Modelling tree size diversity from airborne laser scanning using canopy height models with image texture measures

Özdemir, İbrahim; Donoghue, Daniel N.M.

doi:10.1016/j.foreco.2012.12.044

Cited by 51 publications

(26 citation statements)

References 45 publications

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“…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. The textural features were poorly correlated with the coefficients of variation of the DBH and G values as well as with the number of understory trees.…”

Section: Discussionmentioning

confidence: 59%

“…The main attention was focused on central attributes related to the properties of the forest growing stock, i.e., total stem volume (V), basal area (G), and basal-area weighted mean diameter (D gM ). Due to the earlier reported potential to link the textural features with the diversity of the tree size and vegetation structure [34,71], we additionally analyzed the relationships between the textural features and the number of understory trees and variation of the DBH and G observed from within the 9 m plots and among the satellite plots, respectively.…”

Section: Supervised Prediction Of Forest Attributesmentioning

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: 59%

Section: Supervised Prediction Of Forest Attributesmentioning

confidence: 99%

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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“…relative dispersion (Hosking, 1990). The attention has recently been turned to studying tree size inequality by L-moments, especially with regards to their relations with ALS datasets (Ozdemir and Donoghue, 2013;Valbuena et al, 2013b). Furthermore, the coefficient of LA developed by Damgaard and Weiner (2000) was employed by Valbuena et al (2013a) for characterizing the relation between dominant and subdominant cohorts in multi-layered forests.…”

Section: Introductionmentioning

confidence: 99%

Comparison of airborne laser scanning methods for estimating forest structure indicators based on Lorenz curves

Valbuena

Vauhkonen

Packalén

et al. 2014

ISPRS Journal of Photogrammetry and Remote Sensing

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The purpose of this study was to compare a number of state-of-the-art methods in airborne laser scan-ning (ALS) remote sensing with regards to their capacity to describe tree size inequality and other indi-cators related to forest structure. The indicators chosen were based on the analysis of the Lorenz curve: Gini coefficient (GC), Lorenz asymmetry (LA), the proportions of basal area (BALM) and stem density (NSLM) stocked above the mean quadratic diameter. Each method belonged to one of these estimation strategies: (A) estimating indicators directly; (B) estimating the whole Lorenz curve; or (C) estimating a complete tree list. Across these strategies, the most popular statistical methods for area-based approach (ABA) were used: regression, random forest (RF), and nearest neighbour imputation. The latter included distance metrics based on either RF (NN-RF) or most similar neighbour (MSN). In the case of tree list esti-mation, methods based on individual tree detection (ITD) and semi-ITD, both combined with MSN impu-tation, were also studied. The most accurate method was direct estimation by best subset regression, which obtained the lowest cross-validated coefficients of variation of their root mean squared error CV(RMSE) for most indicators: GC (16.80%), LA (8.76%), BALM (8.80%) and NSLM (14.60%). Similar figures [CV(RMSE) 16.09%, 10.49%, 10.93% and 14.07%, respectively] were obtained by MSN imputation of tree lists by ABA, a method that also showed a number of additional advantages, such as better distributing the residual variance along the predictive range. In light of our results, ITD approaches may be clearly inferior to ABA with regards to describing the structural properties related to tree size inequality in for-ested areas.

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“…In addition to UAVs, other remote sensing data sets have also been used in estimating structural diversity. In [70], plot-level tree size diversity was modelled through ALS-based features, whereas in [71], structural diversity of urban forests was estimated with various satellite imagery. Both studies conclude that the used remote sensing data sets can be utilized in characterizing structural diversity.…”

Section: Discussionmentioning

confidence: 99%

Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

et al. 2018

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Forests are the most diverse terrestrial ecosystems and their biological diversity includes trees, but also other plants, animals, and micro-organisms. One-third of the forested land is in boreal zone; therefore, changes in biological diversity in boreal forests can shape biodiversity, even at global scale. Several forest attributes, including size variability, amount of dead wood, and tree species richness, can be applied in assessing biodiversity of a forest ecosystem. Remote sensing offers complimentary tool for traditional field measurements in mapping and monitoring forest biodiversity. Recent development of small unmanned aerial vehicles (UAVs) enable the detailed characterization of forest ecosystems through providing data with high spatial but also temporal resolution at reasonable costs. The objective here is to deepen the knowledge about assessment of plot-level biodiversity indicators in boreal forests with hyperspectral imagery and photogrammetric point clouds from a UAV. We applied individual tree crown approach (ITC) and semi-individual tree crown approach (semi-ITC) in estimating plot-level biodiversity indicators. Structural metrics from the photogrammetric point clouds were used together with either spectral features or vegetation indices derived from hyperspectral imagery. Biodiversity indicators like the amount of dead wood and species richness were mainly underestimated with UAV-based hyperspectral imagery and photogrammetric point clouds. Indicators of structural variability (i.e., standard deviation in diameter-at-breast height and tree height) were the most accurately estimated biodiversity indicators with relative RMSE between 24.4% and 29.3% with semi-ITC. The largest relative errors occurred for predicting deciduous trees (especially aspen and alder), partly due to their small amount within the study area. Thus, especially the structural diversity was reliably predicted by integrating the three-dimensional and spectral datasets of UAV-based point clouds and hyperspectral imaging, and can therefore be further utilized in ecological studies, such as biodiversity monitoring.

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Modelling tree size diversity from airborne laser scanning using canopy height models with image texture measures

Cited by 51 publications

References 45 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

Comparison of airborne laser scanning methods for estimating forest structure indicators based on Lorenz curves

Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

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