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Updated: May 3, 2026
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
Published on: February 9, 2024
Natural forest biomass estimation based on plantation information using PALSAR data.
Ram Avtar1, Rikie Suzuki2, Haruo Sawada3
1Institute of Industrial Science, The University of Tokyo, Tokyo, Japan ; Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan ; United Nations University Institute for Sustainability and Peace, Tokyo, Japan.
This study used Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data to estimate forest biomass in Cambodia. A model based on cashew plantations provided more accurate biomass estimations than one based on rubber plantations.
Area of Science:
- Remote Sensing
- Forestry
- Ecology
Background:
- Forest biomass monitoring is crucial for understanding terrestrial carbon cycling and climate change impacts.
- Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data offers potential for biomass estimation.
- Cambodia's diverse plantation types present unique challenges and opportunities for radar-based biomass assessment.
Purpose of the Study:
- To investigate the backscattering properties of ALOS PALSAR data in Cambodian cashew and rubber plantations.
- To develop and validate multiple linear regression (MLR) models for estimating natural forest biomass using PALSAR data.
- To compare the efficacy of models derived from cashew versus rubber plantations for biomass estimation.
Main Methods:
- Analysis of ALOS PALSAR backscattering coefficient (σ0) in cashew and rubber plantations.
- Creation of MLR models (C-MLR for cashew, R-MLR for rubber) correlating PALSAR σ0 with field-based biomass measurements.
- Validation of the C-MLR model using forest inventory data for natural forests in Cambodia.
Main Results:
- PALSAR σ0 exhibited differential responses in cashew and rubber plantations due to varying biophysical parameters.
- The cashew-based MLR model (C-MLR) showed better correlation and lower saturation compared to the rubber-based model (R-MLR).
- C-MLR-estimated natural forest biomass demonstrated a strong correlation (R² = 0.64) with field data in deciduous forests, with an RMSE of 23.2 Mg/ha.
Conclusions:
- Cashew plantation data provides a more reliable basis for developing PALSAR-based forest biomass estimation models in Cambodia.
- The C-MLR model shows promise for estimating biomass in deciduous forests but faces limitations in high-biomass evergreen forests due to signal saturation.
- Further research may be needed to address saturation issues in dense, multi-story forest structures for improved biomass monitoring.
