Application of Remote Sensing Data-based Detection of Forest Vegetation Water Content in Forest Fire Prevention

Abstract

Forest fires occurring on Earth cause huge losses every year and their frequency is on the rise. In order to reduce the occurrence of forest fires, there have been a number of new technologies that have emerged to predict and dynamically monitor forest fires based on remote sensing technology. In this paper, the Normalised difference infrared index (NDII) value of the study area is based on remote sensing satellite image data to detect and estimate the water content of the vegetation in the forest area to predict the occurrence of forest fires, so as to strengthen the precautionary measures for forest fires and reduce the probability of forest fires. The results showed that vegetation water content was significantly negatively correlated with the risk of forest fires, and when the vegetation NDII value was lower than -0.8486, it indicated that the water content of the vegetation area had dropped to a very low level, and it would be highly combustible and the probability of fire would be very high. For the study area, the water content of the vegetation tends to be even lower especially during the hot and dry seasons, making the overall increase in the forest fire risk level. This paper further validates that the water content of vegetation is one of the key indicators in forest fire risk assessment. This study provides more prevention indicators for forest fire prevention facilities and improves the prevention system.