2025 |
Fauziah,; Hayati, Nur; Prasetyo, Lilik B Mapping of hotspots and burn areas based on QGIS in relation to Peatland fire vulnerability on Sumatra Island Conference AIP Conference Proceedings, vol. 3250, 2025. Abstract | BibTeX | Tags: hotspot, peatland @conference{nokey,Peatlands in Indonesia cover 10.8% of the country’s land area and are found in Kalimantan, Papua, and Sumatra. Peatlands store large amounts of water and help to prevent floods and droughts in surrounding areas. However, poor management of peatlands has led to frequent wildfires in Indonesia. In 2015, wildfires in Sumatra produced hazardous haze that affected the health of over 100,000 people in Indonesia, Malaysia, and Singapore. Indonesian peatlands store up to 57 billion tons of carbon, which makes it difficult to extinguish underground peat fires. One way to prevent wildfires is to map hotspots and burn areas to identify vulnerable regions. This study used hotspot data from VIIRS and burn area data from MODIS to analyze trends in Sumatra, the largest peatland area in Indonesia. The results showed that the number of hotspots and the size of burn areas in Riau were significantly higher than in other peatland regions. Riau consistently had the highest percentage of hotspots and burn areas, ranging from 6.26% to 90.70% for hotspots and 22.45% to 80.01% for burn areas. |
2018 |
Setiawan, Yudi; Prasetyo, Lilik B; Pawitan, Hidayat; Permatasari, Prita A; Suyamto, Desi; Wijayanto, Arif K Identifying Areas Affected By Fires In Sumatra Based On Time Series Of Remotely Sensed Fire Hotspots And Spatial Modeling Journal Article In: Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management), vol. 8, no. 3, pp. 420–427, 2018, ISSN: 2460-5824. Abstract | Links | BibTeX | Tags: fire, hotspot @article{setiawan2018identifying,Wildfires threaten the environment not only at local scales, but also at wider scales. Rapid monitoring system to detect active wildfires has been provided by satellite remote sensing technology, particularly through the advancement on thermal infrared sensors. However, satellite-based fire hotspots data, even at relatively high temporal resolution of less than one-day revisit period, such as time series of fire hotspots collected from TERRA and AQUA MODIS, do not tell exactly if they are fire ignitions or fire escapes, since other factors like wind, slope, and fuel biomass significantly drive the fire spread. Meanwhile, a number of biophysical fire simulation models have been developed, as tools to understand the roles of biophysical factors on the spread of wildfires. Those models explicitly incorporate effects of slope, wind direction, wind speed, and vegetative fuel on the spreading rate of surface fire from the ignition points across a fuel bed, based on either field or laboratory experiments. Nevertheless, none of those models have been implemented using real time fire data at relatively large extent areas. This study is aimed at incorporating spatially explicit time series data of weather (i.e. wind direction and wind speed), remotely sensed fuel biomass and remotely sensed fire hotspots, as well as incorporating more persistent biophysical factors (i.e. terrain), into an agent-based fire spread model, in order to identify fire ignitions within time series of remotely sensed fire hotspots. |
2017 |
Wijayanto, Arif K; Sani, Octo; Kartika, Nadia D; Herdiyeni, Yeni Classification model for forest fire hotspot occurrences prediction using ANFIS algorithm Proceedings Article In: IOP Conference Series: Earth and Environmental Science, pp. 012059, IOP Publishing IOP Publishing, 2017. Abstract | Links | BibTeX | Tags: ANFIS, fire, hotspot @inproceedings{wijayanto2017classification,This study proposed the application of data mining technique namely Adaptive Neuro-Fuzzy inference system (ANFIS) on forest fires hotspot data to develop classification models for hotspots occurrence in Central Kalimantan. Hotspot is a point that is indicated as the location of fires. In this study, hotspot distribution is categorized as true alarm and false alarm. ANFIS is a soft computing method in which a given inputoutput data set is expressed in a fuzzy inference system (FIS). The FIS implements a nonlinear mapping from its input space to the output space. The method of this study classified hotspots as target objects by correlating spatial attributes data using three folds in ANFIS algorithm to obtain the best model. The best result obtained from the 3rd fold provided low error for training (error = 0.0093676) and also low error testing result (error = 0.0093676). Attribute of distance to road is the most determining factor that influences the probability of true and false alarm where the level of human activities in this attribute is higher. This classification model can be used to develop early warning system of forest fire. |
2025 |
Mapping of hotspots and burn areas based on QGIS in relation to Peatland fire vulnerability on Sumatra Island Conference AIP Conference Proceedings, vol. 3250, 2025. |
2018 |
Identifying Areas Affected By Fires In Sumatra Based On Time Series Of Remotely Sensed Fire Hotspots And Spatial Modeling Journal Article In: Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management), vol. 8, no. 3, pp. 420–427, 2018, ISSN: 2460-5824. |
2017 |
Classification model for forest fire hotspot occurrences prediction using ANFIS algorithm Proceedings Article In: IOP Conference Series: Earth and Environmental Science, pp. 012059, IOP Publishing IOP Publishing, 2017. |
