We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Forest Structure Mapping of Boreal Coniferous Forests Using Multi-Source Remote Sensing Data.
- Authors
Sa, Rula; Fan, Wenyi
- 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.
- Subjects
HEBEI Sheng (China); CHINA; FOREST mapping; CONIFEROUS forests; REMOTE sensing; TAIGAS; PEARSON correlation (Statistics); REDUNDANCY in engineering; MULTISCALE modeling
- Publication
Remote Sensing, 2024, Vol 16, Issue 11, p1844
- ISSN
2072-4292
- Publication type
Article
- DOI
10.3390/rs16111844