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Brief Guidelines to the Global Information and Early Warning System’s (GIEWS) Earth Observation Website











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    Comprendiendo el impacto de El Niño relacionado con la sequía en las zonas agrícolas mundiales
    Evaluación utilizando el Índice de Estrés Agrícola de la FAO (ASI)
    2015
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    FAO monitors the evolution of hazards and provides early indications and warnings on possible impacts on agriculture and food security. One such phenomenon is the periodic occurrence of El Niño. During El Niño episodes the normal patterns of tropical precipitation and atmospheric circulation become disrupted triggering extreme climate events around the globe: droughts, floods and affecting the intensity and frequency of hurricanes. Agriculture is one of the main sectors of the economy that could be severely affected by El Niño phenomena. FAO monitors the El Niño-Southern Oscillation (ENSO) phenomenon, among other weather related hazards, with a special focus on the potential impacts on the agricultural sector. FAO-GIEWS communicates developments during the gestation period and issues alerts and warningsif and when necessary. The objective of this study is to enhance our understanding of the El Niño phenomenon using FAO’s Agricultural Stress Index system (ASIS). FAOASI, developed with t he support of EU/FAO Improved Global Governance for Hunger Reduction Programme, is based on remote sensing data that highlights anomalous vegetation growth and potential drought in arable land during a given cropping season.
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    Evaluation of the project "System for Earth Observation Data Access, Processing and Analysis for Land Monitoring" (SEPAL)
    Project code: GCP/GLO/537/NOR
    2022
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    The System for Earth Observation Data Access, Processing and Analysis for Land Monitoring (SEPAL) is a cloud-based computing platform for fast access and processing of remotely sensed data sources. It is designed to assist national forest monitoring and reporting for the Reducing Emissions from Deforestation and Forest Degradation, Forest Conservation, Sustainable Management of Forests and Enhancement of Carbon Stocks in Developing Countries (REDD+) mechanism. The terminal evaluation of the project found SEPAL to have been largely successful and relevant in achieving its aims. The evaluation recommended certain improvements, notably a “plan B” option to mitigate SEPAL’s dependency on Google Earth Engine, and assurance of continued relevance in Phase II of SEPAL.
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    Dry season forage assessment across senegalese rangelands using earth observation data 2022
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    Strengthening of feed security in the Sahel is urgently needed given the climate change and growing human population. A prerequisite to this is sustainable use of rangeland forage resources for livestock. Many studies have focused on the assessment of rangeland resources during the rainy season, while only a few have focused on the dry season which is the longest and most demanding period for livestock in Sahelian rangelands. The objective of this study is to develop remote sensing-based models for estimating dry season forage vegetation mass. To that end, 29 vegetation indices calculated from each of the MODIS-MCD43A4 (500 m), Landsat-8 (30 m), and Sentinel-2 (10 m) satellite products were used and tested against in situ data collected during three field-measurement campaigns in 2021 at eleven monitoring sites across Senegalese rangelands. Four statistical models were tested, namely, random forest, gradient boosting machines, and simple linear and multiple linear regressions. The two main vegetation mass variables modeled from remote sensing imagery were the standing herbaceous and litter dry mass (BH) and total forage dry mass (BT) with a dry mass of woody plant leaves added to BH. Overall, Sentinel-2 data provided the best performance for the assessment of BH with multiple linear regression (R2 = 0.74; RMSE = 378 kg DM/ha) using NDI5 (Normalized Difference Index5), GRCI (Green Residue Cover Index), SRI (Simple Ratio Index), TCARI (Transformed Chlorophyll Absorption in Reflectance Index), and DFI (Dead Fuel Index) indices. For BT, the best model was also obtained from Sentinel-2 data, including RVI3 (Ratio Vegetation Index3) (R2 = 0.78; RMSE = 496 kg DM/ha). Results showed the suitability of combining the red, green, blue, NIR, SWIR1, and SWIR2 bands in monitoring forage availability during the dry season. Our study revealed that the spectral richness of the optical sensor systems Sentinel-2, Landsat-8, and MODIS-MCD43A4 allowed for accurate assessments of dry-season forage mass of semi-arid rangelands. Adding to this, the high spatial and temporal resolution of Sentinel-2 satellite imagery makes this a promising data source for timely monitoring. These findings can support the monitoring of the animal feed balance in Sahelian countries and contribute to enhancing the resilience of pastoralism toward feed shortage through early warning systems.

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