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.
Highlights
Health changes of temperate forests in the northern hemisphere, of concern to both foresters and ecologists, may be anthropogenic in origin or part of the natural cycle of the forest
Spectral remote sensor data are a fraction of the cost per km2 of small-footprint light detection and ranging (LIDAR), so a method of measuring biophysical variables using spectral remote sensing would allow large geographic coverage at lower cost
LIDAR-derived canopy height statistics, 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
Summary
Health changes of temperate forests in the northern hemisphere, of concern to both foresters and ecologists, may be anthropogenic in origin or part of the natural cycle of the forest. Whether or not decline is observed, forests are often monitored by conducting in situ surveys to acquire various structural metrics. This process is time-consuming, expensive and typically of limited extent. Spectral remote sensor data are a fraction of the cost per km of small-footprint LIDAR, so a method of measuring biophysical variables using spectral remote sensing would allow large geographic coverage at lower cost. Such a method might be used to target more intensive sampling efforts resulting in a more efficient use of limited resources
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