气候变化领域集成服务门户(CCPortal): Comparison of TanDEM-X InSAR data and high-density ALS for the prediction of forest inventory attributes in plantation forests with steep terrain

CCPortal

DOI	10.1016/j.rse.2020.111833
	Comparison of TanDEM-X InSAR data and high-density ALS for the prediction of forest inventory attributes in plantation forests with steep terrain
	Leonardo E.M.C.; Watt M.S.; Pearse G.D.; Dash J.P.; Persson H.J.
发表日期	2020
ISSN	00344257
卷号	246
英文摘要	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°. ALS-based models were substantially more precise than those developed from InSAR for H (R2 = 0.86 vs. 0.60; RMSE% = 5.47 vs. 10.8%), G (R2 = 0.56 vs. 0.32; RMSE% = 21.5 vs. 30.4%), N (R2 = 0.47 vs. 0.09; RMSE% = 32.3 vs. 43.2%), and TSV (R2 = 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 ≤ 1% reduction in the RMSE%. Base metrics (i.e. InSAR observables) also accounted for most of the variation in InSAR models. Addition of metrics from a mixed Canopy Height Model (CHM), derived from InSAR Digital Surface Model (DSM) and ALS Digital Terrain Model (DTM), resulted in reductions in RMSE% of 3.1–5.4% for H, G, and TSV models with addition of textural metrics providing further reductions of 0.2–0.3% for H and G models. Addition of metrics from the radar CHM, derived from the InSAR DTM and DSM, and texture metrics reduced the RMSE% of the base model for N by 2.3% and 0.5%, respectively. The results were generated using a SAR image pair with a height of ambiguity (HOA) that was higher than ideal, which reduced the sensitivity of results to changes in terrain. Despite this limitation, and the steep slopes throughout the forest, the InSAR models described here had comparable precision to developed InSAR models for key inventory metrics from previous studies. © 2020 Elsevier Inc.
英文关键词	Forest inventory; LiDAR; Machine learning; Random forest regression; SAR interferometry; Synthetic aperture radar; TanDEM-X; Volume
语种	英语
scopus关键词	Decision trees; Forestry; Landforms; Textures; Topography; Airborne Laser scanning; Canopy Height Models; Digital surface models; Digital terrain model; Interferometric synthetic aperture radars; Plantation forests; Sensitivity-of-results; Total stem volume; Synthetic aperture radar; digital terrain model; error analysis; forest inventory; image analysis; sustainability; TanDEM-X; terrain; topography; New Zealand
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179297
作者单位	Scion, PO Box 3020, Rotorua, 3010, New Zealand; Scion, 10 Kyle St, Riccarton, Christchurch 8011, New Zealand; Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, 90183, Sweden
推荐引用方式 GB/T 7714	Leonardo E.M.C.,Watt M.S.,Pearse G.D.,et al. Comparison of TanDEM-X InSAR data and high-density ALS for the prediction of forest inventory attributes in plantation forests with steep terrain[J],2020,246.
APA	Leonardo E.M.C.,Watt M.S.,Pearse G.D.,Dash J.P.,&Persson H.J..(2020).Comparison of TanDEM-X InSAR data and high-density ALS for the prediction of forest inventory attributes in plantation forests with steep terrain.Remote Sensing of Environment,246.
MLA	Leonardo E.M.C.,et al."Comparison of TanDEM-X InSAR data and high-density ALS for the prediction of forest inventory attributes in plantation forests with steep terrain".Remote Sensing of Environment 246(2020).