Forest Aboveground Biomass Prediction by Integrating Terrestrial Laser Scanning Data, Landsat 8 OLI-Derived Forest Canopy Density and Spectral Indices

Forest canopy density (FCD) is one of the important parameters for forest mapping and monitoring. Terrestrial Laser Scanning (TLS) is one of the most accurate tools used in field data collection. Hence, the present study aimed to predict forest aboveground biomass (AGB) by integrating TLS data, satellite data-derived FCD and spectral indices. FCD Mapper was used to classify Landsat-8 OLI data into FCD classes, which were validated using field-measured FCD. In the field, point cloud data were collected from each FCD class using TLS. The diameter at breast height (dbh) and height of individual trees were retrieved from the point cloud data and validated with field-measured dbh and height. AGB was estimated from the TLS-derived dbh and height and modelled as a function of Landsat-8 OLI-derived FCD classes and spectral indices. A total of 11 FCD classes were generated, which showed a strong positive correlation (r = 0.96) with the field-measured FCD. From the TLS point cloud data, 96% of individual trees were extracted. Positive correlations were found between TLS-measured dbh and field-measured dbh (r = 0.99), and TLS-measured height and field-measured height (r = 0.96). A linear function best fitted between TLS-estimated AGB and FCD classes was established. Because of the low variability in AGB due to absolute FCD classes, the model was further extended using a few spectral indices. Using a multiple linear model, the average AGB (374 Mg ha(-1)) and total AGB (3,024,550 Mg) of the study area were predicted. The study highlighted that the combined application of TLS and satellite data-derived FCD and spectral indices can be one of the fastest and accurate methods in forest AGB prediction.

Automated summary

The present study aimed to predict forest aboveground biomass (AGB) by integrating Terrestrial Laser Scanning (TLS) data, satellite data-derived Forest Canopy Density (FCD), and spectral indices. TLS data were used to collect point cloud data from different FCD classes, and the derived diameter at breast height (dbh) and height of individual trees were validated with field measurements. A linear function was established to estimate AGB based on TLS-derived dbh and height, FCD classes, and spectral indices. The study emphasized the effectiveness of combining TLS and satellite data-derived FCD and spectral indices in accurately predicting forest AGB.