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All Journal Jurnal Silvikultur Tropika (JST) MEDIA KONSERVASI Teknotan: Jurnal Industri Teknologi Pertanian Habitat Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Jurnal Pertanian Agros Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) JURNAL PENDIDIKAN TAMBUSAI Jambura Agribusiness Journal Jurnal Agrotek Lestari Agro Bali: Agricultural Journal PARAHITA : Jurnal Pengabdian kepada Masyarakat CELEBES Agricultural Room of Civil Society Development Jurnal Pengabdian Sosial Jurnal Pengabdian Masyarakat dan Riset Pendidikan Room of Civil Social Development Jurnal Pertanian Agros Jurnal Rekayasan Lingkungan dan Biosistem
Pramulya, Rahmat
Fakultas Pertanian, Universitas Teuku Umar
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Journal : CELEBES Agricultural

 1 
Vegetation-Water-Built Up Index Combined: Algorithm Indices Combination for Characterization and distribution of Mangrove Forest through Google Earth Engine : The spatial characteristics of Jakarta's urban mangroves Azelia Dwi Rahmawati; Rahmat Asy’Ari; Muhammad Aqbal Fathonah; Priyanto; Neviaty Putri Zamani; Rahmat Pramulya; Yudi Setiawan
CELEBES Agricultural Vol. 3 No. 1 (2022): CELEBES Agricultural
Publisher : Faculty of Agriculture, Tompotika Luwuk University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2499.984 KB) | DOI: 10.52045/jca.v3i1.298

Abstract

Mangroves that live in ecotone areas have a fairly significant role in the economy and ecology. This strategic role requires spatial data to facilitate the management and development of mangrove areas. The mangrove mapping process usually uses a manual method, namely through software, and has shortcomings and limitations in image management that require massive data storage. Cloud computing-based Google Earth Engine (GEE) mapping platform can manage images with an extensive scope and spatiotemporal data processing. However, this platform requires index formulas or combinations to help classify and increase accuracy in mapping the earth’s surface. The innovation with the combined VWB-IC (Vegetation-Water-Built-up Index Combined) formula is projected to classify the characteristics of mangrove areas in Jakarta Bay. The combination consists of three types of indices, namely vegetation index (NDVI, GNDVI, ARVI, EVI, SLAVI, and SAVI), water (NDWI, MNDWI, and LSWI), and buildings (IBI and NDBI). This combination is used to translate the classification of mangroves using the Random Forest (RF) machine learning algorithm method with the Sentinel-2 MSI (Multispectral Instrument) satellite image source and through the GEE platform. This platform generates raster data for land use classification (including mangroves), and then the analysis is continued using ArcMap software. The obtained mangrove area is 220.43 ha, located in Jakarta Bay and divided into the Angke Kapuk Nature Tourism Park and the Pantai Indah Kapuk Mangrove Ecotourism Area. The data from this research is expected to provide a recommendation for a combination index formula for mapping mangrove areas in urban areas. The spatial distribution area can be used as an evaluation material in mangrove areas in Jakarta Bay
High Heterogeneity LULC Classification in Ujung Kulon National Park, Indonesia: A Study Testing 11 Indices, Random Forest, Sentinel-2 MSI, and GEE-based Cloud Computing Rahmat Asy'Ari; Aulia Ranti; Azelia Dwi Rahmawati; Moh Zulfajrin; Lina Lathifah Nurazizah; Made Chandra Aruna Putra; Zayyaan Nabiila Khairunnisa; Faradilla Anggit Prameswari; Rahmat Pramulya; Neviaty P. Zamani; Yudi Setiawan; Ajat Sudrajat; Anggodo
CELEBES Agricultural Vol. 3 No. 2 (2023): CELEBES Agricultural
Publisher : Faculty of Agriculture, Tompotika Luwuk University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1365.903 KB) | DOI: 10.52045/jca.v3i2.381

Abstract

The Ujung Kulon National Park (UKNT) is one of the national parks on the island of Java and has an essential role in saving endemic species in Indonesia. As a form of national park conservation effort, the completeness of LULC spatial data is a primary database that is indispensable in determining national park management policies. Therefore, this research was conducted to map the LULC (Land Use - Land Cover) in the forest landscape with high heterogeneity in UKNT. Sentinel-2 MSI (Multispectral Instrument) image data were classified using the Random Forest (RF) classification algorithm and tested using 11 index algorithms. The classification process takes place on a cloud computing-based geospatial platform, Google Earth Engine (GEE). This test resulted in 10 LULC classes; water had the broadest percentage of 45.44%. Meanwhile, the primary forest has an area of 21,868.41 or about 19.53% of the total area of the national park. However, there are some discrepancies in the spatial information generated by this classification process, so it is considered necessary to evaluate the combination of indexes, training data, and classification algorithms to limit the classification area. Therefore, this study is expected to be considered for further research related to LULC in high-heterogeneity landscapes.
Monitoring and Visualizing the Impact of the Lapindo Mudflow Disaster Using Earth Engine Apps Platform based on Cloud Computing Dzulfigar, Ali; Ramadhan, Muhammad Ikhwan; Pascawisudawati, Azzahra; Asy'Ari, Rahmat; Setiawan, Yudi; Pramulya, Rahmat
CELEBES Agricultural Vol. 4 No. 2 (2024): CELEBES Agricultural
Publisher : Faculty of Agriculture, Tompotika Luwuk University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52045/jca.v4i2.703

Abstract

The Lapindo mudflow disaster at the PT Lapindo Brantas drilling site in Ronokenongo Village, Porong District, Sidoarjo Regency, East Java caused the loss of agricultural and residential areas. The research aimed to detect the areas that are affected by Lapindo mudflow 2006-2022 using Landsat 7 ETM and Landsat 8 OLI-TIRS imageries, as well as visualize their impact using the cloud computing-based Google Earth Engine/GEE platform. Spatiotemporal data analysis was performed on the GEE platform using random forest machine learning as algorithm for supervised land use classification, while visualization was carried out through Earth Engine Apps. The results showed an increase in the mudflow-affected area from 2006 (204.57 ha) to 2012 (542.32 ha) with northeast direction, whereas the increase was insignificant at the following years. Within the detection period, agricultural land was the most affected area, followed by residential areas and bare land. The area ordering was similar during all detected years. The increasing size of the affected area can potentially have both direct and indirect impacts on the surrounding area. Therefore, special action is needed for the surrounding area, such as relocating settlements to safer areas against the Lapindo mudflow disaster.
AgriForScape Model: Optimization of Agricultural Landscape Design in Karawang District as a Pest Control Strategy with an Ecological Approach Selvianing Tiyas; Wildan Maynardy Wicaksono; Usnil Khotimah; Ali Dzulfigar; Danik Septianingrum; Rahmat Asy’ari; Muhammad Ferdiansyah; Neviaty P Zamani; Rahmat Pramulya; Yudi Setiawan
CELEBES Agricultural Vol. 4 No. 2 (2024): CELEBES Agricultural
Publisher : Faculty of Agriculture, Tompotika Luwuk University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52045/jca.v4i2.710

Abstract

Karawang Regency is one of the national rice barns and a major supplier of rice to Jakarta and surrounding areas. However, the productivity of this rice is threatened by the brown planthopper (Nilaparvata lugens) which causes crop failure. The reliance on chemical pesticides to control this pest results in negative impacts on the environment and endangers human health. This caused a decrease in land productivity resulting in the conversion of land use to non-agriculture. This research aims to analyze the conditions and problems of agricultural areas in Karawang Regency and design a strategy for regulating landscape structures in reducing the intensity of pest attacks in Karawang Regency. Optimizing the structure and pattern of agricultural landscapes using the AgriForScape (Agriculture-Forest-Landscape) model can be one of the effective strategies in pest control to increase land productivity by integrating agriculture and forest land covers. Land cover mapping for 2023 and 2000 was conducted using cloud computing, revealing a conversion of 14,000 hectares of rice paddy land over 23 years, leaving 99,713 hectares. AgriForScape focuses on the integration of agriculture and forest conservation to improve ecosystem balance, increase land productivity, and lower the risk of natural disasters. AgriForScape landscape management can be done with several strategies, including the addition of corridors and forest patches as habitat for natural predators of rat pests, and the addition of refugia areas as food sources and natural habitat for insect pest predators. By applying an ecological approach through optimized agricultural landscape design, this strategy aims to reduce pest attack intensity, boost rice productivity, and contribute to food security and climate change mitigation. The findings are expected to advance sustainable agriculture and offer valuable insights for local governments, farmers, and stakeholders seeking environmentally friendly land management solutions.
Data Indo InaFire: Spatial Visualization of Peatland Fire Impact and Ecosystem Restoration Monitoring in PHU Jambi using Earth Engine Apps and Sentinel-2 MSI Imagery Muhammad Ilham; Citra Putri Perdana; Verawati Ayu Lestari; Ali Dzulfigar; Hanum Resti Saputri; Danik Septianingrum; Rahmat Asy’Ari; Yudi Setiawan; Rahmat Pramulya; Neviaty Putri Zamani
CELEBES Agricultural Vol. 4 No. 2 (2024): CELEBES Agricultural
Publisher : Faculty of Agriculture, Tompotika Luwuk University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52045/jca.v4i2.737

Abstract

Peatlands formed from long-term accumulation of partially decomposed organic matter in wetland areas. This particular ecosystem is not only capable of sequestering significant quantities of carbon but also vulnerable to forest and land fires (karhutla). Peatland produces considerable CO₂ emissions during fire occurrences, which consequently requires spatiotemporal monitoring to sustain its ecological roles and functions. This study aims to map the severity of fires in peatland ecosystems, estimate the success of post-fire restoration, and develop an Earth Engine Apps-based monitoring platform for peatland fire monitoring. Fire severity assessment and post-fire restoration success estimation were conducted in Jambi's Peat Hydrological Unit (PHU) in 2019 using the Normalized Burn Ratio (NBR) index derived from Sentinel-2 MSI satellite imagery. Most of Jambi PHU's fire severity and restoration levels are high. The area of PHU Jambi with high fire severity was 7,822.91 hectares, while the area with high restoration success was 23,744.69 hectares. NBR monitoring in PHU Jambi can be used to detect fire severity and restore success. The visualization of forest and land fire severity was successfully displayed on the Data Indo InaFire webGIS platform, an Earth Engine Apps-based monitoring platform.
Development of Spatial Platform Based Earth Engine Apps for Mangrove Carbon Stock: Case Study in Serang Coastal Zone, Banten Province Puspitasari, Raditya Febri; Aisyah; Usnil Khotimah; Mahadika Rifka Nugraha; Ali Dzulfigar; Khairani Putri Marfi; Danik Septianingrum; Rahmat Asy'ari; Rahmat Pramulya; Neviati Putri Zamani; Yudi Setyawan
CELEBES Agricultural Vol. 4 No. 2 (2024): CELEBES Agricultural
Publisher : Faculty of Agriculture, Tompotika Luwuk University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52045/jca.v4i2.746

Abstract

Mangroves exhibited considerable potential in mitigating global climate change, as these ecosystems can sequester and store substantial amounts of carbon in the form of live and decayed plant biomass across coastal areas. This research aimed to estimate carbon stocks and assess the dynamics of carbon reserves in the silvofishery area of Serang City, Banten, utilizing geospatial technology and cloud computing. Additionally, the study sought to develop the Indo InaC Data platform to monitor CO2 uptake on silvofishery land. The methodology employed included mangrove detection through unguided classification, and carbon stock estimation was performed using regression models derived from vegetation indices, specifically the Integrated Remote Sensing and Ecological Index (IRECI) and the Transformed Vegetation Index (TRVI). The results revealed fluctuations in mangrove vegetation cover between 2016 and 2023, with a notable decrease occurring from 2016 to 2017, as the cover declined from approximately 61.91 hectares to 50.53 hectares. This decrease was followed by an increase from 2017 to 2022, during which the area rose to 78.1 hectares; however, a subsequent decrease was observed in 2023, with the area reducing to 66.82 hectares. The estimated carbon reserves in the study area for 2023 amounted to 315 tons, reflecting similar dynamics to those observed in mangrove vegetation cover. The development of the Indo InaC Data platform is anticipated to facilitate ongoing monitoring of CO2 emissions uptake, and it is expected to inform future strategies for managing silvofishery land on an annual basis.
Monitoring Land Use and Land Cover Using Remote Sensing Technology in Kubu Raya Regency, West Kalimantan Province Nur Rahmadhanti, Intan; Salsabila Nur'Aini; Herni Natasha Aulia; Muhammad Ikhwan Ramadhan; Hanum Resti Saputri; Abd Malik A Madinu; Ali Dzulfigar; Rahmat Asy'Ari; Rahmat Pramulya; Yudi Setiawan
CELEBES Agricultural Vol. 5 No. 1 (2024): CELEBES Agricultural
Publisher : Faculty of Agriculture, Tompotika Luwuk University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52045/jca.v5i1.867

Abstract

Kubu Raya Regency is one of the areas that has a peat ecosystem in it. The peat ecosystem has a role and function in mitigating climate change because it has the ability to store quite high carbon reserves. However, peat ecosystems often experience degradation and changes in land cover which can contribute carbon emissions to the atmosphere. Remote sensing is a technology that can be used to detect changes in land cover and use in Kubu Raya Regency. Therefore, this research aims to detect changes in land cover and using remote sensing technology and assess the level of accuracy of the detection results. Analysis of changes in land cover and use from 2000 - 2023 was obtained by guided classification using the Random Forest (RF) algorithm which involves various vegetation, water and built-up land indices. The research results show that there is a decrease in forest land area from 2000 to 2023 amounting to 106,542 ha. The forest area in 2000 was 524,359 ha, while in 2023 it will be 417,817 ha. The results of accuracy measurements show an overall accuracy (OA) value of 98.84% with a kappa statistic of 0.98. It is hoped that the results of these findings will provide an initial picture of the condition of the ecosystem in Kubu Raya Regency, most of which is a peat ecosystem, as a consideration in formulating peat ecosystem conservation policies.
Co-Authors Abd Malik A Madinu Adi Sutrisno Agang, Mohammad Wahyu Agustia, Devi Aisyah Ajat Sudrajat Alfizar Alfizar Ali Dzulfigar Anggodo Ardya Hwardaya Gustawan Ariska, Nana Aswin Nasution Asy'Ari, Rahmat Atmaja, Nanda Aulia Ranti Aulia Ulfa Azelia Dwi Rahmawati Azelia Dwi Rahmawati Azelia Dwi Rahmawati Azelia Dwi Rahmawati Banta Diman Citra Putri Perdana Dabutar, Candra Danik Septianingrum Darmansyah, Dedy Dela Puspita Damanik Dewi Fithria Diman, Banta Dwi Purnomo DWI SANTOSO Dzulfigar, Ali Elida Novita Erliza Noor Fachruddin Fachruddin Faizin, Rusdi Fajri, Maulidil Faojiah, Rahma Septiany Faradilla Anggit Prameswari Hadisah, Hadisah Handian Purwawangsa Hanum Resti Saputri Harahap, Arya Gading Herni Natasha Aulia Irvan Subandar Ismi, Nur Khairani Putri Marfi Kusnadi, Erwan L, Izmi Ahad Lestari, Rachmatika Lina Lathifah Nurazizah Lizmah, Sumeinika Fitria Made Chandra Aruna Putra Mahadika Rifka Nugraha Marfi, Khairani Putri Maulidia, Vina Mauliza, Rahma Moh Zulfajrin Moh. Yani Muhammad Afrillah Muhammad Aqbal Fathonah Muhammad Ikhwan Ramadhan MUHAMMAD ILHAM Muhammad Romli dan Suprihatin Andes Ismayana Muliana, Muliana Naifa Sa'diyya Neviati Putri Zamani Neviaty P Zamani Neviaty P. Zamani Neviaty P. Zamani Neviaty Putri Zamani NEVIATY PUTRI ZAMANI Ningrum, Almyanti Nur Rahmadhanti, Intan Pascawisudawati, Azzahra Pelangi, Putri Priyanto Puspitasari, Raditya Febri Rachman, Habbly Rahmat Asy'Ari Rahmat Asy'ari Rahmat Asy'Ari Rahmat Asy’ari Rahmat Asy’Ari Ramadhan, Muhammad Ikhwan Resta, Muhammad Resta Destyana Rimun Wibowo, Rimun Safrida Safrida Salma, Ummu Salsabila Nur'Aini Sarah, Santi Sayaza, Mas David Sayaza, Mas Davino Selvianing Tiyas Sulindawati, Rishi Tajudin Bantacut Titing, Deny Tjahjo Tri Hartono Usnil Khotimah Verawati Ayu Lestari Wahyu, Adi Wahyuni, Etty Wijaksena, Ego Ibnu Wildan Maynardy Wicaksono Wiwik Handayani Yudi Setiawan Yudi Setiawan Yudi Setiawan Yudi Setiawan Yudi Setiawan Yudi Setyawan Yulisna, Yulisna Zayyaan Nabiila Khairunnisa