Open AccessThis article is
- freely available
Remote Sensing of Vegetation Structure Using Computer Vision
Department of Geography and Environmental Systems, University of Maryland, Baltimore County, 211 Sondheim Hall, 1000 Hilltop Circle, Baltimore, MD 21250, USA
* Author to whom correspondence should be addressed.
Received: 2 March 2010; in revised form: 1 April 2010 / Accepted: 18 April 2010 / Published: 21 April 2010
Abstract: High spatial resolution measurements of vegetation structure in three-dimensions (3D) are essential for accurate estimation of vegetation biomass, carbon accounting, forestry, fire hazard evaluation and other land management and scientific applications. Light Detection and Ranging (LiDAR) is the current standard for these measurements but requires bulky instruments mounted on commercial aircraft. Here we demonstrate that high spatial resolution 3D measurements of vegetation structure and spectral characteristics can be produced by applying open-source computer vision algorithms to ordinary digital photographs acquired using inexpensive hobbyist aerial platforms. Digital photographs were acquired using a kite aerial platform across two 2.25 ha test sites in Baltimore, MD, USA. An open-source computer vision algorithm generated 3D point cloud datasets with RGB spectral attributes from the photographs and these were geocorrected to a horizontal precision of <1.5 m (root mean square error; RMSE) using ground control points (GCPs) obtained from local orthophotographs and public domain digital terrain models (DTM). Point cloud vertical precisions ranged from 0.6 to 4.3 m RMSE depending on the precision of GCP elevations used for geocorrection. Tree canopy height models (CHMs) generated from both computer vision and LiDAR point clouds across sites adequately predicted field-measured tree heights, though LiDAR showed greater precision (R2 > 0.82) than computer vision (R2 > 0.64), primarily because of difficulties observing terrain under closed canopy forest. Results confirm that computer vision can support ultra-low-cost, user-deployed high spatial resolution 3D remote sensing of vegetation structure.
Keywords: vegetation biomass; vegetation carbon; canopy height models; bundle adjustment; Bundler; LiDAR; 3D; carbon; forestry; Ecosynth
Article StatisticsClick here to load and display the download statistics.
Notes: Multiple requests from the same IP address are counted as one view.
Cite This Article
MDPI and ACS Style
Dandois, J.P.; Ellis, E.C. Remote Sensing of Vegetation Structure Using Computer Vision. Remote Sens. 2010, 2, 1157-1176.
Dandois JP, Ellis EC. Remote Sensing of Vegetation Structure Using Computer Vision. Remote Sensing. 2010; 2(4):1157-1176.
Dandois, Jonathan P.; Ellis, Erle C. 2010. "Remote Sensing of Vegetation Structure Using Computer Vision." Remote Sens. 2, no. 4: 1157-1176.