Next Article in Journal
Potential of Using Remote Sensing Techniques for Global Assessment of Water Footprint of Crops
Next Article in Special Issue
Forecasting Areas Vulnerable to Forest Conversion in the Tam Dao National Park Region, Vietnam
Previous Article in Journal
Ten Years of SeaWinds on QuikSCAT for Snow Applications
Previous Article in Special Issue
Using Spatial Structure Analysis of Hyperspectral Imaging Data and Fourier Transformed Infrared Analysis to Determine Bioactivity of Surface Pesticide Treatment
Remote Sens. 2010, 2(4), 1157-1176; doi:10.3390/rs2041157

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 / Revised: 1 April 2010 / Accepted: 18 April 2010 / Published: 21 April 2010
(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)
View Full-Text   |   Download PDF [6930 KB, uploaded 19 June 2014]   |   Browse Figures


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 vegetation biomass; vegetation carbon; canopy height models; bundle adjustment; Bundler; LiDAR; 3D; carbon; forestry; Ecosynth
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote |
MDPI and ACS Style

Dandois, J.P.; Ellis, E.C. Remote Sensing of Vegetation Structure Using Computer Vision. Remote Sens. 2010, 2, 1157-1176.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here


[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert