The province of East Kalimantan is officially designated as the State Capital because the area has the least risk of disaster, even though it cannot be separated from disasters such as forest and land fires. This study aims to determine the spatial pattern of hotspots using SNPP-VIIRS for monitoring potential fires. The research used the descriptive-analytic method to identify the research area and collect secondary data. Secondary data is spatial and nonspatial data consisting of hotspot data from the recording of the SNPP -VIIRS image, including frequency and distribution of hotspots. The data usage from 2012–2021 using SNPP-VIIRS morning and evening recordings. The study results show that the spatial pattern of potential hotspots in the capital city of a new country is quite varied. The spatial pattern of hotspots shows that Kutai Kartanegara Regency as one of the locations for the new State Capital, has the highest number of hotspots, namely 38,970 with the highest accuracy in East Kalimantan Province, namely, 1,616 (low), 36,253 (nominal), and 1,101 (high). The potential for fire disasters in Kutai Kartanegara Regency as an IKN location is high, so planning is urgently needed for future fire prevention, mitigation, and prevention strategies. The spatial pattern of hotspots is known, so it can be used to monitor potential fires and minimize fire occurrences.

This research specifically aims to investigate the most accurate spectral indices in extracting wetlands geospatial information taking South Kalimantan, Indonesia, as an example of wetlands in tropical areas. Ten spectral indices were selected for testing their ability to extract wetlands, those are NDVI, NDWI, MNDWI, MNDWIs2, NDMI, WRI, NDPI, TCWT, AWEInsh, andAWEIsh. Tests were performed on Landsat 8 OLI path/row 117/062 and 117/063. The threshold method which was used to separate the wetland features from the spectral indices imagery is Otsu method. The results of this research showed that generally MNDWIs2 was the most optimal spectral indices in wetlands extraction. Especially tropical wetlands that rich with green vegetation cover. However, MNDWIs2 is very sensitive to dense vegetation, this feature has the potential to be detected as wetlands. Furthermore, to improve the accuracy and prevent detection of the dryland vegetation as wetlands, the threshold value should be determined carefully.

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This research had two objectives. The first objective was to quantity the carbon emissions from fires of various types of tropical wetland vegetation using Sentinel-2 imagery. The second objective was to measure how long the carbon stock will recover using Sentinel-2 imagery. Burned areas were extracted automatically using the Relativized Burn Ratio (RBR). Calculation of carbon emissions and carbon sequestrations were carried out by measuring the differences in Above Ground Biomass (AGB) before the fires, right after the fires, and a few months after the vegetation re-grows after the fires. Therefore, multitemporal Sentinel-2 MSI imageries from three different times are required. All imageries processing was carried out using the ESA SNAP software. The results showed that tropical wetland fires emited an average of 121.61 Mg C/ha, or equivalent to 445.9 Mg CO₂/ha. Furthermore, tropical wetlands had an average rate of about 9.27 months to restore their carbon stocks to their pre-burnt state. Peatland forests took the longest time to recover to its original carbon stock state after burning, which was almost 22 years to recover.