Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Penelitian Kehutanan Wallacea Jurnal Wasian
A, Chairil
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences Environmental Science Physics Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Evaluating Forest and Land Rehabilitation Using Remote Sensing: A Case Study in Maros Regency Rijal, Syamsu; Nursaputra, Munajat; A, Chairil
Jurnal Penelitian Kehutanan Wallacea Vol. 14 No. 1 (2025)
Publisher : Foresty Faculty of Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24259/jpkwallacea.v14i1.36726

Abstract

Remote sensing technology has become crucial in vegetation monitoring, particularly for assessing vegetation density. Despite its broad application, its use in evaluating land rehabilitation efforts remains limited. The increasing extent of degraded lands has underscored the importance of effective forest and land rehabilitation activities. Traditionally, evaluating these efforts involves direct site visits to monitor plant growth annually for three years post-planting, which is time-consuming, labor-intensive, and costly. According to rehabilitation standards, a program is successful if 75% of the planted vegetation survives until the end of the third year. This study presents an efficient alternative by evaluating a rehabilitation site in Maros Regency, using remote sensing technology to monitor planting success over periods of 15 years (2007), nine years (2013), and three years (2019). The evaluation utilizes multispectral drone imagery and Normalized Difference Vegetation Index (NDVI) analysis to assess vegetation density through multi-temporal analysis across wide areas. The findings reveal that the percentage of forested areas after three, nine, and fifteen years of rehabilitation activities was 24.6%, 3.1%, and 23.5%, respectively. This research demonstrates the potential for further application of Unmanned Aerial Vehicle imagery in monitoring the success of land rehabilitation projects.
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

Abstract

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 Adzkia, Ulfa BIMANTARA, TEGUH Irlan Jenos, Alpri Sri Meliani, Meliani Munajat Nursaputra, Munajat Suhartono Syamsu Rijal