Utilization of MODIS data to analyze the forest/land fires frequency and distribution (case study : Central Kalimantan Province)

Sabani, Wahyu; Rahmadewi, Dinda Putri; Rahmi, Khalifah Insan Nur; Priyatna, Muhammad; Kurniawan, Erdin

doi:10.1088/1755-1315/243/1/012032

Cited by 11 publications

(9 citation statements)

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“…Keuntungan penggunaan data MODIS adalah resolusi temporal yang dimiliki MODIS yaitu 2 kali per hari. Resolusi temporal dan tingkat keyakinan data yang beragam membuat data MODIS dapat digunakan dengan baik untuk melakukan analisis titik api (Sabani, Rahmadewi, Rahmi, Priyatna, & Kurniawan, 2019 (Ord & Getis, 1992) dimana xj adalah atribut untuk titik i, wi,j adalah bobot spasial antar titik i dan j, n adalah total jumlah titik. Sedang * adalah z-score (Ord & Getis, 1992).…”

Section: Moderateunclassified

Analisis Pola Spasial Kebakaran Lahan Di Kabupaten Pulang Pisau Tahun 2001-2019

Papilaya

2020

TGEO

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Forest and land fires have become a problem for the Republic of Indonesia in the last few decades. Statistically based on MODIS data for 19 years from 2001 to 2019, Central Kalimantan is the province with the most number of fires totaling 255,334 fire spot or 18% of fire occurance from 34 provinces in Indonesia. This study will look at spatial patterns and spatial correlations of forest and land fires in Pulang Pisau District from 2001 to 2019. Spatial patterns of hotspots were analyzed using a statistical Getis-Ord (Gi *) analysis, the relationship between hotspots was analyzed using Spatial Autocorrelation Moran's I (Index). The results of the hotspot analysis show the high significance of the hotspots that occurred in the subdistricts of Sebangau Kuala, Kahayan Kuala, Kahayan Hilir and Jabiren. The result of spatial autocorrelation of hotspots from 2001-2019 (except in 2010) is that hotspots pattern are clustered. Based on the findings it can be concluded statistically that 99% of the likelihood of a group fire event occurred intentionally. This hotspot incident that arose intentionally can be a clue for the Local Government to be able to better engage the community to prevent and overcome the hotspot by providing coaching and trainingKeywords: Pola Spasial, Hotspot, Spatial Autocorrelation, Pulang Pisau Kebakaran hutan dan lahan telah menjadi masalah bagi Republik Indonesia dalam beberapa dekade terakhir. Secara statistik berdasarkan data MODIS selama 19 tahun mulai dari tahun 2001 sampai 2019, Kalimantan Tengah merupakan provinsi yang paling banyak memiliki titik api sejumlah 255.334 titik atau sebesar 18% kejadian kebakaran hutan dan lahan dari 34 provinsi di Indonesia. Penelitian ini akan melihat pola spasial dan korelasi spasial kebakaran hutan dan lahan di Kabupaten Pulang Pisau pada tahun 2001 sampai 2019. Pola spasial hotspot dianalisa dengan menggunakan analisis Getis-Ord (Gi*) statistic, hubungan antar titik api dianalisa dengan menggunakan Spatial Autocorrelation Moran’s I (Index). Hasil analisis hotspot menunjukan signifikasi tinggi hotspot yang terjadi pada kecamatan Sebangau Kuala, Kahayan Kuala, Kahayan Hilir dan Jabiren. Hasil spatial autocorrelation kejadian titik api dari tahun 2001-2019 (kecuali tahun 2010) adalah titik api memiliki pola yang merupakan berkelompok (clustered). Berdasarkan temuan bisa disimpulkan secara statistik bahwa 99% kemungkinan kejadian titik api yang berkelompok tersebut terjadi secara disengaja. Kejadian titik api yang muncul secara disengaja ini dapat menjadi petunjuk bagi Pemerintah Daerah untuk dapat lebih mengajak masyarakat dalam mencegah dan menanggulangi titik api dengan memberikan pembinaan dan pelatihan. Kata Kunci: Pola Spasial, Hotspot, Spatial Autocorrelation, Pulang Pisau

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Section: Moderateunclassified

Analisis Pola Spasial Kebakaran Lahan Di Kabupaten Pulang Pisau Tahun 2001-2019

Papilaya

2020

TGEO

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“…As a consequence, Modis employs a detection algorithm to determine fire pixels that represent active burning fires when the satellite passes through a location. The bands used for this detection algorithm are band 1 (0.65 µm), band 2 (0,86 µm), band 7 (21 µm), band 21 (4 µm), band 22 (4 µm), band 31 (11 µm), and band 32 (12 µm) [11]. Further, Modis' detection algorithm goes through seven steps of identification when determining the hotspots [12], namely:…”

Section: Introductionmentioning

confidence: 99%

Analysis of Surface Concentration of Black Carbon in Riau Province During the 2019 Forest Fires Using Merra-2 Reanalysis Data

Sari

Andarini

'Adany

et al. 2022

JMG

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Indonesia experienced forest fires almost every year, especially in the area of Riau Province, where as a result, pollutants such carbon dioxide and black carbon present in the air over Riau. Due to its size and its light absorption ability, black carbon is known to have bad impacts on the climate. This study aims to analyse the effect of forest fires that happened in Riau province, Indonesia in September 2019 towards the production of black carbon, as well as to estimate backward air movement trajectory in order to confirm the air mass sources of the black carbon. The data of black carbon concentration used in this study is an hourly temporal data of MERRA-2 with a spatial resolution of 0.5° × 0.625°, while the trajectory calculation uses the HYSPLIT trajectory model. Linear regression of this study has an r value of 0.78 which shows that there is a positive correlation between black carbon concentration and number of hotspots. The range of BC concentrations were 2–11 × 10-9 kg.m-3 with higher concentration occurred during 9–23 September 2019 where it was affected by the south-easterly wind from Jambi and South Sumatera.

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“…Land fires in Kalimantan have occurred since the 17th century, leading to severe physical and social environmental losses. Land fires also caused increased greenhouse gas (GHG) emissions (JICA 2017) and smog (Erwinsyah 2018), impacting various countries such as Malaysia, Singapore, Thailand, and the Philippines (Sabani et al 2019). In Indonesia, smog always occurs during the dry season, from August to October, or during the transitional period.…”

Section: Introductionmentioning

confidence: 99%

Mapping of Factors Affecting Land Fires in the Media Frame for Disaster Mitigation in the Future

et al. 2022

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Land fires are national and international disasters that cause crucial environmental problems and are of local and global concern. This research was conducted to determine the mapping of the factors that affect land fires in a media frame that can be used for disaster mitigation so that the impact of fires can be minimized in the future. In addition, with mitigation, it is hoped that fires can be reduced, namely through restoration programs, including wetting, revegetation and community economic revitalization. This study uses time series data from 2012-2021 and uses the framing analysis method and content analysis. The results of the study show that the mapping of factors that affect land fires in the research area from several media from September 2019 to June 2021 is a factor of human activity and natural factors. The dominant human activities causing fires in the research area are land clearing for agriculture and settlements, land preparation, illegal logging, and lack of public awareness of the dangers of fire. The natural factors that dominate causing fires in the research area are the dry season, not residential areas.

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Utilization of MODIS data to analyze the forest/land fires frequency and distribution (case study : Central Kalimantan Province)

Cited by 11 publications

References 1 publication

Analisis Pola Spasial Kebakaran Lahan Di Kabupaten Pulang Pisau Tahun 2001-2019

Analisis Pola Spasial Kebakaran Lahan Di Kabupaten Pulang Pisau Tahun 2001-2019

Analysis of Surface Concentration of Black Carbon in Riau Province During the 2019 Forest Fires Using Merra-2 Reanalysis Data

Mapping of Factors Affecting Land Fires in the Media Frame for Disaster Mitigation in the Future

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