MAPPING OF FOREST AND LAND FIRE HAZARDOUS USING LANDSAT 8 SATELLITE IMAGERY WITH LAND SURFACE TEMPERATURE (LST) AND NORMALIZED BURN RATIO (NBR) METHODS

Sri Mayang, Dilla Angraina
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Abstract

This study aims (1) to determine the distribution of Land Surface Temperature (LST) in the Baso District in 2022 (2) to determine the Normalized Burn Ratio (NBR) in Baso District in 2022 (3) to map areas prone to forest and land fires by utilizing the Land Surface Temperature (LST) and Normalized Burn Ratio (NBR) algorithms in Baso District in 2022. This study uses the Land Surface Temperature (LST) method to determine the distribution of land surface temperatures in the Baso District in 2022. The Normalized Burn Ratio (NBR) method is used to identify areas that are burned and then weighted overlay using Arcgis to obtain data on land and forest fire vulnerability. in Baso District. The results of this study are (1) showing a minimum temperature value of 13.6oC maximum temperature of 34.5oC and an average temperature of 26oC (2) showing the results of the distribution of areas with a value of -1 which are identified as burnt or those with bad vegetation of 2.5 and areas with a value of 0 indicating vegetation a good area of ​​7,636 Ha (3) on the mapping of areas prone to forest and land fires after the Weighted Overlay was carried out found 4 classes of vulnerability levels not prone to forest and land fires, moderately prone, prone and very prone to forest and land fires.
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基于地表温度(lst)和归一化燃烧比(nbr)方法的landsat 8卫星图像森林和土地火灾危险制图
本研究旨在(1)确定2022年Baso地区的地表温度(LST)分布(2)确定2022年Baso地区的归一化燃烧比(NBR)(3)利用2022年Baso地区的地表温度(LST)和归一化燃烧比(NBR)算法绘制森林和土地火灾易发区域。本研究采用地表温度(LST)方法确定了2022年巴索地区地表温度的分布。采用归一化燃烧比(NBR)方法识别被烧毁的区域,然后利用Arcgis进行加权叠加,获得土地和森林火灾易损性数据。在巴索区。这项研究的结果是(1)显示最低温度13.6摄氏度的价值平均最高温度34.5摄氏度,温度26度(2)的结果显示区域的分布值确定为燃烧或坏植被的2.5和区域的值为0指示植被良好面积7636公顷(3)的映射区域容易发生森林火灾和土地进行加权叠加后发现4类脆弱性水平不容易发生森林和土地火灾,中等容易发生,容易发生和非常容易发生森林和土地火灾。
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