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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)

SDG15 Life on land

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