Integration of ZiYuan-3 Multispectral and Stereo Data for Modeling Aboveground Biomass of Larch Plantations in North China

Abstract

Data saturation in optical sensor data has long been recognized as a major factor that causes underestimation of aboveground biomass (AGB) for forest sites having high AGB, but there is a lack of suitable approaches to solve this problem. The objective of this research was to understand how incorporation of forest canopy features into high spatial resolution optical sensor data improves forest AGB estimation. Therefore, we explored the use of ZiYuan-3 (ZY-3) satellite imagery, including multispectral and stereo data, for AGB estimation of larch plantations in North China. The relative canopy height (RCH) image was calculated from the difference of digital surface model (DSM) data at leaf-on and leaf-off seasons, which were extracted from the ZY-3 stereo images. Image segmentation was conducted using eCognition on the basis of the fused ZY-3 multispectral and panchromatic data. Spectral bands, vegetation indices, textural images, and RCH-based variables based on this segment image were extracted. Linear regression was used to develop forest AGB estimation models, where the dependent variable was AGB from sample plots, and explanatory variables were from the aforementioned remote-sensing variables. The results indicated that incorporation of RCH-based variables and spectral data considerably improved AGB estimation performance when compared with the use of spectral data alone. The RCH-variable successfully reduced the data saturation problem. This research indicated that the combined use of RCH-variables and spectral data provided more accurate AGB estimation for larch plantations than the use of spectral data alone. Specifically, the root mean squared error (RMSE), relative RMSE, and mean absolute error values were 33.89 Mg/ha, 29.57%, and 30.68 Mg/ha, respectively, when using the spectral-only model, but they become 24.49 Mg/ha, 21.37%, and 20.37 Mg/ha, respectively, when using the combined model with RCH variables and spectral band. This proposed approach provides a new insight in reducing the data saturation problem.

Estimating the Growing Stem Volume of Chinese Pine and Larch Plantations based on Fused Optical Data Using an Improved Variable Screening Method and Stacking Algorithm

“…This research showed that the fusing data of high-resolution multispectral bands with medium-resolution multispectral images can improve the accuracy of GSV estimation for Chinese pine and larch plantations in northern China. Some studies have proved that the red band performs much better than other bands when using optical images for AGB or GSV estimation [28,31], which is consistent with the finding of this study that the Red_Landsat image fused by the GF-2 red band with the Landsat 8 multispectral data leads to larger R 2 and smaller RMSEr values than other fused images, including the fusion image Pan-Landsat.…”

“…The k-fold cross validation method is useful for both classifications and estimation without extra data required (where, k is the number of sample plots). Therefore, we used the leave-one-out cross-validation for calculating determination coefficient (R 2 ), adjusted R 2 , RMSE and relative RMSE (RMSEr) between the estimated and observed values to assess the models' prediction performance [28,31]. They were calculated by:…”

“…Optical images often have low saturation levels and thus have low accuracy when they are used for forest AGB or GSV estimation [5,16]. Tree height or canopy height has higher saturation levels and is an important forest stand attribute, so the variables based on height provide great potential for improving AGB or GSV estimation [25,26,28,35]. However, it is expensive to get sufficient photogrammetric and LiDAR data that can be used to extract tree height.…”

“…The new variables are not bio-physically meaningful. In addition, Stepwise Regression Analysis (SRA) [28,30], Random Forest (RF) [13,19,46], the sure independence screening procedure based on Pearson correlation (PC-SIS) [47], the sure independence screening procedure based on distance correlation (DC-SIS) [48], and the fast iterative features selection method for k-nearest neighbor (kNN-FIFS) have also been widely used for variables selection in classification or regression modeling [49]. Unlike PCA, these methods keep the original variables and do not generate new variables when reducing the dimensionality of sample data.…”

Stratification-Based Forest Aboveground Biomass Estimation in a Subtropical Region Using Airborne Lidar Data

“…Meanwhile, we also explored texture measures using the gray level co-occurrence matrix (GLCM) within the size of the sample plot (20 × 20 m) to extract textural images from the CHM data. The texture measures included correlation, contrast, dissimilarity, entropy, homogeneity, second-order moment, and variance [53,54]. These metrics and the corresponding forest AGB of sample plots were exported to SPSS for further analysis.…”