Statistical Analysis for Forest Fire Factors using Geography Information System (GIS) and Remote Sensing Imagery

Abstract

About 33% of permanent forest reserve in Selangor is covered by peat swamp forest. The most important reason of peat swamp forests is to balance the ecosystem. Nevertheless, the evolution of technology and increasing population, with the needs of more space for land development has put peat swamp forest in Selangor under threat particularly because of forest fire. To overcome this risk problem, it is important to identifying and justifying these forest fires and a further study has been carried out to understand the problem. The study is carried out to find the factors that triggering forest fire at Kuala Langat South Forest Reserve (KLSFR) from 2013 until 2020. Temperature, rainfall, relative humidity, wind speed, NDVI, and LULC are choosing as to know the most triggered factors by measure their correlation value. The data are process using GIS and Remote Sensing (Landsat 8). The temperature, rainfall, relative humidity, wind speed data interpolate using Kriging method as a statistical analysis. While LULC is classify using Random Trees method. The value of correlation of temperature (0.4256), rainfall (-0.7613), relative humidity (-0.2484), wind speed (-0.8615), NDVI (0.1945) respectively. Furthermore, LULC is classify into five classes, which are high density forest, medium density forest, agriculture, bare soil, and waterbodies. Bare soil area shows highest correlation compare other classes, which is 0.6381. While rainfall and wind speed were identified as the most trigger factor to forest fire .