Confidence Analysis of Hotspot as Peat Forest Fire Indicator

Shofiana, Dewi Asiah; Sitanggang, Imas Sukaesih

doi:10.1088/1742-6596/1751/1/012035

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…For example, a researcher set a confidence level threshold at 60%, which was applied to cropland areas in Russia [60]. Other researchers have used a 70% threshold in tropical peat areas of Sumatra and Kalimantan [61]. Another set a threshold value at 73% for MODIS-Aqua and 66% for MODIS-Terra in the United States [62].…”

Section: Identification Of Fire Historymentioning

confidence: 99%

“…By considering the confidence levels of the fire incident data, the fire events in the PHU area were identified. Previous researchers had set a range of confidence levels for the hotspot data [40,[60][61][62]; however, considering the similarity of peat types as well as the environmental and climatic conditions, we set a 70% confidence level as the threshold [61]. The distribution of the fire incidents in Kahayan-Sebangau PHU during 2010-2020 is presented in Figure 9.…”

Section: Fire History and Tidal-affected Areamentioning

confidence: 99%

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The Determination of Priority Areas for the Restoration of Degraded Tropical Peatland Using Hydrological, Topographical, and Remote Sensing Approaches

Cahyono

Aditya

Istarno

2022

Land

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Degraded peatland is caused by forest clearing and the construction of artificial water networks. When water management is not implemented across land uses in the entire peatland landscape, then it will be a big issue that causes a water deficit and leads to increasing droughts and fires. Effective restoration must first identify the part of Peatland Hydrological system Units (PHUs) with insufficient water storage and resources. This study used intercorrelated factors of water balance, deficit months, NDMI-NDVI indices, dry periods, recurrent fires, peat depth, and water loss conditions, as the evaluation parameters, within individual sub-PHUs to determine the most degraded areas that require intervention and restoration. Sub-PHU was determined based on the peat hydrological unity concept by identifying streamline, outlet channels, peat-depth, slopes, and network connectivity. Global hydrological data using TerraClimate and CHIRPS, combined with field observations, were used to validate and calculate each sub-PHU’s water balance and dry periods. Soil moisture (NDMI), vegetation density (NDVI), and fire frequency were extracted from multispectral satellite images (e.g., Landsat 8, MODIS-Terra, and MODIS-Aqua). Each parameter was ranked by the score for each sub-PHU. The parameters that can be ranked are only the ordinal type of number. The lowest ranks indicated the most degraded sub-PHUs requiring peat rewetting interventions.

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Section: Identification Of Fire Historymentioning

confidence: 99%

Section: Fire History and Tidal-affected Areamentioning

confidence: 99%

The Determination of Priority Areas for the Restoration of Degraded Tropical Peatland Using Hydrological, Topographical, and Remote Sensing Approaches

Cahyono

Aditya

Istarno

2022

Land

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“…On July 23, 2017, BMKG recapitulated the hotspot data in Riau Province, showing there were five hotspots detected in Rokan Hilir Regency with a confidence level of 50%. The higher the confidence level of hotspots, the stronger it indicates real fire existence [5].…”

Section: Introducingmentioning

confidence: 99%

Hotspot Predictive Modeling Using Regression Decision Tree Algorithm

Shofiana

Utami

Heningtyas

2022

JTI

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Forest fires had always become an international issue influencing many life sectors, including environmental, social, and economic. The forest fire in 2013 was regarded as one of the worst forest fire tragedies in history, not only in Indonesia but also in the world. Detection of hotspots on the earth's surface by the satellite can be an indication of land and forest fire occurrence. This research aims to build a predictive model of monthly hotspots in Rokan Hilir Regency using the regression tree algorithm. Several variables related to weather information are included, such as rainfall, sea surface temperature, and southern oscillation index. This research used 245 training data and 43 testing data, resulting a predictive model with a correlation of 0.875 and an error rate of 0.166. Based on the values, we can conclude that the performance of the model is considerably good.

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