Evaluation of airborne laser scanning data for tree parameters and terrain modelling in forest environment

Mikita, Tomáš; Klimánek, Martin; Cibulka, Miloš

doi:10.11118/actaun201361051339

Cited by 12 publications

(10 citation statements)

References 15 publications

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“…In recent years, airborne laser scanning (ALS), also referred to as airborne light detection and ranging (LiDAR), has become established as a novel technology for estimating forest inventory attributes (e.g., tree or stand height and diameter, basal area, volume -Hyyppä & Inkinen 1999, Mikita et al 2013. The capability of ALS to penetrate tree crowns enables the collection of data on tree and stand characteristics that would otherwise require on-site measurement (Sheridan et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Integration of tree allometry rules to treetops detection and tree crowns delineation using airborne lidar data

Sačkov¹,

Hlásny²,

Bucha³

et al. 2017

iForest

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Airborne laser scanning (ALS) has recently gained increasing attention in forestry, as ALS data may facilitate the efficient assessment of forest inventory attributes and ecological indicators related to forest stand structure. This paper presents a novel workflow for individual tree detection and tree crown delineation using ALS data. The developed point-based approach included several tree allometry rules on permissible tree heights and crown dimensions to increase the likelihood of detecting the actual tree profiles. The accuracy of the method was assessed in a heterogeneous forest with a complex stand structure in Slovakia (Central Europe). ALS measurements were taken using a RIEGL Q680i scanner at 700 m of height with a point density of 20 echoes per m 2 . The ground reference data included the measured positions and dimensions of 1332 trees in nine plots distributed across the region. We found that the number of individual trees detected by the algorithm using ALS data was systematically underestimated by 34 ± 15% relative to the reference data. The delineated crown coverage was underestimated by 2 ± 6% as well, but the latter difference was not statistically significant (p>0.05).

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Section: Introductionmentioning

confidence: 99%

Integration of tree allometry rules to treetops detection and tree crowns delineation using airborne lidar data

Sačkov¹,

Hlásny²,

Bucha³

et al. 2017

iForest

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“…Other modern methods of remote sensing, such as aerial LiDAR (Light Detection and Ranging), reach a significantly lower accuracy with a RMSE of 0.103 m and with a lower density of points around 1-10 points per m 2 (Mikita et al 2013). Ahamed et al (2000) presented that the LiDAR accuracy of each point on the ground is approximately 15 cm in the vertical, and 1.0 m in the horizontal.…”

Section: Discussionmentioning

confidence: 99%

Monitoring of Forest Hauling Roads Wearing Course Damage Using Unmanned Aerial Systems

Hrůza¹,

Mikita²,

Janata³

2016

Acta Univ. Agric. Silvic. Mendelianae Brun.

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Currently, a large part of the forest roads that were built using the bituminous surface technology in the second half of the last century have been worn out. This means that forest owners and forest managers urgently need to determine the amount and extent of this damage and establish a suitable repair plan, which demands both time and staff. The aim of the study is to verify whether it is possible, and with what precision, to detect the damage of the wearing course by means of unmanned aerial systems, which would facilitate and accelerate this process and possibly make it cheaper. A 3D model of a forest road was created using photos of the current state of a damaged part of a forest road. The aerial photographs were taken by an unmanned aircraft. To verify the accuracy of the model, cross sections of the road surface were surveyed tachymetrically and compared with the cross sections created in the 3D model in ArcMap, from photogrammetric pointcloud using aerial photographs from the unmanned aircraft. The RMSE of the values of the control points in the 3D model cross sections compared to the values of the points in the tachymetric measurement of the cross sections reached to within 0.0198 m. The results of the tested road section showed that the unmanned aerial systems can be used to detect the forest road surface damage with the difference in accuracy being up to 2 cm compared with the accuracy of the current tachymetric methods. Based on the results we can conclude that the used method is appropriate for detailed monitoring of the condition of the asphalt wearing course of forest roads and allows for a precise and objective localization and quantification of damage.

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“…Terrestrial surveying based on GNSS and total station measurements in 4 sampling plots in an area of 1 hectare proved the quality of DTM and resulted in an observational error of −0.623 ± 0.12 m (Mikita et al, 2013). Therefore this relatively high systematic bias was eliminated by deducting the diff erence.…”

Section: Data Processingmentioning

confidence: 99%

“…The CHM was smoothed with a Gaussian fi lter to remove small variations on the crown surface. The degree of smoothness was determined on the basis of the most precise detection of tree heights from ALS in comparison to fi eld measured values (Mikita et al, 2013). In the case of this study, we used the Focal Statistic tool of ArcGIS 10.2 with defi ned rectangle surrounding of a kernel of 5 × 5 pixels.…”

Section: : Location Of Tfe Křtinymentioning

confidence: 99%

Usage of Geoprocessing Services in Precision Forestry for Wood Volume Calculation and Wind Risk Assessment

Mikita¹,

Balogh²

2015

Acta Univ. Agric. Silvic. Mendelianae Brun.

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This paper outlines the idea of a precision forestry tool for optimizing clearcut size and shape within the process of forest recovery and its publishing in the form of a web processing service for forest owners on the Internet. The designed tool titled COWRAS (Clearcut Optimization and Wind Risk Assessment) is developed for optimization of clearcuts (their location, shape, size, and orientation) with subsequent wind risk assessment. The tool primarily works with airborne LiDAR data previously processed to the form of a digital surface model (DSM) and a digital elevation model (DEM). In the fi rst step, the growing stock on the planned clearcut determined by its location and area in feature class is calculated (by the method of individual tree detection). Subsequently tree heights from canopy height model (CHM) are extracted and then diameters at breast height (DBH) and wood volume using the regressions are calculated. Information about wood volume of each tree in the clearcut is exported and summarized in a table. In the next step, all trees in the clearcut are removed and a new DSM without trees in the clearcut is generated. This canopy model subsequently serves as an input for evaluation of wind risk damage by the MAXTOPEX tool (Mikita et al., 2012). In the fi nal raster, predisposition of uncovered forest stand edges (around the clearcut) to wind risk is calculated based on this analysis. The entire tool works in the background of ArcGIS server as a spatial decision support system for foresters.

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Evaluation of airborne laser scanning data for tree parameters and terrain modelling in forest environment

Cited by 12 publications

References 15 publications

Integration of tree allometry rules to treetops detection and tree crowns delineation using airborne lidar data

Integration of tree allometry rules to treetops detection and tree crowns delineation using airborne lidar data

Monitoring of Forest Hauling Roads Wearing Course Damage Using Unmanned Aerial Systems

Usage of Geoprocessing Services in Precision Forestry for Wood Volume Calculation and Wind Risk Assessment

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