Real-time detection of wildlife using NOAA/AVHRR data Study area :(Kayamaki Wildlife Refuge)

Forest fire in recent years has paid great attention to climate change and ecosystems. Remote sensing is a quick and inexpensive way to detect and monitor forest fires on a large scale. The purpose of this study was to identify forest and rangeland fire hazards using NOAA / AVHRR in Kayamaki Wildlife Refuge. For the purpose of this study, the history of the fire-burns occurred in MODIS products. Then, the sensor images were prepared based on the date of fire burning. After preprocessing the images, Giglio and IGBP developed algorithms that detected fire. The results of fire detection detection algorithms were evaluated with MODIS products. The results showed that fire detection using the IGBP algorithm is better than the developed algorithms and Giglia. In this way, the IGBP algorithm with the detected fire number of 6 pixels from the 7-pixel fire detection detected by the MODIS product, the Giglio algorithm with the detected fire number is 5 pixels from the 7-pixel fire detection detected by The MODIS and extended algorithm detected the detected fire detected firefight number of 3 pixels from the 7 pixels of fire detection detected by MODIS products. Also IGBP algorithm with error rate of 14% and with fire detection number of 86%, Giglio algorithm with error rate of 28% and number of fire detected 72%, and developed algorithm with 57% error rate and detected fire number 43%.