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Adam Mudinillah
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INDONESIA
Journal of Selvicoltura Asean
Published by Yayasan Adra Karima Hubbi
ISSN : 30481171     EISSN : 30481198     DOI : 10.70177/selvicoltura
Core Subject : Agriculture,
Agriculture, Biological Sciences & Forestry
Journal of Selvicoltura Asean is an international, peer-reviewed, open-access journal that publishes scientific articles primarily but not limited to the area of Forestry Specialist. Journal of Selvicoltura Asean focuses on all dimensions of forest management, including but not limited to planning, conservation, sylviculture, socioeconomics, and the utilization of forest resources, with a focus in particular on the tropical forests of Asia. We are also eager to include contributions from other geographical scopes as long as they can convincingly demonstrate a critical significance to the concerns that are plaguing Asias forested landscape.
Arjuna Subject : Ilmu Pertanian dan Biologi (Semua) - Kehutanan
Articles 5 Documents
 1 
Search results for , issue "Vol. 2 No. 5 (2025)" : 5 Documents clear
“HUTAN ADAT” (INDIGENOUS FORESTS) AS A MODEL FOR SUSTAINABLE FOREST GOVERNANCE: A CASE STUDY OF THE DAYAK COMMUNITY IN BORNEO Wijaya, Wijaya; Suzuki, Sakura; Sato, Haruka
Journal of Selvicoltura Asean Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v2i5.2481

Abstract

Escalating deforestation and the shortcomings of conventional, state-centric forest management paradigms necessitate the exploration of alternative governance models. Hutan Adat (Indigenous Forests), managed through customary laws, represent a long-standing yet frequently overlooked approach to ecological stewardship. This research aims to analyze the principles and practices of Hutan Adat management by the Dayak community in Borneo, evaluating its effectiveness and potential as a replicable model for sustainable forest governance. Employing a qualitative case study approach, this study utilizes ethnographic observation, in-depth interviews with community elders, and participatory mapping. The findings reveal a sophisticated governance system rooted in local wisdom, spiritual values, and collectively enforced customary laws (hukum adat). This system effectively regulates resource extraction, conserves biodiversity, and ensures equitable benefit sharing, resulting in lower deforestation rates and greater ecological integrity compared to adjacent state-managed areas. The study concludes that the Dayak Hutan Adat is a robust and effective model of sustainable forest governance. Its formal recognition and integration into national policy frameworks are crucial for achieving conservation goals while upholding indigenous rights and promoting social justice.
USING ARTIFICIAL INTELLIGENCE AND LIDAR DATA FOR HIGH-RESOLUTION FOREST INVENTORY AND ABOVE-GROUND BIOMASS ESTIMATION IN A SUMATRAN RAINFOREST Nofirman, Nofirman; Shah, Ahmed; Tariq, Usman
Journal of Selvicoltura Asean Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v2i4.2483

Abstract

Accurate quantification of forest carbon stocks is critical for global climate change mitigation initiatives like REDD+. Traditional forest inventory methods are often labor-intensive, costly, and limited in scale, particularly in complex tropical ecosystems such as the Sumatran rainforest. The integration of advanced remote sensing technologies and artificial intelligence (AI) offers a transformative potential for overcoming these limitations. This study aimed to develop and validate a high-resolution model for individual tree detection and above-ground biomass (AGB) estimation in a Sumatran rainforest by synergizing airborne LiDAR data with machine learning algorithms. High-density LiDAR data was acquired over a 10,000-hectare study area. Concurrently, extensive field inventory data from 150 plots were collected to serve as ground truth. A deep learning model, specifically a Convolutional Neural Network (CNN), was trained to perform individual tree crown delineation (ITCD) from the LiDAR-derived canopy height model. Tree-level metrics were then used as predictors in a Random Forest algorithm to estimate AGB, which was calibrated against field-measured biomass. The CNN model successfully identified individual trees with an accuracy of 92.4%. The subsequent Random Forest model demonstrated high predictive power for AGB estimation, yielding a strong coefficient of determination ( = 0.89) and a low Root Mean Square Error (RMSE) of 25.8 Mg/ha. The approach generated a high-resolution (1-meter) AGB map, revealing detailed spatial variations in carbon stock across the landscape. The fusion of AI and LiDAR data provides a highly efficient methodology for forest inventory and AGB mapping in dense tropical rainforests. This approach significantly enhances our capacity to monitor carbon dynamics, forest conservation and climate policy.
THE ECONOMICS OF REDD+ (REDUCING EMISSIONS FROM DEFORESTATION AND FOREST DEGRADATION): A POLICY ANALYSIS OF ITS IMPLEMENTATION IN INDONESIA Judijanto, Loso; Jun, Wang; Mei, Chen
Journal of Selvicoltura Asean Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v2i5.2486

Abstract

Reducing Emissions from Deforestation and Forest Degradation (REDD+) is a pivotal international climate change mitigation mechanism, with Indonesia being a key implementing country due to its vast tropical forests. Despite significant international investment, the economic viability and effectiveness of REDD+ in achieving its goals are contingent upon the design and implementation of national policies. This study aimed to conduct a comprehensive economic policy analysis of REDD+ implementation in Indonesia, evaluating its efficiency, cost-effectiveness, and the equity of its benefit-sharing mechanisms. A policy analysis framework was employed, integrating economic principles with a review of national and sub-national REDD+ policies, regulations, and project implementation documents from 2010 to 2024. The analysis was supplemented by a meta-synthesis of financial reports from REDD+ pilot projects and existing academic literature to assess transaction costs, financial flows, and benefit distribution. The analysis reveals significant economic challenges. High transaction costs, coupled with unclear carbon tenure and property rights, have created substantial inefficiencies and deterred private sector investment. Furthermore, the absence of a consistent national carbon price has undermined the financial incentives for land-use change. Benefit-sharing mechanisms were often found to be ad-hoc, leading to inequitable outcomes that failed to adequately compensate local communities for their opportunity costs. For REDD+ to become an economically viable and effective climate mitigation strategy in Indonesia, significant policy reforms are imperative. Future policies must focus on reducing transaction costs, providing clear and secure carbon tenure, and establishing transparent, equitable, and efficient benefit-sharing mechanisms that reflect the true costs borne by local stakeholders.
INTEGRATING ETHNOFORESTRY AND REMOTE SENSING FOR A HOLISTIC ASSESSMENT OF FOREST HEALTH AND COMMUNITY WELL-BEING IN PAPUA Soleman, Christian; Angrianto, Novaldi Laudi; Kesauliya, Olivia Marie Caesaria
Journal of Selvicoltura Asean Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v2i5.2488

Abstract

Conventional remote sensing often fails to capture the full picture of forest health, ignoring the nuanced knowledge of indigenous communities intrinsically linked to the environment. This study's objective was to develop a holistic framework for assessing forest health by integrating indigenous Papuan ethnoforestry knowledge with advanced remote sensing techniques, and analyzing the link to community well-being. A mixed-methods approach was employed, combining participatory mapping and interviews (collecting local indicators) with time-series analysis of Landsat imagery (deriving biophysical metrics like NDVI). The findings showed a strong positive correlation between community perception and satellite indices. Crucially, the integrated approach revealed subtle degradation (e.g., loss of culturally significant species) undetectable by remote sensing alone. A direct link was established between this degradation and a decline in community well-being (e.g., access to traditional medicine). This integrated framework provides a more accurate and socially relevant assessment, enhancing monitoring, empowering local communities for co-management, and ensuring sustainable livelihoods.
ASSISTED NATURAL REGENERATION AS A CLIMATE-RESILIENT STRATEGY FOR RESTORING DEGRADED PEATLAND FORESTS IN CENTRAL KALIMANTAN Hakim, Dani Lukman; Lima, Lucas; Mendes, Clara
Journal of Selvicoltura Asean Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v2i5.2538

Abstract

Tropical peatland forests in Central Kalimantan, critical global carbon stores, are severely degraded and now act as major greenhouse gas sources due to drainage and fires. This study aimed to evaluate the ecological effectiveness and climate resilience of Assisted Natural Regeneration (ANR) for restoring the hydrological functions and biodiversity of these degraded ecosystems. A field experiment was established across 100 hectares, where the ANR method involved canal blocking to rewet the peat and selective planting of native pioneer species, monitored against control plots over five years. The results showed significant and rapid recovery. Canal blocking successfully raised the water table by an average of 40 cm, drastically reducing fire risk. Furthermore, native tree species richness in ANR plots was over 200% higher than controls, with canopy closure reaching 60%. ANR is highly effective, cost-efficient, and climate-resilient, providing a scalable model that prioritizes rewetting and facilitates natural successional pathways to restore critical ecosystem functions and secure long-term carbon storage.

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