Object-based semi-automatic approach for forest structure characterization using lidar data in heterogeneous Pinus sylvestris stands

Pascual, Cristina; García‐Abril, Antonio; García-Montero, Luis Gonzaga; Martín-Fernández, Susana; Cohen, Warren B.

doi:10.1016/j.foreco.2008.02.055

Cited by 78 publications

(62 citation statements)

References 25 publications

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“…The approach provided a data-driven partitioning of the feature space to the given number of clusters. As there are no objective criteria for determining the number of clusters [74], we followed an earlier example on determining this number [59]. By initially experimenting with different numbers of clusters, we found that 4-7 structural classes could be separated based on our study area and data.…”

Section: Discussionmentioning

confidence: 96%

“…Earlier studies have suggested that the information in the ALS data may be condensed to a few metrics [72,73], the partitioning of which will provide a stratification corresponding closely to the structural complexity observed in the field [59,74,75]. In this study, a similar partitioning was carried out using the textural features, and the applicability of the obtained information was demonstrated by prioritizing the field plots to be measured for predicting plot V using other features.…”

Section: Unsupervised Classification Of the Forested Areamentioning

confidence: 95%

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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: 96%

Section: Unsupervised Classification Of the Forested Areamentioning

confidence: 95%

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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“…For this, two methodologies can be developed (Hyyppä et al, 2008): estimation of dasometric variables calculating regression models between field data and statistics derived from LiDAR data in plots-stands (Nelson et al, 1998;Naesset et al, 2004;Pascual et al, 2008;Li et al,2008;García et al,2010); and studies focused on the estimation of dendrometric variables being necessary a previous delineation of the tree crown (Hyyppä et al, 2001;Holmgren et al, 2003;Persson et al, 2002;Maltamo et al, 2004;Popescu, 2007;Forzieri et al, 2009). In contrast, little research has been conducted in shrub areas because their inherent difficulties (low height and uniform surface).…”

Section: Introductionmentioning

confidence: 99%

Estimation of shrub biomass by airborne LiDAR data in small forest stands

Estornell

Velázquez‐Martí

Fernández-Sarría

2011

Forest Ecology and Management

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The presence of shrub vegetation is very significant in Mediterranean ecosystems.However, the difficulty involved in shrub management and the lack of information about behavior of this vegetation means that these areas are often left out of spatial planning projects. Airborne LiDAR (Light Detection And Ranging) has been used successfully in forestry to estimate dendrometric and dasometric variables that allow to characterize forest structure. In contrast, little research has focused on shrub vegetation.

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“…It covers an area of 96.04 ha (40°45′ N, 4°5′ W), and has altitudes ranging from 1310 to 1790 m, an average annual temperature of 9.4 °C, and average annual rainfall of 1180 mm. Further information can be found in [53]. The strategy consisted of encouraging participation if empathy among participants increased.…”

Section: Case Study and Resultsmentioning

confidence: 99%

Validation of a Methodology for Confidence-Based Participatory Forest Management

Martínez-Falero

González-García

García‐Abril

et al. 2018

Forests

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This paper formulates a new strategy for participatory forest management consisting of encouraging public participation as long as it increases empathy among participants. The strategy requires the homogeneous representation of the opinion of a participant (i.e., to determine how they assess a forest plan and identify the best one). Utility assessments are prepared for participants through pair-comparisons between meaningful points in the territory and from value functions based on forest indicators. The best plan is designed by applying combinatorial optimization algorithms to the utility of a participant. The calculating of empathy-of one participant relative to another-is based on the equivalence of their respective utilities when the current forest plan is modified. This involves calculating the opinions that are due to systematic changes in the collective plan for those participants that each participant supposes will affect the utility of the other participants. Calculating empathy also requires knowing the interactions among participants, which have been incorporated through agent-based simulation models. Application of the above methodology has confirmed the association between increases in empathy and convergence of opinions in different scenarios: well and medium-informed participants and with and without interaction among them, which verifies the proposed strategy. In addition, this strategy is easily integrated into available information systems and its outcomes show advantages over current participatory applications.

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Object-based semi-automatic approach for forest structure characterization using lidar data in heterogeneous Pinus sylvestris stands

Cited by 78 publications

References 25 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

Estimation of shrub biomass by airborne LiDAR data in small forest stands

Validation of a Methodology for Confidence-Based Participatory Forest Management

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