Contenus

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  Mapping of zones at risk in west Africa by using NGI, VCI and SNDVI from the E-Station.

  This work is carried out at the AGRHYMET Regional Centre (ARC)-CILSS as part of the African Monitoring of Environment for Sustainable Development (AMESD) project. The analysis protocol has been improved under the Monitoring of Environment for Security in Africa (MESA) project. The MESA Project has been designed on the achievements of AMESD; its overall objective is to provide African countries with access to Earth Observation data for environmental monitoring and sustainable development. The specific objective of this study is to develop an operational analysis protocol for vegetation monitoring in general and especially for crops and pastures. Three vegetation indices were used: Vegetation Condition Index (VCI), Normalized Growth Index (NGI) and Standardized Normalized Difference Vegetation Index (SNDVI). The analysis of these drought indices is based on taking into account the agro-climatic characteristics of the Sahelian region, the comparison of the NGI profile (per administrative unit) from year X (in progress) to the maximum NGI profiles, minimum and average of the time series data (1998 to year x-1) and evidence convergence. Six years of application of the method and validation actions carried out concluded that it is possible to determine the zones at risk (ZAR) in order to anticipate food crises.
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  AGRHYMET: Adrought monitoring and capacity building center in the West Africa Region

  The AGRHYMET Regional Center, a specialized institution of the Permanent Interstates Committee for Drought Control in the Sahel (CILSS), was created in 1974 at the aftermaths of the severe droughts that affected this region in the early 1970s. The mission assigned to the Center was to train personnel, provide adequate equipment for the meteorological and hydrological stations networks, and set up regional and national multidisciplinary working groups to monitor the meteorological, hydrological, crops and pastures conditions during the rainy season. As such, it can be considered as the West Africa drought monitoring center, similarly to its younger counterparts in Eastern and Southern Africa. After 40 years of existence, AGRHYMET’s scope of activities expend now beyond the geographical boundaries of CILSS member states, to include the whole West Africa thanks to several initiatives it has been implementing on behalf of the Economic Commission of West African States (ECOWAS) on food security and environmental issues, including climate change. Throughout the years, AGRHYMET developed, in collaboration with international research organizations, models and methodologies based on ground and satellite observations to monitor rainfall, food crop water requirements satisfaction and prospective yields, the progress of vegetation front and its seasonal and interannual variations. It has trained about 1200 new experts in agrometeorology, hydrology, equipment maintenance, and plant protection, and more than 6000 professionals on topics related to food security, climate change, and sustainable natural resources (land and water) management. As of now, AGRHYMET staff is involved in several international initiatives on climate change, food security, and environmental monitoring that allow them keep abreast of the best available technologies and methods, and also contribute to generating knowledge on those issues.
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  Mapping of Zones At Risk (ZAR) in west Africa by using NGI, VCI and SNDVI from the E-statuib

  This work is carried out at the AGRHYMET Regional Centre (ARC)-CILSS as part of the African Monitoring of Environment for Sustainable Development (AMESD) project. The analysis protocol has been improved under the Monitoring of Environment for Security in Africa (MESA) project. The MESA Project has been designed on the achievements of AMESD; its overall objective is to provide African countries with access to Earth Observation data for environmental monitoring and sustainable development. The specific objective of this study is to develop an operational analysis protocol for vegetation monitoring in general and especially for crops and pastures. Three vegetation indices were used: Vegetation Condition Index (VCI), Normalized Growth Index (NGI) and Standardized Normalized Difference Vegetation Index (SNDVI). The analysis of these drought indices is based on taking into account the agro-climatic characteristics of the Sahelian region, the comparison of the NGI profile (per administrative unit) from year X (in progress) to the maximum NGI profiles, minimum and average of the time series data (1998 to year x-1) and evidence convergence. Six years of application of the method and validation actions carried out concluded that it is possible to determine the zones at risk (ZAR) in order to anticipate food crises.
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  Performance of similarity analysis in the estimation of forage yields in the Sahelian zone of Niger

  The study aims to test the performance of similarity analysis in herbaceous fodder biomass estimate in the Nigerian pastoral zone, in a context of insecurity and precipitation spatiotemporal variability. It is carried out on the time series of NDVI decadal images of SPOT VEGETATION for the period from 2001 to 2012 and on fodder biomasses measured in situ during the same period. Similarity analysis compares NDVI seasonal patterns to detect similar years using three criteria: the RMSE (Root Mean squared error), the MAD (Mean absolute Deviation), and R². Exploratory statistical analyzes with bootstrap are carried out to better characterize the observations resulting from the simulation. Moreover, the analysis of the parametric and non-parametric correlations is carried out to evaluate the level of link between the simulated data and the real data. The t test and the Wilcoxon test are then carried out in order to compare the means of the actual biomasses with those obtained by the similarity analysis. At the local level, the results indicate that the R² is more efficient than the RMSE and the MAD which have almost the same performances. The results of the similarity calculated with R² can be used as a proxy to the herbaceous phytomass measured in situ, as there is no significant difference between the simulated mean and the mean measured at the 1% threshold. On the other hand, the results of the similarity calculated with the RMSE and the MAD are not exploitable. Parametric and nonparametric correlations are all significant at the 1% threshold. However, the R² are low and vary between 0.32 and 0.45. It therefore seems necessary to continue the research, as numerous studies have revealed very good links between certain indices like the FAPAR, the EVI and the LAI and the aerial phytomasse.