A Proposed Ensemble Feature Selection Method for Estimating Forest Aboveground Biomass from Multiple Satellite Data

Abstract

Feature selection (FS) can increase the accuracy of forest aboveground biomass (AGB) prediction from multiple satellite data and identify important predictors, but the role of FS in AGB estimation has not received sufficient attention. Here, we aimed to quantify the degree to which FS can benefit forest AGB prediction. To this end, we extracted a series of features from Landsat, Phased Array L-band Synthetic Aperture Radar (PALSAR), and climatic and topographical information, and evaluated the performance of four state-of-the-art FS methods in selecting predictive features and improving the estimation accuracy with selected features. We then proposed an ensemble FS method that takes inro account the stability of an individual FS algorithm with respect to different training datasets used; the heterogeneity or diversity of different FS methods; the correlations between features and forest AGB; and the multicollinearity between the selected features. We further investigated the performance of the proposed stability-heterogeneity-correlation-based ensemble (SHCE) method for AGB estimation. The results showed that selected features by SHCE provided a more accurate prediction of forest AGB than existing state-of-the-art FS methods, with R2 = 0.66 ± 0.01, RMSE = 14.35 ± 0.12 Mg ha−1, MAE = 9.34 ± 0.09 Mg ha−1, and bias = 1.67 ± 0.11 Mg ha−1 at 90 m resolution. Boruta yielded comparable prediction accuracy of forest AGB, but could not identify the importance of features, which led to a slightly greater bias than the proposed SHCE method. SHCE not only ranked selected features by importance but provided feature subsets that enabled accurate AGB prediction. Moreover, SHCE provides a flexible framework to combine FS results, which will be crucial in many scenarios, particularly the wide-area mapping of land-surface parameters from various satellite datasets.

A Multi-Scale Forest Above-Ground Biomass Mapping Approach: Employing a Step-by-Step Spatial Downscaling Method with Bias-Corrected Ensemble Machine Learning

“…In accordance with findings from prior research, forest AGB was correlated with various remote sensing parameters, including Nadir bidirectional reflectance distribution function adjusted reflectance (NBAR), net primary productivity (NPP), vegetation continuous fields (VCF), and land surface temperature (LST) [29,30]. In this study, features were extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) NBAR product at a resolution of 500 m (MCD43A4) [31], annual NPP (MOD17A3HGF) at 500 m resolution [32], the VCF product (MOD44B) at 250 m resolution [33], and LST data (MOD11A2) at 1 km resolution [34] for the year 2010, serving as predictors of forest AGB.…”

Estimation of Rubber Plantation Biomass Based on Variable Optimization from Sentinel-2 Remote Sensing Imagery

“…Previous studies indicated that the inclusion of PALSAR can enhance the accuracy of forest AGB estimation [73], but whether the optimized feature parameters derived from PALSAR improve AGB estimation remains unclear. Additionally, elevation, aspect, slope, and other terrain factors, as well as stand age, are also important parameters applied in the estimation of rubber plantation AGB [8,32,74]. Therefore, the parameter optimization approach should be further evaluated using different satellite data, as well as terrain factors and stand age parameters in rubber plantation AGB estimation in the future.…”

“…This implies that it is crucial to select a small, optimal, and sensitivity-aware set of variables for model construction when dealing with a large number of variables. For instance, Zhang et al [32] evaluated the performance of four existing feature selection methods and found that the SHCE selection method for screening remote sensing features achieves the highest estimation performance (R² = 0.66 ± 0.01, RMSE = 14.35 ± 0.12 Mg/ha). These findings suggest that the optimal selection of remote sensing features can enhance the estimation accuracy of forest AGB.…”

Inversion of Forest Aboveground Biomass in Regions with Complex Terrain Based on PolSAR Data and a Machine Learning Model: Radiometric Terrain Correction Assessment

“…In this study, only PolSAR features were applied in AGB inversion. However, it is necessary to include more actual parameters that reflect the geographical environment and physical conditions when constructing a more comprehensive and accurate AGB model [44]. For example, climatic factors (temperature, rainfall, humidity, etc.)…”

“…Feature selection is essential to achieve robust and high-precision estimation of forest AGB based on PolSAR data [43][44][45]. The data and features determine the upper limit of machine learning, while models and algorithms only approach this upper limit [46].…”