Exploring LiDAR–RaDAR synergy—predicting aboveground biomass in a southwestern ponderosa pine forest using LiDAR, SAR and InSAR

Scanning Light Detecting and Ranging (LiDAR), Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) were analyzed to determine (1) which of the three sensor systems most accurately predicted forest biomass, and (2) if LiDAR and SAR/InSAR data sets, jointly considered, produced more accurate...

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Bibliographic Details
Published in:Remote sensing of environment 2007-01, Vol.106 (1), p.28-38
Main Authors: Hyde, Peter, Nelson, Ross, Kimes, Dan, Levine, Elissa
Format: Article
Language:English
Subjects:
Animal, plant and microbial ecology
Applied geophysics
Biological and medical sciences
Biomass
Canopy height
Earth sciences
Earth, ocean, space
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
Internal geophysics
LiDAR
Pinus ponderosa
RaDAR
SAR
Teledetection and vegetation maps
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Summary:Scanning Light Detecting and Ranging (LiDAR), Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) were analyzed to determine (1) which of the three sensor systems most accurately predicted forest biomass, and (2) if LiDAR and SAR/InSAR data sets, jointly considered, produced more accurate, precise results relative to those same data sets considered separately. LiDAR ranging measurements, VHF–SAR cross-sectional returns, and X- and P-band cross-sectional returns and interferometric ranges were regressed with ground-estimated (from dbh) forest biomass in ponderosa pine forests in the southwestern United States. All models were cross-validated. Results indicated that the average canopy height measured by the scanning LiDAR produced the best predictive equation. The simple linear LiDAR equation explained 83% of the biomass variability ( n = 52 plots) with a cross-validated root mean square error of 26.0 t/ha. Additional LiDAR metrics were not significant to the model. The GeoSAR P-band ( λ = 86 cm) cross-sectional return and the GeoSAR/InSAR canopy height (X–P) captured 30% of the forest biomass variation with an average predictive error of 52.5 t/ha. A second RaDAR–FOPEN collected VHF ( λ ∼ 7.8 m) and cross-polarized P-band ( λ = 88 cm) cross-sectional returns, none of which proved useful for forest biomass estimation (cross-validated R 2 = 0.09, RMSE = 63.7 t/ha). Joint consideration of LiDAR and RaDAR measurements produced a statistically significant, albeit small improvement in biomass estimation precision. The cross-validated R 2 increased from 83% to 84% and the prediction error decreased from 26.0 t/ha to 24.9 t/ha when the GeoSAR X–P interferometric height is considered along with the average LiDAR canopy height. Inclusion of a third LiDAR metric, the 60th decile height, further increased the R 2 to 85% and decreased the RMSE to 24.1 t/ha. On this 11 km 2 ponderosa pine study area, LiDAR data proved most useful for predicting forest biomass. RaDAR ranging measurements did not improve the LiDAR estimates.
ISSN:0034-4257
1879-0704
DOI:10.1016/j.rse.2006.07.017