Exploring horizontal area-based metrics to discriminate the spatial pattern of trees and need for first thinning using airborne laser scanning

Pippuri, Inka; Kallio, Eveliina; Maltamo, Matti; Peltola, Heli; Packalén, Petteri

doi:10.1093/forestry/cps005

Cited by 22 publications

(23 citation statements)

References 19 publications

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“…The textural features of the present study were computed from the interpolated CHMs, aiming to improve the characterization of horizontal canopy conditions in ALS forest inventories. From a forest management planning perspective, the horizontal information in terms of the stem density and clustering is related to the timing of thinnings, and the inclusion of the textural features is proposed to improve these decisions [19]. Although Ozdemir and Donoghue [34] and Wood et al [71] indicated a potential to link the textural features with the diversity of the tree size and vegetation structure, respectively, our results did not support this.…”

Section: Discussioncontrasting

confidence: 60%

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. 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.…”

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: Discussioncontrasting

confidence: 60%

Section: Patch Featuresmentioning

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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“…Syrjänen et al 1994). In general clustered spatial patterns of trees make inventory more challenging (Pippuri et al 2012) which is consistent with the RMSE% levels of our study. On the other hand, it is worth noting that the clustered spatial pattern of aspens was also successfully identified from sample plots without the information of tree location.…”

Section: Discussionsupporting

confidence: 90%

Inventory of aspen trees in spruce dominated stands in conservation area

et al. 2015

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Background: The occurrence of aspen trees increases the conservation value of mature conifer dominated forests. Aspens typically occur as scattered individuals among major tree species, and therefore the inventory of aspens is challenging. Methods: We characterized aspen populations in a boreal nature reserve using diameter distribution, spatial pattern, and forest attributes: volume, number of aspens, number of large aspen stems and basal area median diameter. The data were collected from three separate forest stands in Koli National Park, eastern Finland. At each site, we measured breast height diameter and coordinates of each aspen. The comparison of inventory methods of aspens within the three stands was based on simulations with mapped field data. We mimicked stand level inventory by locating varying numbers of fixed area circular plots both systematically and randomly within the stands. Additionally, we also tested if the use of airborne laser scanning (ALS) data as auxiliary information would improve the accuracy of the stand level inventory by applying the probability proportional to size sampling to assist the selection of field plot locations. Results:The results showed that aspens were always clustered, and the diameter distributions indicated different stand structures in the three investigated forest stands. The reliability of the volume and number of large aspen trees varied from relative root mean square error figures above 50% with fewer sample plots (5-10) to values of 25%-50% with 10 or more sample plots. Stand level inventory estimates were also able to detect spatial pattern and the shape of the diameter distribution. In addition, ALS-based auxiliary information could be useful in guiding the inventories, but caution should be used when applying the ALS-supported inventory technique. Conclusions: This study characterized European aspen populations for the purposes of monitoring and management of boreal conservation areas. Our results suggest that if the number of sample plots is adequate, i.e. 10 or more stand level inventory will provide accurate enough forest attributes estimates in conservation areas (minimum accuracy requirement of RMSE% is 20%-50%). Even for the more ecologically valuable attributes, such as diameter distribution, spatial pattern and large aspens, the estimates are acceptable for conservation purposes.

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“…The European CORINE Land Cover (CLC2000) database in a 25 m grid can help in coarse assessments of some biophysical characteristics of the environment (Vihervaara et al 2010), but it cannot provide accurate information on local species assemblages and biotope types that form the basis of ecosystem service supply. More detailed tools, such as airborne laser scanning (ALS), are needed (Hill and Thomson 2005;Pesonen et al 2008;Vehmas et al 2009;Asner et al 2012;Vihervaara et al 2012;Pippuri et al 2012Pippuri et al , 2013. ALS has revolutionized the ways to measure forest structure in 3D.…”

Section: Introductionmentioning

confidence: 99%

How to integrate remotely sensed data and biodiversity for ecosystem assessments at landscape scale

et al. 2014

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Context Biodiversity and ecosystem functioning underpins the delivery of all ecosystem services and should be accounted for in all decision-making related to the use of natural resources and areas. However, biodiversity and ecosystem services are often inadequately accounted for in land use management decisions. Objective We studied a boreal forest ecosystem by linking citizen-science bird data with detailed information on forest characteristics from airborne laser scanning (ALS). In this paper, we describe this method, and evaluate how similar kinds of biological data sets combined with remote sensing can be used for ecosystem assessments at landscape scale. Methods We analysed data for 41 boreal forest bird species and for 14 structural ALS-based forest parameters.Results The results support the use of the selected method as a basis for quantifying spatially-explicit biodiversity indicators for ecosystem assessments, while suggestions for improvements are also reported. Finally, we evaluate the capacity of those indicators to describe biodiversity-ecosystem service relationships, for example with carbon trade-offs. The results showed clear distinctions between the different species as measured, for example, by above-ground forest biomass at the observation sites. We also assess how Electronic supplementary material The online version of this article

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Exploring horizontal area-based metrics to discriminate the spatial pattern of trees and need for first thinning using airborne laser scanning

Cited by 22 publications

References 19 publications

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

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

Inventory of aspen trees in spruce dominated stands in conservation area

How to integrate remotely sensed data and biodiversity for ecosystem assessments at landscape scale

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