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All Journal International Journal of Remote Sensing and Earth Sciences (IJReSES) Jurnal Ilmu Kehutanan
Randi Adrian Saputra
Department of Geography Education, Faculty of Teacher Training and Education, Halu Oleo University
Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences

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COMPARISON OF MACHINE LEARNING ALGORITHMS FOR LAND USE AND LAND COVER ANALYSIS USING GOOGLE EARTH ENGINE (CASE STUDY: WANGGU WATERSHED) Septianto Aldiansyah; Randi Adrian Saputra
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 19 No. 2 (2022)
Publisher : Ikatan Geografi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2022.v19.a3803

Abstract

Human population growth and land use and land cover (LULC) change have always developed side by side. Considering selection of a good Machine Learning (ML) classifier algorithm is needed considering the high estimation of LULC maps based on remote sensing. This study aims to produce a LULC classification of Landsat-8 and Sentinel-2 images by comparing the accuracy performance of three ML algorithms, namely: Classification and Regression Tree (CART), Random Forest (RF), and Support Vector Machine (SVM). Dataset comparison ratios were also explored to find the LULC classification results with the best accuracy. Sentinel-2 is better than Landsat-8 regarding Overall Accuracy (OA) and Coefficient Kappa. The comparison ratio of the training and testing datasets with a good level of accuracy is 70:30 on both images with the average OA Landsat-8 and Sentinel-2 being 92.09% and 94.21%, respectively. The RF algorithm outperforms CART and SVM in both types of satellite imagery. The mean OA of the CART, RF, and SVM classifiers was 92.03%, 94.74%, 83.54% on Landsat-8, 93.14%, 96.15%, and 93.34% on Sentinel-2, respectively.
Pemodelan Kesesuaian Habitat Mandar Gendang (Habroptila wallacii) di Pulau Halmahera, Indonesia Septianto Aldiansyah; Ilyas Madani; Risna Risna; Randi Adrian Saputra
Jurnal Ilmu Kehutanan Vol 19 No 2 (2025): September
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jik.v19i2.18437

Abstract

Drummer Rail (Habroptila wallacii) is a bird species of the Rallidae family with limited ecology and behavior information. The information on the distribution of H. wallacii in Halmahera Island is crucial as it is classified as a vulnerable species. Therefore, this research aims to predict the potential distribution of H. wallacii on Halmahera Island using the Maximum Entropy (MaxEnt) modeling method, which projects species distributions based on presence data and environmental variables. A total of 47 data points on H. wallacii encounters were obtained from open-access data sources and field observation. The variables used were land use land cover (LULC), normalized difference vegetation index (NDVI), elevation, slope, and proximity data (river). The results showed that 33.52% of the area was very suitable for H. wallacii habitat, 32.97% was suitable, and 33.50% was unsuitable. Approximately 29.39% of the suitable habitat was located in limited-production forest areas, while conservation areas covered only 5.19%. These results suggested the need to review spatial planning policies to increase protection of the natural habitat of the species. The results could serve as considerations and recommendations for the Ministry of Environment and Forestry regarding the future management of forest areas for these species.
Co-Authors Ilyas Madani Risna Risna Septianto Aldiansyah Septianto Aldiansyah