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 Student of the D3 Remote Sensing Technology Study Program, Universitas Negeri Padang
  • Dilla Angraina Lecturer Study Program D3 Remote Sensing Technology, Universitas Negeri Padang
DOI: https://doi.org/10.24036/irsaj.v3i2.37
Keywords: Land Surface Temperature (LST), Normalized Burn Ratio (NBR) Weighted Overlay

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.

Published
2023-02-28
How to Cite
Mayang, S., & Angraina, D. (2023). MAPPING OF FOREST AND LAND FIRE HAZARDOUS USING LANDSAT 8 SATELLITE IMAGERY WITH LAND SURFACE TEMPERATURE (LST) AND NORMALIZED BURN RATIO (NBR) METHODS. International Remote Sensing Applied Journal, 3(2), 63-67. https://doi.org/10.24036/irsaj.v3i2.37
Issue
Vol 3 No 2 (2022): International remote sensing application journal (Dec Edition 2022)
Section
Articles

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