Combination of SAR Polarimetric Parameters for Estimating Tropical Forest Aboveground Biomass

Abstract

There is a demand for better information on forest biomass in tropical regions for use in carbon accounting. This needs robust above-ground biomass (AGB) estimation in different forest types. Our study sought to improve biomass estimation by selecting the best regression models based on observations of the contribution of radar signals to AGB in five forest types in Vietnam. Data from PALSAR and PALSAR-2, which covered the forest area, were used to extract 16 polarimetric radar (PolSAR) parameters in 2007 and 2016. This study was designed as a comparative experiment of four regression models: linear, polynomial, support vector machine (SVR) and random forest. First, the contribution of PolSAR data to AGB estimation was evaluated using two approaches: the sample data from all forest types, and the five individual forest types (rich, medium, poor, restoration and bamboo forest). Second, we examined the improvement of AGB prediction by selecting the important variables and assessing the best models for different forest types. The results showed an improvement in the value of R-squared and RMSE using the five individual forest types compared to the combined forest types. In particular, using a multivariate model, RMSE values were enhanced by 9-18% for the rich forest, and by 80-85% for the remaining forest types in all models. SVM provided the best performance for medium and poor forest (RMSE of 8.27 tons ha -1 and 12.38 tons ha -1 , respectively), random forest for bamboo (RMSE of 23.18 tons ha -1 ), and the polynomial regression for the restoration forest (RMSE *of 10.11 tons ha -1 ). Further research is required to derive a more robust AGB estimation model for the rich forest.

The Role of Remote Sensing for the Assessment and Monitoring of Forest Health: A Systematic Evidence Synthesis

“…In addition, synthetic aperture radar (SAR) has the possibility of operating under cloudy weather conditions, unlike optical sensors or LiDAR. Despite this fact, the information that it provides is limited to moisture and structural information [79,80], yet the potential of SAR sensors remains largely unexplored [81].…”

Estimating Forest Above-Ground Biomass in Central Amazonia Using Polarimetric Attributes of ALOS/PALSAR Images

“…Thus, the sum of the parallel polarization polarimetric syntheses responses of many individual scatterers for a given pixel can identify the dominant scattering mechanisms and the degree of randomness of the backscatter signal using the pedestal height as a parameter [59,60]. It is worth mentioning that different types of scattering show different values of pedestal height [63]. Forest typologies were associated with the predominant scattering mechanism, and consequently, the parallel polarimetric response, and their pedestal height, as proposed by [44].…”

Biomass Estimation and Saturation Value Determination Based on Multi-Source Remote Sensing Data

“…Support vector machine is a statistical theory based on the kernel approach that solves multidimensional prediction problems by converting nonlinear regression problems into linear regression problems in a high-dimensional feature space [68,69]. The SVR model has three hyperparameters to set: "kernel", "C", and "epsilon".…”