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Erratum published on 12 May 2017, see Remote Sens. 2017, 9(5), 471.

Open AccessArticle
Remote Sens. 2016, 8(3), 175; doi:10.3390/rs8030175

Automated Detection of Forest Gaps in Spruce Dominated Stands Using Canopy Height Models Derived from Stereo Aerial Imagery

1
Department of Forest Nature Conservation, Forest Research Institute Baden-Württemberg (FVA), Wonnhaldestr. 4, D-79100 Freiburg, Germany
2
Department of Biometry and Information Sciences, Forest Research Institute Baden-Württemberg (FVA), Wonnhaldestr. 4, D-79100 Freiburg, Germany
3
Conservation Biology, Institute of Ecology and Evolution, University of Bern, Baltzerstrasse 6, CH-3012 Bern, Switzerland
*
Author to whom correspondence should be addressed.
Academic Editors: Susan L. Ustin, Randolph H. Wynne and Prasad S. Thenkabail
Received: 30 November 2015 / Revised: 29 January 2016 / Accepted: 9 February 2016 / Published: 25 February 2016
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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Abstract

Forest gaps are important structural elements in forest ecology to which various conservation-relevant, photophilic species are associated. To automatically map forest gaps and detect their changes over time, we developed a method based on Digital Surface Models (DSM) derived from stereoscopic aerial imagery and a LiDAR-based Digital Elevation Model (LiDAR DEM). Gaps were detected and delineated in relation to height and cover of the surrounding forest comparing data from two public flight campaigns (2009 and 2012) in a 1023-ha model region in the Northern Black Forest, Southwest Germany. The method was evaluated using an independent validation dataset obtained by visual stereo-interpretation. Gaps were automatically detected with an overall accuracy of 0.90 (2009) and 0.82 (2012). However, a very high users’ accuracy of more than 0.95 (both years) was counterbalanced by a producer’s accuracy of 0.84 (2009) and 0.73 (2012) as some gaps were not automatically detected. Accuracy was mainly dependent on the shadow occurrence and height of the surrounding forest with user’s accuracies dropping to 0.70 (2009) and 0.52 (2012) in high stands (>8 m tree height). As one important step in the workflow, the class of open forest, an important feature for many forest species, was delineated with a very good overall accuracy of 0.92 (both years) with uncertainties occurring mostly in areas with intermediate canopy cover. Presence of complete or partial shadow and geometric limitations of stereo image matching were identified as the main sources of errors in the method performance, suggesting that images with a higher overlap and resolution and ameliorated image-matching algorithms provide the greatest potential for improvement. View Full-Text
Keywords: aerial imagery; RGBI; photogrammetry; DSM; nDSM; CHM; LiDAR DEM; forest gap; canopy opening; canopy cover aerial imagery; RGBI; photogrammetry; DSM; nDSM; CHM; LiDAR DEM; forest gap; canopy opening; canopy cover
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zielewska-Büttner, K.; Adler, P.; Ehmann, M.; Braunisch, V. Automated Detection of Forest Gaps in Spruce Dominated Stands Using Canopy Height Models Derived from Stereo Aerial Imagery. Remote Sens. 2016, 8, 175.

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