Persson, Henrik
- Department of Forest Resource Management, Swedish University of Agricultural Sciences
Abstract
Satellite interferometric synthetic aperture radar (InSAR) is emerging as a viable low-cost alternative method to airborne laser scanning (ALS) for forest inventory though little research has examined its efficacy for plantation forests located in temperate regions on steep terrain. InSAR and ALS data were collected from Geraldine Forest which is located on rolling to very steep topography in the southeast of New Zealand. These data were combined with an extensive set of plot measurements from which mean top height (H), basal area (G), stem density (N), and total stem volume (TSV) were calculated. InSAR and ALS-based Random Forest models of each variable were developed and compared.Using the ALS data as a reference, the mean RMSE of the InSAR DSM and DTM surfaces were, respectively, 4.58 and 8.09 m and these errors increased to mean values of, respectively, 6.02 and 10.17 m for slopes of 40-50 degrees.ALS-based models were substantially more precise than those developed from InSAR for H (R-2 = 0.86 vs. 0.60; RMSE% = 5.47 vs. 10.8%), G (R-2 = 0.56 vs. 0.32; RMSE% = 21.5 vs. 30.4%), N (R-2 = 0.47 vs. 0.09; RMSE% = 32.3 vs. 43.2%), and TSV (R-2 = 0.70 vs. 0.41; RMSE% = 19.4 vs. 30.7%). The base metrics (i.e. ALS height and canopy cover variables) accounted for most of the variance in the ALS models with addition of further metrics providing
Keywords
Forest inventory; LiDAR; Machine learning; Random forest regression; SAR interferometry; Synthetic aperture radar; TanDEM-X; Volume
Published in
Remote Sensing of Environment
2020, volume: 246, article number: 111833
Publisher: ELSEVIER SCIENCE INC
SLU Authors
Global goals (SDG)
UKÄ Subject classification
Earth Observation
Publication identifier
- DOI: https://doi.org/10.1016/j.rse.2020.111833
Permanent link to this page (URI)
https://res.slu.se/id/publ/106714