Belachew Gizachew Zeleke
Forsker
(+47) 902 48 909
belachew.gizachew@nibio.no
Sted
Ås - Bygg H8
Besøksadresse
Høgskoleveien 8, 1433 Ås
Forfattere
Belachew Gizachew ZelekeSammendrag
Tropical forests, despite their critical environmental and socio-economic roles, remain highly vulnerable to deforestation, forest degradation, and climate-related disturbances. There is a growing demand for robust and transparent forest monitoring systems, particularly under REDD+, the Paris Agreement’s Enhanced Transparency Framework (ETF), and emerging climate-finance mechanisms. Conventional approaches based on field inventories and traditional remote sensing are often constrained by limited or uneven field data, persistent cloud cover, complex forest conditions, and limited institutional and technical capacity. This review examines how artificial intelligence (AI) and machine learning (ML) are being integrated into remote sensing–based tropical forest monitoring to address these structural constraints. Using a semi-systematic synthesis of peer-reviewed studies, complemented by operational platforms and grey literature, the review assesses AI/ML approaches, remote sensing datasets, and applications relevant to national and large-scale monitoring. Evidence is synthesized across five analytical dimensions: AI/ML model families and workflows, multi-sensor datasets and training resources, operational monitoring platforms, application domains (including deforestation, degradation, and biomass/carbon estimation), and cross-cutting technical, institutional, and governance barriers. The review finds that AI/ML-enabled remote sensing, particularly those combining optical, radar, and LiDAR time series within cloud-based platforms, has substantially improved the automation, scalability, and speed of tropical forest monitoring. However, effective and equitable adoption remains constrained by limitations in training and validation data, dependence on proprietary platforms and data, uneven technical capacity, and unresolved governance and ethical challenges. Emerging solutions, including open and representative training datasets, platform-agnostic processing infrastructures, long-term capacity building, and inclusive data-governance frameworks, are identified as critical enablers of credible and nationally owned AI/ML-enabled forest-monitoring systems. The review highlights that AI/ML can play a transformative role in supporting climate mitigation, biodiversity conservation, and informed decision-making. This potential, however, depends on transparent data governance arrangements, long-term capacity building, and platform-agnostic infrastructures that support national ownership.
Sammendrag
Deadwood carbon pool is a crucial component of forest ecosystems and the global carbon cycle. Assessing of deadwood carbon is challenging due to variability in decay status, species and disturbances in tropical forests. Quantifying the magnitude of uncertainty is essential for improving the accuracy of carbon stock estimations. This study aimed to estimate deadwood carbon pool by considering deadwood decay status and different vegetation types as well as the associated uncertainty in carbon stock estimates. Based on the National Forestry Resources Monitoring and Assessment of Tanzania (NAFORMA) sampling design, we analysed 21,946 data points from 1,798 plots. A two-way Analysis of Variance (ANOVA) was used to examine the variation in deadwood carbon stock (rotten and solid) between the primary vegetation types. Tukey’s Honest Significant Difference (HSD), post-hoc test was applied to determine which vegetation types significantly differ in carbon stock while a paired samples t -test was used to compare carbon stock of solid and rotten deadwood. Uncertainty was calculated using Equation 10 of 2006 IPCC Guidelines with 95% confidence interval. The estimated deadwood carbon stock ranged from 0.11 to 1.01 t C ha −1 , with solid deadwood having higher carbon stocks than rotten deadwood, accounting for 0.79% of total estimated carbon stocks. Carbon uncertainty values ranged from 0.0008 to 0.28%, with the highest and lowest uncertainty values from rotten deadwood in cultivated land and woodland, respectively. However, these variations among vegetation types did not significantly impact the deadwood carbon stock. In contrast, decay status had a significant effect on deadwood carbon stock. These findings are crucial for national climate policies, land use contributions to national carbon accounting, REDD+ mechanisms and sustainable management of natural ecosystems.
Forfattere
Dagnew Yebeyen Burru Jayaraman Durai Melaku Anteneh Chinke Gudeta W. Sileshi Yashwant S. Rawat Belachew Gizachew Zeleke Selim Reza Fikremariam Haile Desalegne Kassa Toshe WorassaSammendrag
Highland bamboo (Oldeania alpina) plays a vital role in supporting local livelihoods, fostering biodiversity conservation and sustainable land management. Despite these benefits, its significant potential for carbon sequestration remains underutilized withinEthiopia’s climate mitigation strategies. In this study, we developed site-specific allometric equations to assess the biomass and carbon storage potential of highland bamboo. Datawere collected from the Garamba natural bamboo forest and Hula homestead bamboo stands in the Sidama Regional State, Southern Ethiopia. Data on stand density and structurewere gathered using systematically laid transects and sample plots, while plant samples were analyzed in the laboratory to determine the dry-to-fresh weight ratios. We developedallometric models to estimate the aboveground biomass (AGB) and carbon stock. The study results indicated that homestead bamboo stands exhibited higher biomass accumulationthan natural bamboo stands. The AGB was estimated at 92.3 Mg ha−1in the natural forest and 118.3 Mg ha−1in homestead bamboo stands, with total biomass carbon storage of 52.1 Mg ha−1 and 66.7 Mg ha−1, respectively. The findings highlight the significant potential of highland bamboo for carbon sequestration in both natural stands and homesteads.Sustainable management of natural highland bamboo stands and integrating bamboo into farms can contribute to climate change mitigation, support ecosystem restoration, andenhance the socio-economic development of communities.
Divisjon for skog og utmark
Partnership for Research and Education for Monitoring Coastal Environments in Africa (CoastAfrica)
CoastAfrica is an international research and education partnership that strengthens collaboration between Norwegian and African institutions to monitor coastal environmental change.
Divisjon for skog og utmark
Optimizing Carbon, Soil Health and Yield in Coffee-Forest Systems as a Climate-Smart Land Management in Ethiopia (CoffeeLand)
CoffeeLand is an interdisciplinary research project aimed at advancing climate-smart land management in Ethiopia’s coffee-forest systems, which are critical for biodiversity, livelihoods, and global Arabica coffee genetic resources. These systems support millions of smallholder farmers but are increasingly threatened by climate change, land-use pressure, and declining productivity.