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1. Disentangling the drivers of deforestation and forest degradation in the Miombo landscape: A case study from Mozambique

Author

Sá Nogueira Lisboa, Clovis Grinand, Julie Betbeder, Frédérique Montfort, and Lilian Blanc

Subjects
Beira Corridor, Southern Africa, Machine Learning, Land-Use Change, Underlying Drivers, Physical geography, GB3-5030, Environmental sciences, GE1-350
Abstract

The fragmented and complex landscape in the Miombo landscape makes it a challenge to map and disentangle the various forest change drivers (FCD) associated with these changes and relate them to other underlying drivers. To overcome these challenges, we developed a method to spatially disentangle the drivers of deforestation (smallholder and commercial agriculture, mining, and clean-cutting charcoal), forest degradation (selective charcoal production, wildfires, logging), and forest growth (abandoned land, regrowth including plantations) in the Beira corridor, in central Mozambique. We identified ten potential FCD from the literature and created two land use and land cover (LULC) maps for 2000 and 2020 to identify areas of forest change. We used stratified random sampling based on the LULC change map, visually interpreted high-resolution satellite imagery, and NDVI time series to characterise and collected observation points of the FCD. We derived several potential underlying drivers as explanatory spatial variables. We used the random forest algorithm to evaluate their relative importance and generate a map of FCD. The forest loss due to deforestation and degradation accounts for 82.8 % (38,553.1 ha year−1) and 5.2 % (2,399.1 ha year−1), respectively, while the gain due to plantations accounts for 2.8 % (1,314.4 ha year−1) and regrowth for 9.2 % (4,297 ha year−1) of the total forest change area from 2000 to 2020. Smallholder agriculture (72.2 % of the total forest change), clear-cutting charcoal (9.1 %), abandoned land (5.4 %) and regrowth (5.7 %) were the main FCD in the study area. They are explained mainly by the intensity of change, altitude, population density and proximity to the main road. The results show a satisfactory accuracy for the LULC map (overall accuracy = 88 % and F1-score = 80 % for LULC 2000 and 90 % and 88 % for LULC 2020) and for the FCD map (overall accuracy = 79 % and F1-score = 73 %). This study provides a significant improvement in quantifying FCD by using spatially explicit data. The method could help decision-makers design land use policies better and monitor their impacts.

Published
2024
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2. Estimating crop parameters using Sentinel-1 and 2 datasets and geospatial field data

Author

Audrey Mercier, Julie Betbeder, Julien Denize, Jean-Luc Roger, Fabien Spicher, Jérôme Lacoux, David Roger, Jacques Baudry, and Laurence Hubert-Moy

Subjects
LAI, Biomass, Phenological stages, Water content, Remote sensing, Optical and SAR satellite images, Computer applications to medicine. Medical informatics, R858-859.7, Science (General), Q1-390
Abstract

Crop monitoring is essential for ensuring food security in a global context of population growth and climate change. Satellite images are commonly used to estimate crop parameters over large areas, and the freely available Synthetic Aperture Radar (SAR) Sentinel-1 (S-1) and optical Sentinel-2 (S-2) images are relevant for that purpose combining high temporal resolution and high spatial resolution. For this data article, field surveys were conducted from January to July 2017 in France to sample wheat and rapeseed crop parameters during the entire crops cycle. Phenological stages were identified in 83 wheat fields and 32 rapeseed fields in Brittany and Picardy regions. Moreover, Leaf Area Index (LAI), wet biomass, dry biomass and water content were sampled in three wheat fields and three rapeseed fields in Brittany. We assigned to each field sample 10 spectral bands and 12 vegetation indices from S-2 images and two backscattering coefficients, one backscattering ratio and four polarimetric indicators from S-1 images. This dataset can be used for crop monitoring in other regions, as well as for modelling development.

Published
2021
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3. How wildfires increase sensitivity of Amazon forests to droughts

Author

Renan Le Roux, Fabien Wagner, Lilian Blanc, Julie Betbeder, Valery Gond, Hélène Dessard, Beatriz Funatzu, Clément Bourgoin, Guillaume Cornu, Bruno Herault, Frédérique Montfort, Plinio Sist, Agnes Begue, Vincent Dubreuil, François Laurent, François Messner, Ali Fadhil Hasan, and Damien Arvor

Subjects
Amazon forest, fire, climate, Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350, Science, Physics, QC1-999
Abstract

The phenology of tropical forests is tightly related to climate conditions. In the Amazon, the seasonal greening of forests is conditioned by solar radiation and rainfall. Yet, increasing anthropogenic pressures (e.g. logging and wildfires), raise concerns about the impacts of forest degradation on the functioning of forest ecosystems, especially in a climate change context. In this study, we relied on remote sensing data to assess the contribution of solar radiation and precipitation to forest greening in mature and fire degraded forests, with a focus on the 2015 drought event. Our results showed that forest greening is more dependent on water resources in degraded forests than in mature forests. As a consequence, the expected increase in drought episodes and associated fire occurrences under climate change could lead to a long-term drying of tropical forests.

Published
2022
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4. Automatic Detection of Inland Water Bodies along Altimetry Tracks for Estimating Surface Water Storage Variations in the Congo Basin

Author

Frédéric Frappart, Pierre Zeiger, Julie Betbeder, Valéry Gond, Régis Bellot, Nicolas Baghdadi, Fabien Blarel, José Darrozes, Luc Bourrel, and Frédérique Seyler

Subjects
radar altimetry, wetlands, surface water storage, Congo, Science
Abstract

Surface water storage in floodplains and wetlands is poorly known from regional to global scales, in spite of its importance in the hydrological and the carbon balances, as the wet areas are an important water compartment which delays water transfer, modifies the sediment transport through sedimentation and erosion processes, and are a source for greenhouse gases. Remote sensing is a powerful tool for monitoring temporal variations in both the extent, level, and volume, of water using the synergy between satellite images and radar altimetry. Estimating water levels over flooded area using radar altimetry observation is difficult. In this study, an unsupervised classification approach is applied on the radar altimetry backscattering coefficients to discriminate between flooded and non-flooded areas in the Cuvette Centrale of Congo. Good detection of water (open water, permanent and seasonal inundation) is above 0.9 using radar altimetry backscattering from ENVISAT and Jason-2. Based on these results, the time series of water levels were automatically produced. They exhibit temporal variations in good agreement with the hydrological regime of the Cuvette Centrale. Comparisons against a manually generated time series of water levels from the same missions at the same locations show a very good agreement between the two processes (i.e., RMSE ≤ 0.25 m in more than 80%/90% of the cases and R ≥ 0.95 in more than 95%/75% of the cases for ENVISAT and Jason-2, respectively). The use of the time series of water levels over rivers and wetlands improves the spatial pattern of the annual amplitude of water storage in the Cuvette Centrale. It also leads to a decrease by a factor of four for the surface water estimates in this area, compared with a case where only time series over rivers are considered.

Published
2021
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5. Looking beyond forest cover: an analysis of landscape-scale predictors of forest degradation in the Brazilian Amazon

Author

Clément Bourgoin, Julie Betbeder, Renan Le Roux, Valéry Gond, Johan Oszwald, Damien Arvor, Jacques Baudry, Hugues Boussard, Solen Le Clech, Lucas Mazzei, Hélène Dessard, Peter Läderach, Louis Reymondin, and Lilian Blanc

Subjects
forest degradation, remote sensing, land use/cover, landscape metrics, Brazilian Amazon, fragmentation, Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350, Science, Physics, QC1-999
Abstract

While forest degradation rates and extent exceed deforestation in the Brazilian Amazon, less attention is given to the factors controlling its spatial distribution. No quantified correlation exists between changes of forest structure due to anthropogenic disturbances and dynamics of land use and cover change occurring at broader spatial levels. This study examines the influence of multi-scale landscape structure factors (i.e. spatial composition, configuration and dynamic of land use/cover) on primary forest’s aboveground biomass (AGB), spanning from low to highly degraded, in Paragominas municipality (Pará state). We used random forest models to identify the most important landscape predictors of degradation and clustering methods to analyze their distribution and interactions. We found that 58% of the variance of AGB could be explained by metrics reflecting land use practices and agricultural dynamics around primary forest patches and that their spatial patterns were not randomly distributed. Forest degradation is mainly driven by fragmentation effects resulting from old deforestation and colonization events linked with cropland expansion (e.g. soybean and maize) coupled with high accessibility to market. To a lesser extent, degradation is driven by recent and ongoing (1985–2015) deforestation and fragmentation in slash-and-burn agricultural areas, characterized by heterogeneous mosaics of pastures and fallow lands combined with high use of fire. Our findings highlight the potential of landscape-level framework and remotely sensed land cover data for a thorough understanding of the distribution of forest degradation across human-modified landscapes. Addressing these spatial determinants by looking at agricultural dynamics beyond forest cover is necessary to improve forest management which has major implications for biodiversity, carbon and other ecosystem services.

Published
2021
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6. Detection and Characterization of Hedgerows Using TerraSAR-X Imagery

Author

Julie Betbeder, Jean Nabucet, Eric Pottier, Jacques Baudry, Samuel Corgne, and Laurence Hubert-Moy

Subjects
linear hedgerow network, canopy structure, Radar-SAR imagery, very high resolution, Shannon entropy index, object-oriented classification, Science
Abstract

Whilst most hedgerow functions depend upon hedgerow structure and hedgerow network patterns, in many ecological studies information on the fragmentation of hedgerows network and canopy structure is often retrieved in the field in small areas using accurate ground surveys and estimated over landscapes in a semi-quantitative manner. This paper explores the use of radar SAR imagery to (i) detect hedgerow networks; and (ii) describe the hedgerow canopy heterogeneity using TerraSAR-X imagery. The extraction of hedgerow networks was achieved using an object-oriented method using two polarimetric parameters: the Single Bounce and the Shannon Entropy derived from one TerraSAR-X image. The hedgerow canopy heterogeneity estimated from field measurements was compared with two backscattering coefficients and three polarimetric parameters derived from the same image. The results show that the hedgerow network and its fragmentation can be identified with a very good accuracy (Kappa index: 0.92). This study also reveals the high correlation between one polarimetric parameter, the Shannon entropy, and the canopy fragmentation measured in the field. Therefore, VHSR radar images can both precisely detect the presence of wooded hedgerow networks and characterize their structure, which cannot be achieved with optical images.

Published
2014
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7. Evaluation of Sentinel-1 and 2 Time Series for Land Cover Classification of Forest–Agriculture Mosaics in Temperate and Tropical Landscapes

Author

Audrey Mercier, Julie Betbeder, Florent Rumiano, Jacques Baudry, Valéry Gond, Lilian Blanc, Clément Bourgoin, Guillaume Cornu, Carlos Ciudad, Miguel Marchamalo, René Poccard-Chapuis, and Laurence Hubert-Moy

Subjects
remote sensing, optical and SAR satellite images, feature selection, decision trees, random forests, Brazilian Amazon, cantabrian range, Science
Abstract

Monitoring forest–agriculture mosaics is crucial for understanding landscape heterogeneity and managing biodiversity. Mapping these mosaics from remotely sensed imagery remains challenging, since ecological gradients from forested to agricultural areas make characterizing vegetation more difficult. The recent synthetic aperture radar (SAR) Sentinel-1 (S-1) and optical Sentinel-2 (S-2) time series provide a great opportunity to monitor forest–agriculture mosaics due to their high spatial and temporal resolutions. However, while a few studies have used the temporal resolution of S-2 time series alone to map land cover and land use in cropland and/or forested areas, S-1 time series have not yet been investigated alone for this purpose. The combined use of S-1 & S-2 time series has been assessed for only one or a few land cover classes. In this study, we assessed the potential of S-1 data alone, S-2 data alone, and their combined use for mapping forest–agriculture mosaics over two study areas: a temperate mountainous landscape in the Cantabrian Range (Spain) and a tropical forested landscape in Paragominas (Brazil). Satellite images were classified using an incremental procedure based on an importance rank of the input features. The classifications obtained with S-2 data alone (mean kappa index = 0.59–0.83) were more accurate than those obtained with S-1 data alone (mean kappa index = 0.28–0.72). Accuracy increased when combining S-1 and 2 data (mean kappa index = 0.55–0.85). The method enables defining the number and type of features that discriminate land cover classes in an optimal manner according to the type of landscape considered. The best configuration for the Spanish and Brazilian study areas included 5 and 10 features, respectively, for S-2 data alone and 10 and 20 features, respectively, for S-1 data alone. Short-wave infrared and VV and VH polarizations were key features of S-2 and S-1 data, respectively. In addition, the method enables defining key periods that discriminate land cover classes according to the type of images used. For example, in the Cantabrian Range, winter and summer were key for S-2 time series, while spring and winter were key for S-1 time series.

Published
2019
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8. Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes

Author

Julien Denize, Laurence Hubert-Moy, Julie Betbeder, Samuel Corgne, Jacques Baudry, and Eric Pottier

Subjects
agricultural monitoring, earth observing sensors, multi-temporal classification, optical and SAR time-series, Random Forest algorithm, Support Vector Machine algorithm, Science
Abstract

Monitoring vegetation cover during winter is a major environmental and scientific issue in agricultural areas. From an environmental viewpoint, the presence and type of vegetation cover in winter influences the transport of pollutants to water resources. From a methodological viewpoint, characterizing spatio-temporal dynamics of land cover and land use at the field scale is challenging due to the diversity of farming strategies and practices in winter. The objective of this study was to evaluate the respective advantages of Sentinel optical and SAR time-series to identify land use in winter. To this end, Sentinel-1 and -2 time-series were classified using Support Vector Machine and Random Forest algorithms in a 130 km² agricultural area. From the classification, the Sentinel-2 time-series identified winter land use more accurately (overall accuracy (OA) = 75%, Kappa index = 0.70) than that of Sentinel-1 (OA = 70%, Kappa = 0.66) but a combination of the Sentinel-1 and -2 time-series was the most accurate (OA = 81%, Kappa = 0.77). Our study outlines the effectiveness of Sentinel-1 and -2 for identify land use in winter, which can help to change agricultural practices.

Published
2018
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10. Remote Sensing and Cropping Practices: A Review

Author

Agnès Bégué, Damien Arvor, Beatriz Bellon, Julie Betbeder, Diego de Abelleyra, Rodrigo P. D. Ferraz, Valentine Lebourgeois, Camille Lelong, Margareth Simões, and Santiago R. Verón

Subjects
cropping system, crop succession, rotation, cropping pattern, multiple cropping, agroforestry, irrigation, soil tillage, harvest, intercropping, fallow, Science
Abstract

For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

Published
2018
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19. Evaluation of Sentinel-1 & 2 time series for predicting wheat and rapeseed phenological stages

Author

Jacques Baudry, Audrey Mercier, Vincent Le Roux, Julie Betbeder, Jérôme Lacoux, Laurence Hubert-Moy, Fabien Spicher, David Roger, Signalisation, radiobiologie et cancer, Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN), Biodiversité agroécologie et aménagement du paysage (UMR BAGAP), AGROCAMPUS OUEST-Ecole supérieure d'Agricultures d'Angers (ESA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecologie et Dynamique des Systèmes Anthropisés - UMR CNRS 7058 (EDYSAN), Centre National de la Recherche Scientifique (CNRS)-Université de Picardie Jules Verne (UPJV), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Ecole supérieure d'Agricultures d'Angers (ESA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Rapeseed, F40 - Écologie végétale, 010504 meteorology & atmospheric sciences, Télédétection, Triticum aestivum, 0211 other engineering and technologies, Context (language use), 02 engineering and technology, 01 natural sciences, Normalized Difference Vegetation Index, Crop, Computers in Earth Sciences, Leaf area index, Engineering (miscellaneous), ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, 2. Zero hunger, Phenology, Brassica napus, food and beverages, [SHS.GEO]Humanities and Social Sciences/Geography, Spectral bands, Vegetation, 15. Life on land, technique de prévision, Atomic and Molecular Physics, and Optics, Computer Science Applications, Agronomy, 13. Climate action, [SDE]Environmental Sciences, Environmental science, U30 - Méthodes de recherche, Phénologie
Abstract

In the global context of population growth and climate change, monitoring crops is necessary to sustain agriculture and conserve natural resources. While many studies have demonstrated the ability of optical and SAR remotely sensed data to estimate crop parameters, these data have not been compared or combined to predict crop phenological stages. Despite the high sensitivity of SAR polarimetric data to crop phenological stages, no study has used high temporal resolution data. The freely available SAR Sentinel-1 (S-1) and optical Sentinel-2 (S-2) time series provide a unique opportunity to monitor crop phenology at a high spatial resolution on a weekly basis. The objective of this study was to evaluate the potential of S-1 data alone, S-2 data alone, and their combined use to predict wheat and rapeseed phenological stages. We first analyzed temporal profiles of spectral bands, vegetation indices and leaf area index (LAI) derived from S-2 data, and backscattering coefficients and polarimetric indicators derived from S-1 data. Then, an incremental procedure was used to estimate the contribution of S-1 and S-2 features to the classification of principal and secondary phenological stages of wheat and rapeseed. Results for both crops showed that the classification obtained with combined S-1 & 2 data (mean kappa = 0.53–0.82 and 0.74–0.92 for wheat and rapeseed, respectively) was more accurate than those obtained with S-2 data alone (mean kappa = 0.54–0.75 and 0.67–0.86 for wheat and rapeseed, respectively) or S-1 data alone (mean kappa = 0.48–0.61 and 0.61–0.64 for wheat and rapeseed, respectively). Combining S-1 & 2 data allowed better identification of the beginning and end of tillering for wheat and the beginning and end of ripening for rapeseed. Among S-2 features, the most important were LAI for wheat and the NDVI for rapeseed. For both crops, the S2REP index was one of the most important vegetation indices, while MCARI was less important. For S-1 features, results highlighted the large contribution of the backscatter ratio (σ◦VH:σ◦VV) and the value of using polarimetric indicators (Shannon entropy and span) to monitor rapeseed and wheat phenology. The main novelties of this work are the use of S-1 polarimetric indicators to identify phenological stages of wheat and rapeseed and the mapping of wheat and rapeseed secondary phenological stages using remotely sensed data.

Published
2020
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20. Assessing the joint effects of landscape, farm features and crop management practices on berry damage in coffee plantations

Author

Sergio Vilchez-Mendoza, Ali Romero-Gurdián, Jacques Avelino, Fabrice DeClerck, Pierre Bommel, Julie Betbeder, Christian Cilas, and Leila Bagny Beilhe

Subjects
Ecology, Animal Science and Zoology, Agronomy and Crop Science
Abstract

Coffee berry borer (CBB) (Hypothenemus hampei; Coleoptera: Curculionidae: Scolytinae) is a major insect pest affecting coffee cultivation that causes large economic losses worldwide. Characteristics related to its life cycle makes it very difficult to control. Usually, CBB control measures are carried out at plot scale, with almost no actions taken at wider landscape scales. It is unclear how plot level control strategies and landscape factors act alone or in combination to influence CBB infestation levels. We evaluated the joint effects of crop management at the plot level, of farm features, and of landscape structure at different spatial scales on CBB infestation in 50 Costa Rican coffee farms. On five plants in each farm, we estimated the maximum number of infested berries during the fruiting period. We measured three separate groups of variables related to plot management practices, farm features and landscape structure. To assess their single and joint contributions, their relative importance and the effects of these variables on the number of infested berries we used the variance partitioning approach of the RandomForest algorithm. When evaluating the groups of factors separately, we found that crop management explained 35% of the variability of number of infested berries, farm features 42% and landscape structure 27%. The joint contribution of all three groups of variables explained 48% of variability of the number of infested berries. However, when we assessed the single contributions of each set of variables, i.e., when controlling the other two set of variables, we found that farm features explained 17% of the variance of the number of infested berries, landscape structure 6% and crop management practices only 3%. The larger amount of the variance explained by the joint effect of crop management practices, farm features, and landscape structure suggests that to develop a pest management strategy at a local scale it is important to consider the effect of both local and landscape factors affecting pest abundance. The integrated CBB management plan should consider influences at multiple spatial scales and a coordinated action among farmers that share the same landscape would be beneficial.

Published
2022

21. Threshold and weighted-distance methods: a combined multiscale approach improves explanatory power of forest carabid beetle abundance in agricultural landscape

Author

Benjamin Bergerot, Julie Betbeder, Solène Croci, Olivier Jambon, Jacques Baudry, Françoise Burel, Hugues Boussard, Romain Georges, Pierre-Gilles Lemasle, Ecosystèmes, biodiversité, évolution [Rennes] (ECOBIO), Université de Rennes (UR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Biodiversité agroécologie et aménagement du paysage (UMR BAGAP), Ecole supérieure d'Agricultures d'Angers (ESA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Rennes Angers, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Forêts et Sociétés (UPR Forêts et Sociétés), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Département Environnements et Sociétés (Cirad-ES), Centro Agronómico Tropical de Investigación y Enseñanza - Tropical Agricultural Research and Higher Education Center (CATIE), Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes ), Université de Brest (UBO)-Université de Rennes 2 (UR2)-Nantes Université (Nantes Univ)-Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (Nantes Univ - IGARUN), Nantes Université - pôle Humanités, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Humanités, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (Nantes Univ - IGARUN), Nantes Université (Nantes Univ), Woodnet project, through the 2015-2016 BiodivERsA COFUND, ANR (Agence National de la Recherche, France) French National Research Agency (ANR), MINECO (Ministerio de Asuntos Economicos y Transformacion Digital, Spain), BELSPO (Politique scientifique federale, Belgium), Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Ecole supérieure d'Agricultures d'Angers (ESA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Littoral, Environnement, Télédétection, Géomatique (LETG - Nantes), Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Brest (UBO)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-École pratique des hautes études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0106 biological sciences, Weighted distance, Abax parallelepipedus, Geography, Planning and Development, Species distribution, abondance, Distribution des populations, 010603 evolutionary biology, 01 natural sciences, Abundance (ecology), Statistics, Relative species abundance, Nature and Landscape Conservation, Mathematics, Ecology, biology, [SDE.IE]Environmental Sciences/Environmental Engineering, 010604 marine biology & hydrobiology, 15. Life on land, Explained variation, biology.organism_classification, Écologie des populations, Metric (mathematics), Spatial ecology, Landscape metric, L20 - Écologie animale, Carabidae, Landscape ecology, U30 - Méthodes de recherche, Paysage agricole, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Multiscale approach
Abstract

International audience; Context To analyze the scales at which landscape structure influences ecological processes, two approaches with different underlying ecological assumptions exist; the usual threshold method and the weighted-distance method.Objectives We used abundance of species to test if the combination of weighted-distance and threshold approaches improves the explained variance of landscape metrics. Methods We developed a workflow using the two approaches to calculate metrics computed at multiple scales. The latter was developed using weighted metrics based on different weighted-distance functions, and one metric could be selected for more than one spatial scale. Then, we tested the explained variance of species distribution (the activity-density of Abax parallelepipedus) by these two approaches applied independently and then together in modeling a specific ecological response.Results The combination of metrics computed at multiple scales calculated by both weighted-distance and threshold method improved the predictive performance of the models. More precisely, adding metrics derived from the weighted-distance method to the threshold method significantly increased the explained variance when using the same environmental variables. The mean R-2 values of the selected model for the threshold method was 0.34 +/- 0.10, 0.49 +/- 0.11 with the weighted-distance method, and reached 0.71 +/- 0.07 with the two methods combined. These results demonstrate the importance of combining metrics using the weighted-distance method and the threshold method. In addition, activity-density was better explained by metrics selected at multiple scales.Conclusions This study highlights the importance of combining threshold and weighted-distance method at several scales to improve the explanation of ecological responses based on species abundance.

Published
2022
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22. Looking beyond forest cover: an analysis of landscape-scale predictors of forest degradation in the Brazilian Amazon

Author

Johan Oszwald, Damien Arvor, L. J. M. de Freitas, J. Baudry, Hélène Dessard, Lilian Blanc, R. Le Roux, Louis Reymondin, Julie Betbeder, S. Le Clech, Valéry Gond, Clément Bourgoin, Peter Läderach, H. Boussard, Forêts et Sociétés (UPR Forêts et Sociétés), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN), Biodiversité agroécologie et aménagement du paysage (UMR BAGAP), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Ecole supérieure d'Agricultures d'Angers (ESA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Wageningen University and Research [Wageningen] (WUR), Département Environnements et Sociétés (Cirad-ES), AGROCAMPUS OUEST-Ecole supérieure d'Agricultures d'Angers (ESA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), CLÉMENT BOURGOIN, CIRAD / CIAT, JULIE BETBEDER, CIRAD / CATIE, RENAN LE ROUX, CIRAD, VALÉRY GOND, CIRAD, JOHAN OSZWALD, Université Rennes 2, DAMIEN ARVOR, Université Rennes 2, JACQUES BAUDRY, BAGAP, INRAE, Institut Agro, HUGUES BOUSSARD, BAGAP, INRAE, Institut Agro, SOLEN LE CLECH, Wageningen University and Research, LUCAS JOSE MAZZEI DE FREITAS, CPATU, HÉLÈNE DESSARD, CIRAD, PETER LÄDERACH, CIAT, LOUIS REYMONDIN, CIAT, and LILIAN BLANC, CIRAD.

Subjects
[SDE.MCG]Environmental Sciences/Global Changes, Forest management, Land cover, Ecosystem services, 03 medical and health sciences, Deforestation, Fragmentation, Landscape metrics, Degradação Ambiental, 030304 developmental biology, General Environmental Science, 2. Zero hunger, 0303 health sciences, Forest degradation, geography.geographical_feature_category, Land use, Renewable Energy, Sustainability and the Environment, Agroforestry, Amazon rainforest, Land use/cover, Public Health, Environmental and Occupational Health, Uso da Terra, 04 agricultural and veterinary sciences, [SHS.GEO]Humanities and Social Sciences/Geography, 15. Life on land, Brazilian Amazon, Remote sensing, Old-growth forest, Geography, Environmental Systems Analysis, 13. Climate action, Milieusysteemanalyse, 040103 agronomy & agriculture, Spatial ecology, 0401 agriculture, forestry, and fisheries, Sensoriamento Remoto
Abstract

While forest degradation rates and extent exceed deforestation in the Brazilian Amazon, less attention is given to the factors controlling its spatial distribution. No quantified correlation exists between changes of forest structure due to anthropogenic disturbances and dynamics of land use and cover change occurring at broader spatial levels. This study examines the influence of multi-scale landscape structure factors (i.e. spatial composition, configuration and dynamic of land use/cover) on primary forest's aboveground biomass (AGB), spanning from low to highly degraded, in Paragominas municipality (Pará state). We used random forest models to identify the most important landscape predictors of degradation and clustering methods to analyze their distribution and interactions. We found that 58% of the variance of AGB could be explained by metrics reflecting land use practices and agricultural dynamics around primary forest patches and that their spatial patterns were not randomly distributed. Forest degradation is mainly driven by fragmentation effects resulting from old deforestation and colonization events linked with cropland expansion (e.g. soybean and maize) coupled with high accessibility to market. To a lesser extent, degradation is driven by recent and ongoing (1985?2015) deforestation and fragmentation in slash-and-burn agricultural areas, characterized by heterogeneous mosaics of pastures and fallow lands combined with high use of fire. Our findings highlight the potential of landscape-level framework and remotely sensed land cover data for a thorough understanding of the distribution of forest degradation across human-modified landscapes. Addressing these spatial determinants by looking at agricultural dynamics beyond forest cover is necessary to improve forest management which has major implications for biodiversity, carbon and other ecosystem services. Made available in DSpace on 2021-11-10T19:05:07Z (GMT). No. of bitstreams: 1 Bourgoin-2021-Environ.-Res.-Lett.-16-114045.pdf: 3544341 bytes, checksum: 423582271f80655a3ab4683235bf9dd0 (MD5) Previous issue date: 2021

Published
2021
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23. Automatic Detection of Inland Water Bodies along Altimetry Tracks for Estimating Surface Water Storage Variations in the Congo Basin

Author

Frédérique Seyler, José Darrozes, Fabien Blarel, Régis Bellot, Pierre Zeiger, Luc Bourrel, Valéry Gond, Frédéric Frappart, Nicolas Baghdadi, Julie Betbeder, Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Forêts et Sociétés (UPR Forêts et Sociétés), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Département Environnements et Sociétés (Cirad-ES), Institut National de l'Information Géographique et Forestière [IGN] (IGN), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Géosciences Environnement Toulouse (GET), UMR 228 Espace-Dev, Espace pour le développement, Institut de Recherche pour le Développement (IRD)-Université de Perpignan Via Domitia (UPVD)-Avignon Université (AU)-Université de La Réunion (UR)-Université de Montpellier (UM)-Université de Guyane (UG)-Université des Antilles (UA), This research was funded by CNES TOSCA grants number CASCHMIR and SWHYM., Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD), and Université de Guyane (UG)-Université des Antilles (UA)-Institut de Recherche pour le Développement (IRD)-Université de Perpignan Via Domitia (UPVD)-Avignon Université (AU)-Université de La Réunion (UR)-Université de Montpellier (UM)

Subjects
010504 meteorology & atmospheric sciences, radar altimetry, wetlands, surface water storage, Congo, Wetland, 02 engineering and technology, Imagerie par satellite, 01 natural sciences, 020701 environmental engineering, geography.geographical_feature_category, Water storage, Stockage d'eau, 6. Clean water, [SDE]Environmental Sciences, Erosion, Sediment transport, Télédétection, Science, 0207 environmental engineering, Mouvement de l'eau dans le sol, Altimeter, P10 - Ressources en eau et leur gestion, 0105 earth and related environmental sciences, Hydrology, geography, Cycle hydrologique, Eau superficielle, Cuvette, 13. Climate action, General Earth and Planetary Sciences, Common spatial pattern, Environmental science, U30 - Méthodes de recherche, Surface water
Abstract

International audience; Surface water storage in floodplains and wetlands is poorly known from regional to global scales, in spite of its importance in the hydrological and the carbon balances, as the wet areas are an important water compartment which delays water transfer, modifies the sediment transport through sedimentation and erosion processes, and are a source for greenhouse gases. Remote sensing is a powerful tool for monitoring temporal variations in both the extent, level, and volume, of water using the synergy between satellite images and radar altimetry. Estimating water levels over flooded area using radar altimetry observation is difficult. In this study, an unsupervised classification approach is applied on the radar altimetry backscattering coefficients to discriminate between flooded and non-flooded areas in the Cuvette Centrale of Congo. Good detection of water (open water, permanent and seasonal inundation) is above 0.9 using radar altimetry backscattering from ENVISAT and Jason-2. Based on these results, the time series of water levels were automatically produced. They exhibit temporal variations in good agreement with the hydrological regime of the Cuvette Centrale. Comparisons against a manually generated time series of water levels from the same missions at the same locations show a very good agreement between the two processes (i.e., RMSE _ 0.25 m in more than 80%/90% of the cases and R _ 0.95 in more than 95%/75% of the cases for ENVISAT and Jason-2, respectively). The use of the time series of water levels over rivers and wetlands improves the spatial pattern of the annual amplitude of water storage in the Cuvette Centrale. It also leads to a decrease by a factor of four for the surface water estimates in this area, compared with a case where only time series over rivers are considered.

Published
2021
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24. Assessing the Causes of Tropical Forest Degradation Using Landsat Time Series: A Case Study in the Brazilian Amazon

Author

Clément Bourgoin, Christian Brenez, Renan Le Roux, Guillaume Cornu, Julie Betbeder, Audrey Mercier, Plinio Sist, Lilian Blanc, Mazzei Lucas, Isabelle Tritsch, Hélène Dessard, Valéry Gond, Damien Arvor, Forêts et Sociétés (UPR Forêts et Sociétés), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN), and Département Environnements et Sociétés (Cirad-ES)

Subjects
010504 meteorology & atmospheric sciences, business.industry, Amazon rainforest, Environmental resource management, Logging, 0211 other engineering and technologies, 02 engineering and technology, [SHS.GEO]Humanities and Social Sciences/Geography, 15. Life on land, Tropical forest, 01 natural sciences, [SDE.ES]Environmental Sciences/Environmental and Society, Environmental science, Forest degradation, Illegal logging, Time series, business, Scale (map), ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Degradation (telecommunications)
Abstract

Monitoring forest degradation at fine scale over large area is critical from an environmental point of view since it provides crucial information for many ecological applications. We introduce an automatic method based on optical Landsat time series (2000–2017) to detect and quantify forest disturbances and to identify the causes of forest degradation. The method is based on i) an automatic spectral unmixing to detect forest's disturbances and on ii) landscape metrics and temporal indicators to detect the causes of forest degradation. We applied the approach in the Brazilian Amazon municipality of Paragominas to map forested areas affected by reduced impact logging, conventional logging or illegal logging and fires.

Published
2021
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25. Estimation of Corn Yield by Assimilating SAR and Optical Time Series Into a Simplified Agro-Meteorological Model: From Diagnostic to Forecast

Author

Remy Fieuzal, Julie Betbeder, Maël Ameline, Jean-François Berthoumieu, and Frédéric Baup

Subjects
Synthetic aperture radar, Atmospheric Science, 010504 meteorology & atmospheric sciences, Mean squared error, Télédétection, 0211 other engineering and technologies, Field (mathematics), Scale (descriptive set theory), 02 engineering and technology, 01 natural sciences, Combinatorics, Computers in Earth Sciences, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Physics, Méthode statistique, Series (mathematics), U10 - Informatique, mathématiques et statistiques, A01 - Agriculture - Considérations générales, Sigma, Rendement des cultures, sécurité alimentaire, Yield (chemistry), Climatologie, Production (computer science), U30 - Méthodes de recherche
Abstract

The estimation of crop yield plays a major role in decision making and management of food supply. This paper aims to estimate corn dry masses and grain yield at field scale using an agro-meteorological model. The SAFY-WB model (simple algorithm for yield model combined with a water balance) is controlled by green area index (GAI) derived from optical satellite images (GAI opt ), and the GAI derived from synthetic aperture radar (SAR) satellite images (GAI sar ) acquired over two crop seasons (2015 and 2016) in the south-west of France. Landsat-8 mission provides the optical data. SAR information ( $\sigma _{{\rm{VV}}}^\circ $ , $\sigma _{{\rm{VH}}}^\circ $ , and $\sigma _{{\rm{VH/VV}}}^\circ $ ) is provided by Sentinel-1A mission through two angular normalized orbits (30 and 132) allowing a repetitiveness from 12 to 6 days. $\sigma _{{\rm{VH}}/{\rm{VV}}}^\circ $ is successfully used to derive GAI sar (R 2 = 0.72, relative root mean square error (rRMSE) = 10.4%) over the leaf development stages of the crop cycle from a nonlinear function. Others SAR signal ( $\sigma _{{\rm{VV}}}^\circ $ and $\sigma _{{\rm{VH}}}^\circ $ ) are too much related to soil moisture changes. At the opposite of GAI opt , GAI sar cannot be used alone in the model to accurately estimate vegetation parameters. Finally, the robustness of the results comes from the combination of GAI derived from SAR and optical data. In this condition, the model is able, thanks to the inclusion of a new “production module,” to simulate dry masses and yield (R 2 > 0.75 and rRMSE < 12.75%) with good performances in the diagnostic approach. In the context of forecast, results offer lower performances but stay acceptable, with relative errors inferior to 13.95% (R 2 > 0.69).

Published
2018
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26. SAR analysis of wetland ecosystems: Effects of band frequency, polarization mode and acquisition dates

Author

Sébastien Rapinel, Elodie Fabre, Julien Denize, Julie Betbeder, Eric Pottier, Laurence Hubert-Moy, Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-Université de Nantes (UN)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS), Institut d'Électronique et des Technologies du numéRique (IETR), Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Nantes (UN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Nantes Université (NU), Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche, Centre National d’Etudes Spatiales, Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UNIV-RENNES), Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Université de Nantes (UN)-Université de Rennes 1 (UR1), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

Subjects
Synthetic aperture radar, Marsh, 010504 meteorology & atmospheric sciences, Radar à synthèse d'ouverture, 0211 other engineering and technologies, Polarimetry, Wetland, 02 engineering and technology, Conservation, RADARSAT-2, terre humide, 01 natural sciences, conservation des forêts, conservation des terres, Wetland conservation, Ecosystem, Computers in Earth Sciences, Engineering (miscellaneous), Polarisation, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, geography, geography.geographical_feature_category, ANOVA, Vegetation, Préservation de l'écosystème, [SDE.IE]Environmental Sciences/Environmental Engineering, [SHS.GEO]Humanities and Social Sciences/Geography, 15. Life on land, Polarization (waves), [SDE.ES]Environmental Sciences/Environmental and Society, Atomic and Molecular Physics, and Optics, Computer Science Applications, Random forest, Écosystème forestier, Environmental science, P01 - Conservation de la nature et ressources foncières, U30 - Méthodes de recherche, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, TerraSAR-X
Abstract

International audience; The availability of high spatial resolution synthetic aperture radar (SAR) sensors with a wide range of acquisition modes has increased greatly over the past decade and contributed significantly to the study of wetland ecosystems. However, the relative influence of acquisition configurations (i.e. band frequency, polarization mode, number of acquisition dates) in wetland analysis remains poorly explored. This article investigated the relative influence of X-/C-band frequency, dual-/quad-polarization and single-/multi-acquisition features on discrimination of vegetation types in 632 ha Ramsar-protected temperate riverine marshes (Mont-Saint-Michel Bay, France). Three SAR datasets (i.e. quad-pol/C-band, dual-pol/C-band and dual-pol/X-band) were generated from five pairs of TerraSAR-X and RADARSAT-2 images. First, a set of 25 SAR features, including backscattering coefficients and polarimetric parameters, was extracted from the SAR datasets. Second, correlation between each pair of images was calculated using the polarimetric parameter Shannon entropy to select the most similar pairs in the time series. Third, the importance of each SAR feature and modeling accuracy were calculated using a conditional random forest model for each of the three datasets. Finally, analysis of variance was performed to assess the impact of band frequency, polarization mode and number of acquisition dates on the classification of vegetation types. The results highlighted that although the time-shift of each pair of TerraSAR-X and RADARSAT-2 images was short (3-11 days), only three pairs were sufficiently similar, highlighting the high variability in wetland ecosystems. The polarimetric parameter Shannon entropy was the most discriminating feature, regardless of the frequency or polarization. Most variance in the model accuracy was explained by the number of acquisition dates (68%), followed by the frequency (23%), while polarization explained little. This article will help select the most suitable SAR sensor acquisition modes for wetland conservation.

Published
2020
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27. UAV-based canopy textures assess changes in forest structure from long-term degradation

Author

Clément Bourgoin, Julie Betbeder, Pierre Couteron, Lilian Blanc, Hélène Dessard, Johan Oszwald, Renan Le Roux, Guillaume Cornu, Louis Reymondin, Lucas Mazzei, Plinio Sist, Peter Läderach, Valéry Gond, Forêts et Sociétés (UPR Forêts et Sociétés), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Rennes 2 (UR2), Brazilian Agricultural Research Corporation (Embrapa), International Center for Tropical Agriculture [Colombie] (CIAT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), TOSCA CASTAFIOR project, and Université de Rennes (UNIV-RENNES)

Subjects
0106 biological sciences, Canopy, est degradation, F40 - Écologie végétale, Télédétection, Forest management, General Decision Sciences, forêt tropicale, 010501 environmental sciences, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, F50 - Anatomie et morphologie des plantes, 010603 evolutionary biology, 01 natural sciences, Texture (geology), [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Canopy structure For, Tropical forest, Lacunarity, Forest structure, Texture, K70 - Dégâts causés aux forêts et leur protection, Ecology, Evolution, Behavior and Systematics, 0105 earth and related environmental sciences, Remote sensing, Forest degradation, Tree canopy, Ecology, Logging, Unmanned aerial vehicle, 15. Life on land, [SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics, Environmental science, dégradation des forêts, Couvert forestier, Satellite, Canopy structure, [SDE.BE]Environmental Sciences/Biodiversity and Ecology
Abstract

International audience; Degraded tropical forests dominate agricultural frontiers and their management is becoming an urgent priority. This calls for a better understanding of the different forest cover states and cost-efficient techniques to quantify the impact of degradation on forest structure. Canopy texture analyses based on Very High Spatial Resolution (VHSR) optical imagery provide proxies to assess forest structures but the mechanisms linking them with degradation have rarely been investigated. To address this gap, we used a lightweight Unmanned Aerial Vehicle (UAV) to map 739 ha of degraded forests and acquire both canopy VHSR images and height model. Thirty-three years of degradation history from Landsat archives allowed us to sample 40 plots in undisturbed, logged, over-logged and burned and regrowth forests in tropical forested landscapes (Paragominas, Pará, Brazil). Fourier (FOTO) and lacunarity textures were used to assess forest canopy structure and to build a typology linking degradation history and current states. Texture metrics capture canopy grain, heterogeneity and openness gradients and correlate with forest structure variability (R2 = 0.58). Similar structures share common degradation history and can be discriminated on the basis of canopy texture alone (accuracy = 55%). Over-logging causes a lowering in forest height, which brings homogeneous textures and of finer grain. We identified the major changes in structures due to fire following logging which changes heterogeneous and intermediate grain into coarse textures. Our findings highlight the potential of canopy texture metrics to characterize degraded forests and thus be used as indicators for forest management and degradation mitigation. Inexpensive and agile UAV open promising perspectives at the interface between field inventory and satellite characterization of forest structure using texture metrics.

Published
2020
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28. Evaluation of sentinel-1 and -2 time series for estimating LAI and biomass of wheat and rapeseed crop types

Author

Jacques Baudry, Julie Betbeder, Sébastien Rapinel, Audrey Mercier, Nicolas Jégou, Laurence Hubert-Moy, Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES), Centro Agronómico Tropical de Investigación y Enseñanza - Tropical Agricultural Research and Higher Education Center (CATIE), Université de Montpellier (UM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Biodiversité agroécologie et aménagement du paysage (UMR BAGAP), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Ecole supérieure d'Agricultures d'Angers (ESA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN), Kalideos project - CNESZone Atelier Armorique projectMinistry of Research and Technology of the Republic of Indonesia (RISTEK)2015-2016 BiodivERsA COFUNDFrench National Research Agency (ANR)MINECOBelgian Federal Science Policy Office, and AGROCAMPUS OUEST-Ecole supérieure d'Agricultures d'Angers (ESA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
Synthetic aperture radar, Rapeseed, 010504 meteorology & atmospheric sciences, Télédétection, Radar à synthèse d'ouverture, 0211 other engineering and technologies, Biomass, 02 engineering and technology, 01 natural sciences, Crop, F01 - Culture des plantes, Biomasse, Water content, ComputingMilieux_MISCELLANEOUS, Triticum, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, 2. Zero hunger, Modélisation des cultures, Indice de surface foliaire, Brassica napus, Vegetation, Spectral bands, [SHS.GEO]Humanities and Social Sciences/Geography, 15. Life on land, [SDE]Environmental Sciences, General Earth and Planetary Sciences, Environmental science, U30 - Méthodes de recherche, Scale (map), Analyse de séries chronologiques
Abstract

Monitoring crops at a fine scale is critical because it provides information crucial for assessing the influence of increased food production on sustainable management of agricultural landscapes. We assessed the potential of synthetic aperture radar (SAR) Sentinel-1 (S-1) and optical Sentinel-2 (S-2) images to derive crop biophysical parameters of wheat and rapeseed in Brittany, France. We generated a dataset of 29 features, including spectral bands and vegetation indices derived from S-2 images, and backscattering coefficients and polarimetric indicators derived from S-1 images. Then, we compared the respective value of S-1 and S-2 features to estimate crop LAI, biomass, and water content (WC) using a Gaussian processes regression. The results show that best S-2-based models were achieved using the green band, NIR bands, and vegetation indices for the wheat WC, LAI, and biomass, respectively, and the shortwave-infrared bands for rapeseed biomass. Concerning S-1-based models, the σ0VH : σ0VV ratio was the most relevant feature for wheat LAI and rapeseed biomass, and the Shannon entropy polarization contribution best performed for wheat WC. We highlighted not only the value of optical S-2 images but also the importance of polarimetric indicators derived from SAR S-1 images for estimating crop biophysical parameters.

Published
2020
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29. Evaluation of Sentinel-1 and -2 time series to derive crop phenology and biomass of wheat and rapeseed: northen France and Brittany case studies

Author

Vincent Leroux, Audrey Mercier, Jacques Baudry, Laurence Hubert-Moy, Fabien Spicher, Julie Betbeder, Julien Denize, Jean-Luc Roger, Signalisation normale et pathologique de l'embryon aux thérapies innovantes des cancers, Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN), SAD Paysage (SAD Paysage), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), Nantes Université (NU)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Signalisation normale et pathologique de l'embryon aux thérapies innovante des cancers, Institut Curie-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Brest (UBO)-École pratique des hautes études (EPHE)-Université de Nantes (UN)-Université d'Angers (UA)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 2 (UR2), Normandie Université (NU)-Normandie Université (NU), AGROCAMPUS OUEST-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut d'Electronique et de Télécommunications de Rennes (IETR), Université de Nantes (UN)-Université de Rennes 1 (UR1), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

Subjects
2. Zero hunger, Synthetic aperture radar, Biomass (ecology), Rapeseed, 010504 meteorology & atmospheric sciences, Phenology, 0211 other engineering and technologies, 02 engineering and technology, Vegetation, [SHS.GEO]Humanities and Social Sciences/Geography, 15. Life on land, 01 natural sciences, Crop, [SDE]Environmental Sciences, Temperate climate, Environmental science, Physical geography, Water content, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences
Abstract

Crop monitoring at a fine scale is critical from an environmental perspective since it provide crucial information to combine increased food production and sustainable management of agricultural landscapes. The recent Synthetic Aperture Radar (SAR) Sentinel-1 (S-1) and optical Sentinel-2 (S-2) time series offer a great opportunity to monitor cropland (structure, biomass and phenology) due to their high spatial and temporal resolutions. In this study, we assessed the potential of Sentinel data to derive Wet Biomass (WB), Dry biomass (DB), water content and crop Phenological Stages (PS). This study focuses on wheat and rapeseed, which represent two of the most important seasonal crops of the world in terms of occupied area. Satellites and ground data were collected over two French temperate agricultural landscapes, in northern France and Brittany. Spectral bands and vegetation indices were derived from the S-2 images and backscattering coefficients and polarimetric indicators from the S-1 images. We used linear models to estimate the Crop Parameters (CP) of wheat and rapeseed crops. Satellite images were then classified using a random forest incremental procedure based on the importance rank of the input features to discriminate PS. Results showed that S-1 features were more efficient than S-2 features to estimate CP of rapeseed while S-2 features were better for wheat. We demonstrated the high potential of S-1 and 2 to predict principal PS (kappa=0.75) while secondary PS were misclassified. For wheat, the succession of PS predicted was consistent, further research is required to confirm the potential of S-1 and 2.

Published
2019
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30. Evaluation of using sentinel-1 and -2 time-series to identifywinter land use in agricultural landscapes

Author

Samuel Corgne, Eric Pottier, Julie Betbeder, Laurence Hubert-Moy, Jacques Baudry, Julien Denize, Institut d'Electronique et de Télécommunications de Rennes (IETR), Université de Nantes (UN)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement, Télédétection, Géomatique (LETG - Nantes), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-École pratique des hautes études (EPHE)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement (IGARUN), Université de Nantes (UN)-Université de Nantes (UN), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Biodiversité agroécologie et aménagement du paysage (UMR BAGAP), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST-Ecole supérieure d'Agricultures d'Angers (ESA), Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), Normandie Université (NU)-Normandie Université (NU)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Ecole supérieure d'Agricultures d'Angers (ESA), MESR (Ministry of Higher Education and Research of France), Nantes Université (NU)-Université de Rennes 1 (UR1), Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Brest (UBO)-Université de Caen Normandie (UNICAEN), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Normandie Université (NU)-Normandie Université (NU)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), and Denize, Julien

Subjects
010504 meteorology & atmospheric sciences, P40 - Météorologie et climatologie, Science, 0211 other engineering and technologies, 02 engineering and technology, Land cover, 01 natural sciences, Vegetation cover, Multi-temporal classification, [SPI]Engineering Sciences [physics], Couverture végétale, Agricultural monitoring, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, 2. Zero hunger, Utilisation des terres, Méthode statistique, Land use, business.industry, Optical and SAR time-series, Environmental resource management, Random Forest algorithm, Earth observing sensors, agricultural monitoring, earth observing sensors, multi-temporal classification, optical and SAR time-series, Support Vector Machine algorithm, 15. Life on land, 6. Clean water, Hiver, Random forest, E11 - Économie et politique foncières, Water resources, 13. Climate action, Agriculture, General Earth and Planetary Sciences, Environmental science, U30 - Méthodes de recherche, Paysage agricole, business, Scale (map), Agricultural landscapes
Abstract

International audience; Monitoring vegetation cover during winter is a major environmental and scientific issue in agricultural areas. From an environmental viewpoint, the presence and type of vegetation cover in winter influences the transport of pollutants to water resources. From a methodological viewpoint, characterizing spatio-temporal dynamics of land cover and land use at the field scale is challenging due to the diversity of farming strategies and practices in winter. The objective of this study was to evaluate the respective advantages of Sentinel optical and SAR time-series to identify land use in winter. To this end, Sentinel-1 and -2 time-series were classified using Support Vector Machine and Random Forest algorithms in a 130 km(2) agricultural area. From the classification, the Sentinel-2 time-series identified winter land use more accurately (overall accuracy (OA) = 75%, Kappa index = 0.70) than that of Sentinel-1 (OA = 70%, Kappa = 0.66) but a combination of the Sentinel-1 and -2 time-series was the most accurate (OA = 81%, Kappa = 0.77). Our study outlines the effectiveness of Sentinel-1 and -2 for identify land use in winter, which can help to change agricultural practices.

Published
2019
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31. Deforestation for food production

Author

Laurene Feintrenie, Marie-Gabrielle Piketty, Julie Betbeder, and Laurent Gazull

Subjects
S01 - Nutrition humaine - Considérations générales, F08 - Systèmes et modes de culture, Climate change, Deforestation, K01 - Foresterie - Considérations générales, K70 - Dégâts causés aux forêts et leur protection, Agricultural productivity, Production alimentaire, Utilisation des terres, Agroforestry, business.industry, Intensive farming, Système de production, Subsistence agriculture, Déboisement, E11 - Économie et politique foncières, Geography, Food processing, Food systems, Système de culture, Arable land, business
Abstract

Deforestation contributes to carbon emissions and therefore to climate change. Within food systems, agricultural production is the stage which plays the largest role in deforestation and forest degradation, and it is therefore the focus of this chapter. There is a critical link between food systems and deforestation. Arable lands most often have a forested past. It might be ancestral, with deforestation having happened in the early occupation of land by humans or be very recent on current forest frontiers. Over the past two decades, commercial agriculture has overtaken subsistence agriculture as the main driver of deforestation in LI and LMI countries, especially in tropical areas.

Published
2019
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32. Evaluation of the potentiality of polarimetric C- and L-SAR timeseries images for the identification of winter land-use

Author

Julie Betbeder, Julien Denize, Samuel Corgne, Eric Pottier, Laurence Hubert-Moy, Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), Littoral, Environnement, Télédétection, Géomatique (LETG - Nantes), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN), Forêts et Sociétés (UPR Forêts et Sociétés), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Ministere de l'Education Nationale, de l'Enseignement Superieur et de la Recherche, FEDER, Region Bretagne, Conseil General du Finistere, Brest Metropole, Institut Mines Telecom, Kalideos project - CNES, Zone Atelier Armorique, MacDONALD DETTWILER AND ASSOCIATES LTD, Canadian space agency, Nantes Université (NU)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Brest (UBO)-Université de Caen Normandie (UNICAEN), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Normandie Université (NU)-Normandie Université (NU)-École Pratique des Hautes Études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN)

Subjects
Synthetic aperture radar, Series (stratigraphy), Radarsat-2, 010504 meteorology & atmospheric sciences, Land use, 0211 other engineering and technologies, Polarimetry, Sowing, 02 engineering and technology, Land cover, 15. Life on land, agricultural monitoring, 01 natural sciences, Identification (information), [SPI]Engineering Sciences [physics], 13. Climate action, Alos-2, land-use, Environmental science, Sentinel-1, Polarimetric SAR, Winter season, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract

International audience; Land cover and land use monitoring, particularly during winter season, is still a major environmental challenge. Indeed, the presence of a vegetation cover, the dates of sowing, the length of the intercrop period, and land use types have an impact on pollutant transport to water bodies. The objective of this study was to evaluate the potentiality of polarimetric C- and L-SAR time-series to improve the identification and characterization of vegetation cover during winter season in a 130 km (2) area. Alos-2, Radarsat-2 and Sentinel-1 time-series were classified using RF algorithm. The best results were obtained from Radarsat-2 polarimetric images acquired between August 2016 and May 2017, with an overall accuracy of similar to 80%.

Published
2018
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33. Identification of winter land use in temperate agricultural landscapes based on Sentinel-1 and 2 times-series

Author

Julie Betbeder, Laurence Hubert-Moy, Samuel Corgne, Eric Pottier, Julien Denize, Institut d'Électronique et des Technologies du numéRique (IETR), Nantes Université (NU)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement, Télédétection, Géomatique (LETG - Nantes), Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Brest (UBO)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Forêts et Sociétés (UPR Forêts et Sociétés), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Ministere de l'Education Nationale, de l'Enseignement Superieur et de la Recherche, FEDER, Canadian space agency, CNES, Region Bretagne, Conseil General du Finistere, Brest Metropole, Institut Mines Telecom, Kalideos project - CNES, Zone Atelier Armorique, MacDONALD DETTWILER AND ASSOCIATES LTD, Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), Université de Caen Normandie (UNICAEN), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Normandie Université (NU)-Normandie Université (NU)-École Pratique des Hautes Études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN)

Subjects
optical and SAR time series, 010504 meteorology & atmospheric sciences, Land use, business.industry, Earth observing sensors, Land cover, 010501 environmental sciences, 15. Life on land, 01 natural sciences, Vegetation cover, Identification (information), [SPI]Engineering Sciences [physics], Support Vector Machine classifier, 13. Climate action, Agriculture, Temperate climate, Environmental science, Agricultural monitoring, Physical geography, Multi-temporal classifications, Random Forest classifier, business, Scale (map), Agricultural landscapes, 0105 earth and related environmental sciences
Abstract

International audience; Land cover and land use monitoring, particularly during winter season, is still a major environmental and scientific issue in agricultural areas. From an environmental point of view, the presence and type of vegetation cover in winter have an impact on pollutant transport to water bodies. From a methodological point of view, characterizing spatio-temporal dynamics of winter land cover and land use at a field scale remains a challenge due to the diversity of farming strategies and practices. The objective of this study was to evaluate the potential of optical and SAR time-series to improve the monitoring of winter land use in an area of 130 km(2). For that purpose, Sentinel-1 and 2 time-series were classified using SVM and RF algorithms. Winter land use was identified with an overall accuracy of 81% and a kappa index of 0.77 from a combination of Sentinel-1 and 2 images.

Published
2018
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34. TerraSAR-X dual-pol time-series for mapping of wetland vegetation

Author

Samuel Corgne, Sébastien Rapinel, Eric Pottier, Julie Betbeder, Laurence Hubert-Moy, Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN), Ecosystèmes, biodiversité, évolution [Rennes] (ECOBIO), Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), This work was supported by the DIVA 3-AGRICONNECT program, the CNES and the DLR., Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Rennes (UR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Nantes Université (NU)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

Subjects
010504 meteorology & atmospheric sciences, Radar à synthèse d'ouverture, 0211 other engineering and technologies, Wetland, Imagerie par satellite, 02 engineering and technology, terre humide, 01 natural sciences, change detection, geography.geographical_feature_category, Flooding (psychology), Atomic and Molecular Physics, and Optics, Computer Science Applications, P01 - Conservation de la nature et ressources foncières, Biodiversité, medicine.symptom, Analyse de séries chronologiques, Change detection, Écologie, F40 - Écologie végétale, Télédétection, vegetation formations, SVM, SAR imagery, Temperate climate, medicine, Computers in Earth Sciences, Engineering (miscellaneous), 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, geography, Flood myth, Végétation, 15. Life on land, 13. Climate action, Environmental science, Satellite, U30 - Méthodes de recherche, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, multitemporal image analysis, Scale (map), Vegetation (pathology)
Abstract

International audience; Mapping vegetation formations at a fine scale is crucial for assessing wetland functions and for better landscape management. Identification and characterization of vegetation formations is generally conducted at a fine scale using ecological ground surveys, which are limited to small areas. While optical remotely sensed imagery is limited to cloud-free periods, SAR time-series are used more extensively for wetland mapping and characterization using the relationship between distribution of vegetation formations and flood duration. The aim of this study was to determine the optimal number and key dates of SAR images to be classified to map wetland vegetation formations at a 1:10,000 scale. A series of eight dual-polarization TerraSAR-X images (HH/VV) was acquired in 2013 during dry and wet seasons in temperate climate conditions. One polarimetric parameter was extracted first, the Shannon entropy, which varies with wetland flooding status and vegetation roughness. Classification runs of all the possible combinations of SAR images using different k (number of images) subsets were performed to determine the best combinations of the Shannon entropy images to identify wetland vegetation formations. The classification runs were performed using Support Vector Machine techniques and were then analyzed using the McNemar test to investigate significant differences in the accuracy of all classification runs based on the different image subsets. The results highlight the relevant periods (i.e. late winter, spring and beginning of summer) for mapping vegetation formations, in accordance with ecological studies. They also indicate that a relationship can be established between vegetation formations and hydrodynamic processes with a short time-series of satellite images (i.e. 5 dates). This study introduces a new approach for herbaceous wetland monitoring using SAR polarimetric imagery. This approach estimates the number and key dates required for wetland management (e.g. restoration) and biodiversity studies using remote sensing data.

Published
2015
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35. Synthetic Aperture Radar (SAR) images improve habitat suitability models

Author

Laurence Hubert-Moy, Julie Betbeder, Marianne Laslier, Françoise Burel, Jacques Baudry, Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN), Ecosystèmes, biodiversité, évolution [Rennes] (ECOBIO), Université de Rennes (UR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), SAD Paysage (SAD Paysage), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, DIVA 3 Program of the French Ministry of Environment AGRICONNECT Project, CNES, TerraSAR-X Imagery, Zone Atelier Armorique, Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Brest (UBO)-École pratique des hautes études (EPHE)-Université de Nantes (UN)-Université d'Angers (UA)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 2 (UR2), Normandie Université (NU)-Normandie Université (NU), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Centre National de la Recherche Scientifique (CNRS), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA)

Subjects
0106 biological sciences, 010504 meteorology & atmospheric sciences, Radar à synthèse d'ouverture, Geography, Planning and Development, Imagerie par satellite, landscape connectivity, 01 natural sciences, remote sensing, Abundance (ecology), K01 - Foresterie - Considérations générales, biodiversity, Ecology, Agroforestry, Sampling (statistics), P01 - Conservation de la nature et ressources foncières, Biodiversité, Carabidae, Synthetic aperture radar, habitat suitability, Télédétection, graph theory, canopy cover density, Context (language use), Haie, forest carabid beetles, 010603 evolutionary biology, Écologie forestière, Couvert, 0105 earth and related environmental sciences, Nature and Landscape Conservation, Remote sensing, terrasar-x, Resistance (ecology), hedgerow networks, 15. Life on land, Ecological network, 13. Climate action, Environmental science, U30 - Méthodes de recherche, Landscape ecology, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Landscape connectivity
Abstract

International audience; Context The ability to detect ecological networks in landscapes is of utmost importance for managing biodiversity and planning corridors. Objectives The objective of this study was to evaluate the information provided by a synthetic aperture radar (SAR) image for landscape connectivity modeling compared to aerial photographs (APs). Methods We present a novel method that integrates habitat suitability derived from remote sensing imagery into a connectivity model to explain species abundance. More precisely, we compared how two resistance maps constructed using landscape and/or local metrics derived from AP or SAR imagery yield different connectivity values (based on graph theory), considering hedgerow networks and forest carabid beetle species as a model. Results We found that resistance maps using landscape and local metrics derived from SAR imagery improve landscape connectivity measures. The SAR model is the most informative, explaining 58% of the variance in forest carabid beetle abundance. This model calculates resistance values associated with homogeneous patches within hedgerows according to their suitability (canopy cover density and landscape grain) for the model species. Conclusions Our approach combines two important methods in landscape ecology: the construction of resistance maps and the use of buffers around sampling points to determine the importance of landscape factors. This study was carried out through an interdisciplinary approach involving remote sensing scientists and landscape ecologists. This study is a step forward in developing landscape metrics from satellites to monitor biodiversity.

Published
2017
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36. Scientific Committee

Author

Bernard Mougenot, Bernard Lacaze, Frank Veroustraete, Moise Tsayem Demaze, Johan Oszwald, Florent Mouillot, Eric Maille, Olivier Boucher, Jean François Leon, Emeric Frejafon, Marcello De Michele, Marielle Jappiot, Pascal Lacroix, Pascal Allemand, Arthur Delorme, Olivier Rouzeau, Rémi Andreoli, Eric Pirard, Jean-François Desprats, Jean Luc Froger, Nicolas Villeneuve, Nadine Dessay, Julie Betbeder, Jennifer Amsallem, Cécile Vignoles, Sophie Vanwanbeke, Nicolas Baghdadi, and Mehrez Zribi

Published
2016
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37. Monitoring of Agricultural Landscapes Using Remote Sensing Data

Author

Samuel Corgne, Laurence Hubert-Moy, and Julie Betbeder

Subjects
Télédétection, Land management, Land cover, Surveillance de l’environnement, Land use, land-use change and forestry, Modélisation environnementale, Remote sensing, Changement climatique, Utilisation des terres, Land use, U10 - Informatique, mathématiques et statistiques, business.industry, Impact sur l'environnement, Global change, Changement de couvert végétal, Natural resource, Land information system, Environmental science, Land development, U30 - Méthodes de recherche, Paysage agricole, business
Abstract

In this chapter, agricultural landscapes are defined as ecosystems used by humans for agricultural purposes (crop/livestock productions). Within the framework of climate land use and land cover evolution, which are sometimes responsible of significant impacts on the environment and societies, represent a major challenge for the international community. Monitoring of agricultural landscapes using remote sensing images takes place within a larger scientific framework on the study of land cover and land use changes in general. The Land Change Science, which has emerged over the last 10 years as a full-fledged scientific discipline, aims at understanding land cover and land use dynamics as a complex system based on the humankind-environment relationships, by developing theories, notions and models investigated in the framework of social and environmental sciences. These studies which focused in particular on the analysis of the interactions between land cover changes and global change in general (climate change, loss of biodiversity, impacts on natural resources…), and were developed within the framework of international research programs (LULCC: Land Use and Land Cover Change; GLP: Global Land Project, etc.), have four main objectives: – improving the characterization of the main types of land cover; – improving the identification of space-time dynamics that characterize land cover and land use changes (change dynamics, scales at which they occur, etc.); – identifying the main factors (spatial, biophysical, climatic, socioeconomic, etc.) that motivate land cover and land use changes; – improving and developing new models able to assess the impacts of land use and land cover changes on societies, territories and the environment.

Published
2016
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38. List of Authors

Author

Nicolas Baghdadi, Frédéric Baret, Sébastien Bauwens, Julie Betbeder, Stéphanie Bonnet, Alexandre Bouvet, Nathalie Bréda, Samuel Corgne, Dominique Courault, Valérie Demarez, Sylvain Ferrant, Cécile Gomez, Martine Guérif, Dominique Guyon, Dinh Ho Tang Minh, Laurence Hubert-Moy, Jordi Inglada, Jaco Kemp, Philippe Lagacherie, Philippe Lejeune, Michel Le Page, Thuy Le Toan, Stéphane Mermoz, Adrien Michez, Vincent Simonneaux, Pierre Todoroff, Amanda Veloso, Ludovic Villard, and Mehrez Zribi

Published
2016
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39. Contribution of multitemporal polarimetric synthetic aperture radar data for monitoring winter wheat and rapeseed crops

Author

Remy Fieuzal, Julie Betbeder, Frédéric Baup, Laurent Ferro-Famil, Yannick Philippets, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and Nantes Université (NU)-Université de Rennes 1 (UR1)

Subjects
Synthetic aperture radar, 010504 meteorology & atmospheric sciences, Backscatter, Télédétection, Radar à synthèse d'ouverture, Image spot, 0211 other engineering and technologies, Polarimetry, 02 engineering and technology, Imagerie par satellite, 01 natural sciences, Normalized Difference Vegetation Index, law.invention, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], law, F01 - Culture des plantes, Surveillance des cultures, Radar imaging, Radar, Leaf area index, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, 2. Zero hunger, blé d'hiver, U10 - Informatique, mathématiques et statistiques, Brassica napus, 15. Life on land, [SPI.TRON]Engineering Sciences [physics]/Electronics, General Earth and Planetary Sciences, Environmental science, U30 - Méthodes de recherche, Atmospheric optics, Index de végétation
Abstract

International audience; This paper aims to evaluate the contribution of multitemporal polarimetric synthetic aperture radar (SAR) data for winter wheat and rapeseed crops parameters [height, leaf area index, and dry biomass (DB)] estimation, during their whole vegetation cycles in comparison to backscattering coefficients and optical data. Angular sensitivities and dynamics of polarimetric indicators were also analyzed following the growth stages of these two common crop types using, in total, 14 radar images (Radarsat-2), 16 optical images (Formosat-2, Spot-4/5), and numerous ground data. The results of this study show the importance of correcting the angular effect on SAR signals especially for copolarized signals and polarimetric indicators associated to single-bounce scattering mechanisms. The analysis of the temporal dynamic of polarimetric indicators has shown their high potential to detect crop growth changes. Moreover, this study shows the high interest of using SAR parameters (backscattering coefficients and polarimetric indicators) for crop parameters estimation during the whole vegetation cycle instead of optical vegetation index. They particularly revealed their high potential for rapeseed height and DB monitoring [i.e., Shannon entropy polarimetry (r2=0.70) and radar vegetation index (r2=0.80), respectively]

Published
2016
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40. Assimilation of LAI and dry biomass data from optical and SAR images into an agro-meteorological model to estimate soybean yield

Author

Remy Fieuzal, Frédéric Baup, Julie Betbeder, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Atmospheric Science, 010504 meteorology & atmospheric sciences, Soja, Image spot, 0211 other engineering and technologies, 02 engineering and technology, Imagerie par satellite, 01 natural sciences, F01 - Culture des plantes, skin and connective tissue diseases, Mathematics, 2. Zero hunger, Polarimétrie, Rendement des cultures, Vegetation Index, Agricultural landscapes, Synthetic aperture radar, P40 - Météorologie et climatologie, Télédétection, Polarimetry, Normalized Difference Vegetation Index, Computers in Earth Sciences, Leaf area index, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Agrométéorologie, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Environmental perspective, Radar, fungi, Modèle de simulation, 15. Life on land, [SDE.ES]Environmental Sciences/Environmental and Society, body regions, Satellite, U30 - Méthodes de recherche, Index de végétation
Abstract

Crop monitoring at a fine scale and crop yield estimation are critical from an environmental perspective because they provide essential information to combine increased food production and sustainable management of agricultural landscapes. The aim of this article is to estimate soybean yield using an agro-meteorological model controlled by optical and/or synthetic aperture radar (SAR) multipolarized satellite images. Satellite and ground data were collected over seven working farms. Optical and SAR images were acquired by Formosat-2, Spot-4, Spot-5, and Radarsat-2 satellites during the soybean vegetation cycle. A vegetation index (NDVI) was derived from the optical images, and backscattering coefficients and polarimetric indicators were computed from full quad-pol Radarsat-2 images. An angular normalization of SAR data was performed to minimize the incidence angle effects on SAR signals by using the complementarities provided by SAR and optical data. The best results are obtained when the model is controlled by both the leaf area index (LAI) derived from the optical vegetation index modified triangular vegetation index (MTVI2) or from the SAR backscattering coefficient ${\sigma_{{^{\circ}}{\textsc{vv}}}}$ $({\text{LAI}}_{\text{MTVI2}} $ or ( ${\text{LAI}}_{\sigma^{\circ}{\textsc{vv}}} $ ) and the dry biomass (DB) derived from the SAR Pauli matrix T33 $({\text{DB}}_{{\text{T}}33}) ({\text{r}}^{2} > 0.83)$ , demonstrating the complementary of optical and SAR data.

Published
2016
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41. Estimation of crop parameters using multi-temporal optical and radar polarimetric satellite data

Author

Yannick Philippets, Remy Fieuzal, Julie Betbeder, Frédéric Baup, Laurent Ferro-Famil, Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Nantes Université (NU)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and Université de Nantes (UN)-Université de Rennes 1 (UR1)

Subjects
2. Zero hunger, Synthetic aperture radar, Backscatter, Polarimetry, Vegetation, 15. Life on land, Normalized Difference Vegetation Index, law.invention, Geography, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, law, Satellite, Radar, Leaf area index, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Remote sensing
Abstract

International audience; This paper is concerned with the estimation of wheat and rapeseed crops parameters (height, leaf area index and dry biomass), during their whole vegetation cycle, using satellite time series both acquired in optical and microwave domains. Crop monitoring at a fine scale represents an important stake from an environmental point of view as it provides essential information to combine increase of production and sustainable management of agricultural landscapes. The aim of this paper is to compare the potential of optical and SAR parameters (backscattering coefficients and polarimetric parameters) for crop parameters estimation. Satellite (Formosat-2, Spot-4/5 and Radarsat-2) and ground data were acquired during the MCM 10 experiment conducted by the CESBIO laboratory in 2010. A vegetation index was derived from the optical images: the NDVI and backscattering coefficients and polarimetric parameters were computed from Radarsat-2 images. Results of this study show the high interest of using SAR parameters (backscattering coefficients and polarimetric parameters) for crop parameters estimation during the whole vegetation cycle instead of using optical vegetation index. Polarimetric parameters do not improve wheat parameters estimation (e.g. backscattering coefficient sigma-nought VV corresponds to the best parameter for wheat height estimation (r2 = 0.60)) but show their high potential for rapeseed height and dry biomass monitoring (i.e. Shannon Entropy polarimetry (SEp ; r2 = 0.70) and Radar Vegetation Index (RVI ; r2 = 0.80) respectively).

Published
2015
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42. Detection and Characterization of Hedgerows Using TerraSAR-X Imagery

Author

Samuel Corgne, Julie Betbeder, Jean Nabucet, Eric Pottier, Jacques Baudry, Laurence Hubert-Moy, Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN), Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), SAD Paysage (SAD Paysage), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Prasad S. Thenkabail, Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Brest (UBO)-École pratique des hautes études (EPHE)-Université de Nantes (UN)-Université d'Angers (UA)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 2 (UR2), Normandie Université (NU)-Normandie Université (NU), Institut d'Electronique et de Télécommunications de Rennes (IETR), AGROCAMPUS OUEST-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Nantes Université (NU)-Université de Rennes 1 (UR1)

Subjects
Canopy, 010504 meteorology & atmospheric sciences, Biodiversité et Ecologie, Radar à synthèse d'ouverture, 0211 other engineering and technologies, linear hedgerow network, canopy structure, radar-sar imagery, very high resolution, shannon entropy index, object-oriented classification, 02 engineering and technology, Imagerie par satellite, 01 natural sciences, law.invention, law, K01 - Foresterie - Considérations générales, Radar, Etudes de l'environnement, Mathematics, Shannon entropy index, [SHS.GEO]Humanities and Social Sciences/Geography, Agricultural sciences, Characterization (materials science), Radar-SAR imagery, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Écologie, F40 - Écologie végétale, Télédétection, Science, Polarimetry, Haie, Biodiversity and Ecology, Kappa index, Plante de haie, Couvert, Radar imaging, Environmental studies, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Paysage, Environmental and Society, Fragmentation (computing), 15. Life on land, [SDE.ES]Environmental Sciences/Environmental and Society, Field (geography), General Earth and Planetary Sciences, Environnement et Société, U30 - Méthodes de recherche, Sciences agricoles
Abstract

International audience; Whilst most hedgerow functions depend upon hedgerow structure and hedgerow network patterns, in many ecological studies information on the fragmentation of hedgerows network and canopy structure is often retrieved in the field in small areas using accurate ground surveys and estimated over landscapes in a semi-quantitative manner. This paper explores the use of radar SAR imagery to (i) detect hedgerow networks; and (ii) describe the hedgerow canopy heterogeneity using TerraSAR-X imagery. The extraction of hedgerow networks was achieved using an object-oriented method using two polarimetric parameters: the Single Bounce and the Shannon Entropy derived from one TerraSAR-X image. The hedgerow canopy heterogeneity estimated from field measurements was compared with two backscattering coefficients and three polarimetric parameters derived from the same image. The results show that the hedgerow network and its fragmentation can be identified with a very good accuracy (Kappa index: 0.92). This study also reveals the high correlation between one polarimetric parameter, the Shannon entropy, and the canopy fragmentation measured in the field. Therefore, VHSR radar images can both precisely detect the presence of wooded hedgerow networks and characterize their structure, which cannot be achieved with optical images.

Published
2014
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43. Multitemporal classification of TerraSAR-X data for wetland vegetation mapping

Author

Thomas Corpetti, Laurence Hubert-Moy, Eric Pottier, Samuel Corgne, Sébastien Rapinel, Julie Betbeder, Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN), Ecosystèmes, biodiversité, évolution [Rennes] (ECOBIO), Université de Rennes (UR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Nantes Université (NU), Université de Nantes (UN)-Université de Rennes 1 (UR1), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

Subjects
010504 meteorology & atmospheric sciences, Radar à synthèse d'ouverture, 0211 other engineering and technologies, Wetland, Imagerie par satellite, 02 engineering and technology, terre humide, 01 natural sciences, law.invention, law, Polarization, Radar, ComputingMilieux_MISCELLANEOUS, geography.geographical_feature_category, Contextual image classification, Vegetation, [SHS.GEO]Humanities and Social Sciences/Geography, [SDE]Environmental Sciences, P01 - Conservation de la nature et ressources foncières, Synthetic aperture radar, Écologie, F40 - Écologie végétale, Télédétection, Backscatter, Matrices, Image processing, Radar imaging, Polarimetry, Nonpoint source pollution, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, geography, Flood myth, Water, Végétation, 15. Life on land, 13. Climate action, General Earth and Planetary Sciences, Environmental science, U30 - Méthodes de recherche, Plaine d'inondation
Abstract

This paper is concerned with wetland vegetation mapping using multitemporal syn- thetic aperture radar imagery. Although wetlands play a key role in controlling flooding and nonpoint source pollution, sequestering carbon and providing an abundance of ecological ser- vices, knowledge of the flora and fauna of these environments is patchy, and understanding of their ecological functioning is still insufficient for a reliable functional assessment on areas larger than a few hectares. The aim of this paper is to evaluate multitemporal TerraSAR-X imagery to precisely map the distribution of vegetation formations considering flood duration. A series of six dual-polarization TerraSAR-X images (HH-VV) was acquired in 2012 during dry and wet seasons. One polarimetric parameter, the Shannon entropy (SE), and two intensity parameters (σ° HH and σ° VV), which vary with wetland flooding status and vegetation roughness, were first extracted. These parameters were then classified using support vector machine techniques based on a specific kernel adapted to the comparison of time-series data, K-nearest neighbors, and decision tree (DT) algorithms. The results show that the vegetation formations can be identified very accurately (kappa index ¼ 0.85) from the classification of SE temporal profiles derived from the TerraSAR-X images. They also reveal the importance of the use of polarimetric param- eters instead of backscattering coefficients alone (HH or VV) or combined (HH and VV). © 2014 Society of Photo-Optical Instrumentation Engineers (SPIE) (DOI: 10.1117/1.JRS.8.083648)

Published
2014
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44. ulti-Temporal optical and radar data fusion for crop monitoring: application to an intensive agricultural area in Britanny (France)

Author

Julie Betbeder, Marianne Laslier, Thomas Corpetti, Eric Pottier, Samuel Corgne, Laurence Hubert-Moy, Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN), Institut d'Électronique et des Technologies du numéRique (IETR), Nantes Université (NU)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), Corpetti, Thomas, Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDE] Environmental Sciences, [SDE]Environmental Sciences, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2014

45. Mapping of Central Africa forested wetlands using remote sensing

Author

Gaël Briant, Julie Betbeder, Frédéric Frappart, Valéry Gond, Etienne Bartholomé, Nicolas Baghdadi, Biens et services des écosystèmes forestiers tropicaux : l'enjeu du changement global (UPR BSEF), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Université Laval [Québec] (ULaval), Joint Research Center Institute for the Protection and Security of the Citizen, Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Biens et services des écosystèmes forestiers tropicaux (BSEF) (Cirad-ES-UPR 105 BSEF), Département Environnements et Sociétés (Cirad-ES), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), and Université Laval

Subjects
Atmospheric Science, Floodplain, Zone humide, Télédétection, IMAGERIE, Drainage basin, Wetland, Land cover, Spatial distribution, Facteur climatique, K01 - Foresterie - Considérations générales, Couverture végétale, Computers in Earth Sciences, Forêt tropicale humide, TELEDETECTION, Remote sensing, Hydrology, geography, geography.geographical_feature_category, Cartographie, Inondation, Distribution spatiale, Vegetation, Enhanced vegetation index, 15. Life on land, 13. Climate action, P31 - Levés et cartographie des sols, [SDE]Environmental Sciences, Environmental science, Moderate-resolution imaging spectroradiometer, U30 - Méthodes de recherche, Analyse d'image
Abstract

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-ATTOS; Wetlands represent 6% of the Earth's land cover surface. They are of crucial importance in the global water cycle and climatic dynamics. Nowadays, wetlands are the most threatened land cover type, nevertheless their spatial distribution and ecological functions are poorly documented. Despite the need for more detailed information, wetland mapping is a rarely activity. Few data are available mainly because of the complexity of obtaining good field data. We therefore propose a method based on multi sensor imagery analysis, to characterize land cover patterns of the second largest wetland area of the world (The 'Cuvette Centrale' of the Congo River basin). Time series of MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) images are used to map land cover types based their phenological differences. Flooded areas in the Congo Basin have been mapped during different seasons using L-band synthetic aperture radar (PALSAR) imagery. The associated model has been improved upon by the addition of elevation data as well as mean canopy heights acquired with LIDAR (Light Detection and Ranging) data. The result of this study is the first detailed spatial distribution of four forested wetland types within the 'Cuvette Centrale' of the Congo River basin. This study demonstrates that the spatial organization of the floodplain landscape depends on the extent of flooding. The results also show that land cover phenology is closely related to the time period of flood and solar intensity for this region, similarly to what is observed in the extensive floodplain of the Amazon basin.

Published
2014
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46. Multi-temporal classification of TerraSAR-X data for wetland vegetation mapping

Author

Laurence Hubert Moy, Sébastien Rapinel, Samuel Corgne, Thomas Corpetti, Eric Pottier, Julie Betbeder, Conservatoire Botanique National de Brest (CBN), Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN), Institut d'Électronique et des Technologies du numéRique (IETR), Nantes Université (NU)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Rennes 1 (UR1), and Université de Rennes (UNIV-RENNES)

Subjects
Synthetic aperture radar, Hydrology, multi-temporal classifications, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, Flooding (psychology), Wetland, Vegetation, 15. Life on land, 010501 environmental sciences, synthetic aperture radar (SAR), 01 natural sciences, Ecosystem services, Support vector machine, Geography, Support Vector Machine classifier, 13. Climate action, Abundance (ecology), Wetlands, TerraSAR-X imagery, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Nonpoint source pollution, 0105 earth and related environmental sciences
Abstract

International audience; This paper is concerned with vegetation wetland mapping using multi-temporal SAR imagery. Whilst wetlands play a key role in controlling flooding and nonpoint source pollution, sequestering carbon and providing an abundance of ecological services, knowledge of the flora and fauna of these environments is patchy, and understanding of their ecological functioning is still insufficient for a reliable functional assessment on areas larger than a few ha. The aim of this paper is to evaluate multitemporal TerraSAR-X imagery to map precisely the distribution of vegetation formations within wetlands, in determining seasonally flooded areas of wetlands. A series of six dual-polarization TerraSAR-X images (HH/VV) were acquired in 2012 during dry and wet seasons. Polarimetric and intensity parameters, which present a temporal variation that depends on wetland flooding status and vegetation roughness, were firstly extracted. The parameters were then classified based on Support Vector Machines (SVM) techniques using a specific kernel adapted to the comparison of time-series data. The results show that the Shannon entropy parameter allows discriminating vegetation formations within wetland with more accuracy than intensity parameters.

Published
2013
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47. Monitoring restored riparian vegetation: how can recent developments in remote sensing sciences help?

Author

Simon Dufour, Sébastien Rapinel, Jean Nabucet, Samuel Corgne, Ivan Bernez, Laurence Hubert-Moy, Charles Trollé, Julie Betbeder, Jérôme Sawtschuk, Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Brest (UBO)-École pratique des hautes études (EPHE)-Université de Nantes (UN)-Université d'Angers (UA)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 2 (UR2), Normandie Université (NU)-Normandie Université (NU), Écologie et santé des écosystèmes (ESE), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut de Géoarchitecture, Institut Brestois des Sciences de l'Homme et de la Société (IBSHS), Université de Brest (UBO)-Université de Brest (UBO)-Université de Brest (UBO)-Université de Bretagne Sud (UBS), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Littoral, Environnement, Télédétection, Géomatique ( LETG - Rennes ), Littoral, Environnement, Télédétection, Géomatique ( LETG ), Université de Caen Normandie ( UNICAEN ), Normandie Université ( NU ) -Normandie Université ( NU ) -Université d'Angers ( UA ) -Université de Nantes ( UN ) -École pratique des hautes études ( EPHE ) -Université de Brest ( UBO ) -Université de Rennes 2 ( UR2 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Caen Normandie ( UNICAEN ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Centre National de la Recherche Scientifique ( CNRS ), Écologie et santé des écosystèmes ( ESE ), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique ( INRA ), Institut Brestois des Sciences de l'Homme et de la Société ( IBSHS ), Université de Brest ( UBO ) -Université de Brest ( UBO ) -Maison des Sciences de l'Homme de Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), and Université de Brest ( UBO ) -Université de Brest ( UBO ) -Université de Bretagne Sud ( UBS )

Subjects
010504 meteorology & atmospheric sciences, 01 natural sciences, lcsh:Aquaculture. Fisheries. Angling, [ SHS.GEO ] Humanities and Social Sciences/Geography, law.invention, [ SDE ] Environmental Sciences, remote sensing, law, Radar, Water Science and Technology, lcsh:SH1-691, geography.geographical_feature_category, Ecology, Végétation ripicole, 04 agricultural and veterinary sciences, F70 - Taxonomie végétale et phytogéographie, [SHS.GEO]Humanities and Social Sciences/Geography, Identification (information), Lidar, Remote sensing (archaeology), [SDE]Environmental Sciences, medicine.symptom, Écologie, LiDAR, F40 - Écologie végétale, Télédétection, UAV, Restauration couverture végétale, riparian vegetation, Management, Monitoring, Policy and Law, Aquatic Science, Radar imaging, medicine, 0105 earth and related environmental sciences, Nature and Landscape Conservation, Remote sensing, Riparian zone, geography, 15. Life on land, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, U30 - Méthodes de recherche, Scale (map), Vegetation (pathology), radar
Abstract

Riparian vegetation restoration projects require appropriate tools to monitor actions efficiency. On a large scale remote sensing approaches can provide continuous and detailed data to describe riparian vegetation. In this paper, we illustrated recent developments and perspectives for riparian vegetation monitoring purposes through three examples of image sources: Light Detection And Ranging (LiDAR), radar and Unmanned Aerial Vehicule (UAV) images. We notably focused on the potential of such images to provide 3D information for narrow strips of riparian vegetation with high temporal resolution to allow fine monitoring following restoration program. LiDAR data allows canopy structure identification with a high accuracy level and automatic classifications for heterogeneous riparian corridors. Radar images allow a good identification of riparian vegetation but also of the structure and phenology of vegetation through time with an analysis of the Shannon entropy of the signal. The UAV system used here is a very flexible approach that can easily provide RGB mosaic but also a local digital surface model with very high spatial resolution. Lastly, we discuss the advantages and limitations of each approach from an applied perspective, in terms of flexibility, resolution and technicality.

Published
2013
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48. Landform and landscape mapping, French Guiana (South America)

Author

Jean-Michel Carozza, Cécile Richard-Hansen, Julie Betbeder, Olivier Brunaux, Jean-François Cornu, Stéphane Guitet, Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Département recherche et développement, Direction Régionale de Guyane, Office National des Forêts (ONF), Centre National de la Recherche Scientifique (CNRS), Direction Etudes et recherches, Office National de la Chasse et de la Faune Sauvage, French Ministry of the Environment (‘Ecosyste`mes Tropicaux’ research project), and by ONF, and EU grants (PO-FEDER ‘Habitats’)., Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD [France-Sud]), and GUITET, Stéphane

Subjects
0106 biological sciences, 010504 meteorology & atmospheric sciences, Biodiversité et Ecologie, télédétection, Geography, Planning and Development, 01 natural sciences, shield, K01 - Foresterie - Considérations générales, Earth and Planetary Sciences (miscellaneous), remote-sensing, SRTM, Landscape mapping, geodiversity, geography.geographical_feature_category, Remote sensing, Geography, Principal component analysis, P01 - Conservation de la nature et ressources foncières, Géomorphologie, rainforest, Cartography, Rainforest, Guiana, forêt tropicale humide, Biogéographie, Shuttle Radar Topography Mission, Geomorphologie, Geomorphology, Geodiversity, Guiana shield, Spatial distribution, 010603 evolutionary biology, Biodiversity and Ecology, Cartographie de l'occupation du sol, Transect, Cluster analysis, 0105 earth and related environmental sciences, Paysage, Landform, geomorphology, 15. Life on land, guyane française, U30 - Méthodes de recherche, [SDE.BE]Environmental Sciences/Biodiversity and Ecology
Abstract

In this paper two geomorphologic maps (landform level and landscape level) are presented covering the French Guianan rainforest (84,000 km2) using full-resolution Shuttle Radar Topography Mission (SRTM) data. The entire country was segmented into 224,000 landform units on the basis of an original object-oriented approach using a modified counting box algorithm. A Principal Components Analysis (PCA) followed by k-means clustering (Ward's method) identified 12 different landform types corresponding to theoretical elementary landforms. The landscape map was generated by analyzing the spatial distribution of the different landform types. The different maps and models were compared with topographic field data collected on 92 transects totaling 260 km in length. The object-focused approach is a very efficient method that preserves geomorphologic consistency and discriminates between landforms using simple descriptors that are easily understood by non-geomorphologists. Despite major noise in the data, the landform map proved to be reliable and provided a strong spatial structure for the definition of landscape units. We recommend using the landform map at scales 1: 100,000?1: 250,000. Landscape map, used on a 1:1,000,000?1:2,000,000 scale, enabled us to draw bio-geographical limits in this region and provides exhaustive relief information that usefully supplements the geological map.

Published
2013
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