Sebaran Spasial Dan Temporal Titik Panas (Hotspot) Di Indonesia Dari Satelit Modis Dengan Metode Gridding

Putra, I Dewa Gede Arya; Heryanto, Eko; Sopaheluwakan, Ardhasena; Pradana, Radyan Putra; Haryoko, Urip

doi:10.24895/sng.2018.3-0.1035

Cited by 5 publications

(5 citation statements)

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“…This information on land cover reflects the variation in vegetation types present. These vegetation types are key indicators in determining the amount of fuel available for forest fires (Putra et al, 2019). The weighting process is based on the sensitivity of each vegetation type to fire risk, as classified by Pratondo et al (2006) and Salsabilah et al (2021) which was then adjusted to the conditions in Pangi Binangga Nature Reserve.…”

Section: Land Cover Class Analysismentioning

confidence: 99%

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Spatial Analysis of Forest Fire Potential in Pangi Binangga Nature Reserve, Parigi Moutong District, Central Sulawesi

Muh. Suyuti Hamsi,

Muis,

Rahman

2024

Sainmatika

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This study aims to analyze the potential for forest fires in Pangi Binangga Nature Reserve, Parigi Moutong District, Central Sulawesi, by utilizing geographic information system data and mapping areas based on fire potential. The research method involved field surveys and the analysis of primary and secondary data. Forest fire hazards maps were obtained from the analysis of Landsat satellite images that were classified to provide information on land cover, slope, distance from roads, and categorized based on predetermined weighting categories. The overlay process was conducted using Geographic Information System software. The main findings indicate that the area has diverse land cover, with reed grassland savanna having the highest fire hazards covering an area of 1203.78 ha. Very steep slopes cover 339.94 ha, and the distance from roads and settlements to the highest hazards point is 140 meters. These variables also influence the level of hazards. Implications of this study include the placement of forest fire prevention patrols, the use of weather modification technology, and post-fire support to restore ecosystems.

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Section: Land Cover Class Analysismentioning

confidence: 99%

“…Additionally, implementing fire prevention measures and raising awareness about the dangers of forest fires can help reduce the occurrence of fires in these areas. (Putra et al, 2019).…”

Section: Forest Fire Hazards Level Of Pangi Binangga Nature Reservementioning

confidence: 99%

Spatial Analysis of Forest Fire Potential in Pangi Binangga Nature Reserve, Parigi Moutong District, Central Sulawesi

Muh. Suyuti Hamsi,

Muis,

Rahman

2024

Sainmatika

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“…Titik panas (hotspot) merupakan indikator terjadinya kebakaran lahan di suatu lokasi yang memiliki suhu relatif lebih tinggi dibandingkan dengan suhu disekitarnya [12]. Semakin banyak titik hotspot di suatu wilayah, semakin banyak pula potensi kejadian kebakaran lahan di suatu wilayah tersebut.…”

Section: Metodologi Penelitianunclassified

Algoritma Backpropagation dalam Akurasi Memprediksi Kemunculan Titik Api (Hotspot) pada Wilayah Kerja Dinas Kehutanan

Riska

2022

jsisfotek

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Forest and land fires are an annual disaster issue in Indonesia. The forest area in West Sumatra is ± 2,286,883.10 Ha and 27% or an more than 630,695 Ha of forest area categorized as critical land that has the potential to burn and be damaged. Controlling for forest and land fires in West Sumatra Province was task for Forestry Departement, part of Sumatera Barat Government. One of is task was to reduce the rate of forest destruction. Forest and land fires are an annual disaster issue in Indonesia. The forest area in West Sumatra is ± 2,286,883.10 Ha and 27% or an more than 630,695 Ha of forest area categorized as critical land that has the potential to burn and be damaged. Controlling for forest and land fires in West Sumatra Province was task for Forestry Departement, part of Sumatera Barat Government. One of is task was to reduce the rate of forest destruction. Apart from to extinguishing forest fires directly at the hotspots, preventive action are needed to reduce the possibility of forest and land fires, and one of it is by predicting the possibility hotspots in the future. One of the methods used to predict the possibility hotspots is the use of artificial neural network Backpropagation, this is because Backpropagation has the ability to learn from existing data patterns to calculate the possibility of future events. Data of hotspots that have happened previously and several supporting variables such as air temperature, humidity, rainfall and wind speed, were analyzed and grouped as the basis for the formation of an artificial neural network and for further data training. This learning is done by testing several different network architectures. The results obtained from these tests are the Performance and MSE (Mean Squared Error) values for each network architecture. The test results for each architecture will be compared to determine the best architecture that produces the most accurate predictive value and the smallest MSE. The results of this prediction will later be used as one of the considerations for the Forestry Departement for planning forest and land fire control activities in their area.

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“…According to (Yananto & Dewi, 2016), when a strong El Nino occurred in 2015, which prolonged the dry season, the number of hotspots in the same year was considered the highest number of hotspots in Kalimantan and Sumatra in the last ten years. The highest numbers of hotspots usually occur at the peak of a dry season (Putra et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Hotspot data analysis was conducted by processing the data spatially and temporally. Hotspot data visualization was carried out as described in (Putra et al, 2018) through several data processing stages, one of which was a gridding process. Figure 2 represents a hotspot gridding process illustration in Nagan Raya Regency with 10 x 10 km resolution.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Hotspots Based on Climate Data in the Nagan Raya Regency, Aceh

Abdurahman

Ismail

Putra

2022

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Identifying hotspots as a potential for wildfires due to weather/climate factors needs to be studied in more detail to consider the policies taken by relevant agencies in the Nagan Raya Regency, mainly consisting of peatlands. Rainfall observation data in some areas are incomplete, so alternative data are needed for rainfall estimation for those areas, such as satellite data. However, the satellite data does not necessarily match the conditions in the field, so validation is needed. In this study, satellite data were validated with available observational data in the area, so the results can be used as a reference when field data is unavailable. The data used are GSMaP_GNRT6 and observation data from 5 rainfall observation posts: Beutong, Cut Nyak Dien Meteorological Station, Darul Makmur, PT. Socfindo and Pulo Ie for the period 2010-2019. The satellite and the observation data were correlated with the Pearson method to see the relationship between the two data. The difference between each satellite data and observations at the same time and place is calculated using the formulas Mean Error (ME), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Furthermore, a case study of fire incidents and satellite hotspot data at several locations was also observed simultaneously. In addition, the validated rainfall data were also used to calculate the Standardized Precipitation Index (SPI) value. The result shows the validation of rainfall data with GSMaP_GNRT6 satellite data has a moderate correlation with the MAE value ranging from 101.3 to 195.12 from the five rainfall observation posts. The results also show that a 10-day base of rainfall before the occurrence of the wildfires was in a low category (86%). The number of hotspot occurrences was also supported by the negative monthly SPI value, high air temperature, and the type of land in the study area. Keywords: Hotspot, Rainfall, Air Temperature, Wildfires, Peat Lands

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Sebaran Spasial Dan Temporal Titik Panas (Hotspot) Di Indonesia Dari Satelit Modis Dengan Metode Gridding

Cited by 5 publications

References 0 publications

Spatial Analysis of Forest Fire Potential in Pangi Binangga Nature Reserve, Parigi Moutong District, Central Sulawesi

Spatial Analysis of Forest Fire Potential in Pangi Binangga Nature Reserve, Parigi Moutong District, Central Sulawesi

Algoritma Backpropagation dalam Akurasi Memprediksi Kemunculan Titik Api (Hotspot) pada Wilayah Kerja Dinas Kehutanan

Evaluation of Hotspots Based on Climate Data in the Nagan Raya Regency, Aceh

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