37 results on '"Laurin, Gaia Vaglio"'
Search Results
2. SnowWarp: An open science and open data tool for daily monitoring of snow dynamics
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Laurin, Gaia Vaglio, Francini, Saverio, Penna, Daniele, Zuecco, Giulia, Chirici, Gherardo, Berman, Ethan, Coops, Nicholas C., Castelli, Giulio, Bresci, Elena, Preti, Federico, and Valentini, Riccardo
- Published
- 2022
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- View/download PDF
3. A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps
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Araza, Arnan, de Bruin, Sytze, Herold, Martin, Quegan, Shaun, Labriere, Nicolas, Rodriguez-Veiga, Pedro, Avitabile, Valerio, Santoro, Maurizio, Mitchard, Edward T.A., Ryan, Casey M., Phillips, Oliver L., Willcock, Simon, Verbeeck, Hans, Carreiras, Joao, Hein, Lars, Schelhaas, Mart-Jan, Pacheco-Pascagaza, Ana Maria, da Conceição Bispo, Polyanna, Laurin, Gaia Vaglio, Vieilledent, Ghislain, Slik, Ferry, Wijaya, Arief, Lewis, Simon L., Morel, Alexandra, Liang, Jingjing, Sukhdeo, Hansrajie, Schepaschenko, Dmitry, Cavlovic, Jura, Gilani, Hammad, and Lucas, Richard
- Published
- 2022
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- View/download PDF
4. Corrigendum to “Validating GEDI tree canopy cover product across forest types using co-registered aerial LiDAR data” [ISPRS J. Photogramm. Remote Sens. 207 (2024) 326–337]
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Li, Xiao, primary, Li, Linyuan, additional, Ni, Wenjian, additional, Mu, Xihan, additional, Wu, Xiaodan, additional, Laurin, Gaia Vaglio, additional, Vangi, Elia, additional, Stereńczak, Krzysztof, additional, Chirici, Gherardo, additional, Yu, Shiyou, additional, and Huang, Huaguo, additional
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- 2024
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5. Inferring plant functional diversity from space: the potential of Sentinel-2
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Ma, Xuanlong, Mahecha, Miguel D., Migliavacca, Mirco, van der Plas, Fons, Benavides, Raquel, Ratcliffe, Sophia, Kattge, Jens, Richter, Ronny, Musavi, Talie, Baeten, Lander, Barnoaiea, Ionut, Bohn, Friedrich J., Bouriaud, Olivier, Bussotti, Filippo, Coppi, Andrea, Domisch, Timo, Huth, Andreas, Jaroszewicz, Bogdan, Joswig, Julia, Pabon-Moreno, Daniel E., Papale, Dario, Selvi, Federico, Laurin, Gaia Vaglio, Valladares, Fernando, Reichstein, Markus, and Wirth, Christian
- Published
- 2019
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6. Characteristics of Tropical Tree Species in Hyperspectral and Multispectral Data
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Ferreira, Matheus Pinheiro, primary, Amaral, Cibele Hummel do, additional, Laurin, Gaia Vaglio, additional, Kokaly, Raymond, additional, de Souza Filho, Carlos Roberto, additional, and Shimabukuro, Yosio Edemir, additional
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- 2018
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7. Vegetation structure derived from airborne laser scanning to assess species distribution and habitat suitability: The way forward
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Moudrý, Vítězslav, primary, Cord, Anna F., additional, Gábor, Lukáš, additional, Laurin, Gaia Vaglio, additional, Barták, Vojtěch, additional, Gdulová, Kateřina, additional, Malavasi, Marco, additional, Rocchini, Duccio, additional, Stereńczak, Krzysztof, additional, Prošek, Jiří, additional, Klápště, Petr, additional, and Wild, Jan, additional
- Published
- 2022
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8. Space For The Unccd And The Desertwatch Project
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Pace, Gaetano, Laurin, Gaia Vaglio, Do Rosario, Lucio Pires, Sciortino, Maurizio, Marini, Alberto, editor, and Talbi, Mohamed, editor
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- 2009
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9. Imperfect detection and wildlife density estimation using aerial surveys with infrared and visible sensors.
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Delisle, Zackary J., McGovern, Patrick G., Dillman, Brian G., Swihart, Robert K., Sankey, Temuulen, and Laurin, Gaia Vaglio
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AERIAL surveys ,WHITE-tailed deer ,DETECTORS ,DENSITY ,ELEVATING platforms ,INFRARED cameras ,THERMOGRAPHY - Abstract
Aerial vehicles equipped with infrared thermal sensors facilitate quick density estimates of wildlife, but detection error can arise from the thermal sensor and viewer of the infrared video. We reviewed published research to determine how commonly these sources of error have been assessed in studies using infrared video from aerial platforms to sample wildlife. The number of annual articles pertaining to aerial sampling using infrared thermography has increased drastically since 2018, but past studies inconsistently assessed sources of imperfect detection. We illustrate the importance of accounting for some of these types of error in a case study on white‐tailed deer Odocoileus virginianus in Indiana, USA, using a simple double‐observer approach. In our case study, we found evidence of false negatives associated with the viewer of infrared video. Additionally, we found that concordance between the detections of two viewers increased when using a red‐green‐blue camera paired with the infrared thermal sensor, when altitude decreased and when more stringent criteria were used to classify thermal signatures as deer. We encourage future managers and ecologists recording infrared video from aerial platforms to use double‐observer methods to account for viewer‐induced false negatives when video is manually viewed by humans. We also recommend combining infrared video with red‐green‐blue video to reduce false positives, applying stringent verification standards to detections in infrared and red‐green‐blue video and collecting data at lower altitudes over snow when needed. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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10. Prospects for monitoring bird migration along the East Asian‐Australasian Flyway using weather radar.
- Author
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Shi, Xu, Hu, Cheng, Soderholm, Joshua, Chapman, Jason, Mao, Huafeng, Cui, Kai, Ma, Zhijun, Wu, Dongli, Fuller, Richard A., Lecours, Vincent, and Laurin, Gaia Vaglio
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BIRD migration ,WEATHER radar networks ,RADAR meteorology ,MIGRATION flyways ,BIRD surveys ,BIRD communities ,MIGRATORY birds - Abstract
Each year, billions of birds migrate across the globe, and interpretation of weather radar signals is increasingly being used to document the spatial and temporal migration patterns in Europe and America. Such approaches are yet to be applied in the East Asian‐Australasian Flyway (EAAF), one of the most species‐rich and threatened flyways in the world. Logistical challenges limit direct on‐ground monitoring of migratory birds in many parts of the EAAF, resulting in knowledge gaps on population status and site use that limit evidence‐based conservation planning. Weather radar data have great potential for achieving comprehensive migratory bird monitoring along the EAAF. In this study, we discuss the feasibility and challenges of using weather radar to complement on‐ground bird migration surveys in the flyway. We summarize the location, capacity and data availability of weather radars across EAAF countries, as well as the spatial coverage of the radars with respect to migrants' geographic distribution and migration hotspots along the flyway, with an exemplar analysis of biological movement patterns extracted from Chinese weather radars. There are more than 430 weather radars in EAAF countries, covering on average half of bird species' passage and non‐breeding distributions, as well as 70% of internationally important sites for migratory shorebirds. We conclude that the weather radar network could be a powerful resource for monitoring bird movements over the full annual cycle throughout much of the EAAF, providing estimates of migration traffic rates, site use, and long‐term population trends, especially in remote and less‐surveyed regions. Analyses of weather radar data would complement existing ornithological surveys and help understand the past and present status of the avian community in a highly threatened flyway. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
11. Space For The Unccd And The Desertwatch Project
- Author
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Pace, Gaetano, primary, Laurin, Gaia Vaglio, additional, Do Rosario, Lucio Pires, additional, and Sciortino, Maurizio, additional
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- 2009
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12. Global Airborne Laser Scanning Data Providers Database (GlobALS)—A New Tool for Monitoring Ecosystems and Biodiversity
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Stereńczak, Krzysztof, primary, Laurin, Gaia Vaglio, additional, Chirici, Gherardo, additional, Coomes, David A., additional, Dalponte, Michele, additional, Latifi, Hooman, additional, and Puletti, Nicola, additional
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- 2020
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13. Satellite open data to monitor forest damage caused by extreme climate-induced events: a case study of the Vaia storm in Northern Italy.
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Laurin, Gaia Vaglio, Francini, Saverio, Luti, Tania, Chirici, Gherardo, Pirotti, Francesco, and Papale, Dario
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FOREST monitoring ,STORM damage ,SYNTHETIC apertures ,SYNTHETIC aperture radar ,CLOUDINESS - Abstract
The frequency of extreme storm events has significantly increased in the past decades, causing significant damage to European forests. To mitigate the impacts of extreme events, a rapid assessment of forest damage is crucial, and satellite data are an optimal candidate for this task. The integration of satellite data in the operational phase of monitoring forest damage can exploit the complementarity of optical and Synthetic Aperture Radar (SAR) open datasets from the Copernicus programme. This study illustrates the testing of Sentinel 1 and Sentinel 2 data for the detection of areas impacted by the Vaia storm in Northern Italy. The use of multispectral Sentinel 2 provided the best performance, with classification overall accuracy (OA) values up to 86 percent; however, optical data use is seriously hampered by cloud cover that can persist for months after the event and in most cases cannot be considered an appropriate tool if a fast response is required. The results obtained using SAR Sentinel 1 were slightly less accurate (OA up to 68 percent), but the method was able to provide valuable information rapidly, mainly because the acquisition of this dataset is weather independent. Overall, for a fast assessment Sentinel 1 is the better of the two methods where multispectral and ground data are able to further refine the initial SAR-based assessment. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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14. Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data
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Laurin, Gaia Vaglio, primary, Balling, Johannes, primary, Corona, Piermaria, primary, Mattioli, Walter, primary, Papale, Dario, primary, Puletti, Nicola, primary, Rizzo, Maria, primary, Truckenbrodt, John, primary, and Urban, Marcel, primary
- Published
- 2018
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15. An integrated pan‐tropical biomass map using multiple reference datasets
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Avitabile, Valerio, primary, Herold, Martin, additional, Heuvelink, Gerard B. M., additional, Lewis, Simon L., additional, Phillips, Oliver L., additional, Asner, Gregory P., additional, Armston, John, additional, Ashton, Peter S., additional, Banin, Lindsay, additional, Bayol, Nicolas, additional, Berry, Nicholas J., additional, Boeckx, Pascal, additional, Jong, Bernardus H. J., additional, DeVries, Ben, additional, Girardin, Cecile A. J., additional, Kearsley, Elizabeth, additional, Lindsell, Jeremy A., additional, Lopez‐Gonzalez, Gabriela, additional, Lucas, Richard, additional, Malhi, Yadvinder, additional, Morel, Alexandra, additional, Mitchard, Edward T. A., additional, Nagy, Laszlo, additional, Qie, Lan, additional, Quinones, Marcela J., additional, Ryan, Casey M., additional, Ferry, Slik J. W., additional, Sunderland, Terry, additional, Laurin, Gaia Vaglio, additional, Gatti, Roberto Cazzolla, additional, Valentini, Riccardo, additional, Verbeeck, Hans, additional, Wijaya, Arief, additional, and Willcock, Simon, additional
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- 2016
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16. COSMO-SkyMed potential to detect and monitor Mediterranean maquis fires and regrowth: a pilot study in Capo Figari, Sardinia, Italy.
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Laurin, Gaia Vaglio, Avezzano, Ruggero, Bacciu, Valentina, Del Frate, Fabio, Papale, Dario, and Virelli, Maria
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WILDFIRES , *FOREST fires , *FORESTS & forestry , *FOREST ecology , *CLIMATE change , *ECOSYSTEM dynamics - Abstract
Mediterranean maquis is a complex and widespread ecosystem in the region, intrinsically prone to fire. Many species have developed specific adaptation traits to cope with fire, ensuring resistance and resilience. Due to the recent changes in socio-economy and land uses, fires are more and more frequent in the urban-rural fringe and in the coastlines, both now densely populated. The detection of fires and the monitoring of vegetation regrowth is thus of primary interest for local management and for understanding the ecosystem dynamics and processes, also in the light of the recurrent droughts induced by climate change. Among the main objectives of the COSMO-SkyMed radar constellation mission there is the monitoring of environmental hazards; the very high revisiting time of this mission is optimal for post-hazard response activities. However, very few studies exploited such data for fire and vegetation monitoring. In this research, Cosmo-SkyMed is used in a Mediterranean protected area covered by maquis to detect the burnt area extension and to conduct a mid-term assessment of vegetation regrowth. The positive results obtained in this research highlight the importance of the very high-resolution continuous acquisitions and the multi-polarization information provided by COSMO-SkyMed for monitoring fire impacts on vegetation. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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17. Tree species diversity of three Ghanaian reserves.
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Gatti, Roberto Cazzolla, Laurin, Gaia Vaglio, and Valentini, Riccardo
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PLANT diversity , *PLANT species , *FORESTS & forestry , *DATABASES , *ACQUISITION of data - Abstract
Among tropical areas, Africa is considered to be poor in terms of biodiversity as compared with Amazon or South-East Asia, especially with respect to forest diversity. Despite this lower diversity, some African tropical zones, such as Ghana, harbour a plethora of species, particularly of trees. Unfortunately, as a result of anthropogenic impacts, biological diversity in West Africa dramatically decreased in the last decades, with very limited reference to evaluate the amount of the loss. Due to these growing pressure, a collection of relevant biodiversity information in this region seems to be urgent. We surveyed 127 temporary plots randomly distributed within 3 protected areas in Ghana and we collected data on tree (dbh>10 cm) species richness and their abundances. We also performed α, and β diversity analyses, and estimated the effective number of species, adopting various indices and approaches to provide further information on each assemblage. The main goals of this research were: (i) to provide a wide tree species database (abundance-based data), together with some biodiversity analyses; (ii) to estimate the sampling effort needed for next biodiversity surveys in the same and similar regions; and (iii) to calculate some indices useful to monitor the future of these protected areas both in terms of conservation and biodiversity research. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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18. Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates.
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Laurin, Gaia Vaglio, Pirotti, Francesco, Callegari, Mattia, Qi Chen, Cuozzo, Giovanni, Lingua, Emanuele, Notarnicola, Claudia, and Papale, Dario
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FOREST biomass , *CARBON sequestration , *REMOTE sensing , *LIDAR , *COMPARATIVE studies - Abstract
Remote sensing supports carbon estimation, allowing the upscaling of field measurements to large extents. Lidar is considered the premier instrument to estimate above ground biomass, but data are expensive and collected on-demand, with limited spatial and temporal coverage. The previous JERS and ALOS SAR satellites data were extensively employed to model forest biomass, with literature suggesting signal saturation at low-moderate biomass values, and an influence of plot size on estimates accuracy. The ALOS2 continuity mission since May 2014 produces data with improved features with respect to the former ALOS, such as increased spatial resolution and reduced revisit time. We used ALOS2 backscatter data, testing also the integration with additional features (SAR textures and NDVI from Landsat 8 data) together with ground truth, to model and map above ground biomass in two mixed forest sites: Tahoe (California) and Asiago (Alps). While texture was useful to improve the model performance, the best model was obtained using joined SAR and NDVI (R² equal to 0.66). In this model, only a slight saturation was observed, at higher levels than what usually reported in literature for SAR; the trend requires further investigation but the model confirmed the complementarity of optical and SAR datatypes. For comparison purposes, we also generated a biomass map for Asiago using lidar data, and considered a previous lidar-based study for Tahoe; in these areas, the observed R² were 0.92 for Tahoe and 0.75 for Asiago, respectively. The quantitative comparison of the carbon stocks obtained with the two methods allows discussion of sensor suitability. The range of local variation captured by lidar is higher than those by SAR and NDVI, with the latter showing overestimation. However, this overestimation is very limited for one of the study areas, suggesting that when the purpose is the overall quantification of the stored carbon, especially in areas with high carbon density, satellite data with lower cost and broad coverage can be as effective as lidar. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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19. Does degradation from selective logging and illegal activities differently impact forest resources? A case study in Ghana.
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Laurin, Gaia Vaglio, Hawthorne, William D., Chiti, Tommaso, Di Paola, Arianna, Gatti, Roberto Cazzolla, Marconi, Sergio, Noce, Sergio, Grieco, Elisa, Pirotti, Francesco, and Valentini, Riccardo
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- *
ECOSYSTEM management , *NATURE conservation , *BIODIVERSITY , *LOGGING , *LAND use - Abstract
Degradation, a reduction of the ecosystem's capacity to supply goods and services, is widespread in tropical forests and mainly caused by human disturbance. To maintain the full range of forest ecosystem services and support the development of effective conservation policies, we must understand the overall impact of degradation on different forest resources. This research investigates the response to disturbance of forest structure using several indicators: soil carbon content, arboreal richness and biodiversity, functional composition (guild and wood density), and productivity. We drew upon large field and remote sensing datasets from different forest types in Ghana, characterized by varied protection status, to investigate impacts of selective logging, and of illegal land use and resources extraction, which are the main disturbance causes in West Africa. Results indicate that functional composition and the overall number of species are less affected by degradation, while forest structure, soil carbon content and species abundance are seriously impacted, with resources distribution reflecting the protection level of the areas. Remote sensing analysis showed an increase in productivity in the last three decades, with higher resiliency to change in drier forest types, and stronger productivity correlation with solar radiation in the short dry season. The study region is affected by growing anthropogenic pressure on natural resources and by an increased climate variability: possible interactions of disturbance with climate are also discussed, together with the urgency to reduce degradation in order to preserve the full range of ecosystem functions. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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- View/download PDF
20. Airborne LiDAR Detects Selectively Logged Tropical Forest Even in an Advanced Stage of Recovery.
- Author
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Kent, Rafi, Lindsell, Jeremy A., Laurin, Gaia Vaglio, Valentini, Riccardo, and Coomes, David A.
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FOREST canopy gaps ,OLD growth forest conservation ,DEFORESTATION ,SELECTIVE logging ,LIDAR ,AIRBORNE-based remote sensing - Abstract
Identifying historical forest disturbances is difficult, especially in selectively logged areas. LiDAR is able to measure fine-scale variations in forest structure over multiple kilometers. We use LiDAR data from ca. 16 km2 of forest in Sierra Leone, West Africa, to discriminate areas of old-growth from areas recovering from selective logging for 23 years. We examined canopy height variation and gap size distributions. We found that though recovering blocks of forest differed little in height from old-growth forest (up to 3 m), they had a greater area of canopy gaps (average 10.2% gap fraction in logged areas, compared to 5.6% in unlogged area); and greater numbers of gaps penetrating to the forest floor (162 gaps at 2 m height in logged blocks, and 101 in an unlogged block). Comparison of LiDAR measurements with field data demonstrated that LiDAR delivered accurate results. We found that gap size distributions deviated from power-laws reported previously, with substantially fewer large gaps than predicted by power-law functions. Our analyses demonstrate that LiDAR is a useful tool for distinguishing structural differences between old-growth and old-secondary forests. That makes LiDAR a powerful tool for REDD+ (Reduction of Emissions from Deforestation and Forest Degradation) programs implementation and conservation planning. [ABSTRACT FROM AUTHOR]
- Published
- 2015
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- View/download PDF
21. Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estiates.
- Author
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Dengsheng Lu, Qi Chen, Guangxing Wang, Moran, Emilio, Batistella, Mateus, Maozhen Zhang, Laurin, Gaia Vaglio, and Saah, David
- Abstract
Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM's shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data. [ABSTRACT FROM AUTHOR]
- Published
- 2012
- Full Text
- View/download PDF
22. Historical trends of degradation, loss, and recovery in the tropical forest reserves of Ghana.
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Wimberly, Michael C., Dwomoh, Francis K., Numata, Izaya, Mensah, Foster, Amoako, Jacob, Nekorchuk, Dawn M., and McMahon, Andrea
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FOREST reserves ,TROPICAL forests ,FOREST degradation ,FOREST restoration ,FOREST protection ,RANDOM forest algorithms ,ECOSYSTEMS - Abstract
The Upper Guinean Forest region of West Africa, a globally significant biodiversity hotspot, is among the driest and most human-impacted tropical ecosystems. We used Landsat to study forest degradation, loss, and recovery in the forest reserves of Ghana from 2003 to 2019. Annual canopy cover maps were generated using random forests and results were temporally segmented using the LandTrendr algorithm. Canopy cover was predicted with a predicted-observed r
2 of 0.76, mean absolute error of 12.8%, and mean error of 1.3%. Forest degradation, loss, and recovery were identified as transitions between closed (>60% cover), open (15–60% cover) and low tree cover (< 15% cover) classes. Change was relatively slow from 2003 to 2015, but there was more disturbance than recovery resulting in a gradual decline in closed canopy forests. In 2016, widespread fires associated with El Niño drought caused forest loss and degradation across more than 12% of the moist semi-deciduous and upland evergreen forest types. The workflow was implemented in Google Earth Engine, allowing stakeholders to visualize the results and download summaries. Information about historical disturbances will help to prioritize locations for future studies and target forest protection and restoration activities aimed at increasing resilience. [ABSTRACT FROM AUTHOR]- Published
- 2022
- Full Text
- View/download PDF
23. Co-limitation towards lower latitudes shapes global forest diversity gradients
- Abstract
The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025 degrees x 0.025 degrees) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from similar to 1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers.
- Published
- 2022
- Full Text
- View/download PDF
24. Co-limitation towards lower latitudes shapes global forest diversity gradients
- Abstract
The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025 degrees x 0.025 degrees) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from similar to 1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers.
- Published
- 2022
25. Co-limitation towards lower latitudes shapes global forest diversity gradients
- Abstract
The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025 degrees x 0.025 degrees) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from similar to 1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers.
- Published
- 2022
26. Co-limitation towards lower latitudes shapes global forest diversity gradients
- Abstract
The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025 degrees x 0.025 degrees) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from similar to 1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers.
- Published
- 2022
- Full Text
- View/download PDF
27. A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps
- Abstract
Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the f
- Published
- 2022
28. A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps
- Abstract
Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the f
- Published
- 2022
29. A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps
- Abstract
Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the f
- Published
- 2022
30. Performance of Various Speckle Filter Methods in Modelling Forest Aboveground Biomass using Sentinel-1 Data: Case Study of Barru Regency, South Sulawesi.
- Author
-
Giri Ananto, Wahyu Hendardi, Sandhini Putri, Ade Febri, Hadi, Haeydar Anggara, Hanum, Difa Nisrina, Puji Wiryawan, Bayu Kurnia, Prabaswara, Rifqi Rizaldy, and Arjasakusuma, Sanjiwana
- Published
- 2020
- Full Text
- View/download PDF
31. ESTIMATION METHODOLOGY FOR FOREST BIOMASS IN MONGOLIA USING REMOTE SENSING.
- Author
-
Altanchimeg, T., Renchin, T., De Maeyer, P., Natsagdorj, E., Tseveen, B., and Norov, B.
- Subjects
FOREST biomass ,REMOTE sensing ,BIOMASS estimation ,LEAF area index ,SOIL moisture - Abstract
The forest biomass is one of the most important parameters for the global carbon stock. Information on the forest volume, coverage and biomass are important to develop the global perspective on the CO
2 concentration changes. Objective of this research is to estimate forest biomass in the study area. The study area is Hangal sum, Bulgan province, Mongolia. Backscatter coefficients for vertical transmit and vertical receive (VV), for vertical transmit and horizontal receive (VH) from Sentinel data and Leaf Area Index (LAI) from Landsat data were used in the study area. We developed biomass estimation approach using ground truth data which is DBH, height and soil moisture. The coefficient α, β, δ, γ were found from the approach. The output map from the approach was compared with VV and VH, LAI data. The relationship between output map and VH data shows a positive result R2 = 0.61. This study suggests that the biomass estimation using Remote sensing data can be applied in forest region in the North. [ABSTRACT FROM AUTHOR]- Published
- 2019
- Full Text
- View/download PDF
32. Inferring plant functional diversity from space : the potential of Sentinel-2
- Abstract
Plant functional diversity (FD) is an important component of biodiversity that characterizes the variability of functional traits within a community, landscape, or even large spatial scales. It can influence ecosystem processes and stability. Hence, it is important to understand how and why FD varies within and between ecosystems, along resources availability gradients and climate gradients, and across vegetation successional stages. Usually, FD is assessed through labor-intensive field measurements, while assessing FD from space may provide a way to monitor global FD changes in a consistent, time and resource efficient way. The potential of operational satellites for inferring FD, however, remains to be demonstrated. Here we studied the relationships between FD and spectral reflectance measurements taken by ESA's Sentinel-2 satellite over 117 field plots located in 6 European countries, with 46 plots having in-situ sampled leaf traits and the other 71 using traits from the TRY database. These field plots represent major European forest types, from boreal forests in Finland to Mediterranean mixed forests in Spain. Based on in-situ data collected in 2013 we computed functional dispersion (FDis), a measure of FD, using foliar and whole-plant traits of known ecological significance. These included five foliar traits: leaf nitrogen concentration (N%), leaf carbon concentration (%C), specific leaf area (SLA), leaf dry matter content (LDMC), leaf area (LA). In addition they included three whole-plant traits: tree height (H), crown cross-sectional area (CCSA), and diameter-at-breast-height (DBH). We applied partial least squares regression using Sentinel-2 surface reflectance measured in 2015 as predictive variables to model in-situ FDis measurements. We predicted, in cross-validation, 55% of the variation in the observed FDis. We also showed that the red-edge, near infrared and shortwave infrared regions of Sentinel-2 are more important than the visible region for predict
- Published
- 2019
33. An integrated pan-tropical biomass map using multiple reference datasets
- Abstract
We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N–23.4 S) of 375 Pg dry mass, 9–18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15–21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha−1 vs. 21 and 28 Mg ha−1 for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets.
34. An integrated pan-tropical biomass map using multiple reference datasets
- Abstract
We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N–23.4 S) of 375 Pg dry mass, 9–18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15–21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha−1 vs. 21 and 28 Mg ha−1 for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets.
- Published
- 2016
35. An integrated pan-tropical biomass map using multiple reference datasets
- Abstract
We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N–23.4 S) of 375 Pg dry mass, 9–18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15–21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha−1 vs. 21 and 28 Mg ha−1 for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets.
- Published
- 2016
36. Acknowledgement to Reviewers of Remote Sensing in 2014.
- Subjects
- REMOTE Sensing (Book), ANDERSON, Neil
- Abstract
People whom the author would like to thank for their assistance in the creation of the book "Remote Sensing" are mentioned including James V. Aanstoos, Henok Alemu and Neil Anderson.
- Published
- 2015
- Full Text
- View/download PDF
37. Classification of Semideciduous Seasonal Forest successional stages using Sentinel-1-2 and SRTM data on Google Earth Engine/Classificacao de estagios sucessionais da Floresta Estacional Semidecidua utilizando dados Sentinel-1-2 e SRTM no Google Earth Engine
- Author
-
da Costa, Vinicius Lorini and de Freitas, Marcos Wellausen Dias
- Published
- 2024
- Full Text
- View/download PDF
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