A wide range of environmental and societal issues such as food security policy implementation requires accurate information on biomass productivity and its underlying drivers at both regional and local scales. While many studies in West Africa are conducted with coarse resolution earth observation data, few have tried to relate vegetation trends to explanatory factors, as is generally done in land use and land cover change (LULCC) studies at finer scales. In this study we proposed to make a bridge between vegetation trend analysis and LULCC studies to improve the understanding of the various factors that influence the biomass production changes observed in satellite time series (using integrated Normalized Difference Vegetation Index [NDVI] as a proxy). The study was conducted in two steps. In the first step we analyzed MODIS NDVI linear trends together with TRMM growing season rainfall over the Sahel region from 2000 to 2015. A classification scheme was proposed that enables better specification of the relative role of the main drivers of biomass production dynamics. We found that 16% of the Sahel is re-greening—but found strong evidence that rainfall is not the only important driver of biomass increase. Moreover, a decrease found in 5% of the Sahel can be chiefly attributed to factors other than rainfall (88%). In the second step, we focused on the “Degré Carré de Niamey” site in Niger. Here, the observed biomass trends were analyzed in relation to land cover changes and a set of potential drivers of LULCC using the Random Forest algorithm. We observed negative trends (29% of the Niger site area) mainly in tiger bush areas located on lateritic plateaus, which are particularly prone to pressures from overgrazing and overlogging. The significant role of accessibility factors in biomass production trends was also highlighted. Our methodological framework may be used to highlight changing areas and their major drivers to identify target areas for more detailed studies. Finer-scale assessments of the long-term vulnerability of populations can then be made to substantiate food security management policies. (Résumé d'auteur

UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitièresNumerous agronomical applications of remote sensing have been proposed in recent years, including water stress assessment at field by thermal imagery. The miniaturization of thermal cameras allows carrying them onboard the unmanned aerial vehicles (UAVs), but these systems have no temperature control and, consequently, drifts during data acquisition have to be carefully corrected. This manuscript presents a comprehensive methodology for radiometric correction of UAV remotely-sensed thermal images to obtain (combined with visible and near-infrared data) multispectral ortho-mosaics, as a previous step for further image-based assessment of tree response to water stress. On summer 2013, UAV flights were performed over an apple tree orchard located in Southern France, and 4 dates and 5 h of the day were tested. The 6400 m2 field plot comprised 520 apple trees, half well-irrigated and half submitted to progressive summer water stress. Temperatures of four different on-ground stable reference targets were continuously measured by thermo-radiometers for radiometric calibration purposes. By using self-developed software, frames were automatically extracted from the thermal video files, and then radiometrically calibrated using the thermal targets data. Once ortho-mosaics were obtained, root mean squared error (RMSE) was calculated. The accuracy obtained allowed multi-temporal mosaic comparison. Results showed a good relationship between calibrated images and on-ground data. Significantly higher canopy temperatures were found in water-stressed trees compared to well-irrigated ones. As high resolution field ortho-mosaics were obtained, comparison between trees opens the possibility of using multispectral data as phenotypic variables for the characterization of individual plant response to drought

Abstract:Accurate cropland maps at the global and local scales are crucial for scientists, government and nongovernment agencies, farmers and other stakeholders, particularly in food-insecure regions, such as Sub-Saharan Africa. In this study, we aim to qualify the crop classes of the MODIS Land Cover Product (LCP) in Sub-Saharan Africa using FAO (Food and Agricultural Organisation) and AGRHYMET (AGRiculture, Hydrology and METeorology) statistical data of agriculture and a sample of 55 very-high-resolution images. In terms of cropland acreage and dynamics, we found that the correlation between the statistical data and MODIS LCP decreases when we localize the spatial scale (from R 2 = 0.86 *** at the national scale to R 2 = 0.26 *** at two levels below the national scale). In terms of the cropland spatial distribution, our findings indicate a strong relationship between the user accuracy and the fragmentation of the agricultural landscape, as measured by the MODIS LCP; the accuracy decreases as the crop fraction increases. In addition, thanks to the Pareto boundary method, we were able to isolate and quantify the part of the MODIS classification error that could be directly linked to the performance of the adopted classification algorithm. Finally, based on these results, (i) a regional map of the MODIS LCP user accuracy estimates for cropland classes was produced for the entire Sub-Saharan region; this map presents a better accuracy in the western part of the region (43%-70%) compared to the eastern part (17%-43%); (ii) Theoretical user and producer accuracies for OPEN ACCESS Remote Sens. 2014, 6 8542 a given set of spatial resolutions were provided; the simulated future Sentinel-2 system would provide theoretical 99% user and producer accuracies given the landscape pattern of the region.

Bats are the second most species‐rich Mammalian order and provide a wide range of ecologically important and economically significant ecosystem services. Nipah virus is a zoonotic emerging infectious disease for which pteropodid bats have been identified as a natural reservoir. In Cambodia, Nipah virus circulation has been reported in Pteropus lylei , but little is known about the spatial distribution of the species and the associated implications for conservation and public health. We deployed Global Positioning System (GPS) collars on 14 P. lylei to study their movements and foraging behavior in Cambodia in 2016. All of the flying foxes were captured from the same roost, and GPS locations were collected for 1 month. The habitats used by each bat were characterized through ground‐truthing, and a spatial distribution model was developed of foraging sites. A total of 13,643 valid locations were collected during the study. Our study bats flew approximately 20 km from the roost each night to forage. The maximum distance traveled per night ranged from 6.88–105 km and averaged 28.3 km. Six of the 14 bats visited another roost for at least one night during the study, including one roost located 105 km away. Most foraging locations were in residential areas (53.7%) followed by plantations (26.6%). Our spatial distribution model confirmed that residential areas were the preferred foraging habitat for P. lylei , although our results should be interpreted with caution due to the limited number of individuals studied. Synthesis and applications : Our findings suggest that the use of residential and agricultural habitats by P. lylei may create opportunities for bats to interact with humans and livestock. They also suggest the importance of anthropogenic habitats for conservation of this vulnerable and ecologically important group in Cambodia. Our mapping of the probability of occurrence of foraging sites will help identification of areas where public awareness should be promoted regarding the ecosystem services provided by flying foxes and potential for disease transmission through indirect contact.