Predicting the spatial pattern of trees by airborne laser scanning

Packalén, Petteri; Vauhkonen, Jari; Kallio, Eveliina; Peuhkurinen, Jussi; Pitkänen, Juho; Pippuri, Inka; Strunk, Jacob L.; Maltamo, Matti

doi:10.1080/01431161.2013.787501

Cited by 44 publications

(49 citation statements)

References 18 publications

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“…However, there is no evidence that the spatial pattern of the trees could be correctly classified based on remotely sensed data [20]. Even if such a classification were useful for forestry applications, departures from the assumptions could cause severe error trends [82].…”

Section: Discussionmentioning

confidence: 99%

“…In [19], a threshold of 5 m was used to assign all CHM values above that height as tree patches. In [20], an adaptive difference between the maximum height (h max ) and the standard deviation of the height values (h std ) was reported as the best canopy threshold for determining the spatial pattern of trees. Both the aforementioned constant and adaptive thresholds were tested in this study, and the patch features described in Table 3 were extracted from the thresholded CHMs.…”

Section: Patch Featuresmentioning

confidence: 99%

“…The spatial pattern of trees was modeled by thresholding CHMs for representing ground and tree patches [19,20]. In [19], a threshold of 5 m was used to assign all CHM values above that height as tree patches.…”

Section: Patch Featuresmentioning

confidence: 99%

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Extracting Canopy Surface Texture from Airborne Laser Scanning Data for the Supervised and Unsupervised Prediction of Area-Based Forest Characteristics

Niemi

2016

Remote Sensing

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Abstract:Area-based analyses of airborne laser scanning (ALS) data are an established approach to obtain wall-to-wall predictions of forest characteristics for vast areas. The analyses of sparse data in particular are based on the height value distributions, which do not produce optimal information on the horizontal forest structure. We evaluated the complementary potential of features quantifying the textural variation of ALS-based canopy height models (CHMs) for both supervised (linear regression) and unsupervised (k-Means clustering) analyses. Based on a comprehensive literature review, we identified a total of four texture analysis methods that produced rotation-invariant features of different order and scale. The CHMs and the textural features were derived from practical sparse-density, leaf-off ALS data originally acquired for ground elevation modeling. The features were extracted from a circular window of 254 m 2 and related with boreal forest characteristics observed from altogether 155 field sample plots. Features based on gray-level histograms, distribution of forest patches, and gray-level co-occurrence matrices were related with plot volume, basal area, and mean diameter with coefficients of determination (R 2 ) of up to 0.63-0.70, whereas features that measured the uniformity of local binary patterns of the CHMs performed poorer. Overall, the textural features compared favorably with benchmark features based on the point data, indicating that the textural features contain additional information useful for the prediction of forest characteristics. Due to the developed processing routines for raster data, the CHM features may potentially be extracted with a lower computational burden, which promotes their use for applications such as pre-stratification or guiding the field plot sampling based solely on ALS data.

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Section: Discussionmentioning

confidence: 99%

Section: Patch Featuresmentioning

confidence: 99%

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Extracting Canopy Surface Texture from Airborne Laser Scanning Data for the Supervised and Unsupervised Prediction of Area-Based Forest Characteristics

Niemi

2016

Remote Sensing

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“…Due to depicting solely canopy geometry, certain simplifications to the visualizing other elements were adopted. The number or the spatial pattern of the tree stems could not be precisely modeled, which is a typical result of ALS-based analyses (e.g., Packalén et al 2013). However, the landscape-level visualizations were found to be more realistic with at least some shadowing caused by the stems.…”

Section: Visualizing the Landscape And Harvests Of Canopy Biomassmentioning

confidence: 99%

“…All trees with either diameter at breast height (DBH) ≥ 4 cm or height ≥ 4 m were mapped for locations and measured for species, DBH, and height. The measurements are described in more detail by Packalén et al (2013). For simplicity, the plot size used in this study was standardized to 400 m 2 , rather than varying from 400 to 900 m 2 as in the earlier publications.…”

Section: Study Area and Datamentioning

confidence: 99%

Reconstructing forest canopy from the 3D triangulations of airborne laser scanning point data for the visualization and planning of forested landscapes

Ruotsalainen

2017

Annals of Forest Science

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& Key messageWe present a data-driven technique to visualize forest landscapes and simulate their future development according to alternative management scenarios. Gentle harvesting intensities were preferred for maintaining scenic values in a test of eliciting public's preferences based on the simulated landscapes. & Context Visualizations of future forest landscapes according to alternative management scenarios are useful for eliciting stakeholders' preferences on the alternatives. However, conventional computer visualizations require laborious tree-wise measurements or simulators to generate these observations. & Aims We describe and evaluate an alternative approach, in which the visualization is based on reconstructing forest canopy from sparse density, leaf-off airborne laser scanning data. & Methods Computational geometry was employed to generate filtrations, i.e., ordered sets of simplices belonging to the three-dimensional triangulations of the point data. An appropriate degree of filtering was determined by analyzing the topological persistence of the filtrations. The topology was further utilized to simulate changes to canopy biomass, resembling harvests with varying retention levels. Relative priorities of recreational and scenic values of the harvests were estimated based on pairwise comparisons and analytic hierarchy process (AHP). & Results The canopy elements were co-located with the tree stems measured in the field, and the visualizations derived from the entire landscape showed reasonably realistic, despite a low numerical correspondence with plot-level forest attributes. The potential and limitations to improve the proposed parameterization are discussed. & Conclusion Although the criteria to evaluate the landscape visualization and simulation models were not conclusive, the results suggest that forest scenes may be feasibly reconstructed based on data already covering broad areas and readily available for practical applications.

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