Presenter : Chingchai Humhong
In recent years, the damage from droughts to the environment and economies of some countries was extensive, and the death toll of livestock and wildlife was unprecedented. Drought monitoring is typically based on continuous climatic data which often lack the full spatial coverage area and rapid information accessibility. The data are derived drought that requires long-term to analyze. The utilization of satellite images is an alternative for identifying drought patterns. The purpose of this study is to analyze a new vegetation drought indicator based on the Normalized Difference Drought Index (NDDI) through Web Processing Service (WPS) using ZOO-Project platform. NDDI is combines information from both the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) data derived from MODIS data. pyModis library has been developed which are able to automatically download and process data using pyModis scripts. Then, to deployed as WPS using the ZOO-Project open source implementation. The latter provide an OGC WPS 1.0.0 compliant engine and allows to create and chain web services. The resulting of NDDI data is then automatically published as Web Map Service and can be highlighted potential on a web application. It can identify pattern of drought and distribution of drought maps that can enhance the effectiveness of decision making.