Field-Calibrated Sentinel-2 Assessment of Biomass and Carbon Storage in Mixed Pinus halepensis–Cedrus atlantica Stands Affected by Cedar Decline in Northeastern Algeria—A Sustainability Perspective

Reliable forest biomass and carbon-stock assessment is essential for climate-change mitigation and sustainable forest management, particularly in semi-arid Mediterranean mountain forests affected by drought, wildfire and tree decline. Here, we estimated above-ground biomass (AGB), total biomass, carbon stock and CO2-equivalent storage in the Ouled Yagoub Forest, northeastern Algeria, by combining field inventory data, species-specific allometric equations, Sentinel-2 vegetation indices and elevation. We established 60 circular plots of 0.1 ha in April 2026 and retained 54 stocked plots for biomass modelling. The field inventory included 661 trees: 467 Pinus halepensis and 194 Cedrus atlantica, including 122 healthy and 72 declining cedar individuals. Field estimates indicated an AGB of 338.07 Mg, equivalent to 62.61 Mg ha−1 for stocked plots. Total biomass reached 436.11 Mg, while carbon stock and CO2-equivalent storage reached 238.56 Mg C and 874.78 Mg CO2eq, respectively. The NDVI-based model explained 78.3% of AGB variability, with Pearson’s r = 0.885, RMSE = 48.77 Mg ha−1 and MAE = 39.14 Mg ha−1. Cross-validation confirmed acceptable model stability, with LOOCV R2 = 0.755 and RMSE = 51.86 Mg ha−1, and 5-fold CV R2 = 0.762 and RMSE = 51.07 Mg ha−1. SAVI produced identical predictive performance because it was perfectly collinear with NDVI. Therefore, we selected the NDVI-only model as the most parsimonious model for spatial prediction. Declining Cedrus atlantica individuals showed lower estimated biomass and carbon storage than healthy cedar individuals, although this difference represents an observed pattern rather than a direct causal quantification of carbon loss due to dieback. The resulting maps provide field-calibrated exploratory spatial estimates rather than independently validated carbon maps. The research directly aligns with the principles of Sustainability, contributing to climate change mitigation and resilient forest management in semi-arid Mediterranean ecosystems. In addition, this work provides a stocked-plot carbon baseline and supports forest carbon monitoring, cedar conservation and restoration planning in the Aurès Mountains.

Authors

Institutions

Publication Details

Journal
Sustainability
Published
2026-08-26
DOI
https://doi.org/10.3390/su18178741
Primary Topic
Forest ecology and management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Field-Calibrated Sentinel-2 Assessment of Biomass and Carbon Storage in Mixed Pinus halepensis–Cedrus atlantica Stands Affected by Cedar Decline in Northeastern Algeria—A Sustainability Perspective

Mohamed S. Shokr, Ahmed S. Abuzaid, O Meghithi, Toufik Aliat et al.
Sustainability
Forest ecology and management
article

Field-Calibrated Sentinel-2 Assessment of Biomass and Carbon Storage in Mixed Pinus halepensis–Cedrus atlantica Stands Affected by Cedar Decline in Northeastern Algeria—A Sustainability Perspective

Mohamed S. Shokr, Ahmed S. Abuzaid, O Meghithi, Toufik Aliat, Yassine Benabbes
article en

Abstract

Reliable forest biomass and carbon-stock assessment is essential for climate-change mitigation and sustainable forest management, particularly in semi-arid Mediterranean mountain forests affected by drought, wildfire and tree decline. Here, we estimated above-ground biomass (AGB), total biomass, carbon stock and CO2-equivalent storage in the Ouled Yagoub Forest, northeastern Algeria, by combining field inventory data, species-specific allometric equations, Sentinel-2 vegetation indices and elevation. We established 60 circular plots of 0.1 ha in April 2026 and retained 54 stocked plots for biomass modelling. The field inventory included 661 trees: 467 Pinus halepensis and 194 Cedrus atlantica, including 122 healthy and 72 declining cedar individuals. Field estimates indicated an AGB of 338.07 Mg, equivalent to 62.61 Mg ha−1 for stocked plots. Total biomass reached 436.11 Mg, while carbon stock and CO2-equivalent storage reached 238.56 Mg C and 874.78 Mg CO2eq, respectively. The NDVI-based model explained 78.3% of AGB variability, with Pearson’s r = 0.885, RMSE = 48.77 Mg ha−1 and MAE = 39.14 Mg ha−1. Cross-validation confirmed acceptable model stability, with LOOCV R2 = 0.755 and RMSE = 51.86 Mg ha−1, and 5-fold CV R2 = 0.762 and RMSE = 51.07 Mg ha−1. SAVI produced identical predictive performance because it was perfectly collinear with NDVI. Therefore, we selected the NDVI-only model as the most parsimonious model for spatial prediction. Declining Cedrus atlantica individuals showed lower estimated biomass and carbon storage than healthy cedar individuals, although this difference represents an observed pattern rather than a direct causal quantification of carbon loss due to dieback. The resulting maps provide field-calibrated exploratory spatial estimates rather than independently validated carbon maps. The research directly aligns with the principles of Sustainability, contributing to climate change mitigation and resilient forest management in semi-arid Mediterranean ecosystems. In addition, this work provides a stocked-plot carbon baseline and supports forest carbon monitoring, cedar conservation and restoration planning in the Aurès Mountains.

SustainabilityVol. 18(17)
Benha University (EG), Tanta University (EG), Russian New University (RU), Higher National Veterinary School (DZ), Higher National School of Forests
Life in Land
Openalex Percentile: Top 7%
Forest ecology and management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.