Remote Sens. 2013, 5(1), 155-182; doi:10.3390/rs5010155

Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

1 National Geospatial-Intelligence Agency, 7500 GEOINT Drive, N55-PSCF, Springfield, VA 22150, USA 2 Department of Geosciences and Center for Advanced Spatial Technologies (CAST), 113 Ozark Hall, University of Arkansas, Fayetteville, AR 72701, USA
* Author to whom correspondence should be addressed.
Received: 20 October 2012; in revised form: 9 December 2012 / Accepted: 19 December 2012 / Published: 7 January 2013
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Abstract: 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 m2/ha RMSE when the average basal area across the study area was ~12 m2/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.
Keywords: LIDAR; hyperspectral; deciduous forest; structure; canopy height; basal area

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MDPI and ACS Style

Defibaugh y Chávez, J.; Tullis, J.A. Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing. Remote Sens. 2013, 5, 155-182.

AMA Style

Defibaugh y Chávez J, Tullis JA. Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing. Remote Sensing. 2013; 5(1):155-182.

Chicago/Turabian Style

Defibaugh y Chávez, Jason; Tullis, Jason A. 2013. "Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing." Remote Sens. 5, no. 1: 155-182.

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