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DETEKSI KONDISI KETAHANAN PANGAN BERAS MENGGUNAKAN PEMODELAN SPASIAL KERENTANAN PANGAN Dede Dirgahayu; I Nengah Surati Jaya; Florentina Sri Hardiyanti Purwadhi; Muhammad Ardiansyah; Hermanu Triwidodo
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol. 2 No. 2 (2012): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (JPSL)
Publisher : Graduate School Bogor Agricultural University (SPs IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.2.2.85

Abstract

In 2005 and 2009, BKP and WFP has provided food security conditions in Indonesia on Food Insecurity Map which were developed using food availability, food accessibility, food absorption and food vulnerability. There are 100 out of 265 districts in Indonesia or about 37,7%, which fall into the vulnerable to very vulnerable categories, where 11 districts were found in Java. The main objective of this research is to develope a spatial model of the rice production vulnerability (KPB) based on Remote Sensing and GIS technologies for estimating the food insecurity condition. Several criteria used to obtain food vulnerability information are percentage level of green vegetation (PV), rainfall anomaly (ACH), land degradation due to erosion (Deg), and paddy harvest failure due to drought and flood in paddy field (BK). Dynamic spatial information on the greenness level of land cover can be obtained from multitemporal EVI (Enhanced vegetation Index) of MODIS (Moderate Resolution Imaging Spectroradiometer) data. Spatial information of paddy harvest failure caused by drought and flood was estimated by using vegetation index, land surface temperature, rainfall and moisture parameters with advance image processing of multitemporal EVI MODIS data. The GIS technology were used to perform spatial modelling based on weighted overlay index (multicriteria analysis). The method for computing weight of factors in the vulnerability model was AHP (Analytical Hierarchy Process). The spatial model of production vulnerability (KPB) developed in this study is as follows: KPB = 0,102 PV + 0,179 Deg + 0,276 ACH + 0,443 BK. In this study, level of production vulnerability can be categorized into six classes, i.e.: (1) invulnerable; (2) very low vulnerability; (3) low vulnerability; (4) moderately vulnerable; (5) highly vulnerable; and (6) extremely vulnerable. The result of spatial modelling then was used to evaluate progress production vulnerability condition at several sub-districts in Indramayu Regency. According to the investigation results of WFP in 2005, this area fall into moderately vulnerable category. Only few sub-districts that fall into highly and extremely vulnerable during the period of May ~ August 2008, namely: Kandanghaur, Losarang, part of Lohbener, and Arahan.Keywords: remote sensing, GIS, food vulnerability, vegetation index, AHP
Application of Random Forest Algorithm to Analyze the Confidence Level of Forest Fire Hotspots in Riau Peatland Unik, Mitra; Sukaesih Sitanggang, Imas; Syaufina, Lailan; Surati Jaya, I Nengah
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol 15 No 2 (2025): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (JPSL)
Publisher : Pusat Penelitian Lingkungan Hidup, IPB (PPLH-IPB) dan Program Studi Pengelolaan Sumberdaya Alam dan Lingkungan, IPB (PS. PSL, SPs. IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.15.2.255

Abstract

Forest fires pose a significant challenge in Riau Province, Indonesia, especially in peatland areas. This study employs the Random Forest (RF) algorithm to analyze the confidence levels of hotspots, aiming to predict potential fire occurrences and improve fire management strategies. The research focuses on peatlands spanning 3.86 million ha, using key variables such as NDVI, surface temperature, and peat thickness derived from satellite data. The model achieved an average AUC of 0.732 and a classification accuracy of 70.3%, with medium-confidence hotspots demonstrating the best predictive performance (AUC: 0.707, F1-score: 0.804). However, the model struggled with low-confidence hotspots, reflecting challenges in distinguishing less prominent patterns in the data. Compared to other methods, RF demonstrates strong potential in handling complex environmental datasets, making it a valuable tool for hotspot prediction. This study contributes to understanding forest fire risks in peatlands and provides actionable insights for improving preparedness and mitigation efforts.
Co-Authors Abdul Rosyid Adelia Juli Kardika Agung Budi Cahyono Agung Budi Cahyono Agus P. Kartono Ahyar Gunawan Andry Indrawan Anita Zaitunah Anita Zaitunah Antonius B Wijanarto Antonius B Wijanarto Antonius B Wijanarto Bambang Hero Saharjo Bambang Sapto Pratomosunu Bejo Slamet Beni Iskandar Boedi Tjahjono Bramasto Nugroho Budi Kuncahyo Cecep Kusmana Dahlan Dahlan Dahlan Dahlan Darwo Darwo Darwo Darwo Dede Dirgahayu Dewayany Sutrisno Dewayany Sutrisno Diana Septriana Dito Cahya Renaldi Dito Cahya Renaldi Dwi Noventasari Dwi Putra Apriyanto Dwi Shanty Apriliani Gunadi Elias Elias Ema Kurnia ENDANG SUHENDANG Endang Suhendang Endang Suhendang Suhendang Endes Nurfilmarasa Dahlan Eva Achmad F Gunarwan Suratmo Fahmi Amhar Faid Abdul Manan Fairus Mulia Fairus Mulia Farida H. Susanty Farida Herry Susanty Farida Herry Susanty Florentina Sri Hardiyanti Purwadhi Hanifah Ikhsani Hardian, Dwika Hardjanto Hardjanto Hardjanto Hariaji Setiawan Haryo Tabah Wibisono Hasriani Muis Hendrayanto . Hendri Nurwanto Hermanu Triwidodo Herry Purnomo Herry Purnomo Hidayat Pawitan I Gusti Bagus Wiksuana Iin Arianti Imas Sukaesih Sitanggang Irdika Mansur Ismail HJ Hashim Israr Albar Ita Carolita Iwan Gunawan Jarunton Boonyanuphap Kartodihardjo, Hariadi Kukuh Murtilakono Kukuh Murtilaksono Kukuh Murtilaksono Kusnadi Lailan Syaufina LILIK BUDIPRASETYO Liu Qian Liu Qian Lukman Hakim Lukman Mulyanto M. Bismark Makin Basuki Marlina, Etty Moch. Anwar Muhammad Ardiansyah Muhammad Buce Saleh Muhammad Ikhwan Mulyaningrum Mulyaningrum Muzailin Affan Muzailin Affan N Nurhendra Naik Sinukaban Naik Sinukaban Nanin Anggraini Nining Puspaningsih Nitya Ade Santi Nitya Ade Santi Nitya Ade Santi Nitya Ade Santi Nobuyuki Abe Nurdin Sulistiyono Omo Rusdiana Oteng Haridjaja Oteng Haridjaja Patrich Phill Edrich Papilaya Pratiwi Pratiwi Pratiwi Pratiwi Purnama, Edwin Setia R Assyfa El Lestari Rahimahyuni Fatmi Noor'an Robert Parulian Silalahi Rudi Ichsan Ismail Samsuri Samsuri Samsuri Samsuri Samsuri Samsuri Samsuri, Samsuri Sendi Yusandi Sigit Nugroho Soedari Hardjoprajitno Sri Wahyuni Suria Darma Tarigan Susilawati Suyadi Suyadi Suyadi Suyadi Syamsu Rijal Tatang Tiryana Teddy Rusolono Tien Lastini Tien Lastini Tirta Negara Tirta Negara Tomi Yuwono Tomi Yuwono, Tomi Unik, Mitra Uus Saepul Mukarom Wang Xuenjun Wang Xuenjun Wibisono, Haryo Tabah Wibisono, Haryo Tabah Wibisono, Haryo Tabah Widi Atmaka Widyananto Basuki Aryono Wijanarto, Antonius B. Wijanarto, Antonius B. Yadi Setiadi YANTO SANTOSA Zhang Yuxing