Intense human activity in urban areas plays an important role in increasing temperatures, which is reflected in the high surface temperature over built-up areas in cities. The aim of this study is to analyze the Urban Heat Island (UHI) phenomenon in Jakarta and Surabaya, using Landsat data associated with spectral indices and Land Use Land Cover (LULC), specifically for built-up areas. We used spectral indices for detecting built-up areas such as Index-based Built- Up Index (IBI), New Built-up Index (NBI), Normalized Difference Built-up Index (NDBI), and Urban Index (UI) to analyze UHI over a 10-year period from 2012 to 2022. Machine learning algorithms were employed to map the LULC, achieving an overall accuracy of 84% with the Support Vector Machine algorithm and 83% with the Random Forest. The analysis revealed that IBI has the highest correlation (0.69-0.84) with LST, compared to other built-up indices in Jakarta and Surabaya. The UHI classification based on LULC showed that residential areas had the highest average temperature compared to bare land, industrial areas, vegetation, and water bodies, with a temperature of 43.3°C for Jakarta and 43.°C for Surabaya, due to the high density of residential areas and buildings in the city. The spectral index correlation results show that IBI has the highest value, 0.69 for Jakarta and 0.85 for Surabaya.. Further research needs to explore high spatial resolution data to distinguish detailed built-up objects in the city.
Nusantara is a city currently under construction to serve as the future capital city of Indonesia, replacing DKI Jakarta. It is located on the island of Kalimantan/Borneo, approximately 1200 Km away from DKI Jakarta on the island of Java. Initially, a significant portion of the Nusantara Capital City was covered with forests and vegetation. The objective of this study is to assess the land cover changes occurring in the Nusantara Capital City using multi-temporal remote sensing satellite imagery. The satellite images used in this study are obtained from Planet's Doves satellite, which consists of four bands (Blue, Green, Red, and Near Infra-Red), as well as SuperDove, which offers eight bands (Ocean Blue, Blue, Green I, Green, Yellow, Red, Red Edge, and Near Infra-Red). Despite being categorized as small satellites, they have a high spatial resolution of 3-5 meters. Remote sensing indices were used to facilitate the land cover classification in areas of interest (AoI), especially the normalized difference vegetation index (NDVI), considering the nature of the land cover. Land cover changes from several different times, starting from 2021, were compared to determine the extent of changes that have occurred. The carbon stock loss in Nusantara was also approximated quarterly using NDVI. As of June 2023, the results indicate that approximately 8.3% of the total AoI has experienced a loss in vegetation, with the most significant decline observed in March 2023. These findings contribute to expanding our understanding of the evolving landscape in the Nusantara Capital City.
Smoke information serves as a crucial marker for detecting peatland fires. Practically, smoke identification utilizing remote sensing satellites, based on visual interpretation techniques, proves inefficient in processing time and high subjectivity. The application of machine learning technology for smoke detection remains limited in the tropics, especially in peatland areas. This study aims to identify smoke from peatland fires using machine learning techniques. The dataset comprises Visible Infrared Imaging Radiometer Suite (VIIRS) images and the VIIRS’s hotspots on September 11st 2019, coinciding with a major peat fire incident in Indonesia. Various machine learning techniques were tested, encompassing Random Forest (RF), Classification and Regression Trees (CART), Support Vector Machine (SVM), Naive Bayes (NB), and Gradient Tree Boost (GTB). Object classification includes thin smoke, thick smoke, clouds/smoke, clouds, vegetation, water body, and bare land. The accuracy assessment involved both qualitative assessment based on true-color images and quantitative evaluation through the 70:30 sample splitting accuracy assessment method. The analysis of spectral distance for the seven object types reveals that band 5, 10, and 12 exhibit the highest value. Successfully identification of peatland fire smoke is achieved via supervised machine learning, particularly logic-based algorithms (RF, CART, GTB) and support vector machine methods (SVM), while statistical method (NB) yield comparatively less success. Qualitative validation using true-color VIIRS image indicates strong alignment between thick smoke and the RF all-bands approach. Quantitative validation, based on accuracy assessment with 1531 samples, establishes SVM as the most accurate method, boasting an overall accuracy of 0.93, followed by GTB at 0.91, RF at 0.90, and CART at 0.88.
KEYWORDS: Landsat, Vegetation, Air temperature, Surface air temperature, Climate change, Land cover, Earth observing sensors, Near infrared, Temperature metrology, Statistical analysis
Climate change has led to an increase in global air temperatures, posing a threat to the liability of capital cities. This study focuses on understanding the Surface Urban Heat Island (SUHI) phenomena, which occurs over cities and is exacerbated by climate change. Spectral indices derived from Landsat data were used to understand SUHI, while trends in air temperature, precipitation, and relative humidity were analyzed using ground observation data collected from 1992 to 2022 in the capital city of Indonesia, Jakarta. The spectral indices used were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Moisture Index (NDMI). Land Surface Temperature (LST) was used to represent SUHI. Results indicate that NDMI has the highest Pearson’s correlation coefficient with LST (-0.73), followed by EVI (-0.41), SAVI (-0.4), and NDVI (-0.4). Trend analysis using Mann-Kendal test and Sen’s Slope showed a statistically significant increase in air temperature with a slope estimation of 0.03°C per year, while rainfall and relative humidity did not significantly differ over 30 years. SUHI trend analysis showed a statistically significant increase with a slope estimation of 0.1°C from 1992-2022. Mean surface temperature increased from 38.9°C in 1992 to 39.4°C in 2022. Jakarta's surface temperature ranged from 24°C – 57°C across water bodies, vegetation, bare land, urban, and industry, analyzed using Support Vector Machine. This study provides insight into the condition of SUHI over time, allowing the government to make efforts to mitigate the impact of climate change.
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