Analysis of Land Surface Temperature (LST) in Bur Ni Geureudong Geothermal Field, Aceh, Indonesia Using Landsat 8 OLI / TIRS Images

Putri, Deviyani R.; Ismail, Nazli; Idroes, Rinaldi; Rizal, Samsul; Abdulmadjid, Syahrun Nur; Nanda, Muhammad

doi:10.12982/cmujns.2021.084

Cited by 3 publications

(2 citation statements)

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“…The LST was analyzed utilizing Landsat 8 satellite imagery spanning the years 2019 to 2021. The processing of the LST underwent several intricate stages, as detailed in the study [26]:…”

Section: Data Processingmentioning

confidence: 99%

Temporal-Spatial Impact of the COVID-19 Pandemic on Land Surface Temperatures in Kuta, Bali: An Analysis Using Landsat 8 Satellite Imagery

Wiguna,

Sutari

2023

ATG

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In 2020, the world witnessed an unprecedented event: the outbreak of the COVID-19 pandemic, leading to significantly curtailed human activities. This study sought to elucidate the potential spatial ramifications of this on land surface temperatures (LSTs) in the renowned tourist locale of Kuta, Bali, Indonesia. Landsat 8 satellite imagery from 2019-2021, complemented by spatial data from local agencies, was employed for this analysis. LST processing was achieved through the calculation of Spectral Radiance/Top of Atmosphere, Brightness Temperature, and the conversion of Brightness Temperature to actual LST. In 2019, observed LSTs in Kuta District varied from 20.1℃ to over 32℃, with the predominant temperature range being 28.1℃ -31.99℃, covering an expansive 1487.03 ha or 70.26% of the entire area. By 2020, a notable decline was discerned with temperatures peaking at 27.99 ℃ and the most prevalent temperature range being 24.1℃ -27.99℃, encompassing an area of 1105.46 ha (52.23%). Contrarily, 2021 experienced an upswing, with the apex temperature touching 31.99℃, and the dominant temperature bracket being 28.1℃ -31.99℃, spanning 974.90 ha (46.06%). A discernable correlation was identified between tourism activities and LST fluctuations, with temperature reductions conspicuous in zones endowed with tourism amenities.

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“…The LST was analyzed utilizing Landsat 8 satellite imagery spanning the years 2019 to 2021. The processing of the LST underwent several intricate stages, as detailed in the study [26]:…”

Section: Data Processingmentioning

confidence: 99%

Temporal-Spatial Impact of the COVID-19 Pandemic on Land Surface Temperatures in Kuta, Bali: An Analysis Using Landsat 8 Satellite Imagery

Wiguna,

Sutari

2023

ATG

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“…The (i) land use land cover, (ii) slope, (iii) land surface temperature, (iv) surface albedo, (v) lineament density, and (vi) normalized differential vegetation index was generated. Applying Equations (1-4) are the existing equations researchers have adopted (Putri et al, 2021;Bello et al, 2021) in processing various factors identi ed as possible indicators of geothermal potential. The normalized differential vegetation index (NDVI) was processed and analyzed to produce thematic themes.…”

Section: Database Preparation Using Gis Remote Sensing and Geophysica...mentioning

confidence: 99%

Geothermal energy potentiality modeling using GIS-based machine learning algorithm concept in Southwestern Nigeria: insights from geophysical and remote sensing data sets

Mogaji,

Adeniyi,

KOMOLAFE

2023

Preprint

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The paucity of energy orchestrated by the demerits of non-renewable energy sources has posed significant challenges to global demand for energy. Salvaging this difficulty, geothermal renewable energy resource is considered as an alternative. This study investigated the effectiveness of a GIS-based machine learning algorithm to analyze remote sensing and geophysical datasets to address this task. The acquired remote sensing dataset was processed to derive surface-induced geothermal conditioning factors (GCFs): land use land cover, normalized difference vegetation index, lineament density, land surface temperature, and slope percent. Spectral analysis was carried out on aeromagnetic data to derive sub-surface induced GCFs, including Curie point depth, heat flow, and geothermal gradient. Geospatial analysis module, the thematic maps for the GCFs were produced in a GIS environment. With MATLAB program coding, the optimized weights for the produced GCFs thematic maps were determined using machine learning algorithms by applying adaptive neuro-fuzzy inference system (ANFIS) models incorporated with metaheuristic optimization mechanisms. The output of various metaheuristic optimization algorithms, such as Genetic Algorithm (GA), Invasive Weed Optimization (IWO), and Particle Swarm Optimization (PSO), was processed in the GIS platform to create maps of the geothermal potential predictive index (GPPI) for the study area. Receiver Operating Characteristics (ROC) curve, Root Mean Square Error (RMSE), and multifaceted geology were used to validate the produced GPPI model maps. The multifaceted geology approach validation results revealed that a high probability of geothermal manifestation predominates the Ifewara shear zone. The results of the ROC-AUC on the optimized ANFIS models, namely: IWO-ANFIS, GA-ANFIS, and PSO-ANFIS, are 77.2%, 81.1%, and 77.5%, respectively, compared to 73.5% from the ANFIS model. The observed RMSE validation results also determined the prediction values of 0.041803, 0.10281, and 0.10734 for IWO-ANFIS, GA-ANFIS, and PSO-ANFIS compared to 0.021803 for the conventional ANFIS model. The GA-ANFIS model performed better than all investigated machined learning algorithm models. The study shows that the GPPI model map created by GA-ANFIS could be employed for accurate decision-making in geothermal resource exploitation in the investigated area and other regions with comparable geologic terrain.

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Monitoring land use land cover changes and its impacts on land surface temperature over Mardan and Charsadda Districts, Khyber Pakhtunkhwa (KP), Pakistan

Khan

Basir

et al. 2022

Environ Monit Assess

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Analysis of Land Surface Temperature (LST) in Bur Ni Geureudong Geothermal Field, Aceh, Indonesia Using Landsat 8 OLI / TIRS Images

Cited by 3 publications

References 0 publications

Temporal-Spatial Impact of the COVID-19 Pandemic on Land Surface Temperatures in Kuta, Bali: An Analysis Using Landsat 8 Satellite Imagery

Temporal-Spatial Impact of the COVID-19 Pandemic on Land Surface Temperatures in Kuta, Bali: An Analysis Using Landsat 8 Satellite Imagery

Geothermal energy potentiality modeling using GIS-based machine learning algorithm concept in Southwestern Nigeria: insights from geophysical and remote sensing data sets

Monitoring land use land cover changes and its impacts on land surface temperature over Mardan and Charsadda Districts, Khyber Pakhtunkhwa (KP), Pakistan

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