Estimating vertical canopy cover using dense image-based point cloud data in four vegetation types in southern Sweden

Granholm, Ann-Helen; Lindgren, Nils; Olofsson, Kenneth; Nyström, Mattias; Allard, Anna; Olsson, Håkan

doi:10.1080/01431161.2017.1283074

Cited by 19 publications

(11 citation statements)

References 34 publications

(56 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The inter-comparison of selected algorithms for the purposes of producing point clouds for forest attribute prediction has typically focused on a comparison of two software packages, rather than a systematic evaluation. Ullah et al [48] and Kukkonen et al [68] compared software in the context of the ABA for forest attributes, for canopy cover prediction by Granholm et al [76], and for miscellaneous targets in Remondino et al [45]. Both Ullah et al [48] and Kukkonen et al [68] found that data derived from image-matching techniques were capable of predicting forest inventory attributes with comparable accuracies to those from ALS, which is the consensus from other comparative analyses [42,58,77].…”

Section: Image-matching Algorithmsmentioning

confidence: 90%

“…They found negligible differences in generated digital surface models and indicated that both algorithms were capable, accurate, and consistent (±~2% RMSE for all attributes) at providing forest attribute predictions with the pre-condition that an ALS DTM was available. Granholm et al [76] compared the MATCH-T and SURE algorithms for estimating vertical canopy cover and found differences in point cloud outputs, but not in generated metrics. All studies, however, were cautious in their recommendation of a particular algorithm due to the potential differences that could arise from software tuning, forest type, and solar illumination.…”

Section: Image-matching Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review of Opportunities, Challenges, and Future Directions

2019

View full text Add to dashboard Cite

Purpose of Review Three-dimensional (3D) data on forest structure have transformed the level of detail and accuracy of forest information. While these 3D data have primarily been derived from airborne laser scanning (ALS), there has been growing interest in the use of 3D data derived from digital aerial photogrammetry (DAP) and image-matching algorithms. In particular, research and operational forestry communities are interested in using DAP data to update existing ALS-derived enhanced forest inventories. Although DAP depends on accurate terrain information provided by ALS to normalize digital surface models to heights above ground, in an inventory update scenario, DAP data currently have cost advantages over repeat ALS acquisitions. Recent Findings Extensive research across a broad range of forest types has demonstrated that DAP data can provide comparable accuracies to ALS for estimating inventory attributes such as volume, basal area, and height when used in an area-based approach with co-located ground plot information. Summary Herein, we review research relevant to the use of DAP for updating area-based forest inventories in subsequent inventory cycles, highlighting issues and opportunities for DAP data in this context. We examine the use of DAP for areabased forest inventory applications, comparing data inputs, algorithms, and outcomes across numerous studies and forest environments. Lastly, we outline outstanding research gaps that require further inquiry including benchmarking of acquisition parameters and image-matching algorithms. Keywords Digital aerial photogrammetry. Image-matching. Airborne laser scanning. Forest inventory update. Digital stereo imagery. Forest structure. Image based point clouds This article is part of the Topical Collection on Remote Sensing

show abstract

Section: Image-matching Algorithmsmentioning

confidence: 90%

Section: Image-matching Algorithmsmentioning

confidence: 99%

Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review of Opportunities, Challenges, and Future Directions

2019

View full text Add to dashboard Cite

show abstract

“…UAS products commonly use cm-level resolution and have high accuracy [19]. During recent years, the use of UAS in forestry has increased rapidly due to the advantages of low-cost, flexibility, and repeatability, for example in forest inventory parameters (e.g., tree location, tree height, crown width, and volume) estimation [20,21], forest change and recovery monitoring [22,23], canopy cover estimation [24,25], and individual tree crown segmentation [26,27]. UAS makes the on-demand acquisition of multiple temporal and high spatial resolution imagery possible [28].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Forest Structural Attributes Using Spectral Indices and Point Clouds from UAS-Based Multispectral and RGB Imageries

et al. 2019

View full text Add to dashboard Cite

Forest structural attributes are key indicators for parameterization of forest growth models, which play key roles in understanding the biophysical processes and function of the forest ecosystem. In this study, UAS-based multispectral and RGB imageries were used to estimate forest structural attributes in planted subtropical forests. The point clouds were generated from multispectral and RGB imageries using the digital aerial photogrammetry (DAP) approach. Different suits of spectral and structural metrics (i.e., wide-band spectral indices and point cloud metrics) derived from multispectral and RGB imageries were compared and assessed. The selected spectral and structural metrics were used to fit partial least squares (PLS) regression models individually and in combination to estimate forest structural attributes (i.e., Lorey’s mean height (HL) and volume(V)), and the capabilities of multispectral- and RGB-derived spectral and structural metrics in predicting forest structural attributes in various stem density forests were assessed and compared. The results indicated that the derived DAP point clouds had perfect visual effects and that most of the structural metrics extracted from the multispectral DAP point cloud were highly correlated with the metrics derived from the RGB DAP point cloud (R2 > 0.75). Although the models including only spectral indices had the capability to predict forest structural attributes with relatively high accuracies (R2 = 0.56–0.69, relative Root-Mean-Square-Error (RMSE) = 10.88–21.92%), the models with spectral and structural metrics had higher accuracies (R2 = 0.82–0.93, relative RMSE = 4.60–14.17%). Moreover, the models fitted using multispectral- and RGB-derived metrics had similar accuracies (∆R2 = 0–0.02, ∆ relative RMSE = 0.18–0.44%). In addition, the combo models fitted with stratified sample plots had relatively higher accuracies than those fitted with all of the sample plots (∆R2 = 0–0.07, ∆ relative RMSE = 0.49–3.08%), and the accuracies increased with increasing stem density.

show abstract

“…Analysing trends in the experiments, better results and relationships between direct measurements from a botanical campaign were for individuals with a large crown and great crown density, depending on the tree and shrub species. Granholm et al [58] also indicated that the accuracy of tree heights estimated using semiglobal matching (SGM) is low when canopy cover values are low. Regarding LIDAR technology, which also provides a CHM, Leckie et al [59] achieved better results, similar to terrain measurements, when the tree density is higher.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Using Dense Image Matching Techniques to Study the Process of Secondary Succession in Non-Forest Natura 2000 Habitats

et al. 2019

View full text Add to dashboard Cite

Secondary succession is considered a threat to non-forest Natura 2000 habitats. Currently available data and techniques such as airborne laser scanning (ALS) data processing can be used to study this process. Thanks to these techniques, information about the spatial extent and the height of research objects—trees and shrubs—can be obtained. However, only archival aerial photographs can be used to conduct analyses of the stage of succession process that took place in the 1960s or 1970s. On their basis, the extent of trees and shrubs can be determined using photointerpretation, but height information requires stereoscopic measurements. State-of-the-art dense image matching (DIM) algorithms provide the ability to automate this process and create digital surface models (DSMs) that are much more detailed than ones obtained using image matching techniques developed a dozen years ago. This research was part of the HabitARS project on the Ostoja Olsztyńsko-Mirowska Natura 2000 protected site (PLH240015). The source data included archival aerial photographs (analogue and digital) acquired from various phenological periods from 1971–2015, ALS data from 2016, and data from botanical campaigns. First, using the DIM algorithms, point clouds were generated and converted to DSMs. Heights interpolated from the DSMs were compared with stereoscopic measurements (1971–2012) and ALS data (2016). Then, the effectiveness of tree and shrub detection was analysed, considering the relationship between the date and the parameters of aerial images acquisition and DIM effects. The results showed that DIM can be used successfully in tree and shrub detection and monitoring, but the source images must meet certain conditions related to their quality. Based on the extensive material analysed, the detection of small trees and shrubs in aerial photographs must have a scale greater than 1:13,000 or a 25 cm GSD (Ground Sample Distance) at most, an image acquisition date from June–September (the period of full foliage in Poland), and good radiometric quality.

show abstract

Estimating vertical canopy cover using dense image-based point cloud data in four vegetation types in southern Sweden

Cited by 19 publications

References 34 publications

Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review of Opportunities, Challenges, and Future Directions

Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review of Opportunities, Challenges, and Future Directions

Estimation of Forest Structural Attributes Using Spectral Indices and Point Clouds from UAS-Based Multispectral and RGB Imageries

Analysis of Using Dense Image Matching Techniques to Study the Process of Secondary Succession in Non-Forest Natura 2000 Habitats

Contact Info

Product

Resources

About