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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Jurnal Ilmu Pertanian Indonesia Jurnal Ilmu Tanah dan Lingkungan (Journal of Soil Science and Environment) Jurnal Planologi Sultan Agung Journal of Mathematical and Fundamental Sciences Journal of Engineering and Technological Sciences Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) JURNAL ILMIAH GEOMATIKA MAJALAH ILMIAH GLOBE Indonesian Journal of Electrical Engineering and Computer Science Makara Journal of Technology The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Bambang Hendro Trisasongko
Department Of Soil Science And Land Resources And P4W/CRESTPENT, Bogor Agricultural University. Jalan Meranti, Dramaga Campus. Bogor 16680. Indonesia.
Agriculture, Biological Sciences & Forestry Astronomy Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Modeling Land Use/Land Cover Change in Berau Pantai Forests, Berau Regency, East Kalimantan Province Andhi Trisnaputra; Baba Barus; Bambang Hendro Trisasongko
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol 13 No 3 (2023): 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.13.3.386-397

Abstract

Land demands increase with the rise of population and regional development. This results in considerable pressure on forest resources which is characterized by an increasing rate of deforestation. To further explore the impact of deforestation and forest management in regional planning process, this study specifically aimed 1) to identify patterns of land use/land cover changes, 2) to analyze driving factors and 3) to model future land use/land cover. This study employed Landsat imageries to construct land use/land cover maps and their variation across time. Driving factors were analyzed using binary logistic regression. Land use prediction was made through Artificial Neural Network approach. Multitemporal analysis indicated that the research area experienced a decreasing trend of natural forest and shrubs, with substantial extension of existing plantation forests, plantations, agricultural lands and settlements. Indicated driving factors included accessibility, slope class, soil type, forest permit, forest function, RTRW and population density. A forecast in 2030 suggested that natural forests and built-up land would increase from current figures.
Modeling Landslide Hazard Using Machine Learning: A Case Study of Bogor, Indonesia Tjahjono, Boedi; Firdiana, Indah; Trisasongko, Bambang Hendro
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol 14 No 2 (2024): 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.14.2.407

Abstract

Landslides occur in many parts of the world. Well-known drivers, such as geological activities, are often enhanced by violent precipitation in tropical regions, creating complex multi-hazard phenomena that complicate mitigation strategies. This research investigated the utility of spatial data, especially the digital elevation model of SRTM and Landsat 8 remotely sensed data, for the estimation of landslide distribution using a machine learning approach. Bogor Regency was chosen to demonstrate the approach considering its vast hilly/mountainous terrain and high rainfall. This study aimed to model landslide hazards in Sukajaya District using random forests and analyze the key variables contributing to the isolation of highly probable landslides. The initial model, using the default settings of random forest, demonstrated a notable accuracy of 93%, with an accuracy ranging from 91 to 94%. The three main predictors of landslides are rainfall, elevation, and slope inclination. Landslides were found to occur primarily in areas with high rainfall (2,668–3,228 mm),elevations of 500 to 1,500 m, and steep slopes (25–45%). Approximately 4,536 ha were potentially prone to landslides, while the remaining area (> 12,000 ha) appeared relatively sound.
Co-Authors A Kasno Achmad, Alfredian Adi F. Ramly, Adi F. Alinda F. M. Zain Andhi Trisnaputra Andi Tahir ANTONIUS KASNO Aufa H. A. Syafril, Aufa H. A. Baba Barus Boedi Tjahjono Chiharu Hongo, Chiharu Dyah R Panuju Dyah R. Panuju Dyah R. Panuju Dyah R. Panuju Dyah Retno Panuju Febria Heidina, Febria Firdiana, Indah H.A. Syafril, H.A. Hari Wijayanto Harimurti Harimurti Heru Subroto, Heru Irzaman, Irzaman Khursatul Munibah La Ode Syamsul Iman Luluk Dwi Wulan Handayani Mahmud A. Raimadoya Mahmud A. Raimadoya Mahmud Arifin Raimadoya Nindita, Wida Nur Etika Karyati Panuju, Dyah R. Ramdhani Reyna Prachmayandini Reyna Prachmayandini, Reyna Rizqi I’anatus Sholihah Sigit, Gunardi Suria Darma Tarigan Uciningsih, Winda Wahyudi Hasbi Widiatmaka Yunito, Muhammad Rahmanda