Carbon stocks of tree plantations in a Western Ghats landscape, India: influencing factors and management implications.

Biomass and carbon stock assessments in data-deficient plantations and identifying the factors influencing tree growth, distribution, and carbon stocks are extremely important for implementing sound silvicultural management and monitoring practices to achieve REDD+ goals. We conducted carbon stock assessments in five major plantation types in a regional landscape in the central Western Ghats, India, by establishing fifty 0.1-ha plots across the landscape. We quantified the overall carbon stocks by summing the carbon pools across mature trees, deadwood, and soil (0 -15 cm) components. Allometric equations were compared to address the uncertainty in the tree biomass carbon. The tree biomass carbon and soil organic carbon varied significantly across the plantation types (F = 55.23, p < 0.00). The present study yielded the highest carbon stocks in Pinus plantation (201.91 ± 9.52 Mg ha-1) and the least in Eucalyptus (122.63 ± 9.73 Mg ha-1). The correlation analysis displayed a strong influence of mean annual precipitation and edaphic factors on soil organic carbon, while basal area and elevation were good predictors of tree biomass carbon. The principal component analysis revealed an association of predictor variables in the distribution of plantation types. We found a strong association between mean annual precipitation on Pinus plantation and mean annual temperature on Eucalyptus and Acacia plantations. On the other hand, teak pure plantation was associated with structural and topographic variables, while edaphic factors mainly influenced the distribution of teak mixed plantations. The findings of the present study conclude substantial carbon storage ability of the plantations in the studied landscape which can play a significant role in mitigating the effects of climate change and reaching carbon neutrality.

LiDAR-based reference aboveground biomass maps for tropical forests of South Asia and Central Africa.

Accurate mapping and monitoring of tropical forests aboveground biomass (AGB) is crucial to design effective carbon emission reduction strategies and improving our understanding of Earth's carbon cycle. However, existing large-scale maps of tropical forest AGB generated through combinations of Earth Observation (EO) and forest inventory data show markedly divergent estimates, even after accounting for reported uncertainties. To address this, a network of high-quality reference data is needed to calibrate and validate mapping algorithms. This study aims to generate reference AGB datasets using field inventory plots and airborne LiDAR data for eight sites in Central Africa and five sites in South Asia, two regions largely underrepresented in global reference AGB datasets. The study provides access to these reference AGB maps, including uncertainty maps, at 100 m and 40 m spatial resolutions covering a total LiDAR footprint of 1,11,650 ha [ranging from 150 to 40,000 ha at site level]. These maps serve as calibration/validation datasets to improve the accuracy and reliability of AGB mapping for current and upcoming EO missions (viz., GEDI, BIOMASS, and NISAR).