Forest Structure Mapping of Boreal Coniferous Forests Using Multi-Source Remote Sensing Data

Abstract

Modeling forest structure using multi-source satellite data is beneficial to understanding the relationship between vertical and horizontal structure and image features to provide more comprehensive and abundant information for the study of forest structural complexity. This study investigates and models forest structure as a multivariate structure based on sample data and active-passive remote sensing data (Landsat8, Sentinel-2A, and ALOS-2 PALSAR) from the Saihanba Forest in Hebei Province, Northern China, to measure forest structural complexity, relying on a relationship-driven model between field and satellite data. In this study, we considered the effects of the role of satellite variables in different vertical structure types and horizontal structure ranges, used two methods to stepwise select significant variables (stepwise forward selection and Pearson correlation coefficient), and employed a multivariate modeling technique (redundancy analysis) to derive a forest composite structure index (FSI), combining both horizontal and vertical structure attributes. The results show that optical texture can better represent forest structure characteristics, polarization interferometric radar information can represent the vertical structure information of forests, and combining the two can represent 77% of the variance of multiple forest structural attributes. The new FSI can explain 93% of the relationship between stand structure and satellite variables, and the linear fit R2 to the measured data reaches 0.91, which largely shows the situation of the measured data. The generated forest structure map more accurately reflects the complexity of the forest structure in the Saihanba Forest, achieving a supplementary explanation of the measured data.

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

“…This is because all the subjects in this study are artificial coniferous forests, which means that the canopy structure is complicated and has numerous branches. The horizontal structure index can capture biochemical information in the upper canopy and provide more two-dimensional distribution information in the horizontal range of the canopy [86]. And the horizontal structural indices combine ratio techniques with bands and textures, eliminating the effects of topography and sensors and better expressing vegetation parameter information [87,88].…”

“…The polarization decomposition variables and parameters introduced by the three variable selection methods are all related to double and volume scattering, suggesting that they are the main polarization features in the AGB estimation. This is because the subjects of this study are artificial coniferous forests, which have complex canopy structures and more branches inside the canopy, resulting in stronger double and volume scattering [86].…”