Land cover/use changes specially the forest cover changes affect the local surface temperature (LST) of the earth. In this study, a combination of remote sensing and GIS techniques was used to scrutinize the interactions between LST anomalies and deforestation in Sardasht County, NW Iran. The land cover/use change layers of the study area were extracted from Landsat satellite imagery based on Binary Encoding classification and change detection technique. The radiometric correction analysis were done for each Landsat image to derive LST map layers. According to the results, a descending trend in forest cover with a total 2560 ha decline in area and an ascending trend of about 4 degrees rise in surface temperature values on both forest and non-forest areas were detected in the study area from 1984 to 2017. The temporal and spatial analysis yielded high rates of reverse temporal correlation (-0.81) between forest areas and LST anomalies while the correlation value of 0.76 was found for non-forest areas and LST. The regression analysis of the values confirmed the correlation results to be trustable at 99 percent. It was also found that the deforested areas of the study area correlate with the LST rise spatially with a very high correlation (0.98) from which a tangible interaction of the parameters can be inferred.

Forest fire is one of the most common natural hazards occurring in the Western Ghats region of Kerala and is one of the reasons for forest degradation. This natural disaster causes considerable damage to the biodiversity of this region during the dry fire season. The area selected for the present study, Eravikulam National Park, which is predominantly of grassland vegetation, is also prone to forest fires. This study aims to delineate the forest fire risk zones in Eravikulam National Park using remote sensing (RS) data and geographic information system (GIS) techniques. In the present study, methods such as Analytic Hierarchy Process (AHP) and Frequency Ratio (FR) were used to derive the weights, and the results were compared. We have used seven factors, i.e. land cover types, normalized difference vegetation index, normalized difference water index, slope angle, slope aspect, distance from the settlement, and distance from the road to prepare the fire risk zone map. The area of the prepared risk zone maps is divided into three zones, namely low, moderate, and high. From the study, it was found that the fire occurring in this area is due to natural as well as anthropogenic factors. The prepared forest fire risk zone maps are validated using the fire incidence data for the period from January 2003 to June 2019 collected from the records of the Forest Survey of India. The investigation revealed that 72% and 24% of the fire incidences occurred in the high risk zone of the maps prepared using the AHP and FR methods, respectively, which ascertained the superiority of the AHP method over the FR method for forest fire risk zone mapping. The receiver operating characteristic (ROC) curve analysis gives an area under the ROC curve (AUC) value of 0.767 and 0.567 for the AHP and FR methods, respectively. The risk zone maps will be useful for staff of the forest department, planners, and officials of the disaster management department to take effective preventive and mitigation measures.

The classification accuracy of the various categories on the classified remotely sensed images are usually evaluated by two different measures of accuracy, namely, producer’s accuracy (PA) and user...