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

机译:使用MODIS影像和农业生态分层对西非耕地进行制图

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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.
机译:为了预测和应对饥荒和其他形式的粮食不安全状况,不同的预警系统正在使用基于卫星的信息,对作物状况和农业生产进行远程分析。为了改善这些预测,必须对全国范围内的耕地面积进行可靠的估算。在这项研究中,我们根据中分辨率遥感MODIS图像的时间序列中捕获的时间行为,开发了一种提取耕地的方法。我们还使用了更高分辨率的SPOT和LANDSAT图像来识别训练中使用的耕地。我们在国家范围内的塞内加尔和马里对这种方法进行了测试。从生态学和遥感的角度来看,两个研究区域都被划分为同质区域,以减少数据集中的地表反射率变异性,从而提高分类效率。最终,在MODIS NDVI时间序列上对每个农业生态区域进行了时空分类(K均值)。对于塞内加尔,我们获得了更新的作物面积图,其分辨率比美国国际开发署的图(1 km分辨率),并且塞内加尔地区的命名比POSTEL地图中建议的命名更具体。对于马里而言,结果表明,MODIS数据集可以与外部数据相结合,在一个FEWS区域内完全令人满意地表示耕地。正在处理全国范围的结果,并将在会议上进行介绍。

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