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

Zielewska-Büttner, Katarzyna; Adler, Petra; Ehmann, Michaela; Braunisch, Veronika

doi:10.3390/rs8030175

Cited by 45 publications

(45 citation statements)

References 45 publications

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“…For instance, CHM can be used for change detection, canopy gap dynamic, and single tree detection (Pitkänen et al 2004, Koch et al 2006, Zielewska-Büttner et al 2016. Height and density metrics derived from CHMs at plot level can be used for the assessment of forest height, forest timber volume, biomass, basal area and mean diameter-at-breastheight point (DBH) using the area-based approach (ABA - Bohlin et al 2012, Naesset 2004, Ota et al 2015, Rahlf et al 2014, Straub et al 2013a, White et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Comparing image-based point clouds and airborne laser scanning data for estimating forest heights

Ullah¹,

Adler²,

Dees³

et al. 2017

iForest

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Accurate and updated knowledge of forest tree heights is fundamental in the context of forest management. However, measuring canopy height over large forest areas using traditional inventory techniques is laborious, time-consuming and excessively expensive. In this study, image-based point clouds produced from stereo aerial photographs (AP) were used to estimate forest height, and compared to Airborne Laser Scanning (ALS) data. We generated image-based Canopy Height Models (CHM) using different image-matching algorithms (SGM: Semi-Global Matching; eATE: enhanced Automatic Terrain Extraction), which were compared with a pure ALS-derived CHM. Additionally, plotlevel height and density metrics were extracted from CHMs and used as explanatory variables for predicting the Lorey's mean height (LMH), which was measured at 296 reference points on the ground. CHMSGM and CHMALS showed similar results in predicting LMH at sample plot locations (RMSE% = 8.54 vs. 7.92, respectively), while CHMeATE had lower accuracy (RMSE% = 13.23). Similarly, CHMSGM showed a lower normalized median absolute deviation (NMAD) from CHMALS (0.68 m) compared to CHMeATE (1.1 m). Our study revealed that image-based point clouds using SGM in the presence of high-resolution ALSderived digital terrain model (DTM) provide comparable results with ALS data, while the performance of image-based point clouds using eATE is poorer than ALS for forest height estimation. The findings of this study provide a viable and cost-effective option for assessing height-related forest structural parameters. The proposed methodology can be usefully applied in all those countries where AP are updated on a regular basis and pre-existing historical ALS-derived DTMs are available.

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Section: Introductionmentioning

confidence: 99%

Comparing image-based point clouds and airborne laser scanning data for estimating forest heights

Ullah¹,

Adler²,

Dees³

et al. 2017

iForest

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“…In recent years, forest growth models [6], forest growth analysis [7], and remote sensing methods [8] have increasingly been used, and as a result have helped to further define the relevant principles of sustainable forest management [9,10].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Forest Management in Protected Areas Based on Multidisciplinary Research

Machar

Simon

Rejšek

et al. 2016

Forests

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Abstract:The remnants of primeval Norway spruce forests in the European temperate zone are crucial for maintaining forest biodiversity in high mountain landscapes. This paper presents results of a multidisciplinary research and evaluation project on the management practices for mountain spruce forests in the Natura 2000 site (National Nature Reserve Serak-Keprnik in the Hruby Jesenik Mountains, the Czech Republic). Results are based on combining research on the historical development of the forest ecosystem and predictions of future dynamics using a forest growth simulation model. The presented results show that a non-intervention management strategy for mountain spruce forest in the next 50 years complies with the Natura 2000 requirement to maintain the existing character of the forest habitat. Thus, the results indicate that the current management plan for the spruce forests does not require significant corrections in the context of its conservation goals (i.e., maintaining biodiversity and current character of the forest ecosystem dominated by Norway spruce). The results of this study suggest that combining the knowledge of historical development with forest inventory data using forest growth simulation represents a suitable support tool for the assessment of management practices for forest habitats in protected areas.

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“…The authors would like to correct Tables 6 and 8 and the relevant text of this article [1] as follows, as the column values in the tables were unintentionally exchanged. Please also note an updated correspondence E-mail.…”

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confidence: 99%

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

et al. 2017

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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 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 not automatically detected. Accuracy was mainly dependent on the shadow occurrence and height of the surrounding forest with producer'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. Mapping AccuracyThe comparison of automatic gap-detection with the visually identified gaps revealed good agreement (Tables 5 and 6) with an overall accuracy of 0.90 and

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

Cited by 45 publications

References 45 publications

Comparing image-based point clouds and airborne laser scanning data for estimating forest heights

Comparing image-based point clouds and airborne laser scanning data for estimating forest heights

Assessment of Forest Management in Protected Areas Based on Multidisciplinary Research

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

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