Forest and land fires occur every year in Indonesia. Efforts to handle forest and land fires have not been optimal because fires occur in too many places with unclear patterns and densities. The study analyzed the spatiotemporal patterns of burned areas and fire density in fire-prone areas in Indonesia. Data of burned areas were taken from http://sipongi.menlhk.go.id/. The website collected its data from NOAA (National Oceanic and Atmospheric Administration) images. Data were analyzed using the hot spot analysis to determine the spatiotemporal patterns of the burned areas and the kernel density analysis to examine the density of land fires. Findings showed that the spatiotemporal pattern from 2016 to 2019 formed a hot spot value in the peatland area with a confidence level of 90–99%, meaning that land fires were clustered in that area. In addition, the highest density of land fires also occurred in the peatland areas. Clustered burned areas with high fire density were found in areas with low–medium vegetation density—they were the peatland areas. The peatland areas must become the priority to prevent and handle forest and land fires to reduce fire risks.

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Shifting cultivation is the dominant land-use system in the Loksado Subdistrict. The shifting cultivation products provide various valuable subsistence products for Meratus Dayak farmers. The shift farming system is controversial because it is closely related to environmental problems. Shifting cultivation has undergone a drastic change to market-oriented land use. However, there is limited information on geographic data in the form of the spatial distribution of shifting fields. This is very important for the monitoring and evaluation of shifting agriculture. Remote sensing techniques provide an effective way to detect, monitor the location and extent of shifting cultivation. The method used is through visual interpretation of Sentinel 2 satellite images. The total increase in the number of shifting fields is 159 fields or 11.5% and the area of shifting cultivation has increased by 219.5 hectares or 8.17% in 2019. bags.

Wetland areas are volatile and have high iron content. In this study, through a remote sensing approach, especially using Landsat Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) imagery, we discussed the method to estimate the presence of iron oxide in the wetlands of South Kalimantan in 2018, 2019, and 2020. Interpretation of the Landsat OLI TIRS was employed in April 2018, August 2018, February 2019, August 2019, March 2020, and August 2020. The band ratio method was used to determine the distribution of samples in this study. The results of the iron oxide index from the image were performed regression and correlation analysis with field measurement and laboratory test results to validate the oxide index values. The results showed that the iron oxide index value in the dry season was higher than in the rainy season. Iron oxide index value in open land was higher than in vegetation cover. The wetland was in dry condition during the dry season, making it easier to detect iron oxide values. Vegetation cover could reduce the iron oxide index value on the soil surface so that the iron oxide value was more easily identified in open land. The results of linear regression testing for the wet season sample obtained a coefficient of determination R² = 0.413, while the results of linear regression testing for the dry season sample obtained a coefficient of determination R² = 0.667. Thus, the Landsat image has strong enough to estimate the iron oxide index in the wetland area of Kalimantan.