NDVI and LST correlation analysis using Landsat 8 image based on cloud compute: Google Earth Engine (case study of Wonosobo District, Wonosobo Regency) Ahmad Syafril Romadhon, Umar El Izzudin Kiat, Arrohman Mardiyansyah
Muhammadiyah University of Surakarta
Sinergi Visi Utama Company
Abstract
The rapid development of technology has a positive impact on several parties, such as the assessment of vegetation density index and land surface temperature. This study aims to determine changes in vegetation density and surface temperature in 2019 and 2023 and their correlation with the Wonosobo District study area. The methods used in this research are Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) by utilizing Landsat 8 Level 2 Tier 2 image data processed through Google Earth Engine and ArcMap 10.8. This research resulted in changes in the highest vegetation index value in the range of 0.74 - 0.92, while the lowest index value is in the value range between 0.03 - 0.36 which indicates that the area is non / rarely vegetated. In the vulnerable years 2019 to 2023, the vegetation density experienced changes in the form of the highest reduction in vegetation land of 108.009108 hectares. Field accuracy testing resulted in 10 spots with a fairly high level of accuracy. The results of observations of land surface temperature on the map show that there is a significant change in temperature in the central to western parts of Wonosobo District. There is a negative correlation between NDVI and LST
