Mapping cultivated area in West Africa using modis imagery and agroecological stratification

To predict and respond to famine and other forms of food insecurity, different early warning systems are using remote analyses of crop condition and agricultural production, using satellite-based information. To improve these predictions, a reliable estimation of the cultivated area at national scale must be carried out. In this study, we develop a methodology for extracting cultivated domain based on their temporal behaviour as captured in time-series of moderate resolution remote sensing MODIS images. We also used higher resolution SPOT and LANDSAT images for identifying cultivated areas used in training. We tested this methodology in Senegal and Mali at national scale. Both studied areas were stratified in homogeneous areas from an ecological and a remote sensing point of view, to reduce the land surface reflectance variability in the dataset in order to improve the classification efficiency. A spatiotemporal (K-means) classification was finally made on the MODIS NDVI time series, inside each of the agro-ecological regions For Senegal, we obtained an updated map of crop area with a better resolution than the USAID map (which is 1 km resolution) and with a nomenclature more specific of the Senegal region than suggested in the POSTEL map. For Mali, the results showed that MODIS data set can provide a completely satisfactory representation of the cultivated domain in one FEWS zone, in combination with external data. Results at national scale are being processed and will be presented at the conference. (Résumé d'auteur

From plot to regional scale, spatial modelling of crop systems using interaction graphs. [P35]

Developing a climate-smart agriculture towards the " triple win " (food security, adaptation and mitigation) objective requires drawing-up policies that take into account the evolution of agrarian systems. To do so, it is necessary to develop tools to analyse the agricultural production trends from plot to regional scale. In developing countries, monitoring tools are facing the issue of heterogeneous and sparse information available: limited information networks, small average area of cultivated plots, fragmented plot organisation and diverse management modes. Moreover, the agrarian dynamics are the result of many processes occurring at different scales, which raises the issue of documenting the main trends without distorting the information when trying to upscale or downscale it. We propose a methodology to estimate the spatial variability and the time dynamics of agrarian systems at scales appropriate for seasonal risk monitoring and land policy planning. To do so, we use a mixed scaling approach based on the modelling of spatial dynamics which combines various information sources coming from ground networks, expert knowledge, thematic maps, crop models and remote sensing images. The novelty of the proposed approach is to use a spatial dynamics modelling language, Ocelet, based on interaction graphs: the graphs allow us to link information at different scales, and to integrate the spatial constraints and variability, central to the understanding of agrarian dynamics. The 1500 km² studied area is located in the cotton region of West Burkina Faso. This region displays high spatial climatic variability and has undergone notable transformations these last two decades due to high population growth and cultivated area reaching its saturation point. The main result presented is the simulation of the expansion of cultivated areas at the expense of forests, and also the evolution of cropping systems, taking account farmers strategies, climatic variability and spatial heterogeneities. (Texte intégral

Disentangling factors of landscape changes in Burkina Faso, the nexus between spatial modeling and remote sensing

Rural areas of West Burkina Faso have seen notable transformations these last two decades due to high population growth and farming systems evolution. Satellite images acquired frequently and covering large areas are essential for detecting such landscape changes and long term trends. However, these images generally have coarse spatial resolutions and can only provide information about changes in the main vegetation patterns. The factors causing these changes are more difficult to determine, although there are essential for monitoring landscape evolution. We hereby present a method based on multi-scalar modelling of past landscape dynamics crossed with changes in vegetation trends identified from coarse resolution satellite images. The aim of our presentation is to use the model to simulate and illustrate how land cover and land use changes may impact vegetation response by improving the qualification and understanding of the observed trends. The cropping systems dynamics of the study area, the Tuy province of West Burkina Faso, were modelled with the Ocelet Modelling Platform over the last fifteen years through a multi-scalar model. The model was validated at local scale with information derived from high resolution images. At the same time, vegetation trends were analysed using Ordinary Least Square regressions based on MODIS NDVI time series. Simulated cropland change maps were then used to decompose the remote sensing-based trends. This allowed the spatial identification of factors responsible for the vegetation changes. The original approach we proposed here opens new opportunities for the understanding and monitoring of landscape changes using time series of coarse resolution satellite images