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All Journal JTSL (Jurnal Tanah dan Sumberdaya Lahan) Jurnal Hutan dan Masyarakat Jurnal EBONI Jurnal Wasian
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Decision Sciences, Operations Research & Management Earth & Planetary Sciences Environmental Science Physics Other

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Journal : Jurnal Wasian

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Will Indonesia's Forests Survive Development Pressure? Machine Learning Predictions for Energy-Critical Tropical Watersheds Utami A, Widyanti; Irlan, Irlan; Syahrir, Nur Hilal A; Rosmaeni, Rosmaeni
Jurnal Wasian Vol. 12 No. 01 (2025): June
Publisher : Forestry Department, University of Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62142/hjs6a555

Abstract

Land Use and Land Cover (LULC) changes play an important role in influencing the hydrological conditions of a watershed. The conversion of land such as forests, shrubs and grasslands into agricultural land can disrupt the hydrological balance of the watershed. The availability of information related to LULC dynamics in the future is needed to assist sustainable watershed management planning. Machine learning technology, such as Cellular Automata, can provide accurate predicting. The objective of this research is to simulate LULC based on machine learning in the Mamasa Sub-watershed. Two model combinations were employed to simulate LULC: Artificial Neural Network-Cellular Automata (ANN-CA) and Logistic Regression-Cellular Automata (LR-CA). The research results found that the ANN-CA model achieved percent of correctness and overall kappa of 83.6745 and 0.75412, respectively, which were higher than those of the LR-CA model (82.3498 and 0.73361). The prediction results of both model combinations still fall below the actual LULC values, especially in the case of large LULC classes such as forests, range-shrub, rice, and pasture. Conversely, higher accuracy is observed for smaller classes such as wetlands-forested, orchard, residential, and oak. However, it should be noted that this research did not include several socio-economic variables, such as population and income level, which are considered to influence changes in LULC. Future research is expected to analyse the influence of each variable and include some socio-economic variables that may have a significant influence on LULC change.
Individual Tree Segmentation in TropicalNatural Forest Based on Point CloudGenerated from UAV RGB Image Irlan, Irlan; Adzkia, Ulfa; Suhartono, Suhartono; Meliani, Meliani; Jenos, Alpri Sri; Bimantara, Teguh; A, Chairil
Jurnal Wasian Vol. 12 No. 02 (2025): December
Publisher : Forestry Department, University of Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62142/kx7bhn83

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Different techniques have been developed for segmenting individual trees using point clouds from UAVs and other remote sensing technologies. A more accurate and reasonably priced method is still required, nevertheless, especially for tropical natural forests. This study evaluates the accuracy of individual tree segmentation using point clouds derived from RGB images in Indonesian natural forests. Compared to other sensors like LiDAR, RGB-based point clouds are significantly more cost-effective. We employed a point cloud-based segmentation algorithm, which has demonstrated superior performance over raster-based or hybrid methods. The results show that this approach is feasible for segmenting individual trees, although it tends to produce over-segmentation. This was attributed to the constraints of incomplete ground measurements resulting from dense canopy cover. The method achieved an overall segmentation accuracy of r (0.68), p (0.76), and F (0.72). Tree position accuracy had an RMSE of 1.95 meters, while the RMSE for crown radius was 1.59 meters. Future work will focus on enhancing the quality of RGB point clouds and improving algorithms to increase segmentation accuracy in natural forests.
Co-Authors A, Chairil A, Widyanti Utami Adzkia, Ulfa Andi Khairil A.Samsu arham, ihsan BIMANTARA, TEGUH Burhan, Abd Rukman Irlan Irundu, Daud Irwansyah Irwansyah Jenos, Alpri Sri Kurniati Mahendra, Yusril Mas'ud, Emban Ibnurusyd Meliani, Meliani Prayogi Sudiatmoko rosmaeni, rosmaeni Sanjaya, Muhammad Fahyu Sri Sukmawati Suhartono Syahrir, Nur Hilal A. Utami A, Widyanti