Brief Introduction: The Sahel is experiencing significant Land Cover and Land Use (LULC) change (LUCC) in the last decades due to climatic and anthropic forcing. Important research efforts have been invested in the analysis of the driving factors of LULC and its potential impact on ecosystems, both in global change studies and to support land and water management in the Sahel. The project supported by the National Natural Science Foundation of China aims to study the response of LULC to climatic and anthropic forcing in the Sahel, as well as the impact of LUCC on water balance. The project first mapped LULC in the Sahel at a spatial resolution of 30 m and at intervals of 5 years over the past 30 years. A new classification approach is proposed and particular attention will be paid on increasing accuracy in LULC classification in arid and semi-arid lands. The spatiotemporal variation of LUCC has been explored to understand whether the Sahel is undergoing desertification or greening. Based on the satellite and auxiliary data, the two most important driving factors of LUCC, i.e. climatic and the anthropic forcing, were evaluated in the Sahel both in space and time. The response of LUCC to climatic and the anthropic forcing was analyzed to identify the causal relationship between LUCC and climatic/anthropic forcing in the Sahel. Further, the impact of LUCC on the water balance in the Sahel was characterized. Finally, this study provides scientific support to decision making on water and soil conservation and ecosystem management, within the framework of the Great Green Wall Initiative (GGWI), so to improve the capacity of adaptation to climate change in the Sahel region.
Number of Datasets: 4
The Surface water body extent and area dataset in pan-Sahel region includes the changes of surface water body (≥1km2) in pan-Sahelian 23 countries during 2000-2020. The dataset was produced based on the global surface water extent dataset (GSWED). Firstly, the misclassification caused by the dynamic threshold in the original GSWED data was eliminated by establishing the mask of area size and observation frequency to obtain an improved surface water data set. Then, the improved surface water surface data set was objected, and manually revised combination with global River widths from Landsat and lake data (HydroLAKES). Finally, based on the revised surface water body data set, the water body extent and area change in the Pan Sahel region in 21 years was counted. The dataset is in the vector file format (.shp) and has the geographic coordinate system of WGS 1984. It not only reduces the redundancy of data but increases the surface water from pixel scale to object scale, which is of more practical significance in geo-analysis. The dataset covers the Sahel and West Africa and provides data support for the assessment and research of surface water resources in the region.
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This data set is a 30m land use / cover classification product in the Sahel region of Africa every five years from 1990 to 2020. The product is based on a collaborative framework of land cover classification integrating machine learning and multiple data fusion, and integrates supervised land cover classification with existing thematic land cover maps by using Google Earth engine (GEE) cloud computing platform. The classification system adopts FROM_ GLC classification system includes 8 categories: cultivated land, forest, grassland, shrub, wetland, water body, impervious surface and bare land. The data set has been verified by a large number of seasonal samples in the Sahel region. The overall accuracy of the data set is about 75%, and the accuracy of change area detection is more than 70%. It is also very similar to FAO and the existing land cover map. The data set can provide data support for the sustainable use of land resources and environmental protection in the Sahel region of Africa.
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This data set includes year-by-year nighttime light annual images (totally 28 images) in Northern Equatorial Africa and Sahel from 1992 to 2020. By establishing the calibration relationship by fitting the median NPP-VIIRS nighttime light radiance and the DMSP-OLS nighttime light DN values, the DMSP-OLS nighttime light stable data from 1992 to 2013 were calibrated, and the synthesized DMSP-OLS data after 2013 are generated based on NPP-VIIRS nighttime light data. The spatial resolution is 0.00833 ° (about 1km); Raster data type is GeoTIFF. The grid pixel value is radiance with unit 10 − 9 w ∙ cm − 2 ∙ sr − 1. This data set can be used for the study on human activities in Northern Equatorial Africa and Sahel, such as the analysis of temporal and spatial changes of human activities.
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The data set contains annual NPP-VIIRS night time light data images of equatorial northern Africa and the Sahel region from 2013 to 2020. Based on the monthly average night time light image data of visible infrared imaging radiometer Suite (VIIRS) of national polar orbiting partnership (NPP) satellite, this dataset is generated by separating the unstable night light caused by biomass combustion from the stable night light information caused by human activities. The spatial resolution of the data is 500 m, and the grid data type is GeoTIFF. The grid pixel value is radiance, and the unit is 10 − 9 w ∙ cm − 2 ∙ SR − 1. The data set improves the ability of noctilucent images to identify small-scale, scattered and unstable urban information in northern equatorial Africa and Sahel to a certain extent, and can be further applied to the research on human activities in northern equatorial Africa and Sahel.
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