Aboveground Forest Biomass Estimation by the Integration of TLS and ALOS PALSAR Data Using Machine Learning

Abstract

Forest inventory parameters play an important role in understanding various biophysical processes of forest ecosystems. The present study aims at integrating Terrestrial Laser Scanner (TLS) and ALOS PALSAR L-band Synthetic Aperture Radar (SAR) data to assess Aboveground Biomass (AGB) in the Barkot Forest Range, Uttarakhand, India. The integration was performed to overcome the AGB saturation issue in ALOS PALSAR L-band SAR data for the high biomass density forest of the study area using 13 plots. Various parameters, namely, Gray-Level Co-Occurrence Matrix (GLCM) texture measures, Yamaguchi decomposition components, polarimetric parameters, and backscatter values of HH and HV band intensity, were derived from the ALOS SAR data. However, TLS was used to obtain the diameter at breast height (dbh) and tree height for the sample plots. A total of 23 parameters was retrieved using TLS and SAR data for integration with the LiDAR footprint. The integration was performed using Random Forest (RF) and Artificial Neural Network (ANN). The statistical measures for RF were found to be promising compared with ANN for AGB estimation. The R2 value obtained for the RF was 0.94, with an RMSE of 59.72 ton ha−1 for the predicted biomass value. The RMSE% was 15.92, while the RMSECV was 0.15. The R2 value for ANN was 0.77, with an RMSE of 98.46 ton ha−1. The RMSE% was 26.0, while the RMSECV was 0.26. RF performed better in estimating the biomass, which ranged from 122.46 to 581.89 ton ha−1, while uncertainty ranged from 15.75 to 85.14 ton ha−1. The integration of SAR and LiDAR data using machine learning shows great potential in overcoming AGB saturation of SAR data.

Forest Biomass Assessment Using Multisource Earth Observation Data: Techniques, Data Sets and Applications

“…Moreover, LiDAR data from terrestrial and spaceborne platforms have proven highly effective in accurately estimating AGB across various forest types in India (Dhanda et al, 2017;Mohite et al, 2024;Singh et al, 2023;Singhal et al, 2021). Integration of multi-sensor data, including passive optical, SAR, and LiDAR data, has demonstrated superior AGB estimation compared to single sensor data (Fararoda et al, 2021;Malhi et al, 2022;.…”

Forest Structure Measurement in Tropical Rainforest using Laser Scanning Technology

“…The last element was a basal area referred to as the common term used to describe the average amount of an area (usually an acre) occupied by tree stems. All of the tree elements were chosen because of their major roles in presenting the relationship and pattern between trees in the forest that also reflected the forest structure [3,27].…”

Prediction of Regional Forest Biomass Using Machine Learning: A Case Study of Beijing, China

“…Forests are the dominant terrestrial plant communities, which not only maintain the balance of the global ecosystem, but also play an important role in the global carbon balance [1][2][3]. As the most basic quantitative characteristic of forest ecosystems, forest biomass and its dynamic changes are closely related to the carbon cycle, climate change, forest productivity and biodiversity, which makes them suitable to monitor the dynamic changes in forest resources at all levels as well as to measure the quality and productivity of the forest ecosystem [4][5][6].…”