Airborne Laser Scanning - the Status and Perspectives for the Application in the South-East European Forestry

Balenović, Ivan; Alberti, Giorgio; Marjanović, Hrvoje

doi:10.15177/seefor.13-07

Cited by 12 publications

(11 citation statements)

References 62 publications

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“…the accuracy of LiDARestimated tree heights depends on many factors such as: LiDAR system characteristics (pulse density, footprint diameter, laser pulse divergence, scanning angle), the algorithms used for data processing, and forest characteristics (tree species, stand density, percentage and height of understory and ground vegetation, etc.) (Hyyppä et al 2008, Balenović et al 2013. According to the results obtained from this research ( Figure 5, Table 4), the method did not lead to constant and systematic underestimations of tree heights.…”

Section: Discussionsupporting

confidence: 50%

Estimation of the mean tree height of forest stands by photogrammetric measurement using digital aerial images of high spatial resolution

et al. 2015

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. Estimation of the mean tree height of forest stands by photogrammetric measurement using digital aerial images of high spatial resolution. Ann. For. Res. 58(1): 125-143.Abstract. Tree height is one of the more fundamental measurements in forest inventories. In addition to classical field measurements, tree height may be estimated by remote sensing methods, such as by photogrammetric measurements of aerial images. Since it has been found and generally accepted that the extraction of forest and tree data from classical analogue aerial photographs has certain limitations, especially in the densely canopied forests, the usefulness of photogrammetric-based forest inventory in many countries remains a controversial issue. Therefore, this paper focuses on investigating the possibility of applying digital photogrammetric method to estimate mean stand height. Photogrammetric stereo-measurements of tree height were conducted on colour infrared images of high spatial resolution (ground sample distance -GSD -of 30 cm and 10 cm) using a digital photogrammetric workstation. The height of each tree within 183 sample plots (14 subcompartments) were calculated as the difference between the tree top elevations determined with the aerial images and the corresponding tree bottom elevations determined from the digital terrain model. To compare the photogrammetric-and field-estimated mean stand heights, the mean plot heights were calculated for both photogrammetric and field estimates of tree heights. Repeated measurements using ANOVA testing did not reveal a statistically significant difference (p > 0.05) between the field-estimated and photogrammetric-estimated mean stand heights using the 30 cm and 10 cm GSD digital aerial images. Deviations of the mean stand heights estimated using the images of both spatial resolutions were similar to the field-estimated heights. Using the 30 cm images the deviations of the photogrammetrically estimated mean stand height amounted to 0.35 m (1.59%) on average, whereas using the 10 cm images the deviations amounted to 0.31 m (1.41%) compared to the field estimation. Therefore, it can be concluded that the 30 cm GSD aerial images allow for the photogrammetric measurement of mean stand heights with accuracy similar to 10 cm GSD aerial images. In addition, 30 cm GSD aerial images are more favourable financially since the same area of interest could be covered with a considerably smaller number of images than of the 10 cm GSD aerial images.

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Section: Discussionsupporting

confidence: 50%

Estimation of the mean tree height of forest stands by photogrammetric measurement using digital aerial images of high spatial resolution

et al. 2015

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“…The first applications were topographically oriented. Since then, the development of this technology was fast and ALS are currently used in a wide range of applications, including forestry (Balenović et al 2013, Maas 2013. The history of the development and use of ALS in forestry are described in Hyyppä et al (2009).…”

Section: Introductionmentioning

confidence: 99%

“…The best results are obtained for dominant trees in the canopy layer: in younger stands and trees in the understorey layer, the number of trees is likely to be underestimated (Pitkänen et al 2004, Heurich 2008. A multitude of algorithms and procedures for the identification of individual trees have been proposed in the literature (Jing et al 2014, Kaartinen et al 2012, Khosravipour et al 2015, Sterenczak & Miscicki 2012, Vauhkonen et al 2012, which can be grouped in two basic approaches: (i) an area-based approach where forest characteristics are estimated using statistical analyses and models between ALS data and terrestrial measurements; and (ii) an individual tree-based approach whereby individual trees are identified from ALS data, visually or by segmentation processes, and dendrometric parameters are extracted for individual trees (Balenović et al 2013, Kaartinen et al 2012, Vauhkonen et al 2012. Accuracy of tree detection is usually higher in conifer stands as compared to stands with deciduous trees (Sterenczak 2013).…”

Section: Introductionmentioning

confidence: 99%

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Determining basic forest stand characteristics using airborne laser scanning in mixed forest stands of Central Europe

Smreček¹,

Michňová²,

Sačkov³

et al. 2018

iForest

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(1) This study focused on the derivation of basic stand characteristics from airborne laser scanning (ALS) data, aiming to elucidate which characteristics (mean height and diameter, dominant height and diameter) are best approximated by the variables obtained using ALS data. The height of trees of different species in four permanent plots located in the Slovak Republic was derived from the normalised digital surface model (nDSM) representing the canopy surface, using an automatic approach to identify local maxima (individual treetops). Tree identification was carried out using four different spatial resolutions of the nDSM (0.5 m, 1.0 m, 1.5 m, and 2.0 m) and the number of trees identified was compared with reference data obtained from field measurements. The highest percentage of tree detection (69-75%) was observed at the spatial resolutions of 1.0 and 1.5 m. Absolute differences of tree height between reference and ALS datasets ranged from 0 to 36% at all spatial resolutions. The smallest difference in mean height was obtained using the higher spatial resolution (0.5 m), while the smallest difference in the dominant height of the relative number of thickest trees (h10% and h20%) was observed using the lower spatial resolution (2 m). The same trends also apply to diameters. The average errors at resolution of 1.0 and 1.5 m was 8.7%, 5.9% and 9.7% for mean height, h20% and h10%, respectively. ALS-derived diameters (obtained using regression models from reference data and ALS-derived individual height as predictor) showed absolute errors in the range 0-48% at all spatial resolutions. The deviation in mean diameter at a resolution of 0.5 m ranged from -12.1% to 15.3%.

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“…Such enriched point clouds offer an added value to features from discrete ALS for vegetation and forest mapping and for individual tree identification and classification [2,3]. Since 2004, FWF ALS systems are increasingly used for object detection and classification [4,5], and for the derivation of vegetation characteristics in forestry [6][7][8][9][10][11], agriculture [12], and in urban areas [13]. Research efforts focusing on vegetation are expanding due to an increasing need to quantify vegetation characteristics and to model environmental dynamics [14].…”

Section: Introductionmentioning

confidence: 99%

Full-Waveform Airborne Laser Scanning in Vegetation Studies—A Review of Point Cloud and Waveform Features for Tree Species Classification

Koenig

Höfle

2016

Forests

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Abstract:In recent years, small-footprint full-waveform airborne laser scanning has become readily available and established for vegetation studies in the fields of forestry, agriculture and urban studies. Independent of the field of application and the derived final product, each study uses features to classify a target object and to assess its characteristics (e.g., tree species). These laser scanning features describe an observable characteristic of the returned laser signal (e.g., signal amplitude) or a quantity of an object (e.g., height-width ratio of the tree crown). In particular, studies dealing with tree species classification apply a variety of such features as input. However, an extensive overview, categorization and comparison of features from full-waveform airborne laser scanning and how they relate to specific tree species are still missing. This review identifies frequently used full-waveform airborne laser scanning-based point cloud and waveform features for tree species classification and compares the applied features and their characteristics for specific tree species detection. Furthermore, limiting and influencing factors on feature characteristics and tree classification are discussed with respect to vegetation structure, data acquisition and processing.

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Airborne Laser Scanning - the Status and Perspectives for the Application in the South-East European Forestry

Cited by 12 publications

References 62 publications

Estimation of the mean tree height of forest stands by photogrammetric measurement using digital aerial images of high spatial resolution

Estimation of the mean tree height of forest stands by photogrammetric measurement using digital aerial images of high spatial resolution

Determining basic forest stand characteristics using airborne laser scanning in mixed forest stands of Central Europe

Full-Waveform Airborne Laser Scanning in Vegetation Studies—A Review of Point Cloud and Waveform Features for Tree Species Classification

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