Abstract:
Accurate quantification of forest above-ground live carbon stock is crucial for sustaining forests as a climate change mitigation mechanism. However, the major challenge is to find an accurate technique for timely processing and driving biomass information estimates at the extent of big data. This study is thus mainly aimed in estimating and modeling forest aboveground biomass and carbon stock using field inventory data and automated scenario based analysis of satellite images in Google Earth Engine cloud computing environment. The carbon stock estimation was computed at three stages viz: at plot level, at full-hectare basis and using satellite images. The plot level biomass was estimated based on field inventory of 20 circular plots which then extrapolated to determine the biomass of the study area. The resulting biomass was then converted into carbon based on IPCC guidelines. For satellite based biomass and carbon stock estimation, 10m surface reflectance bands and respective vegetation indices were geocomputed for the years 2016 to 2020. These were later correlated with derived satellite variables to validate the AGC derived from sentinel-2 and SAR images of 2019. The coefficient of determination (R2) between observed and the predicted AGC was then used to validate the estimated result. The sentinel 2 NDVI showed the strongest correlation (r = 0.9) with AGC in the study area. Sentinel 1 variables revealed moderate correlation (r = 0.08 to 0.7) with the on-situ AGC. To develop AGB predictive model, NDVI, NDWI, EVI and Entropy were selected based on their correlation coefficient and variable importance. The model has a coefficient of determination value of 0.86. Forest above ground carbon stock map was produced by the developed model and masked by the forest cover produced from fusion image composited from B2, B3, B4, B8, NDVI, EVI, NDWI, VH, elevation and slope band. The per-pixel AGC for the study area ranged from 0.538 – 1.6153, 0.353 -1.518, 0.319-1.480, 0.517 – 1.614 tons for the years 2016 to 2020 respectively. The total plot level per pixel AGC was found between 6 and 7.4 tons. The fusion of Sentinel-2 variables with sentinel-1 GRD images, GLCM and ALOS DSM scenario performed better in estimating AGB and carbon stock compared to use of sentinel-1 and Sentinel-2 alone with model accuracy of 98%. Overall, integrating field data with multisensor remote sensing method increases the accuracy of modeling and estimating forest AGC stock
