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The Desert Locust (Schistocerca gregaria) is the most feared of all the locusts worldwide. Satellite imagery can provide a continuous overview of ecological conditions (i.e., vegetation, soil moisture) suitable for the Desert Locust at the continental scale and in near real time. To monitor green vegetation, most remote sensing techniques are based on vegetation indices (e.g., NDVI). However, several limitations have been observed for this index based approaches in sparsely vegetated areas. To guarantee a more robust and reliable image-independent discrimination between vegetation and non-vegetated surface types, an innovative multi-temporal and multi-spectral image analysis method was developed based on a combination of MIR, NIR and Red reflectance measurements. The proposed approach is based on a transformation of the RGB color space into HSV that decouples chromaticity and luminance. A complete automatic processing chain combining the daily observations of MODIS and SPOT VEGETATION, was designed to provide user-friendly vegetation dynamic maps at 250 m resolution over the entire locust area every 10 days. This new product informs users about the location of green vegetation and its temporal evolution. The methodology is currently implemented at the Vlaamse instelling voor technologisch onderzoek (VITO) to provide vegetation dynamic maps every dekade to the Desert Locust Information Service at FAO.
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of (Volume:4 , Issue: 2 )
Date of Publication: June 2011