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Mining Drought from Remote Sensing Images

Remote sensing advancements offered new opportunities to access timely information for drought monitoring. In traditional NDVI based drought monitoring, the common practice is to apply crisp classification techniques, based on vegetation stress intensity values. Such approach is less effective for monitoring low vegetation stress, which, when extended over a long period of time can cause severe drought. Drought monitoring from images often require time series of images that are difficult to process using traditional image processing methods. The extraction and analysis of large amount of data can be achieved with the use of image mining techniques. In this study, the main objective is to develop a framework that uses image mining techniques to monitor drought. The proposed framework considers both vegetation stress intensity and duration to characterize drought from remote sensing images. As a case, the drought that occurred in East Africa during the period of December 2005 to February 2006, is taken and analyzed. The data used for this study are NOAA-AVHRR derived NDVI images. A fuzzy membership function is used to characterize drought on space. The patterns of these droughts objects on space are extracted and tracked over time. The extracting and tracking algorithms are automated to analyze a series of images. The results obtained formed the gained knowledge about drought in space and time. This knowledge is used for further understanding the behavior of drought characteristics based on the vegetation conditions. The proposed approach allows an easy monitoring of drought in space and time. Such model can be used to monitor other vague phenomena, which are characterized based on intensity and duration parameters. This research is promising for data mining processes applied on vague phenomenon monitoring.