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.
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