Conference paper, 2015
Estimating vertical canopy cover with dense point cloud data from matching of digital aerial photosGranholm, Ann-Helen; Lindgren, Nils; Olofsson, Kenneth; Allard, Anna; Olsson, Håkan
AbstractThis study aims to explore the use of dense point clouds from matching of aerial photos for estimation of vertical canopy cover (VCC), defined as the proportion of the forest floor covered by the vertical projection of the tree crowns. VCC is commonly estimated using vegetation ratio (VR) derived from airborne laser scanner (ALS) data. A reliable measure of VCC from matching aerial photos would aid in vegetation mapping and reduce the need for repeated ALS data acquisition. The test area is located in southern Sweden and covers a variety of vegetation types. In total 367 sample plots were placed in parts of the study area representing VCC ranging from 0 % up to close to 100 %. ALS data with a density of 20 returns per m2 was used for calculating the VR as the proportion of first returns above a threshold. Aerial imagery with a ground sample distance of 0.25 m was matched to produce dense point cloud data, which was used to derive digital surface models (DSMs) with grid size from 0.25 m up to 2.0 m. Local maxima (LM) detection was applied to the DSMs with search windows of 0.5 m size up to 2.0 m. The heights of the LM were normalized using a digital elevation model (DEM) derived from ALS data. Regression analysis was applied with the VR as dependent variable and the sum of the height of LM within sample plots as independent variable. Results from linear regression using heights of LM detected in a DSM of 0.25 m resolution with a 0.5 m search window gave an root mean square error (RMSE) of 5.5 % and relative RMSE (rRMSE) of 9.3 % in forest on rocky outcrops and boulders, while wooded pasture gave RMSE = 6.3 % and rRMSE = 19 %.
Keywordscanopy cover, photogrammetry, aerial image, laser
Publisher: European Association of Remote Sensing Laboratories
ConferenceThe 35th EARSel Symposium European Remote Sensing: Progress, Challenges and Opportunities
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