Quantifying forest carbon stocks by integrating satellite images and forest inventory data

Reliable biomass and carbon stock estimation are central to obtain reference levels for quantifying carbon emissions. Forest inventory data combined with remote sensing data provides opportunities to map and monitor forest areas at various spatio-temporal scales. The current research is a pilot study focussed on the biomass and carbon estimation and mapping of subtropical scrub forests of Khanpur range, Haripur Forest Division, Pakistan considering 20 inventory plots using Sentinel-2A and Landsat-8 data. Six forest areas (Garamthun, Chhoi, Moharagutta, Sanaba, Dobandi and Saradana) were considered covering a total area of 697.3 ha. Average biomass of the assessed plots was 104.6 t/ha and mean carbon stock was 49.7 t/ha. Garamthun forest had the highest values for both biomass (187.30 t/ha) and carbon (87.98 t/ha) followed by Choi with 148.22 t/ha of biomass and 69.6 t/ha carbon respectively. The total estimated carbon stock for these six forest types was 43570.9 t. The biomass was then correlated with spectral indices computed from Sentinel 2 image (NDVI, SAVI, DVI, PVI and MSAVI). NDVI performed significantly well among five other indices with the values of R2 of 0.81 followed by 0.7 and 0.58 for SAVI and DVI respectively. PVI and MSAVI responded poorly to biomass as compared to the other indices with the value of R2 of 0.20 and 0.11 respectively. Spatial distribution of biomass was mapped using NDVI, which was selected as the best model based on the values of R2. Further, Landsat-8 was also used and the similar five indices were derived for Landsat-8 imagery. Finally, both the indices derived from Sentinel-2A and Landsat-8 were compared. Scrub forests of Khanpur showed the largest potential for carbon sequestration and storage. It is suggested that this method is not only used for the Haripur district in Khyber Pakhtunkhwa, whose forest division extends merely over the area of 42491 ha; rather it should be applied to the entire forest area of Pakistan for national forest inventory. The research concluded that Sentinel 2 has the best combination of spectral capabilities and broad spectrum of applicability.