Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest

Anderson, Jeanne E.; Plourde, Lucie; Martin, Mary E.; Braswell, B. H.; Smith, Mike; Dubayah, Ralph; Hofton, M. A.; Blair, J. B.

doi:10.1016/j.rse.2007.09.009

Cited by 186 publications

(125 citation statements)

References 33 publications

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“…One trend apparent in the literature is interest in the use of light detection and ranging (LIDAR) remote sensing for extraction of forest biophysical parameters,, such as aboveground biomass and various measures of forest structure [19][20][21][22]. Some of these studies have shown encouraging results [23].…”

Section: Remote Sensing Of Forested Environmentsmentioning

confidence: 99%

“…However, the appropriate use of LIDAR data to calibrate a spectral reflectance model, which can then be applied beyond the LIDAR data footprint, has the potential for reducing time and costs in regional scale studies. For example, both Hyde et al [31] and Anderson et al [19] used hyperspectral reflectance data to map biophysical variables and used LIDAR-derived structural data to validate their results and to increase the accuracy of their respective models.…”

Section: Remote Sensing Of Forested Environmentsmentioning

confidence: 99%

“…Other studies that have combined LIDAR with spectral data have approached the problem in terms of LIDAR return intensity [19]. A unique contribution of this research was to assess a predictive relationship between hyperspectral derivatives and near-coincident LIDAR-derived canopy height statistics (e.g., understory, upper canopy), with hyperspectral relationship to in situ measured basal area provided as a comparison.A spectral remote sensing-assisted method that allows rapid detection and assessment of changes in forest structure has value both for resource management and environmental monitoring.…”

Section: Objectivesmentioning

confidence: 99%

“…Mean basal area, , was computed as: (2) where is plot basal area measured and is the total number of measured trees in a plot. This is a more useful reference variable as it applies to the aged oak-hickory stands of the Ozark Plateau [19]. For this reason, the latter Equation (2) was incorporated in this study.…”

Section: In Situ Reference Datamentioning

confidence: 99%

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Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

Chávez

Tullis

2013

Remote Sensing

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Coverage and frequency of remotely sensed forest structural information would benefit from single orbital platforms designed to collect sufficient data. We evaluated forest structural information content using single-date Hyperion hyperspectral imagery collected over full-canopy oak-hickory forests in the Ozark National Forest, Arkansas, USA. Hyperion spectral derivatives were used to develop machine learning regression tree rule sets for predicting forest neighborhood percentile heights generated from near-coincident Leica Geosystems ALS50 small footprint light detection and ranging (LIDAR). The most successful spectral predictors of LIDAR-derived forest structure were also tested with basal area measured in situ. Based on the machine learning regression trees developed, Hyperion spectral derivatives were utilized to predict LIDAR forest neighborhood percentile heights with accuracies between 2.1 and 3.7 m RMSE. Understory predictions consistently resulted in the highest accuracy of 2.1 m RMSE. In contrast, hyperspectral prediction of basal area measured in situ was only found to be 6.5 m 2 /ha RMSE when the average basal area across the study area was ~12 m 2 /ha. The results suggest, at a spatial resolution of 30 × 30 m, that orbital hyperspectral imagery alone can provide useful structural information related to vegetation height. Rapidly calibrated biophysical remote sensing techniques will facilitate timely assessment of regional forest conditions.

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Section: Remote Sensing Of Forested Environmentsmentioning

confidence: 99%

Section: Remote Sensing Of Forested Environmentsmentioning

confidence: 99%

Section: Objectivesmentioning

confidence: 99%

Section: In Situ Reference Datamentioning

confidence: 99%

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Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

Chávez

Tullis

2013

Remote Sensing

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“…These resources include: various types of sensors (Zolkos et al, 2013), regression approaches (Chen et al, 2010; The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B8, 2016XXIII ISPRS Congress, 12-19 July 2016, Prague, Czech Republic 2015Dalponte et al, 2008;Garcıa-Gutiérreza et al;Gleason and Im, 2012), and whether or not optical remote sensing data is fused with lidar data (Anderson et al, 2008;Koetz et al, 2007;Lefsky et al, 2005;Swatantran et al, 2011;Laurin et al, 2014). However, these sources introducing errors to lidar-based AGB models are more considered as external factors rather than the intrinsic limitations of further improving the performance of lidar in biomass estimation.…”

Section: Resultsmentioning

confidence: 99%

Impacts of Tree Height-DBH Allometry on Lidar-Based Tree Aboveground Biomass Modeling

Fang¹

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VIII, WG VIII/7 KEY WORDS: tree aboveground biomass, lidar, allometric equations, simulation, regression ABSTRACT:Lidar has been widely used in tree aboveground biomass (AGB) estimation at plot or stand levels. Lidar-based AGB models are usually constructed with the ground AGB reference as the response variable and lidar canopy indices as predictor variables. Tree diameter at breast height (dbh) is the major variable of most allometric models for estimating reference AGB. However, lidar measurements are mainly related to tree vertical structure. Therefore, tree height-dbh allometric model residuals are expected to have a large impact on lidar-based AGB model performance. This study attempts to investigate sensitivity of lidar-based AGB model to the decreasing strength of height-dbh relationship using a Monte Carlo simulation approach. Striking decrease in R 2 and increase in relative RMSE were found in lidar-based AGB model, as the variance of height-dbh model residuals grew. I, therefore, concluded that individual tree height-dbh model residuals fundamentally introduce errors to lidar-AGB models.

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Modelling catchment processes

Downs

Asua²

2016

Tools in Fluvial Geomorphology

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Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest

Cited by 186 publications

References 33 publications

Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

Impacts of Tree Height-DBH Allometry on Lidar-Based Tree Aboveground Biomass Modeling

Modelling catchment processes

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