Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests
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. &...
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doaj-123ea9cbcaf940c48adad80a356c267f2020-11-24T22:56:46ZengMDPI AGRemote Sensing2072-42922012-06-01461758178010.3390/rs4061758Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed ForestsRoss F. NelsonJames J. ReisMark SanfordValerie A. ThomasAlicia PeduzziRandolph H. WynneThe 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. <em>In situ</em> 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 <em>R<sup>2</sup></em> 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.http://www.mdpi.com/2072-4292/4/6/1758deciduous forestsconiferous forestssilvicultureleaf area indexremote sensinglaser scanningInSARdual band single pass interferometric synthetic aperture radar |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ross F. Nelson James J. Reis Mark Sanford Valerie A. Thomas Alicia Peduzzi Randolph H. Wynne |
spellingShingle |
Ross F. Nelson James J. Reis Mark Sanford Valerie A. Thomas Alicia Peduzzi Randolph H. Wynne Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests Remote Sensing deciduous forests coniferous forests silviculture leaf area index remote sensing laser scanning InSAR dual band single pass interferometric synthetic aperture radar |
author_facet |
Ross F. Nelson James J. Reis Mark Sanford Valerie A. Thomas Alicia Peduzzi Randolph H. Wynne |
author_sort |
Ross F. Nelson |
title |
Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests |
title_short |
Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests |
title_full |
Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests |
title_fullStr |
Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests |
title_full_unstemmed |
Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests |
title_sort |
combined use of airborne lidar and dbinsar data to estimate lai in temperate mixed forests |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2012-06-01 |
description |
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. <em>In situ</em> 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 <em>R<sup>2</sup></em> 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. |
topic |
deciduous forests coniferous forests silviculture leaf area index remote sensing laser scanning InSAR dual band single pass interferometric synthetic aperture radar |
url |
http://www.mdpi.com/2072-4292/4/6/1758 |
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