Authors: Erik Borg; Bernd Fichtelmann; Kurt P. Guenther
Addresses: German Aerospace Center, German Remote Sensing Data Center, Kalkhorstweg 53, 17235 Neustrelitz, Germany ' German Aerospace Center, German Remote Sensing Data Center, Kalkhorstweg 53, 17235 Neustrelitz, Germany ' German Aerospace Center, German Remote Sensing Data Center, Oberpfaffenhofen, Postfach 1116, 82230 Wessling, Germany
Abstract: Within the ESA CCI 'Fire Disturbance' project (Guenther et al., 2012), a dynamic self-learning water masking approach was developed for AATSR, MERIS-FR(S), MERIS-RR, and for SPOT VEGETATION (SPOT-VGT) data. The primary goal of the development was to find for all sensors a generic algorithm by combining static water masks on a global scale with a self-learning algorithm. Our approach results in the generation of a dynamic water mask which helps to distinguish burned areas from other dark areas as, e.g., cloud or topographic shadows or coniferous forests. The use of static water masks as training areas for the learning algorithm must take into account that small and shallow water bodies may change in time and that a precise geo-location of the static water mask and the scene under investigation is mandatory. The comparison of the water masks derived from all sensors for a region in Kazakhstan demonstrates the quality of the new dynamic water masks. In addition, the advantages to other water masking algorithms (MOD44W, Hansen_GFC or IDEPIX) are shown. Furthermore, the dynamic water masks of AATSR, MERIS and SPOT-VGT for the same region are presented and discussed together with the use of more detailed static water masks.
Keywords: self-learning algorithm; land-water mask; interpretation; remote sensing; MERIS; AATSR; SPOT-VEGETATION; global.
International Journal of Business Intelligence and Data Mining, 2017 Vol.12 No.2, pp.95 - 118
Available online: 15 May 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article