scholarly journals Erfassung struktureller Waldparameter mithilfe von flugzeuggetragenem Laserscanning | Deriving structural forest parameters using airborne laser scanning

2011 ◽  
Vol 162 (6) ◽  
pp. 164-170
Author(s):  
Felix Morsdorf
Keyword(s):  
Laser Scanning ◽  
Recognition Rate ◽  
Tree Height ◽  
Field Measurements ◽  
Airborne Laser Scanning ◽  
Ecosystem Research ◽  
Airborne Laser ◽  
Habitat Analysis ◽  
Ecosystem Studies ◽  
Individual Trees

Airborne laser scanning is a relatively young and precise technology to directly measure surface elevations. With today's high scanning rates, dense 3-D pointclouds of coordinate triplets (xyz) can be provided, in which many structural aspects of the vegetation are contained. The challenge now is to transform this data, as far as possible automatically, into manageable information relevant to the user. In this paper we present two such methods: the first extracts automatically the geometry of individual trees, with a recognition rate of over 70% and a systematic underestimation of tree height of only 0.6 metres. The second method derives a pixel map of the canopy density from the pointcloud, in which the spatial patterns of vegetation cover are represented. These patterns are relevant for habitat analysis and ecosystem studies. The values derived by this method correlate well with field measurements, giving a measure of certainty (R2) of 0.8. The greatest advantage of airborne laser scanning is that it provides spatially extensive, direct measurements of vegetation structure which show none of the extrapolation errors of spot measurements. A large challenge remains in integrating these new products into the user's processing chains and workflows, be it in the realm of forestry or in that of ecosystem research.

scholarly journals ALGORITHM FOR THE AUTOMATIC ESTIMATION OF AGRICULTURAL TREE GEOMETRIC PARAMETERS USING AIRBORNE LASER SCANNING DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 629-632 ◽  
Author(s):  
E. Hadaś ◽  
A. Borkowski ◽  
J. Estornell
Keyword(s):  
Laser Scanning ◽  
Tree Height ◽  
Field Measurements ◽  
Point Clouds ◽  
Average Density ◽  
Geometric Parameters ◽  
Airborne Laser Scanning ◽  
Maximum Height ◽  
Airborne Laser ◽  
Crown Base Height

The estimation of dendrometric parameters has become an important issue for the agricultural planning and management. Since the classical field measurements are time consuming and inefficient, Airborne Laser Scanning (ALS) data can be used for this purpose. Point clouds acquired for orchard areas allow to determine orchard structures and geometric parameters of individual trees. In this research we propose an automatic method that allows to determine geometric parameters of individual olive trees using ALS data. The method is based on the α-shape algorithm applied for normalized point clouds. The algorithm returns polygons representing crown shapes. For points located inside each polygon, we select the maximum height and the minimum height and then we estimate the tree height and the crown base height. We use the first two components of the Principal Component Analysis (PCA) as the estimators for crown diameters. The α-shape algorithm requires to define the radius parameter <i>R</i>. In this study we investigated how sensitive are the results to the radius size, by comparing the results obtained with various settings of the R with reference values of estimated parameters from field measurements. Our study area was the olive orchard located in the Castellon Province, Spain. We used a set of ALS data with an average density of 4 points&thinsp;m<sip>&minus;2</sup>. We noticed, that there was a narrow range of the <i>R</i> parameter, from 0.48&thinsp;m to 0.80&thinsp;m, for which all trees were detected and for which we obtained a high correlation coefficient (>&thinsp;0.9) between estimated and measured values. We compared our estimates with field measurements. The RMSE of differences was 0.8&thinsp;m for the tree height, 0.5&thinsp;m for the crown base height, 0.6&thinsp;m and 0.4&thinsp;m for the longest and shorter crown diameter, respectively. The accuracy obtained with the method is thus sufficient for agricultural applications.

scholarly journals ALGORITHM FOR THE AUTOMATIC ESTIMATION OF AGRICULTURAL TREE GEOMETRIC PARAMETERS USING AIRBORNE LASER SCANNING DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 629-632
Author(s):  
E. Hadaś ◽  
A. Borkowski ◽  
J. Estornell
Keyword(s):  
Laser Scanning ◽  
Tree Height ◽  
Field Measurements ◽  
Point Clouds ◽  
Average Density ◽  
Geometric Parameters ◽  
Airborne Laser Scanning ◽  
Maximum Height ◽  
Airborne Laser ◽  
Crown Base Height

The estimation of dendrometric parameters has become an important issue for the agricultural planning and management. Since the classical field measurements are time consuming and inefficient, Airborne Laser Scanning (ALS) data can be used for this purpose. Point clouds acquired for orchard areas allow to determine orchard structures and geometric parameters of individual trees. In this research we propose an automatic method that allows to determine geometric parameters of individual olive trees using ALS data. The method is based on the α-shape algorithm applied for normalized point clouds. The algorithm returns polygons representing crown shapes. For points located inside each polygon, we select the maximum height and the minimum height and then we estimate the tree height and the crown base height. We use the first two components of the Principal Component Analysis (PCA) as the estimators for crown diameters. The α-shape algorithm requires to define the radius parameter &lt;i&gt;R&lt;/i&gt;. In this study we investigated how sensitive are the results to the radius size, by comparing the results obtained with various settings of the R with reference values of estimated parameters from field measurements. Our study area was the olive orchard located in the Castellon Province, Spain. We used a set of ALS data with an average density of 4 points&thinsp;m&lt;sip&gt;&minus;2&lt;/sup&gt;. We noticed, that there was a narrow range of the &lt;i&gt;R&lt;/i&gt; parameter, from 0.48&thinsp;m to 0.80&thinsp;m, for which all trees were detected and for which we obtained a high correlation coefficient (&gt;&thinsp;0.9) between estimated and measured values. We compared our estimates with field measurements. The RMSE of differences was 0.8&thinsp;m for the tree height, 0.5&thinsp;m for the crown base height, 0.6&thinsp;m and 0.4&thinsp;m for the longest and shorter crown diameter, respectively. The accuracy obtained with the method is thus sufficient for agricultural applications.

scholarly journals Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon

2021 ◽  
Vol 13 (2) ◽  
pp. 261
Author(s):  
Francisco Mauro ◽  
Andrew T. Hudak ◽  
Patrick A. Fekety ◽  
Bryce Frank ◽  
Hailemariam Temesgen ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Field Measurements ◽  
Point Clouds ◽  
Semiparametric Models ◽  
Airborne Laser Scanning ◽  
Parametric Models ◽  
Fuel Load ◽  
Forest Fuels ◽  
Airborne Laser

Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas. In this study, we analyze different factors that can contribute to developing a regional strategy to use information from completed ALS data acquisitions and develop maps of multiple forest attributes in new ALS project areas in a rapid manner. This study is located in Oregon, USA, and analyzes six forest structural attributes for differences between: (1) synthetic (i.e., not-calibrated), and calibrated predictions, (2) parametric linear and semiparametric models, and (3) models developed with predictors computed for point clouds enclosed in the areas where field measurements were taken, i.e., “point-cloud predictors”, and models developed using predictors extracted from pre-rasterized layers, i.e., “rasterized predictors”. Forest structural attributes under consideration are aboveground biomass, downed woody biomass, canopy bulk density, canopy height, canopy base height, and canopy fuel load. Results from our study indicate that semiparametric models perform better than parametric models if no calibration is performed. However, the effect of the calibration is substantial in reducing the bias of parametric models but minimal for the semiparametric models and, once calibrations are performed, differences between parametric and semiparametric models become negligible for all responses. In addition, minimal differences between models using point-cloud predictors and models using rasterized predictors were found. We conclude that the approach that applies semiparametric models and rasterized predictors, which represents the easiest workflow and leads to the most rapid results, is justified with little loss in accuracy or precision even if no calibration is performed.

scholarly journals Spatial Models With Inter-Tree Competition From Airborne Laser Scanning Improve Estimates of Genetic Variance

2021 ◽  
Vol 11 ◽  
Author(s):  
David Pont ◽  
Heidi S. Dungey ◽  
Mari Suontama ◽  
Grahame T. Stovold
Keyword(s):  
Laser Scanning ◽  
Management Practices ◽  
Spatial Models ◽  
Airborne Laser Scanning ◽  
Tree Level ◽  
Residual Variance ◽  
Spatial Effects ◽  
Individual Tree ◽  
Airborne Laser ◽  
Individual Trees

Phenotyping individual trees to quantify interactions among genotype, environment, and management practices is critical to the development of precision forestry and to maximize the opportunity of improved tree breeds. In this study we utilized airborne laser scanning (ALS) data to detect and characterize individual trees in order to generate tree-level phenotypes and tree-to-tree competition metrics. To examine our ability to account for environmental variation and its relative importance on individual-tree traits, we investigated the use of spatial models using ALS-derived competition metrics and conventional autoregressive spatial techniques. Models utilizing competition covariate terms were found to quantify previously unexplained phenotypic variation compared with standard models, substantially reducing residual variance and improving estimates of heritabilities for a set of operationally relevant traits. Models including terms for spatial autocorrelation and competition performed the best and were labelled ACE (autocorrelation-competition-error) models. The best ACE models provided statistically significant reductions in residuals ranging from −65.48% for tree height (H) to −21.03% for wood stiffness (A), and improvements in narrow sense heritabilities from 38.64% for H to 14.01% for A. Individual tree phenotyping using an ACE approach is therefore recommended for analyses of research trials where traits are susceptible to spatial effects.

scholarly journals Digital Aerial Photogrammetry (DAP) and Airborne Laser Scanning (ALS) as Sources of Information about Tree Height: Comparisons of the Accuracy of Remote Sensing Methods for Tree Height Estimation

2020 ◽  
Vol 12 (11) ◽  
pp. 1808 ◽  
Author(s):  
Miłosz Mielcarek ◽  
Agnieszka Kamińska ◽  
Krzysztof Stereńczak
Keyword(s):  
Laser Scanning ◽  
Tree Height ◽  
Airborne Laser Scanning ◽  
Surface Model ◽  
Sources Of Information ◽  
Related Factors ◽  
Height Estimation ◽  
Aerial Photogrammetry ◽  
Airborne Laser ◽  
The Difference

The rapid developments in the field of digital aerial photogrammetry (DAP) in recent years have increased interest in the application of DAP data for extracting three-dimensional (3D) models of forest canopies. This technology, however, still requires further investigation to confirm its reliability in estimating forest attributes in complex forest conditions. The main purpose of this study was to evaluate the accuracy of tree height estimation based on a crown height model (CHM) generated from the difference between a DAP-derived digital surface model (DSM) and an airborne laser scanning (ALS)-derived digital terrain model (DTM). The tree heights determined based on the DAP-CHM were compared with ground-based measurements and heights obtained using ALS data only (ALS-CHM). Moreover, tree- and stand-related factors were examined to evaluate the potential influence on the obtained discrepancies between ALS- and DAP-derived heights. The obtained results indicate that the differences between the means of field-measured heights and DAP-derived heights were statistically significant. The root mean square error (RMSE) calculated in the comparison of field heights and DAP-derived heights was 1.68 m (7.34%). The results obtained for the CHM generated using only ALS data produced slightly lower errors, with RMSE = 1.25 m (5.46%) on average. Both ALS and DAP displayed the tendency to underestimate tree heights compared to those measured in the field; however, DAP produced a higher bias (1.26 m) than ALS (0.88 m). Nevertheless, DAP heights were highly correlated with the heights measured in the field (R2 = 0.95) and ALS-derived heights (R2 = 0.97). Tree species and height difference (the difference between the reference tree height and mean tree height in a sample plot) had the greatest influence on the differences between ALS- and DAP-derived heights. Our study confirms that a CHM computed based on the difference between a DAP-derived DSM and an ALS-derived DTM can be successfully used to measure the height of trees in the upper canopy layer.

scholarly journals Estimation of forest parameters using airborne laser scanning data

2015 ◽  
Vol 5 (2) ◽  
pp. 549-576
Author(s):  
J. Cohen
Keyword(s):  
Laser Scanning ◽  
Tree Height ◽  
National Forest Inventory ◽  
Airborne Laser Scanning ◽  
Forest Mapping ◽  
Airborne Laser ◽  
Forest Region ◽  
Digital Elevation ◽  
Industry Applications ◽  
Elevation Model

Abstract. Methods for the estimation of forest characteristics by airborne laser scanning (ALS) data have been introduced by several authors. Tree height (TH) and canopy closure (CC) describing the forest properties can be used in forest, construction and industry applications, as well as research and decision making. The National Land Survey has been collecting ALS data from Finland since 2008 to generate a nationwide high resolution digital elevation model. Although this data has been collected in leaf-off conditions, it still has the potential to be utilized in forest mapping. A method where this data is used for the estimation of CC and TH in the boreal forest region is presented in this paper. Evaluation was conducted in eight test areas across Finland by comparing the results with corresponding Multi-Source National Forest Inventory (MS-NFI) datasets. The ALS based CC and TH maps were generally in a good agreement with the MS-NFI data. As expected, deciduous forests caused some underestimation in CC and TH, but the effect was not major in any of the test areas. The processing chain has been fully automated enabling fast generation of forest maps for different areas.

scholarly journals Evaluation automatischer Einzelbaumerkennung aus luftgestützten Laserscanning-Daten

2017 ◽  
Vol 168 (3) ◽  
pp. 151-159 ◽  
Author(s):  
Julia Menk ◽  
Luuk Dorren ◽  
Johannes Heinzel ◽  
Mauro Marty ◽  
Markus Huber
Keyword(s):  
Protective Effect ◽  
Minimum Distance ◽  
Laser Scanning ◽  
Simulation Models ◽  
Airborne Laser Scanning ◽  
Existing Structures ◽  
Airborne Laser ◽  
Individual Trees ◽  
Steep Slopes

Evaluation of automated single-tree recognition from airborne laser scanning data In the present study, we investigated whether the detection tool FINT (Find Individual Trees) can identify single trees out of canopy height models (CHM) precisely enough to assess the protective effect of forests, even on steep slopes. For this purpose, reference trees were measured and described in twelve randomly selected sample plots in the Bündner Herrschaft and Schanfigg regions (Canton Graubünden, Switzerland). CHMs of different resolution and smoothing were generated from airborne laser scanning data for each sample plot and subsequently processed with FINT. In addition, we tested whether the use of a model that defines the minimum distance between a tree and its neighbours based on its height (MBA model) improved the quality of the results. The study showed that a finer-resolution CHM combined with stronger smoothing produced results comparable to those obtained with an unsmoothed and lower-resolution CHM. The smallest difference between the numbers of trees measured and detected was achieved with the 1-m resolution CHM, with no smoothing and no MBA model. In conclusion, FINT can provide a basis for assessing the protective effect of a forest with its existing structures, and its results – after evaluation in the field – can be directly integrated into natural hazard simulation models.

scholarly journals Classification of tree species composition using a combination of multispectral imagery and airborne laser scanning data

2017 ◽  
Vol 63 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Maroš Sedliak ◽  
Ivan Sačkov ◽  
Ladislav Kulla
Keyword(s):  
Species Composition ◽  
Tree Species ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Tree Species Composition ◽  
Object Based Image Analysis ◽  
Airborne Laser ◽  
Object Based ◽  
Individual Trees

AbstractRemote Sensing provides a variety of data and resources useful in mapping of forest. Currently, one of the common applications in forestry is the identification of individual trees and tree species composition, using the object-based image analysis, resulting from the classification of aerial or satellite imagery. In the paper, there is presented an approach to the identification of group of tree species (deciduous - coniferous trees) in diverse structures of close-to-nature mixed forests of beech, fir and spruce managed by selective cutting. There is applied the object-oriented classification based on multispectral images with and without the combination with airborne laser scanning data in the eCognition Developer 9 software. In accordance to the comparison of classification results, the using of the airborne laser scanning data allowed identifying ground of terrain and the overall accuracy of classification increased from 84.14% to 87.42%. Classification accuracy of class “coniferous” increased from 82.93% to 85.73% and accuracy of class “deciduous” increased from 84.79% to 90.16%.

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