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Ecosystem service (ES) models can only inform policy design adequately if they incorporate ecological processes. We used the Lund-Potsdam-Jena managed Land (LPJmL) model, to address following questions for Mexico, Bolivia and Brazilian Amazon: (i) How different are C stocks and C sequestration quantifications under standard (when soil and litter C and heterotrophic respiration are not considered) and comprehensive (including all C stock and heterotrophic respiration) approach? and (ii) How does the valuation of C stock and C sequestration differ in national payments for ES and global C funds or markets when comparing both approach? We found that up to 65% of C stocks have not been taken into account by neglecting to include C stored in soil and litter, resulting in gross underpayments (up to 500 times lower). Since emissions from heterotrophic respiration of organic material offset a large proportion of C gained through growth of living matter, we found that markets and decision-makers are inadvertently overestimating up to 100 times C sequestrated. New approaches for modelling C services relevant ecological process-based can help accounting for C in soil, litter and heterotrophic respiration and become important for the operationalization of agreements on climate change mitigation following the COP21 in 2015. Made available in DSpace on 2018-11-28T23:41:43Z (GMT). No. of bitstreams: 1 2018056.pdf: 4636292 bytes, checksum: f0c3c2bb494dc8dc92d947d954894047 (MD5) Previous issue date: 2018-11-28

International audience; With the unprecedented availability of satellite data and the rise of global binary maps, the collection of shared reference data sets should be fostered to allow systematic product benchmarking and validation. Authoritative global reference data are generally collected by experts with regional knowledge through photo-interpretation. During the last decade, crowdsourcing has emerged as an attractive alternative for rapid and relatively cheap data collection, beckoning the increasingly relevant question: can these two data sources be combined to validate thematic maps? In this article, we compared expert and crowd data and assessed their relative agreement for cropland identification, a land cover class often reported as difficult to map. Results indicate that observations from experts and volunteers could be partially conflated provided that several consistency checks are performed. We propose that conflation, i.e., replacement and augmentation of expert observations by crowdsourced observations, should be carried out both at the sampling and data analytics levels. The latter allows to evaluate the reliability of crowdsourced observations and to decide whether they should be conflated or discarded. We demonstrate that the standard deviation of crowdsourced contributions is a simple yet robust indicator of reliability which can effectively inform conflation. Following this criterion, we found that 70% of the expert observations could be crowdsourced with little to no effect on accuracy estimates, allowing a strategic reallocation of the spared expert effort to increase the reliability of the remaining 30% at no additional cost. Finally, we provide a collection of evidence-based recommendations for future hybrid reference data collection campaigns.

This database contains nine land use / land cover datasets collected in a standardized manner between 2013 and 2020 in seven tropical countries within the framework of the international JECAM initiative: Burkina Faso (Koumbia), Madagascar (Antsirabe), Brazil (Sao Paulo and Tocantins), Senegal (Nioro and Niakhar), Kenya (Muranga), Cambodia (Kandal) and South Africa (Mpumalanga) (cf Study_sites.kml). These quality-controlled datasets are distinguished by ground data collected at field scale by local experts, with precise geographic coordinates, and following a common protocol. This database, which contains 27 074 records (20 257 crop and 6 817 non-crop) is a geographic layer in Shapefile format in a Geographic Coordinates System with Datum WGS84. Field surveys were conducted yearly in each study zone, either around the growing peak of the cropping season, for the sites with a main growing season linked to the rainy season such as Burkina Faso, or seasonally, for the sites with multiple cropping (e.g. Sao Paulo site). The GPS waypoints were gathered following an opportunistic sampling approach along the roads or tracks according to their accessibility, while ensuring the best representativity of the existing cropping systems in place. GPS waypoints were also recorded on different types of non-crop classes (e.g. natural vegetation, settlement areas, water bodies) to allow differentiating crop and non-crop classes. Waypoints were only recorded for homogenous fields/entities of at least 20 x 20 m². To facilitate the location of sampling areas and the remote acquisition of waypoints, field operators were equipped with GPS tablets providing access to a QGIS project with Very High Spatial Resolution (VHSR) images ordered just before the surveys. For each waypoint, a set of attributes, corresponding to the cropping practices (crop type, cropping pattern, management techniques) were recorded (for more informations about data, see data paper being published). These datasets can be used to validate existing cropland and crop types/practices maps in the tropics, but also, to assess the performances and the robustness of classification methods of cropland and crop types/practices in a large range of Southern farming systems.

O GeoInfo é um repositório que disponibiliza os dados de pesquisa espacial gerados pela Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) para compreender a dinâmica da agricultura no território brasileiro. Considerando os esforços necessários para a implantação e institucionalização do repositório, a ausência de estudos da Ciência da Informação relacionados à investigação do impacto dos usos feitos a partir dos dados e informações disponibilizados em repositórios de dados de pesquisa, bem como o processo de avaliação de impactos utilizado pela EMBRAPA desde 1989, é oportuno investigar o impacto do repositório para seu público-alvo, qual seja, especialistas de geociências. Assim, o objetivo deste trabalho é apresentar os resultados de uma avaliação de impactos realizada com o GeoInfo, a partir da metodologia - Ambitec-TICs: Módulo de critérios e indicadores de impactos para Tecnologias de Informação e Comunicação -, especificamente para a dimensão social, existente no Ambitec-TICs. Made available in DSpace on 2021-05-06T02:11:16Z (GMT). No. of bitstreams: 1 5899.pdf: 589235 bytes, checksum: 4985b2b0753250e89f4c9a3f1e10121d (MD5) Previous issue date: 2021

International audience; The adoption of new cropping practices such as integrated Crop-Livestock systems (iCL) aims at improving the land use sustainability of the agricultural sector in the Brazilian Amazon. The emergence of such integrated systems, based on crop and pasture rotations over and within years, challenges the remote sensing community who needs to implement accurate and efficient methods to process satellite image time series (SITS) in order to come up with a monitoring protocol. These methods generally include a SITS preprocessing step which can be time consuming. The aim of this study is to assess the importance of preprocessing operations such as temporal smoothing and computation of phenological metrics on the mapping of main cropping systems (i.e. pasture, single cropping, double cropping and iCL), with a special emphasis on the iCL class. The study area is located in the state of Mato Grosso, an important producer of agriculture commodities located in the Southern Brazilian Amazon. SITS were composed of a set of 16-day composites of MODIS Vegetation Indices (MOD13Q1 product) covering a one year period between 2014 and 2015. Two widely used classifiers, i.e. Random Forest (RF) and Support Vector Machine (SVM), were tested using five data sets issued from a same SITS but with different preprocessing levels: (i) raw NDVI; (ii) raw NDVI + raw EVI; (iii) smoothed NDVI; (iv) NDVI-derived phenometrics; (v) raw NDVI + phenometrics. Both RF and SVM classification results showed that the "raw NDVI + raw EVI" data set achieved the highest performance (RF OA = 0.96, RF Kappa = 0.94, SVM OA = 0.95, SVM Kappa = 0.93), followed closely by the "raw NDVI" and the "raw NDVI + phenometrics" datasets. The "NDVIderived phenometrics" alone achieved the lowest accuracies (RF OA = 0.58 and SVM OA = 0.66). Considering that the implementation of preprocessing steps is computationally expensive and does not provide significant gains in terms of classification accuracy, we recommend to use raw vegetation indices for mapping cropping practices in Mato Grosso, including the integrated Crop-Livestock systems.

Changes in the carbon (C) and nitrogen (N) compartments that result from the addition of organic material (OM) to the soil are predicted by the transformation of added OM (TAO) model with three parameters: very labile (P′L) and stable (PS) fractions of the OM and the rate of remineralization (kremin) of nitrogen immobilized by microorganisms. We propose relations between P′L, PS, kremin and various chemical groups in the OM identified by their 13C nuclear magnetic resonance (NMR) spectra. The aromatic content increased the predicted PS in accordance with published results. The O-aromatic content also increased PS, but much less so than the aromatic content. The carboxyl content decreased PS and increased P′L as in the TAO model based on infrared spectrometry. The carbonyl content decreased P′L, whereas di-O-alkyl increased P′L. The chemical composition of the population of decomposer organisms did not appear to be homeostatic, but was related rather to the composition of the substrate: kremin was positively correlated with the carboxyl and di-O-alkyl content and negatively correlated with the alkyl content. Solid state 13C NMR spectroscopy gave better predictions of the transformations that resulted from adding OM than biochemical fractionation and near infrared reflectance spectrometry (NIRS). It is fast and non-destructive and provides new insights into the processes that control decomposition for research into waste recycling, agro-ecology and climate change. (Resume d'auteur)

ropical dry forests and savannas harbour unique biodiversity and provide critical ES, yet they are under severe pressure globally. We need to improve our understanding of how and when this pressure provokes tipping points in biodiversity and the associated social-ecological systems. We propose an approach to investigate how drivers leading to natural vegetation decline trigger biodiversity tipping and illustrate it using the example of the Dry Diagonal in South America, an understudied deforestation frontier. The Dry Diagonal represents the largest continuous area of dry forests and savannas in South America, extending over three million km² across Argentina, Bolivia, Brazil, and Paraguay. Natural vegetation in the Dry Diagonal has been undergoing large-scale transformations for the past 30 years due to massive agricultural expansion and intensification. Many signs indicate that natural vegetation decline has reached critical levels. Major research gaps prevail, however, in our understanding of how these transformations affect the unique and rich biodiversity of the Dry Diagonal, and how this affects the ecological integrity and the provisioning of ES that are critical both for local livelihoods and commercial agriculture. Fil: Thonicke, Kirsten. Institute for Climate Impact Research ; Alemania Fil: Langerwisch, Fanny. Institute for Climate Impact Research ; Alemania. Czech University of Life Sciences Prague; República Checa Fil: Baumann, Matthias. Humboldt Universität zu Berlin; Alemania. Technische Universitat Carolo Wilhelmina Zu Braunschweig.; Alemania Fil: Leitão, Pedro J.. Humboldt Universität zu Berlin; Alemania. Technische Universitat Carolo Wilhelmina Zu Braunschweig.; Alemania Fil: Václavík, Tomáš. Helmholtz Centre for Environmental Research; Alemania. Palacký University Olomouc; República Checa Fil: Alencar, Anne. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; Brasil Fil: Simões, Margareth. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA); Brasil Fil: Scheiter, Simon. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA); Brasil. Universidade Federal do Rio de Janeiro; Brasil Fil: Langan, Liam. Senckenberg Biodiversity and Climate Research Centre; Alemania Fil: Bustamante, Mercedes. Universidade do Brasília; Brasil Fil: Gasparri, Nestor Ignacio. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto de Ecología Regional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina Fil: Hirota, Marina. Universidade Federal de Santa Catarina; Brasil. Universidade Estadual de Campinas; Brasil Fil: Börner, Jan. Universitat Bonn; Alemania Fil: Rajao, Raoni. Universidade Federal de Minas Gerais; Brasil Fil: Soares Filho, Britaldo. Universidade Federal de Minas Gerais; Brasil Fil: Yanosky, Alberto. Consejo Nacional de Ciencia y Tecnología; Paraguay Fil: Ochoa Quinteiro, José Manuel. Instituto de Investigación de Recursos Biológicos Alexander von Humboldt; Colombia Fil: Seghezzo, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energía no Convencional. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Física. Instituto de Investigaciones en Energía no Convencional; Argentina Fil: Conti, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; Argentina Fil: de la Vega Leiner, Anne Cristina. Universität Greifswald; Alemania

International audience; Cropland maps derived from satellite imagery have become a common source of information to estimate food production, support land use policies, and measure the environmental impacts of agriculture. Cropland classification models are typically calibrated with data collected from roadside surveys which enable the sampling of large areas at a relatively low cost. However, there is a risk of providing biased data as environmental and management gradients may not be fully captured from road networks, thereby violating the assumption of representativeness of calibration data. Despite being widely adopted, the potential biases of roadside sampling have so far not been thoroughly addressed. In this study, we looked for evidence of these biases by comparing three sampling strategies: Random sampling, Roadside sampling, and Transect sampling - a spatially constrained variant of Roadside sampling. In these three strategies, non-cropland data are randomly distributed as they can be photo-interpreted. Based on reference maps at 30 m in four study sites, we followed a Monte Carlo approach to generate multiple realizations of each sampling strategy for ten sample sizes. The effect of the sampling strategy was then assessed in terms of representativeness of the data set collected and accuracy of the resulting maps. Results showed that data sets obtained from Roadside sampling were significantly less representative than those obtained from Random sampling but the resulting maps were only marginally less accurate (2% difference). Transect sampling captured systematically less variability than Random or Roadside sampling which led to differences in accuracy as large as 15%. The effect of sample size on accuracy varied across sites but generally leveled off after reaching 3000 pixels. Augmenting the size of Transect samples improved the classification accuracy but not sufficiently to match the performance of the other sampling strategies. Finally, we found that Random and Roadside training sets with similar representativeness yield comparable accuracy. Therefore, we conclude that roadside sampling can be a viable source of training data for cropland mapping if the range of environmental and management gradients is surveyed. This underlines the importance of survey planning to identify those routes that capture most variability.

No ano de 2009, o Governo do Brasil apresentou o Plano de Agricultura de Baixa Emissao de Carbono, cujo objetivo e a organizacao de acoes para a adocao de tecnologias de producao de alimentos alinhadas aos compromissos de reducao de emissoes de GEE. Entre diversas praticas destaca-se a implementacao da integracao lavoura-pecuaria (iLP). No entanto, a falta de procedimentos de monitoramento limita a governanca do Plano. Os sistemas iLP podem ser adotados em duas abordagens distintas: Inter-anual e intra-anual. Este artigo tem como objetivo utilizar dados provenientes de sensoriamento remoto para identificar areas de iLP na abordagem interanual no estado do Mato Grosso, na contribuicao de um protocolo de monitoramento. Com o resultado, identificou-se que a implantacao dos iLP tem seu auge em 2016 quando chega a 5% de toda area de agricultura e pecuaria do estado do Mato Grosso.

Human pressure on ecosystems has undesirable impacts on human well-being. After the Millennium Project, much interdisciplinary research has been developed worldwide aiming to understand these impacts on ecosystem flows and processes, and to learn about the costs and the benefits of ecosystem services for production. Soil provides many ecosystem services, since its multi-functionality is the basis for food production, water filtration, nutrient cycling, and other goods essential to life. This article presents the main concepts and classifications of soil ecosystem services and of its functions; the indicators and the methods for assessment, modeling, and valuation of ecosystem services; some recent applications to assess and evaluate impacts of agricultural management practices on soil ecosystem services; as well as challenges and opportunities for research and for development of public policies related to agro-environmental sustainability in Brazil. Although the role of soil in supplying ecosystem services is yet undervalued, scientists are gradually recognizing soil processes and functions as fundamental to assess ecosystem services and the effects of land use and management on them. Interdisciplinary approaches to integrate science and public policies are necessary to build governance based on ecosystem services. Resumo A pressão humana sobre os serviços ecossistêmicos tem resultado em impactos indesejáveis sobre o bem-estar humano. Com o Projeto Millennium, várias pesquisas interdisciplinares têm sido desenvolvidas em todo o mundo com o objetivo de entender esses impactos sobre os fluxos e os processos dos ecossistemas e internalizar os custos e os benefícios dos serviços ecossistêmicos para a produção. O solo fornece muitos serviços ecossistêmicos, uma vez que sua multifuncionalidade é a base para a produção de alimentos, filtração de água, ciclagem de nutrientes e outros bens essenciais à vida. Este artigo apresenta os principais conceitos e classificações dos serviços ecossistêmicos do solo e de suas funções; os indicadores e os métodos de avaliação, modelagem e valoração dos serviços ecossistêmicos; algumas aplicações recentes para avaliar impactos de práticas de manejo agrícola sobre os serviços ecossistêmicos do solo; bem como os desafios e as oportunidades para a pesquisa e para o desenvolvimento de políticas públicas relacionadas à sustentabilidade agroambiental no Brasil. Apesar de o papel do solo para prestação de serviços ecossistêmicos ainda ser subestimado, os cientistas têm gradualmente reconhecido os processos e as funções do solo como fundamentais para avaliar os serviços ecossistêmicos e os efeitos do uso e manejo da terra sobre eles. Abordagens interdisciplinares que integrem ciência e políticas públicas são necessárias para construir uma governança com base em serviços ecossistêmicos.

With the amount of multitemporal and multiresolution images growing exponentially, the number of image segmentation applications is recently increasing and, simultaneously, new challenges arise. Hence, there is a need to explore new segmentation concepts and techniques that make use of the data temporality. This study describes a spatio-temporal segmentation that adapts the traditional region growing technique to detect homogeneous regions in space and time in optical remote sensing images. Tests were conducted by considering the Dynamic Time Warping measure as the homogeneity criterion. Study cases on high temporal resolution for sequences of MODIS and Landsat-8 OLI vegetation indices products and comparisons with other distance measurements provided satisfactory outcomes.

International audience; The recent decoupling of agricultural production and deforestation in the southern Amazon has been made possible thanks to (1) the adoption of intensive agricultural practices, including irrigation, and (2) the diversification of economic activities, including fish farming. Whereas this new agricultural model has brought out positive results to contain deforestation, it also implied new pressures on the environment , and especially on water resources. Many small artificial water reservoirs have been built with different uses, e.g. crop irrigation, energy generation, fish farming or livestock watering. In this paper, we introduce a method to automatically map small water bodies based on time series of Landsat images. The method was tested in the municipality of Sorriso (state of Mato Grosso, Brazil). The statistical results (Overall Accuracy = 0.872; Kappa index = 0.745) validated the efficiency of the methodology although the spatial resolution of Landsat images limited the detection of very small and linear reservoirs. In Sorriso, we estimated that the cumulated area and the number of small water reservoirs increased more than tenfold (from 153 to 1707 ha) and fivefold (86 to 522), respectively, between 1985 and 2015. We discuss the numerous socio-environmental implications raised by the cumulated impacts of these proliferating small reservoirs. We conclude that integrated whole-landscape approaches are necessary to assess how anthro-pized hydrosystems can counteract or exacerbate the socio-environmental impacts of deforestation and intensive agriculture. Ó

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. Fil: Bégué, Agnès. Institut National de la Recherche Agronomique; Francia. Université Montpellier II; Francia Fil: Arvor, Damien. Centre National de la Recherche Scientifique; Francia Fil: Bellon, Beatriz. Institut National de la Recherche Agronomique; Francia. Université Montpellier II; Francia Fil: Betbeder, Julie. Institut National de la Recherche Agronomique; Francia Fil: de Abelleyra, Diego. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; Argentina. Instituto de Clima y Agua; Argentina Fil: Ferraz, Rodrigo P. D.. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; Brasil Fil: Lebourgeois, Valentine. Institut National de la Recherche Agronomique; Francia Fil: Lelong, Camille. Institut National de la Recherche Agronomique; Francia Fil: Simões, Margareth. Universidade do Estado de Rio do Janeiro; Brasil. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; Brasil Fil: Verón, Santiago Ramón. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; Argentina

Cropping systems’ maps at fine scale over large areas provide key information for further agricultural production and environmental impact assessments, and thus represent a valuable tool for effective land-use planning. There is, therefore, a growing interest in mapping cropping systems in an operational manner over large areas, and remote sensing approaches based on vegetation index time series analysis have proven to be an efficient tool. However, supervised pixel-based approaches are commonly adopted, requiring resource consuming field campaigns to gather training data. In this paper, we present a new object-based unsupervised classification approach tested on an annual MODIS 16-day composite Normalized Difference Vegetation Index time series and a Landsat 8 mosaic of the State of Tocantins, Brazil, for the 2014–2015 growing season. Two variants of the approach are compared: an hyperclustering approach, and a landscape-clustering approach involving a previous stratification of the study area into landscape units on which the clustering is then performed. The main cropping systems of Tocantins, characterized by the crop types and cropping patterns, were efficiently mapped with the landscape-clustering approach. Results show that stratification prior to clustering significantly improves the classification accuracies for underrepresented and sparsely distributed cropping systems. This study illustrates the potential of unsupervised classification for large area cropping systems’ mapping and contributes to the development of generic tools for supporting large-scale agricultural monitoring across regions.

Aim Tropical forests store 25% of global carbon and harbour 96% of the world's tree species, but it is not clear whether this high biodiversity matters for carbon storage. Few studies have teased apart the relative importance of forest attributes and environmental drivers for ecosystem functioning, and no such study exists for the tropics. Location Neotropics. Methods We relate aboveground biomass (AGB) to forest attributes (diversity and structure) and environmental drivers (annual rainfall and soil fertility) using data from 144,000 trees, 2050 forest plots and 59 forest sites. The sites span the complete latitudinal and climatic gradients in the lowland Neotropics, with rainfall ranging from 750 to 4350 mm year−1. Relationships were analysed within forest sites at scales of 0.1 and 1 ha and across forest sites along large-scale environmental gradients. We used a structural equation model to test the hypothesis that species richness, forest structural attributes and environmental drivers have independent, positive effects on AGB. Results Across sites, AGB was most strongly driven by rainfall, followed by average tree stem diameter and rarefied species richness, which all had positive effects on AGB. Our indicator of soil fertility (cation exchange capacity) had a negligible effect on AGB, perhaps because we used a global soil database. Taxonomic forest attributes (i.e. species richness, rarefied richness and Shannon diversity) had the strongest relationships with AGB at small spatial scales, where an additional species can still make a difference in terms of niche complementarity, while structural forest attributes (i.e. tree density and tree size) had strong relationships with AGB at all spatial scales. Main conclusions Biodiversity has an independent, positive effect on AGB and ecosystem functioning, not only in relatively simple temperate systems but also in structurally complex hyperdiverse tropical forests. Biodiversity conservation should therefore be a key component of the UN Reducing Emissions from Deforestation and Degradation strategy.

Ecosystem integrity (EI) may be defined as an equilibrium state of a given natural system able to self-regulate throughout many functional processes. The concept of biodiversity is quite diverse, and it is related to different levels of biological systems ranging from the level of genes, species taxonomic richness to functional groups. In this way, depending on the approach, several indicators, conceptually unrelated, can be used to characterize and quantify the biodiversity of a given natural system. However, in practical terms, the biodiversity, as a characteristic of ecosystems, is an indicator of the ecosystem´s stage regarding its pristine conditions. Then, Ecosystem Integrity has emerged as an important indicator to assess the relationship between biodiversity loss and the impacts on ecosystem services in tropical forests, once EI represents a connection between biodiversity and the ability of ecosystems to maintain the self-organization process. The objective of this chapter is to present a methodological approach developed for generating an Ecosystem Integrity index at regional scale, for different phyto-physiognomies patterns of landscapes, using a probabilistic model based on Bayesian Belief Networks (BBN), and totally free web-available satellite products. The methodology was applied to Brazil’s Legal Amazon region. The results show that it is possible to quantify areas of the Amazon rainforest with high or low Ecosystem Integrity. Using the same Bayesian network, with updated satellite data, it becomes possible to monitor the EI over time, and may even serve to establish a monitoring protocol and planning of mitigation/adaptation procedures.

Tropical forests store and sequester high amounts of carbon and are the most diverse terrestrial cosystem. Studies show potentially important effects of biodiversity on carbon storage and equestration, but a complete understanding of this relationship across spatiotemporal scales relevant for climate change mitigation needs three approaches: empirical, remote sensing and ecosystem modelling. Here, we review the contribution of these individual approaches to the understanding of the relationship of biodiversity with carbon storage and sequestration, and find short-term and long term benefits of biodiversity at both broad and fine spatial scales. We argue that enhanced understanding is obtained by combining approaches, i.e., by using output from one approach to improve another approach and thus results in better input, validation and comparison between approaches. This can be further improved by integrating approaches through using ‘boundary objects’(i.e., variables) that can be understood and measured by all approaches, such as the diversity of leaf traits of the upper canopy and forest structure indices. Combining and especially integrating approaches will therefore lead to a better understanding of biodiversity effects on climate change mitigation. This is crucial for making sound policy decisions.

Assessing the impact/adaptation of human activities on/to climate change is a key issue, especially in the tropics that concentrate major anthropogenic dynamics such as deforestation and nearly two-thirds of the planetary rainfall. However, this task is often made tough because human activities such as agricultural dynamics are usually analysed at local or regional scale whereas climate related studies are led at large to global scales due to a lack of reliable data, especially in the tropics. In this article we argue that the increased spatial resolution of remote sensing-based rainfall estimates enables assessing the spatiotemporal variability of rainfall regimes at regional and local scales, thus allowing fine analysis of the interactions with human activities. We processed Tropical Rainfall Measuring Mission (TRMM) 3B42 daily rainfall estimates over the state of Mato Grosso (southern Brazilian Amazon) for the 1998-2012 study period in order to compute rainfall metrics such as annual rainfall and duration, onset and end dates of the rainy season based on the Anomalous Accumulation methodology (at a 0.25◦ spatial resolution). We then crossed these metrics with agricultural maps (produced at a 250m spatial resolution) and proved that the adoption of intensive agricultural practices such as double cropping systems is partly the result of a strategy to adapt practices to local climatic conditions. Finally, we discuss how such results raise important issues regarding the sustainability of the agricultural development model in the Southern Amazon.

This study explored the suitability of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectrometer (MODIS) obtained for six sugar management zones, over nine years (2002?2010), to forecast sugarcane yield on an annual and zonal base. To take into account the characteristics of the sugarcane crop management (15-month cycle for a ratoon, accompanied with continuous harvest in Western Kenya), the temporal series of NDVI was normalized through an original weighting method that considered the growth period of the sugarcane crop (wNDVI), and correlated it with historical yield datasets. Results when using wNDVI were consistent with historical yield and significant at P-value = 0.001, while results when using traditional annual NDVI integrated over the calendar year were not significant. This correlation between yield and wNDVI is mainly drawn by the spatial dimension of the data set (R2 = 0.53, when all years are aggregated together), rather than by the temporal dimension of the data set (R2 = 0.1, when all zones are aggregated). A test on 2012 yield estimation with this model realized a RMSE less than 5 t·ha?1. Despite progress in the methodology through the weighted NDVI, and an extensive spatio-temporal analysis, this paper shows the difficulty in forecasting sugarcane yield on an annual base using current satellite low-resolution data. This is particularly true in the context of small scale farmers with fields measuring less than the size of MODIS 250 m pixel, and in the context of a 15-month crop cycle with no seasonal cropping calendar. Future satellite missions should permit monitoring of sugarcane yields using image resolutions that facilitate extraction of crop phenology from a group of individual plots. Made available in DSpace on 2021-09-20T18:03:35Z (GMT). No. of bitstreams: 1 remotesensing-05-02184-published-abril-2013.pdf: 1435839 bytes, checksum: f2aace4d430ddfad84b4e787fbe76285 (MD5) Previous issue date: 2013

International audience; The aim of this study was to evaluate the indicators for Water Sustainability Assessment of Agricultural Systems, proposed by Ferraz (2012), by conducting a study case to assess the sugarcane activity water sustainability in 10 municipalities in the southwest region of the Goiás State, Brazil. These indicators are direct indexes obtained from data extracted through geoprocessing techniques from spatial models, such as: (i) Sugarcane Agroecological Zoning (Embrapa, 2009); (ii) Water contamination vulnerability by agricultural effluents model (Barbalho e Campos, 2010); (iii) Specific flow spatially model (Ferraz, 2012.). The indexes allow indicate: (i) pedoclimatic favorability areas: (ii) the surface and groundwater contamination vulnerability areas; (iii) sugarcane water sustainability level. The results show that the indicators were able to characterize and distinguish the differences between the units of analysis (municipalities) and, considering the scale and the detail level required, spatial models used were appropriate.

O presente artigo objetivou apresentar os procedimentos metodológicos e as técnicas de geoprocessamento utilizadas para a extração de dados de modelos espacializados ou de bases cartográficas temáticas visando o cálculo de indicadores para a avaliação do processo de expansão da cultura canavieira em quatro Microrregiões do Estado de Goiás: Sudoeste de Goiás, Vale do Rio dos Bois, Quirinópolis e Meia Ponte. Os diversos dados espaciais em formato shape, adquiridos de fontes secundárias ou de modelos especificamente elaborados, foram introduzidos em um Sistema de Informações Geográficas (SIG), no qual, por meio de técnicas simples de geoprocessamento, foram realizados ajustes e a integração dos dados espaciais para a obtenção dos dados (grandezas físicas) requeridos para o cálculo de diversos índices propostos pelo Sistema de Indicadores para a Avaliação do Potencial de Sustentabilidade Hídrica da Atividade Canavieira - SISH-Cana (FERRAZ, 2012). Os resultados demonstram a pertinência das técnicas utilizadas. O nível de detalhamento dos modelos espaciais adotados foi adequado, considerando que os índices propostos se aplicam à caracterização de unidades territoriais de analise de grande abrangência geográfica (microrregiões) em subsídio a planejamentos setoriais (Recursos hídricos; Agricultura) de nível estratégico.

A very large number of data sources on environment, energy, and natural resources are available worldwide. Unfortunately, users usually face several problems when they want to search and use relevant information. In the Ecobase project, we address these problems in the context of several environmental applications in Brazil and Europe. We propose a distributed architecture for environmental information systems (EIS) based on the Le Select middleware developed at INRIA. In this paper, we present this architecture and its capabilities, and discuss the lessons learned and open issues.

National audience; This article aimed to assess the sugarcane expansion process in four microregionsconsidered sugarcane expansion hotspots in the State of Goiás, Brazil, from the use ofindicators proposed by the Indicators System of Sugarcane Water Sustainability Assessment- SISH-Cana (Ferraz, 2012) Thus, it became possible to perform an analytical andcomparative evaluation of the sugarcane expansion process occurred in 2005/2006 to2010201 period, in the following microregions of South of the State of Goiás, Brazil:Sudoeste de Goiás, Vale do Rio dos Bois, Quirinópolis and Meia Ponte, adopted as territorialunits of analysis. The results of the indicators suggest that the proportions of sugarcaneoccupation in microregions studied are still relatively small in relation to the total areas oftheir territories. They also suggest that the sugar-alcohol sector has expanded mainly on themost appropriate areas, precisely those with slope gradients lower than 12 and with soilswith preferential agricultural suitability, where it becomes possible to develop sugarcaneactivity with high technological level. They have also shown that sugarcane expansionprocess is occurring primarily on areas traditionally occupied with agriculture, but alsopointed out the trend of gradual pastures replacement, while the native vegetation removalis relatively small. In relation to the balance between demand and water availability, theresults demonstrated the sugarcane culture still has a high possibility to develop in themicroregions considered.

Characterization of landscapes is crucial in modelling potential soil erosion to ascertain environmental services that are provided by the main land use in the ecosystem. Remote sensing techniques have proved successful in characterization of landscapes. In this study area of a rain-fed Kibos-Miwani sugar zone of Kenya, we used Normalized Difference Vegetation Index (NDVI) data extracted from satellite imagery to characterize the spatial and temporal heterogeneity of the vegetation conditions, and to model potential soil erosion. Data used included Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m NDVI acquired in the period 2000 to 2012; 30 m Landsat5 time series images acquired between November 2010 and June 2011; a 30 m digital elevation model (DEM); and ground observations (land cover and soil characteristics). Ground observations were cross tabulated and analysed under ISO 17025 laboratory procedures. Temporal NDVI was extracted directly from MODIS 250 m images to study the changes in seasonal vegetation at the region scale, while spatial NDVI was extracted by analysing Landsat 5 images at the field scale. NDVI extracted from Landsat images for a specific date, represented vegetation conditions for that simulation period. To compute potential soil erosion, we ran three simulations using the spatially explicit Fuzzy-based dynamic soil erosion model (FuDSEM) based on identified vegetative conditions, thanks to MODIS data. Input datasets included Landsat 5 NDVI, the slope, aspect, curvature and soil physical properties. Results of land cover presented sugarcane as the main land use, occupying 76% of the land scape. Results of NDVI analysis were consistent with crop management practices, illustrating a spatially heterogeneous land scape with varied vegetation conditions throughout the year. Results of the simulations were not significantly different for the different periods of the year. Out of simulations, we noted a homogeneous low erosion risk in areas with natural land cover with a global mean of 0.42. Medium to intense erosion risk in cropped areas was evident, with erosion risk varying from one pixel to the other. Simulation results suggest that crop management practices (planting and harvesting processes) are the drivers of erosion in sugar cane cultivated areas. (résumé d'auteur)

COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR A tomada de consciência dos riscos ambientais em escala global e o desenvolvimento científico e tecnológico têm incrementado a demanda pelo sensoriamento das condições ambientais marítimas. A distribuição da população do fitoplâncton é o indicador mais utilizado para avaliar a qualidade e a biomassa de ambientes marinhos. Uma característica diferencial do fitoplâncton é a presença da clorofila a, que apresenta fluorescência in vivo na região do vermelho do espectro visível, e que permite o uso de técnicas de sensoriamento remoto para sua detecção. Um radar-laser baseado na detecção da fluorescência do alvo é mais comumente denominado LIF-LIDAR (Laser Induced Fluorescence - LIght Detection And Ranging) e se constitui em uma ferramenta útil para o monitoramento da distribuição de clorofila a nos oceanos, principalmente quando se utiliza uma radiação laser na faixa de comprimento de onda associada à cor verde. O LIDAR-PUC, cuja fonte de excitação é o 2º harmônico de um laser de Nd-YAG, foi ajustado para a detecção e discriminação espectral de emissões inelásticas decorrentes da excitação a 532nm; em especial, a fluorescência da clorofila a. Nesta tese foram desenvolvidos algoritmos para a extração de parâmetros de interesse ambiental, em especial a concentração relativa da clorofila a. Estas metodologias foram testadas quanto a sua repetitividade, linearidade e aplicabilidade em ambiente real. Finalmente, os valores calculados foram analisados em conjunto, indicando sua utilidade para o monitoramento efetivo de ambientes marinhos. Global scale environmental risks and scientific and technological development have increased demands on marine environment monitoring. Phytoplankton distribution is the most frequently used marker employed to assess biomass in marine environment. Analysis of chlorophyll-a plays a central role on phytoplankton studies once this pigment, present in every phytoplankton algae species, exhibits fluorescence in the red region of visible spectrum, thus allowing its detection by remote sensing techniques. A radar-laser based on fluorescence detection, usually called a LIF-LIDAR (Laser Induced Fluorescence - LIght Detection And Ranging), is a valuable tool for monitoring chlorophyll-a distribution in ocean waters, especially when green light lasers are employed. LIDAR-PUC was settled to function with its second harmonic Nd-YAG laser as excitation source. The equipment was adjusted for detection and spectral discrimination of inelastic emissions resulting from excitation at 532nm, with special attention to chlorophyll a fluorescence. In this study, algorithms were developed for the extraction of environmental parameters such as relative chlorophyll a concentration. These algorithms were analyzed regarding the following aspects: repeatability, linearity and applicability to the real world. Finally, ensembles were analyzed, pointing to their usefulness for effective marine environment monitoring.

O presente artigo objetivou apresentar os procedimentos metodológicos e as técnicas de geoprocessamento utilizadas para a extração de dados de bases cartográficas temáticas e de modelos hídricos espacializados requeridos para o cálculo de indicadores de sustentabilidade hídrica da cultura canavieira em quatro Microrregiões do Estado de Goiás: Sudoeste de Goiás, Vale do Rio dos Bois, Quirinópolis e Meia Ponte. Os diversos dados espaciais em formato shape, adquiridos de fontes secundárias e de modelos especificamente elaborados para este fim foram introduzidos em um Sistema de Informações Geográficas (SIG), no qual, por meio de técnicas simples de geoprocessamento, foram ajustados e integrados para a obtenção dos dados (grandezas físicas) utilizados para a determinação de indicadores (índices) propostos pelo Sistema de Indicadores para a Avaliação do Potencial de Sustentabilidade Hídrica da Atividade Canavieira - SISH-Cana (FERRAZ, 2012). Os resultados demonstram a pertinência das técnicas utilizadas e do nível de precisão e detalhamento dos modelos espaciais adotados, considerando que os índices aplicam-se para a avaliação de unidades territoriais de analise em nível de microrregião com vistas a planejamentos territoriais e/ou setoriais em nível estratégico. Made available in DSpace on 2020-09-15T04:40:02Z (GMT). No. of bitstreams: 1 Revista-Geotecnia-artigo-pedogeo.pdf: 3804451 bytes, checksum: 84f467ef524074a8d5df616d4dc9216b (MD5) Previous issue date: 2013

A expansão da cultura da soja no sul da Amazonia Brasileira na decada de 1990 permitiu a emergência de novos territórios integrados a “economia mundial” depois da fase pioneira das décadas de 1970 e 1980. Mas, a queda dos preços, a “ferrugem asiática” (doença da soja), a variabilidade climática e as tensões ambientais levaram o setor agrícola a uma crise profunda entre 2004 e 2007. Esta crise demonstrou os limites deste modelo de desenvolvimento bem ilustrado pelo município de Sorriso (maior produtor de soja do Brasil). Os produtores procuram agora aproveitar as lições aprendidas a partir dos fracassos do passado e adaptar-se as novas normas impostas pela sociedade civil e os mercados internacionais (certificação ambiental, aplicação do código florestal, dentre outros). Vários esforços estão sendo realizados para intensificar e diversificar as produções, principalmente através da cultura do milho e do algodão. Enfim, projetos locais de desenvolvimento sustentável baseados na educação ambiental e sensibilização ao reflorestamento estão sendo criados. Isto comprova a vontade de parte dos atores locais de mudar o padrão de desenvolvimento da região.After the pioneer phase that occurred during the 1970's and 1980's, the expansion of mechanized agriculture (especially soybean crops) in the Southern part of the Brazilian Amazon basin led to the emergence of new territories integrated in " the world economy". However, low prices, the "Asian rust" (soybean's disease), the climatic variability and the environmental tensions pulled the agricultural sector in a deep crisis between 2004 and 2007. This crisis highlighted the limits of this model of development, well illustrated by the county of Sorriso (first soybean's producing county in Brazil). The producers are now learning from past failures in order to adapt themselves to the new standards imposed by the civil society and the international markets (environmental certification, forest code application, to mention some). Numerous efforts for the intensification and diversification of productions, especially by corn and cotton crops, were done. Furthermore, local sustainability projects based on environmental education and incentives to replantation are being developed. They illustrate the desire of part of the local actors to change the regional way of development., L'expansion de la culture du soja au sud de l'Amazonie brésilienne dans les années 1990 a permis l'émergence de nouveaux territoires intégrés à « l'économie monde » après la phase pionnière des décennies 1970 et 1980. Cependant, la baisse des cours, la « rouille asiatique » (maladie du soja), la variabilité climatique et les tensions environnementales ont entraîné la filière agricole dans une crise profonde entre 2004 et 2007. Cette crise a alors mis en exergue les limites de ce modèle de développement, bien illustré par la commune de Sorriso (première commune productrice de soja du Brésil). Les producteurs cherchent à présent à tirer les leçons des échecs du passé et à s'adapter aux nouvelles normes imposées par la société civile et les marchés internationaux (certification environnementale, application du code forestier,...). De nombreux efforts pour l'intensification et la diversification des productions notamment par le maïs et le coton sont réalisés. De plus, des projets locaux de développement durable axés sur l'éducation environnementale et l'incitation au reboisement voient le jour et témoignent également de la volonté d'une partie des acteurs locaux de changer le mode de développement de la région.