English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Forest gaps are important structural elements in forest ecology to which variousconservation-relevant, photophilic species are associated. To automatically map forest gaps and detecttheir changes over time, we developed a method based on Digital Surface Models (DSM) derived fromstereoscopic aerial imagery and a LiDAR-based Digital Elevation Model (LiDAR DEM). Gaps weredetected and delineated in relation to height and cover of the surrounding forest comparing datafrom two public ight campaigns (2009 and 2012) in a 1023-ha model region in the Northern BlackForest, Southwest Germany. The method was evaluated using an independent validation datasetobtained by visual stereo-interpretation. Gaps were automatically detected with an overall accuracyof 0.90 (2009) and 0.82 (2012). However, a very high user’s accuracy of more than 0.95 (both years)was counterbalanced by a producer’s accuracy of 0.84 (2009) and 0.72 (2012) as some gaps were notautomatically detected. Accuracy was mainly dependent on the shadow occurrence and height ofthe surrounding forest with producer’s accuracies dropping to 0.70 (2009) and 0.52 (2012) in highstands (>8 m tree height). As one important step in the workow, the class of open forest, an importantfeature 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 orpartial shadow and geometric limitations of stereo image matching were identied as the main sourcesof errors in the method performance, suggesting that images with a higher overlap and resolution andameliorated image-matching algorithms provide the greatest potential for improvement.3.3.1. Mapping AccuracyThe comparison of automatic gap-detection with the visually identied gaps revealed goodagreement (Tables 5 and 6) with an overall accuracy of 0.90 and 0.82 in 2009 and 2012, respectively,

remote sensing

Erratum: 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