Spatiotemporal analysis of biomass carbon and forest dynamics through remote sensing data in Alemsaga Forest, Ethiopia
- 1Department of Geography and Environmental Studies, GIS and Remote Sensing, College of Social Sciences and Humanities, University of Gondar
Abstract
Climate change poses a profound global threat, with forest ecosystems serving as essential carbon sinks that mitigate atmospheric CO₂ accumulation. Despite Ethiopia’s commitment to forest restoration under initiatives like the Green Legacy Initiative (GLI), quantitative data on long-term forest cover dynamics and carbon stocks in key areas such as Alemsaga Forest remain limited, hindering evidence-based conservation and climate policy. This study aimed to quantify forest cover changes in Alemsaga Forest over 30 years (1992–2022) and estimate its carbon stocks to inform national mitigation strategies. Forest cover was mapped using Landsat satellite imagery (1992, 2003, 2013, and 2022) via supervised classification in ERDAS Imagine. Ground-truth data were collected from 36 randomly selected 16 m × 16 m plots, measuring tree diameter at breast height (DBH) and height for 64 species. Aboveground biomass (AGB) and belowground biomass (BGB) were calculated using algometric equations; soil organic carbon (SOC) was analysed from 1 m × 1 m samples in 16 plots. Carbon stocks were summed across pools (AGB, BGB, deadwood, SOC). The results indicate a 35.34% increase in dense forest cover, which currently constitutes 48.25% of the total forest area (888.30 ha), indicating significant forest regeneration. Average carbon stocks were 191.72 ± 57.92 t/ha (AGB), 92.96 ± 75.74 t/ha (BGB), 2.61 ± 2.26 t/ha (deadwood), and 87.25 ± 51.72 t/ha (SOC), totaling 374.54 ± 187.64 t/ha and equivalent to 1374.76 t/ha CO₂. Alemsaga Forest acts as a dynamic carbon sink, validating the efficacy of ongoing reforestation efforts. Policymakers should strengthen GLI through species-specific allometric models, integrate remote sensing into national carbon registries, and establish community-led monitoring to sustain gains and support Ethiopia’s GLI.
Related
- Biomass carbon stocks and sequestration potential of remnant forests in the subtropical highlands of Ethiopia
- Spatiotemporal Dynamics of Adenovirus Membrane Rupture and Endosomal Escape
- Spatiotemporal Dynamics, Climate Lag Effects, and Environmental Associations of Vegetation Cover Across the Western Sichuan Plateau During 2001–2024
- Spatiotemporal Control of Genetic and Epigenetic Editing through Covalent Tethering of CRISPR Nanoparticles to Zwitterionic Microgels
- STAVelo: deciphering spatiotemporal cellular dynamics via spatial RNA velocity inference from spatial transcriptomics data
BibTeX
@article{gessesse2026spatiotemporal,
title = {{Spatiotemporal analysis of biomass carbon and forest dynamics through remote sensing data in Alemsaga Forest, Ethiopia}},
author = {Agenagnew A. Gessesse and Anbaw Tigabu},
journal = {Discover Sustainability},
year = {2026},
month = jul,
doi = {10.1007/s43621-026-03475-4},
url = {https://doi.org/10.1007/s43621-026-03475-4},
note = {CC BY},
}
RIS
TY - JOUR
TI - Spatiotemporal analysis of biomass carbon and forest dynamics through remote sensing data in Alemsaga Forest, Ethiopia
AU - Agenagnew A. Gessesse
AU - Anbaw Tigabu
PY - 2026
DA - 2026/07/01
JO - Discover Sustainability
DO - 10.1007/s43621-026-03475-4
UR - https://doi.org/10.1007/s43621-026-03475-4
AB - Climate change poses a profound global threat, with forest ecosystems serving as essential carbon sinks that mitigate atmospheric CO₂ accumulation. Despite Ethiopia’s commitment to forest restoration under initiatives like the Green Legacy Initiative (GLI), quantitative data on long-term forest cover dynamics and carbon stocks in key areas such as Alemsaga Forest remain limited, hindering evidence-based conservation and climate policy. This study aimed to quantify forest cover changes in Alemsaga Forest over 30 years (1992–2022) and estimate its carbon stocks to inform national mitigation strategies. Forest cover was mapped using Landsat satellite imagery (1992, 2003, 2013, and 2022) via supervised classification in ERDAS Imagine. Ground-truth data were collected from 36 randomly selected 16 m × 16 m plots, measuring tree diameter at breast height (DBH) and height for 64 species. Aboveground biomass (AGB) and belowground biomass (BGB) were calculated using algometric equations; soil organic carbon (SOC) was analysed from 1 m × 1 m samples in 16 plots. Carbon stocks were summed across pools (AGB, BGB, deadwood, SOC). The results indicate a 35.34% increase in dense forest cover, which currently constitutes 48.25% of the total forest area (888.30 ha), indicating significant forest regeneration. Average carbon stocks were 191.72 ± 57.92 t/ha (AGB), 92.96 ± 75.74 t/ha (BGB), 2.61 ± 2.26 t/ha (deadwood), and 87.25 ± 51.72 t/ha (SOC), totaling 374.54 ± 187.64 t/ha and equivalent to 1374.76 t/ha CO₂. Alemsaga Forest acts as a dynamic carbon sink, validating the efficacy of ongoing reforestation efforts. Policymakers should strengthen GLI through species-specific allometric models, integrate remote sensing into national carbon registries, and establish community-led monitoring to sustain gains and support Ethiopia’s GLI.
SN - 2662-9984
ER -