Terrestrial Laser Scanning and Ground Truth Data for Characterizing Downed Dead Wood

The feasibility of terrestrial laser scanning (TLS) in characterizing standing trees has been frequently investigated, while less effort has been put in quantifying downed dead wood using TLS. To advance dead wood characterization using TLS, we collected TLS point clouds and downed dead wood information from 20 sample plots (32 m x 32 m in size) located in southern Finland. This data set can be used in developing new algorithms for downed dead wood detection and characterization as well as for understanding spatial patterns of downed dead wood in boreal forests.

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Poster on Detecting and Characterizing Dead Wood Using Terrestrial Laser Scanning

10.31219/osf.io/jdfzp ◽

2020 ◽

Author(s):

Tuomas Yrttimaa ◽

Ninni Saarinen ◽

Ville Luoma ◽

Topi Tanhuanpää ◽

Ville Kankare ◽

...

Keyword(s):

Forest Ecosystem ◽

Structural Component ◽

Laser Scanning ◽

Dead Wood ◽

Terrestrial Laser Scanning ◽

Automatic Method ◽

Forest Inventories ◽

Standing Trees ◽

Downed Dead Wood

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Detecting and characterizing downed dead wood using terrestrial laser scanning

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2019.03.007 ◽

2019 ◽

Vol 151 ◽

pp. 76-90 ◽

Cited By ~ 8

Author(s):

Tuomas Yrttimaa ◽

Ninni Saarinen ◽

Ville Luoma ◽

Topi Tanhuanpää ◽

Ville Kankare ◽

...

Keyword(s):

Laser Scanning ◽

Dead Wood ◽

Terrestrial Laser Scanning ◽

Downed Dead Wood

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Detailed point cloud data on stem size and shape of Scots pine trees

10.1101/2020.03.09.983973 ◽

2020 ◽

Author(s):

Ninni Saarinen ◽

Ville Kankare ◽

Tuomas Yrttimaa ◽

Niko Viljanen ◽

Eija Honkavaara ◽

...

Keyword(s):

Scots Pine ◽

Boreal Forests ◽

Laser Scanning ◽

Point Clouds ◽

Data Set ◽

Cloud Data ◽

Stem Size ◽

Size And Shape ◽

Pine Trees ◽

Tree Stem

AbstractQuantitative assessment of the effects of forest management on tree size and shape has been challenging as there has been a lack of methodologies for characterizing differences and possible changes comprehensively in space and time. Terrestrial laser scanning (TLS) and photogrammetric point clouds provide three-dimensional (3D) information on tree stem reconstructions required for characterizing differences between stem shapes and growth allocation. This data set includes 3D reconstructions of stems of Scots pine (Pinus sylvestris L.) trees from sample plots with different thinning treatments. The thinning treatments include two intensities of thinning, three thinning types as well as control (i.e. no thinning treatment since the establishment). The data set can be used in developing point clouds processing algorithms for single tree stem reconstruction and for investigating variation in stem size and shape of Scots pine trees. Additionally, it offers possibilities in characterizing the effects of various thinning treatments on stem size and shape of Scots pine trees from boreal forests.Data setZenodo https://zenodo.org/record/3701271Data set licenseAttribution 4.0 International (CC BY 4.0)

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AUTOMATED STRUCTURAL FOREST CHANGES USING LIDAR POINT CLOUDS AND GIS ANALYSES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2021-603-2021 ◽

2021 ◽

Vol XLIII-B3-2021 ◽

pp. 603-608

Author(s):

A. Novo ◽

H. González-Jorge ◽

J. Martínez-Sánchez ◽

J. M. Fernández-Alonso ◽

H. Lorenzo

Keyword(s):

Spatial Data ◽

Forest Structure ◽

Laser Scanning ◽

Structural Model ◽

Canopy Cover ◽

Ground Truth ◽

Point Clouds ◽

Forest Community ◽

Structural Behaviour ◽

Ground Truth Data

Abstract. Forest spatial structure describes the relationships among different species in the same forest community. Automation in the monitoring of the structural forest changes and forest mapping is one of the main utilities of applications of modern geoinformatics methods. The obtaining objective information requires the use of spatial data derived from photogrammetry and remote sensing. This paper investigates the possibility of applying light detection and ranging (LiDAR) point clouds and geographic information system (GIS) analyses for automated mapping and detection changes in vegetation structure during a year of study. The research was conducted in an area of the Ourense Province (NWSpain). The airborne laser scanning (ALS) data, acquired in August 2019 and June of 2020, reveal detailed changes in forest structure. Based on ALS data the vegetation parameters will be analysed.To study the structural behaviour of the tree vegetation, the following parameters are used in each one of the sampling areas: (1) Relationship between the tree species present and their stratification; (2) Vegetation classification in fuel types; (3) Biomass (Gi); (4) Number of individuals per area; and (5) Canopy cover fraction (CCF). Besides, the results were compared with the ground truth data recollected in the study area.The development of a quantitative structural model based on Aerial Laser Scanning (ALS) point clouds was proposed to accurately estimate tree attributes automatically and to detect changes in forest structure. Results of statistical analysis of point cloud show the possibility to use UAV LiDAR data to characterize changes in the structure of vegetation.

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RIGOROUS STRIP ADJUSTMENT OF AIRBORNE LASERSCANNING DATA BASED ON THE ICP ALGORITHM

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-ii-3-w5-73-2015 ◽

2015 ◽

Vol II-3/W5 ◽

pp. 73-80 ◽

Cited By ~ 6

Author(s):

P. Glira ◽

N. Pfeifer ◽

C. Briese ◽

C. Ressl

Keyword(s):

Measurement Errors ◽

Laser Scanning ◽

Ground Truth ◽

Point Clouds ◽

Redundant Information ◽

Ground Truth Data ◽

Icp Algorithm ◽

Airborne Laser ◽

Fully Automatic

Airborne Laser Scanning (ALS) is an efficient method for the acquisition of dense and accurate point clouds over extended areas. To ensure a gapless coverage of the area, point clouds are collected strip wise with a considerable overlap. The redundant information contained in these overlap areas can be used, together with ground-truth data, to re-calibrate the ALS system and to compensate for systematic measurement errors. This process, usually denoted as <i>strip adjustment</i>, leads to an improved georeferencing of the ALS strips, or in other words, to a higher data quality of the acquired point clouds. We present a fully automatic strip adjustment method that (a) uses the original scanner and trajectory measurements, (b) performs an on-the-job calibration of the entire ALS multisensor system, and (c) corrects the trajectory errors individually for each strip. Like in the Iterative Closest Point (ICP) algorithm, correspondences are established iteratively and directly between points of overlapping ALS strips (avoiding a time-consuming segmentation and/or interpolation of the point clouds). The suitability of the method for large amounts of data is demonstrated on the basis of an ALS block consisting of 103 strips.

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Detecting and characterizing downed dead wood using terrestrial laser scanning

10.31219/osf.io/t2h8r ◽

2020 ◽

Author(s):

Tuomas Yrttimaa ◽

Ninni Saarinen ◽

Ville Luoma ◽

Topi Tanhuanpää ◽

Ville Kankare ◽

...

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Method Development ◽

Dead Wood ◽

Terrestrial Laser Scanning ◽

Detection Accuracy ◽

Visual Interpretation ◽

Wood Volume ◽

Downed Dead Wood ◽

Forest Conditions

Dead wood is a key forest structural component for maintaining biodiversity and storing carbon. Despite its important role in a forest ecosystem, quantifying dead wood alongside standing trees has often neglected when investigating the feasibility of terrestrial laser scanning (TLS) in forest inventories. The objective of this study was therefore to develop an automatic method for detecting and characterizing downed dead wood with a diameter exceeding 5 cm using multi-scan TLS data. The developed four-stage algorithm included 1) RANSAC-cylinder filtering, 2) point cloud rasterization, 3) raster image segmentation, and 4) dead wood trunk positioning. For each detected trunk, geometry-related quality attributes such as dimensions and volume were automatically determined from the point cloud. For method development and validation, reference data were collected from 20 sample plots representing diverse southern boreal forest conditions. Using the developed method, the downed dead wood trunks were detected with an overall completeness of 33% and correctness of 76%. Up to 92% of the downed dead wood volume were detected at plot level with mean value of 68%. We were able to improve the detection accuracy of individual trunks with visual interpretation of the point cloud, in which case the overall completeness was increased to 72% with mean proportion of detected dead wood volume of 83%. Downed dead wood volume was automatically estimated with an RMSE of 15.0 m3/ha (59.3%), which was reduced to 6,4 m3/ha (25.3%) as visual interpretation was utilized to aid the trunk detection. The reliability of TLS-based dead wood mapping was found to increase as the dimensions of dead wood trunks increased. Dense vegetation caused occlusion and reduced the trunk detection accuracy. Therefore, when collecting the data, attention must be paid to the point cloud quality. Nevertheless, the results of this study strengthen the feasibility of TLS-based approaches in mapping biodiversity indicators by demonstrating an improved performance in quantifying ecologically most valuable downed dead wood in diverse forest conditions.

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Investigating the Feasibility of Multi-Scan Terrestrial Laser Scanning to Characterize Tree Communities in Southern Boreal Forests

Remote Sensing ◽

10.3390/rs11121423 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1423 ◽

Cited By ~ 8

Author(s):

Tuomas Yrttimaa ◽

Ninni Saarinen ◽

Ville Kankare ◽

Xinlian Liang ◽

Juha Hyyppä ◽

...

Keyword(s):

Boreal Forests ◽

Laser Scanning ◽

Size Variation ◽

Terrestrial Laser Scanning ◽

Basal Area ◽

Point Clouds ◽

Tree Size ◽

Sample Plot ◽

Tree Detection ◽

Plot Size

Terrestrial laser scanning (TLS) has proven to accurately represent individual trees, while the use of TLS for plot-level forest characterization has been studied less. We used 91 sample plots to assess the feasibility of TLS in estimating plot-level forest inventory attributes, namely the stem number (N), basal area (G), and volume (V) as well as the basal area weighed mean diameter (Dg) and height (Hg). The effect of the sample plot size was investigated by using different-sized sample plots with a fixed scan set-up to also observe possible differences in the quality of point clouds. The Gini coefficient was used to measure the variation in tree size distribution at the plot-level to investigate the relationship between stand heterogeneity and the performance of the TLS-based method. Higher performances in tree detection and forest attribute estimation were recorded for sample plots with a low degree of tree size variation. The TLS-based approach captured 95% of the variation in Hg and V, 85% of the variation in Dg and G, and 67% of the variation in N. By increasing the sample plot size, the tree detection rate was decreased, and the accuracy of the estimates, especially G and N, decreased. This study emphasizes the feasibility of TLS-based approaches in plot-level forest inventories in varying southern boreal forest conditions.

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Targetless Coregistration of Terrestrial Laser Scanning Point Clouds Using a Multi Surrounding Scan Image-Based Technique

Surveying and Geospatial Engineering Journal ◽

10.38094/sgej112 ◽

2020 ◽

Vol 1 (1) ◽

pp. 01-07

Author(s):

Bashar Alsadik

Keyword(s):

Image Matching ◽

Point Cloud ◽

Laser Scanning ◽

Terrestrial Laser Scanning ◽

Experimental Tests ◽

Point Clouds ◽

Data Set ◽

Panoramic Images ◽

Fine Registration ◽

Rgb Values

The coregistration of terrestrial laser point clouds is widely investigated where different techniques are presented to solve this problem. The techniques are divided either as target-based or targetless approaches for coarse and fine coregistration. The targetless approach is more challenging since no physical reference targets are placed in the field during the scanning. Mainly, targetless methods are image-based and they are applied through projecting the point clouds back to the scanning stations. The projected 360 point cloud images are normally in the form of panoramic images utilizing either intensity or RGB values, and an image matching is followed to align the scan stations together. However, the point cloud coregistration is still a challenge since ICP like methods are applicable for fine registration. Furthermore, image-based approaches are restricted when there is: a limited overlap between point clouds, no RGB data accompanied to intensity values, and unstructured scanned objects in the point clouds. Therefore, we present in this paper the concept of a multi surrounding scan MSS image-based approach to overcome the difficulty to register point clouds in challenging cases. The multi surrounding scan approach means to create multi-perspective images per laser scan point cloud. These multi-perspective images will offer different viewpoints per scan station to overcome the viewpoint distortion that causes the failure of the image matching in challenging situations. Two experimental tests are applied using point clouds collected in Enschede city and the published 3D toolkit data set in Bremen city. The experiments showed a successful coregistration approach even in challenging settings with different constellations.

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Forest aboveground volume assessments with terrestrial laser scanning: a ground-truth validation experiment in temperate, managed forests

Annals of Botany ◽

10.1093/aob/mcab110 ◽

2021 ◽

Author(s):

Miro Demol ◽

Kim Calders ◽

Hans Verbeeck ◽

Bert Gielen

Keyword(s):

Laser Scanning ◽

Basic Density ◽

Terrestrial Laser Scanning ◽

Ground Truth ◽

Stem Volume ◽

Calibration Data ◽

Ground Truth Data ◽

Allometric Models ◽

Volumetric Errors ◽

Validation Experiment

Abstract Background and Aims Quantifying the Earth’s forest aboveground biomass (AGB) is indispensable for effective climate action and developing forest policy. Yet, current allometric scaling models (ASM) to estimate AGB suffer several drawbacks related to model selection and calibration data traceability uncertainties. Terrestrial laser scanning (TLS) offers a promising non-destructive alternative. Tree volume is reconstructed from TLS point clouds with Quantitative Structure Models (QSM) and converted to AGB with wood basic density. Earlier studies have found overall TLS-derived forest volume estimates to be accurate, but highlighted problems for reconstructing finer branches. Our objective was to evaluate TLS for estimating tree volumes by comparison with reference volumes and volumes from ASMs. Methods We quantified the woody volume of 65 trees in Belgium (77 – 2.800 L; Pinus sylvestris, Fagus sylvatica, Larix decidua, Fraxinus excelsior) with QSMs and destructive reference measurements. We tested a volume expansion factor (VEF) approach by multiplying the solid and merchantable volume from QSM with literature VEF values. Key Results Stem volume was reliably estimated with TLS. Total volume was overestimated by +21% using original QSMs, by +9% and -12% using two sets of VEF-augmented QSMs, and by -7.3% using best-available allometric models. The most accurate method differed per site, and the prediction errors for each method varied considerably between sites. Conclusions VEF-augmented QSMs were only slightly better than original QSMs for estimating tree volume for common species in temperate forests. Despite satisfying estimates with ASMs, the model choice was a large source of uncertainty, and species-specific models did not always exist. Therefore, we advocate for further improving tree volume reconstructions with QSMs, especially for fine branches, instead of collecting more ground-truth data to calibrate VEF and allometric models. Promising developments such as improved coregistration and smarter filtering approaches are ongoing to further constrain volumetric errors in TLS-derived estimates.

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Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas

Remote Sensing ◽

10.3390/rs13030507 ◽

2021 ◽

Vol 13 (3) ◽

pp. 507

Author(s):

Tasiyiwa Priscilla Muumbe ◽

Jussi Baade ◽

Jenia Singh ◽

Christiane Schmullius ◽

Christian Thau

Keyword(s):

Remote Sensing ◽

Vegetation Structure ◽

Laser Scanning ◽

Spatial Scales ◽

Terrestrial Laser Scanning ◽

Point Clouds ◽

Parameter Extraction ◽

Airborne Lidar ◽

Special Focus ◽

Vegetation Parameter

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.

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