Abstract
Few studies have evaluated the precision of IKONOS stereo data for measuring forest canopy height. The high cost of airborne light detection and ranging (LiDAR) data collection for large area studies and the present lack of a spaceborne instrument lead to the need to explore other low cost options. The US Government currently has access to a large archive of commercial high-resolution imagery, which could be quite valuable to forest structure studies. At 1 m resolution, we here compared canopy height models (CHMs) and height data derived from Goddard’s airborne LiDAR Hyper-spectral and Thermal Imager (G-LiHT) with three types of IKONOS stereo derived digital surface models (DSMs) that estimate CHMs by subtracting National Elevation Data (NED) digital terrain models (DTMs). We found the following in three different forested regions of the US after excluding heterogeneous and disturbed forest samples: (1) G-LiHT DTMs were highly correlated with NED DTMs with R2 > 0.98 and root mean square errors (RMSEs) < 2.96 m; (2) when using one visually identifiable ground control point (GCP) from NED, G-LiHT DSMs and IKONOS DSMs had R2 > 0.84 and RMSEs of 2.7 to 4.1 m; and (3) one GCP CHMs for two study sites had R2 > 0.7 and RMSEs of 2.6 to 3 m where data were collected less than four years apart. Our results suggest that IKONOS stereo data are a useful LiDAR alternative where high-quality DTMs are available.
Highlights
Forests contain the largest proportion of above ground biomass in terrestrial ecosystems and are subject to natural and human induced disturbances that can reduce their carbon (C) storage [1]
We evaluated two existing stereo forest canopy height model (CHM) approaches, one without ground control points (GCPs) and one with GCPs as others have shown substantial improvement in IKONOS mapping accuracy with
We evaluated whether forest height estimates could be improved with GCPs derived from National Elevation Data (NED) digital terrain models (DTMs) data in bare earth locations and compared results to airborne Light Detection And Ranging (LiDAR)
Summary
Forests contain the largest proportion of above ground biomass in terrestrial ecosystems and are subject to natural and human induced disturbances that can reduce their carbon (C) storage [1]. The ongoing rise of anthropogenic C emissions has influenced ecosystem functioning [2,3], and forests should be monitored and evaluated for changes in above ground biomass, canopy cover, and other structural parameters. RAdio Detection And Ranging (RADAR), Light Detection And Ranging (LiDAR), and photogrammetric methods using stereo imagery, have all been used to measure forest structure and each approach has varying accuracies and implementation costs [4,6]. We evaluated two existing stereo forest canopy height model (CHM) approaches, one without ground control points (GCPs) and one with GCPs as others have shown substantial improvement in IKONOS mapping accuracy with
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