Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests

Remote Sensing

Alicia Peduzzi²  Randolph H. Wynne²  Valerie A. Thomas²  Ross F. Nelson³  James J. Reis¹ 

[1] IEEE, Baltimore, MD 21201, USA; E-Mail:;Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute and State University, 319 Cheatham Hall (Mail Code 0324), Blacksburg, VA 24061, USA; E-Mails:;NASA/GSFC, Mail Code 618, Greenbelt, MD 20771, USA; E-Mail:

关键词: deciduous forests;    coniferous forests;    silviculture;    leaf area index;    remote sensing;    laser scanning;    InSAR;    dual band single pass interferometric synthetic aperture radar;   

DOI  :  10.3390/rs4061758

来源: mdpi

【 摘 要 】

The objective of this study was to determine whether leaf area index (LAI) in temperate mixed forests is best estimated using multiple-return airborne laser scanning (lidar) data or dual-band, single-pass interferometric synthetic aperture radar data (from GeoSAR) alone, or both in combination. In situ measurements of LAI were made using the LiCor LAI-2000 Plant Canopy Analyzer on 61 plots (21 hardwood, 36 pine, 4 mixed pine hardwood; stand age ranging from 12–164 years; mean height ranging from 0.4 to 41.2 m) in the Appomattox-Buckingham State Forest, Virginia, USA. Lidar distributional metrics were calculated for all returns and for ten one meter deep crown density slices (a new metric), five above and five below the mode of the vegetation returns for each plot. GeoSAR metrics were calculated from the X-band backscatter coefficients (four looks) as well as both X- and P-band interferometric heights and magnitudes for each plot. Lidar metrics alone explained 69% of the variability in LAI, while GeoSAR metrics alone explained 52%. However, combining the lidar and GeoSAR metrics increased the R² to 0.77 with a CV-RMSE of 0.42. This study indicates the clear potential for X-band backscatter and interferometric height (both now available from spaceborne sensors), when combined with small-footprint lidar data, to improve LAI estimation in temperate mixed forests.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.