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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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Detecting Rice Growth Using ALOS Multispectral and Synthetic Aperture Radar Bambang Hendro Trisasongko; Dyah Retno Panuju; La Ode Syamsul Iman
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 7: July 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i7.pp5613-5620

Abstract

Rice monitoring is a substantial to Asian countries, including Indonesia, since most inhabitants consume rice on a daily basis. As field survey requires substantial time and budget, one relies on remotely sensed data, especially taken through spaceborne platform. This research discusses multispectral and linearly polarized Synthetic Aperture Radar (SAR) from Advanced Land Observing Satellite and their applications to observe various rice growth information. It appears that both sensors provided useful rice growth data leading to the possibility on improving rice field information extraction. Classification scheme by means of Random Forest suggested that both data were fairly acceptable for timely monitoring.
Simulation on the Use of LOSAT Data for Rice Field Mapping Trisasongko, Bambang H.; Panuju, Dyah R; Tjahjono, Boedi; Barus, Baba; Wijayanto, Hari; Raimadoya, Mahmud A.; Irzaman, Irzaman
Makara Journal of Technology Vol. 14, No. 2
Publisher : UI Scholars Hub

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Abstract

Simulation on the Use of LOSAT Data for Rice Field Mapping. Since the launch of LAPAN-TUBSAT satellite in 2007, Indonesia has been developing mission on earth observation missions for various applications. The next generation mission, called LAPAN-ORARI Satellite (LOSAT), is currently under development and expected to be launched in 2011. In order to facilitate the applications, a thorough assessment of the sensor should be made. This paper presents an examination of simulated LOSAT data for rice monitoring and mapping purposes coupled with QUEST statistical tree. We found that three-band simulated LOSAT data were suitable for the task with reasonably high accuracy.
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.
Food Crop Land Allocation: Integrating Land Suitability Analysis and Spatial Forestry, Study Case Katingan, Indonesia Ramdhani; Widiatmaka; Trisasongko, Bambang Hendro
Jurnal Manajemen Hutan Tropika Vol. 29 No. 3 (2023)
Publisher : Institut Pertanian Bogor (IPB University)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.7226/jtfm.29.3.187

Abstract

The Indonesian government strives to expand agricultural lands, primarily beyond Java, through food estate programs. However, there has been a strong likelihood that this endeavor might intersect with forests and forest designation areas. This study aims to determine land suitability and its potential allocation for food crops at the interface of forestry and agriculture in Katingan District. Paddy (Oryza sativa L) and sorghum (Sorghum bicolor L) were selected as the crop species being analyzed, employing a coupling of the analytical hierarchy process and GIS. Forest area designation and land cover maps were incorporated into land allocation scenarios. The results showed that there were 74.254 ha in the "highly suitable" (S1) class and 130.634 ha in the "moderately suitable" (S2) class for paddy. However, after applying the scenario, they decreased by 4% and 12%, respectively. Sorghum has S1 and S2 areas of 108.956 ha and 377.493 ha, which declined by 15% and 14%, respectively, after scenario. Based on the allocation scenario, we found potential deforestation of 67 thousand ha for paddy and 205 thousand ha for sorghum, respectively. We highlighted convertible production forests (HPK) and production forests (HP) as having considerable potential for the allocation of land for food production.
IDENTIFICATION OF AGE CLASS AND VARIETIES OF RICE PLANT USING SPECTRORADIOMETRY AND CHLOROPHYLL CONTENT INDEX: (Identifikasi Kelas Umur dan Varietas Tanaman Padi Menggunakan Spektroradiometri dan Indeks Kandungan Klorofil) Munibah, Khursatul; Trisasongko, Bambang Hendro; Barus, Baba; Tjahjono, Boedi; Achmad, Alfredian; Uciningsih, Winda; Sigit, Gunardi; Hongo, Chiharu
Majalah Ilmiah Globe Vol. 24 No. 1 (2022): GLOBE VOL 24 NO 1 TAHUN 2022
Publisher : Badan Informasi Geospasial

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Abstract

Rice is the staple food for Indonesian society because more than 90% population eat rice every day. Estimation of the rice production can be monitored from the plant growth phase by utilizing remote sensing data. Spectroradiometry can be used to validate the remote sensing spectral because it has a wide wavelength range. Research objectives are to identify transplanting age class and varieties of rice plant based on spectroradiometry and its vegetation index, to analyze the relationship between spectroradiometry and chlorophyll content index (CCI). The results show that the transplanting date of 14 days, 21-32 days, and 56-68 days in three varieties (Inpari32; Padjadjaran Agritan; Siliwangi Agritan) are difficult to be distinguished at visible wavelength but it easy at infrared wavelength. The plant age class for the Siliwangi Agritan can be distinguished well on NDVI, SAVI, EVI while the Pajajaran Agritan is only on NDVI and EVI. All vegetation indexes, where the plant age of 14 days and 21-32 days for the Inpari32 are difficult to be distinguished between them, but easy to be distinguished with 56-68 days. This is due to the high sensitivity of chlorophyll to infrared wavelengths and the characteristics of rice plants itself (many tillers and plant height). Meanwhile, rice plants of every veriety are difficult to be distinguished, either on visible wavelength, infrared wavelength or on all vegetation indexes. Spectroradiometry has a high correlation with chlorophyll content index (CCI) (R2=0,88). This shows that the higher chlorophyll content in rice plants, the higher spectroradiometry for infrared wavelength.
The Impact of the Determination of the Mamminasata National Strategic Area on Rice Fields in Maros Regency Yunito, Muhammad Rahmanda; Barus, Baba; Trisasongko, Bambang Hendro
Jurnal Planologi Vol 22, No 2 (2025): October 2025
Publisher : Universitas Islam Sultan Agung Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30659/jpsa.v22i2.46466

Abstract

The Integration of the Mamminasata National Strategic Area (KSN), encompassing Maros, Gowa, Takalar, and Makassar City, significantly impacts productive rice fields, particularly in Maros Regency. This study evaluates the effects of KSN designation on rice fields by analyzing spatial planning policies, farmers perceptions of rice field protection, and changes in rice field area from 2007 to 2024 using Boolean overlay, interview and image interpretation. The research reveals that only 58.57% of rice fields complies to either KSN or spatial planning (RTRW), highlighting the need for spatial plan policy synchronization. Most farmers were landowners and cultivators over 50 years old, with limited interest among younger generations to continue farming. Farmers have heavily relied on agriculture as their primary livelihood, yet their awareness of the Protected Rice Field (LSD) policy remained low, especially on easily convertible lands. Majority of farmers supported rice field protection and agreed to LSD designation, hoping for assistance and incentives. This research found that rice field areas continued to decline due to urban expansion driven by population growth, infrastructure development, and national strategic projects, especially near Makassar City. Integrated policies are therefore essential to sustain agriculture and farmer welfare.
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.
COMPARISON OF MACHINE LEARNING MODELS FOR LAND COVER CLASSIFICATION Bambang H. Trisasongko; Dyah R. Panuju; Nur Etika Karyati; Rizqi I’anatus Sholihah
International Journal of Remote Sensing and Earth Sciences Vol. 19 No. 1 (2022)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2022.v19.a3786

Abstract

Land cover data remain one of crucial information for public use. Â With rapid human-associated land alteration, this information needs to be frequently updated. Remotely-sensed data provide the best option to construct land cover maps with numerous methods available in the literature. While disagreement exists to select the robust one, further exploration should be made to extend the understanding on the behavior of machine learners, in particular, for classification problems. This article discusses performance of pixel-based machine learning algorithms, frequently used in research or implementation. Five popular algorithms were evaluated to distinguish five rural land cover classes, i.e. built-ups, crops, mixed garden, oil palm plantations and rubber estates, from Sentinel-2 data. This research found that the benchmark, classification and regression tree, was unable to differentiate woody vegetation, although the overall accuracy was sufficiently moderate. This suggested that overall accuracy cannot be seen as the only measure for assessing the quality of the thematic output. Meanwhile, support vector machines and random forest competed to yield the highest accuracy and class detection capability, although the latter was in favor with 98% accuracy level. A newly developed model, like extreme gradient boosting, achieved a similar level of accuracy. This research implies that modern machine learning approaches would be invaluable for land cover classification; hence, access to these modeling toolkits is substantial.
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