Change detection of vegetation during the grazing season using multi temporal data of WiFS-IRS in Semirom region

Accurate and up-to-date global land cover data sets are necessary for various global change research studies including climatic change, biodiversity conservation, ecosystem assessment, and environmental modeling. The aim of the present research was to study change detection of vegetation during the grazing season using multi temporal data of WiFS in Semirom region. Various preprocessing, including geometric correction were applied using topographic maps of 1:250000 with an RMSe 0.35 pixel for sensor IRS-WiFS. The atmospheric and topographic corrections were carried out using dark-object subtraction method and the Lambert method. Field data collection was started on June 2005 on 800,000 ha. Multi-temporal data of IRS-WiFS sets were used for this study. Image processing including FCC, PCA, vegetation indices and supervised classification were employed to produce the vegetation canopy cover map. Various vegetation types were sampled using stratified random sampling method. twenty random sampling points were selected and canopy cover percentage was estimated. Digital data and the indices maps were used as independent data and the field data as dependent variables. The produced models were processed and then resulted images were categorized in 5 classes. Also post classification method was used to determine change detections. Finally the produced maps were controlled for their accuracies. The results confirmed the high correlations of used WiFS indices with field data. In the current study, more than 30 percent of the study area has been affected during the grazing season. Also the NDVI, SAVI and DVI indices which employ RED and NIR bands had relatively highly correlations with rangeland data. Result showed vegetation maps produced with IRS-WiFS data set had very high accuracy.