Mapping Growing Stem Volume Using Dual-Polarization GaoFen-3 SAR Images in Evergreen Coniferous Forests

Abstract

Unaffected by cloud cover and solar illumination, synthetic aperture radar (SAR) images have great capability to map forest growing stem volume (GSV) in complex biophysical environments. Up to now, c-band dual-polarization Gaofen-3 (GF-3) SAR images, acquired by the first Chinese civilian satellite equipped with multi-polarized modes, are rarely applied in mapping forest GSV. To evaluate the capability of dual-polarization GF-3 SAR images in mapping forest GSV, several proposed derived features were initially extracted by mathematical operations and applied to obtain optimal feature sets by different feature sorting methods and feature selection methods. Then, the maps of GSV in an evergreen coniferous forest were inverted by various machine learning algorithms and stacking ensemble learning methods with different strategies. The results implied that backscattering coefficients and partially proposed derived features showed high sensitivity to the forest GSV, and the saturation phenomenon also obviously occurred once the forest GSV was larger than 300 m3/ha. Furthermore, the results showed that the accuracy of the mapped GSV was significantly improved using the stacking ensemble learning methods. Using various optimal feature sets and base models (MLR, KNN, SVM, and RF), the rRMSE values mainly ranged from 30% to 40%. After using the stacking ensemble learning methods, the values of rRMSE ranged from 16.71% to 20.51%. This confirmed that dual-polarization GF-3 images have great potential to map forest GSV in evergreen coniferous forests.

Interpreting the Response of Forest Stock Volume with Dual Polarization SAR Images in Boreal Coniferous Planted Forest in the Non-Growing Season

“…Commonly, these backscattering coefficients of dual polarization SAR include σHH, σHV (GF-3 and ALOS-2) and σVH, σVV(Sentinel-1), for a total of six backscattering coefficients. For increasing the number of alternative features, mathematical operations between backscattering coefficients with different polarizations were applied and eighteen derived features [1] were extracted from each type of SAR image, for a total of 54 derived features from three SAR images in this study (Table 3). Furthermore, Gray Level Co-occurrence Matrix (GLCM) was also employed to extract textural information from each polarized intensity image (σHH, σHV, σVH, and σVV); there were eight textural features, including mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation, which were obtained from each image with various window sizes (5 × 5, 7 × 7, 9 × 9).…”

“…Then the features were ordered in descending order according to the Pearson correlation coefficient. After obtaining sorted features, the sequential forward selection method and four machine learning models were employed to construct wrapped feature selection methods [1,34]. Finally, optimal feature sets were obtaining by removing the features with poor contributions to the accuracy of estimating FSV.…”

“…Planted forests play a significant role in making substantial contributions to climate change mitigation and promoting the carbon cycle within ecosystems. The forest stock volume (FSV) is regarded as one of key indices in evaluating the quality of planted forests [1,2]. Normally, traditional methods of investigating FSV are obtained by time-consuming and costly ground measurements, with which it is hard to satisfy the requirements of modern forest resource monitoring [3].…”

Editorial for the Special Issue “SAR for Forest Mapping II”

“…This Special Issue consists of nine research papers [1][2][3][4][5][6][7][8][9], which investigate several aspects related to forest parameter estimation and retrieval using SAR-based methods, such as the mapping of forest cover [1][2][3], above-ground biomass (AGB) [4], and growing stem volume (GSV) [5], as well as forest height inversion [6], forest structure characterization [7], the estimation of phenological changes [8], and damages in drought-affected forests [9] using SAR data time series. All the methods proposed in these papers were validated using real SAR data and benchmarked with state-of-the-art approaches, thus comprehensively demonstrating the theoretical and practical contributions of the research.…”

“…SAR images have great potential for mapping the forest growing stem volume (GSV) in complex biophysical environments. The objective of the study published in [5] is to evaluate the capability of C-band dual-polarization SAR images acquired by the Chinese civilian Gaofen-3 (GF-3) satellite to map the GSV in evergreen coniferous forests. Several proposed features are extracted and applied to obtain optimal feature sets using different sorting and selection methods.…”