COMPARISON OF FOREST STRUCTURE METRICS DERIVED FROM UAV LIDAR AND ALS DATA

Abstract. Point clouds derived from airborne laser scanning (ALS) and from LiDAR sensors mounted on unmanned aerial vehicles (ULS) reveal differences caused by the different sensor systems and acquisition geometries. These differences in the system characteristics are reflected in forest structure metrics that are derived from the respective point clouds. In our study, we investigate the completeness of scene coverage between the two systems and address differences between structure metrics derived from ULS and ALS, namely in point height quantiles, fractional cover (fc), the vertical complexity index (VCI) and the number of canopy layers (nLayers). The metrics are evaluated for raster cell sizes of 1&ndash;10&thinsp;m in order to investigate the spatial scale on which the sensor systems provide comparable metrics. We found highest correspondences between ALS and ULS in the VCI- and the nLayers-metrics, while fc revealed large differences. For the height quantiles, the absolute differences were larger for the 10%- (h10) and the 50%- (h50) than for the 90%- (h90) height quantile. Furthermore, we found differences between ALS- and ULS-metrics to decrease for larger cell sizes, except for fc, for which the differences increased, and h50 and h90, respectively, for which the differences were relatively stable for all cell sizes.

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Estimation of forest structure metrics relevant to hydrologic modelling using coordinate transformation of airborne laser scanning data

Hydrology and Earth System Sciences ◽

10.5194/hess-16-3749-2012 ◽

2012 ◽

Vol 16 (10) ◽

pp. 3749-3766 ◽

Cited By ~ 17

Author(s):

A. Varhola ◽

G. W. Frazer ◽

P. Teti ◽

N. C. Coops

Keyword(s):

Solar Radiation ◽

Forest Structure ◽

Laser Scanning ◽

Forest Canopy ◽

Airborne Laser Scanning ◽

Water Dynamics ◽

Hydrologic Models ◽

Hydrologic Modelling ◽

Airborne Laser ◽

Structure Metrics

Abstract. An accurate characterisation of the complex and heterogeneous forest architecture is necessary to parameterise physically-based hydrologic models that simulate precipitation interception, energy fluxes and water dynamics. While hemispherical photography has become a popular method to obtain a number of forest canopy structure metrics relevant to these processes, image acquisition is field-intensive and, therefore, difficult to apply across the landscape. In contrast, airborne laser scanning (ALS) is a remote-sensing technique increasingly used to acquire detailed information on the spatial structure of forest canopies over large, continuous areas. This study presents a novel methodology to calibrate ALS data with in situ optical hemispherical camera images to obtain traditional forest structure and solar radiation metrics. The approach minimises geometrical differences between these two techniques by transforming the Cartesian coordinates of ALS data to generate synthetic images with a polar projection directly comparable to optical photography. We demonstrate how these new coordinate-transformed ALS metrics, along with additional standard ALS variables, can be used as predictors in multiple linear regression approaches to estimate forest structure and solar radiation indices at any individual location within the extent of an ALS transect. We expect this approach to substantially reduce fieldwork costs, broaden sampling design possibilities, and improve the spatial representation of forest structure metrics directly relevant to parameterising fully-distributed hydrologic models.

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Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds

Forests ◽

10.3390/f7030062 ◽

2016 ◽

Vol 7 (12) ◽

pp. 62 ◽

Cited By ~ 250

Author(s):

Luke Wallace ◽

Arko Lucieer ◽

Zbyněk Malenovský ◽

Darren Turner ◽

Petr Vopěnka

Keyword(s):

Structure From Motion ◽

Forest Structure ◽

Laser Scanning ◽

Point Clouds ◽

Airborne Laser Scanning ◽

Airborne Laser

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Stand delineation based on laser scanning data and simulated annealing

European Journal of Forest Research ◽

10.1007/s10342-021-01384-x ◽

2021 ◽

Author(s):

Yusen Sun ◽

Weifang Wang ◽

Timo Pukkala ◽

Xingji Jin

Keyword(s):

Simulated Annealing ◽

Unmanned Aerial Vehicles ◽

Laser Scanning ◽

Optimization Problem ◽

Combinatorial Optimization Problem ◽

Canopy Height ◽

Echo Intensity ◽

Airborne Laser ◽

Aerial Vehicles ◽

Raster Cell

AbstractThe use of airborne laser scanning (LS) is increasing in forestry. Scanning can be conducted from manned aircrafts or unmanned aerial vehicles (UAV). The scanning data are often used to calculate various attributes for small raster cells. These attributes can be used to segment the forest into homogeneous areas, called segments, micro-stands, or, like in this study, stands. Delineation of stands from raster data is equal to finding the most suitable stand number for each raster cell, which is a combinatorial optimization problem. This study tested the performance of the simulated annealing (SA) metaheuristic in the delineation of stands from grids of UAV-LS attributes. The objective function included three criteria: within-stand variation of the LS attributes, stand area, and stand shape. The purpose was to create delineations that consisted of homogeneous stands with a low number of small stands and a regular and roundish stand shape. The results showed that SA is capable of producing stand delineations that meet these criteria. However, the method tended to produce delineations where the stands often consisted of disconnected parts and the stand borders were jagged. These problems were mitigated by using a mode filter on the grid of stand numbers and giving unique numbers for all disconnected parts of a stand. Three LS attributes were used in the delineation. These attributes described the canopy height, the height of the bottom of the canopy and the variation of echo intensity within 1-m2 raster cells. Besides, a texture variable that described the spatial variation of canopy height in the proximity of a 1-m2 raster cell was found to be a useful variable. Stand delineations where the average stand area was about one hectare explained more than 80% of the variation in canopy height.

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Estimation of forest structure metrics relevant to hydrologic modeling using coordinate transformation of airborne laser scanning data

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-5531-2012 ◽

2012 ◽

Vol 9 (4) ◽

pp. 5531-5573 ◽

Cited By ~ 1

Author(s):

A. Varhola ◽

G. W. Frazer ◽

P. Teti ◽

N. C. Coops

Keyword(s):

Solar Radiation ◽

Forest Structure ◽

Laser Scanning ◽

Forest Canopy ◽

Canopy Structure ◽

Airborne Laser Scanning ◽

Water Dynamics ◽

Hydrologic Models ◽

Airborne Laser ◽

Structure Metrics

Abstract. Accurate characterizations of the complex and heterogeneous forest architecture are necessary to parameterize physically-based hydrologic models that simulate precipitation interception, energy fluxes and water dynamics. While hemispherical photography has become a popular method to obtain a number of forest canopy structure metrics relevant to these processes, image acquisition is field-intensive and therefore difficult to apply across the landscape. In contrast, airborne laser scanning (ALS) is a remote sensing technique increasingly used to acquire detailed information on the spatial structure of forest canopies over large, continuous areas. This study presents a novel methodology to calibrate ALS data with in-situ optical hemispherical camera images to obtain traditional forest structure and solar radiation metrics. The approach minimizes geometrical differences between these two techniques by transforming the Cartesian coordinates of ALS data to generate synthetic images with a polar projection directly comparable to optical photography. We demonstrate how these new coordinate-transformed ALS metrics, along with additional standard ALS variables, can be used as predictors in multiple linear regression to estimate forest structure and solar radiation indices at any individual location within the extent of an ALS transect. This approach is expected to substantially reduce fieldwork costs, broaden sampling design possibilities, and improve the spatial representation of forest structure metrics directly relevant to parameterize hydrologic models.

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An Adaptive Surface Interpolation Filter Using Cloth Simulation and Relief Amplitude for Airborne Laser Scanning Data

Remote Sensing ◽

10.3390/rs13152938 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2938

Author(s):

Feng Li ◽

Haihong Zhu ◽

Zhenwei Luo ◽

Hang Shen ◽

Lin Li

Keyword(s):

Laser Scanning ◽

Point Clouds ◽

Airborne Laser Scanning ◽

Superior Performance ◽

Cloth Simulation ◽

Cloud Data ◽

Interpolation Filter ◽

Landscape Characteristics ◽

Surface Interpolation ◽

Airborne Laser

Separating point clouds into ground and nonground points is an essential step in the processing of airborne laser scanning (ALS) data for various applications. Interpolation-based filtering algorithms have been commonly used for filtering ALS point cloud data. However, most conventional interpolation-based algorithms have exhibited a drawback in terms of retaining abrupt terrain characteristics, resulting in poor algorithmic precision in these regions. To overcome this drawback, this paper proposes an improved adaptive surface interpolation filter with a multilevel hierarchy by using a cloth simulation and relief amplitude. This method uses three hierarchy levels of provisional digital elevation model (DEM) raster surfaces with thin plate spline (TPS) interpolation to separate ground points from unclassified points based on adaptive residual thresholds. A cloth simulation algorithm is adopted to generate sufficient effective initial ground seeds for constructing topographic surfaces with high quality. Residual thresholds are adaptively constructed by the relief amplitude of the examined area to capture complex landscape characteristics during the classification process. Fifteen samples from the International Society for Photogrammetry and Remote Sensing (ISPRS) commission are used to assess the performance of the proposed algorithm. The experimental results indicate that the proposed method can produce satisfying results in both flat areas and steep areas. In a comparison with other approaches, this method demonstrates its superior performance in terms of filtering results with the lowest omission error rate; in particular, the proposed approach retains discontinuous terrain features with steep slopes and terraces.

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

Remote Sensing ◽

10.3390/rs13020261 ◽

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.

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Highway alignments extraction and 3D modeling from airborne laser scanning point clouds

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2021.102429 ◽

2021 ◽

Vol 102 ◽

pp. 102429

Author(s):

Yuzhou Zhou ◽

Ronggang Huang ◽

Tengping Jiang ◽

Zhen Dong ◽

Bisheng Yang

Keyword(s):

3D Modeling ◽

Laser Scanning ◽

Point Clouds ◽

Airborne Laser Scanning ◽

Airborne Laser

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Potential for Individual Tree Monitoring in Ponderosa Pine-Dominated Forests Using Unmanned Aerial System Structure from Motion Point Clouds

Canadian Journal of Forest Research ◽

10.1139/cjfr-2020-0433 ◽

2021 ◽

Cited By ~ 1

Author(s):

Matthew B. Creasy ◽

Wade Travis Tinkham ◽

Chad M. Hoffman ◽

Jody C. Vogeler

Keyword(s):

Forest Structure ◽

Ponderosa Pine ◽

Laser Scanning ◽

Point Clouds ◽

System Structure ◽

Detection Methods ◽

Unmanned Aerial System ◽

Individual Tree ◽

Detection Rates ◽

Ponderosa Pine Forests

Characterization of forest structure is important for management-related decision making, monitoring, and adaptive management. Increasingly, observations of forest structure are needed at both finer resolutions and across greater extents to support spatially explicit management planning. Unmanned aerial system (UAS)-based photogrammetry provides an airborne method of forest structure data acquisition at a significantly lower cost and time commitment than existing methods such as airborne laser scanning (LiDAR). This study utilizes nearly 5,000 stem-mapped trees in ponderosa pine-dominated forests to evaluate several algorithms for detecting individual tree locations and characterizing crown area across tree sizes. Our results indicate that adaptive variable-window detection methods with UAS-based canopy height models have greater tree detection rates compared to fixed window analysis across a range of tree sizes. Using the UAS approach, probability of detecting individual trees decreases from 97% for dominant overstory to 67% for suppressed understory trees. Additionally, crown radii were correctly determined within 0.5 m for approximately two-thirds of sampled trees. These findings highlight the potential for UAS photogrammetry to characterize forest structure through the detection of trees and tree groups in open-canopy ponderosa pine forests. Further work should investigate how these methods transfer to more diverse species compositions and forest structures.

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Accuracy Assessment of Point Clouds from LiDAR and Dense Image Matching Acquired Using the UAV Platform for DTM Creation

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7090342 ◽

2018 ◽

Vol 7 (9) ◽

pp. 342 ◽

Cited By ~ 28

Author(s):

Adam Salach ◽

Krzysztof Bakuła ◽

Magdalena Pilarska ◽

Wojciech Ostrowski ◽

Konrad Górski ◽

...

Keyword(s):

Land Cover ◽

Laser Scanning ◽

Point Clouds ◽

Data Sources ◽

Digital Terrain Models ◽

Vegetation Height ◽

Digital Terrain ◽

Terrain Models ◽

Airborne Laser ◽

Vertical Accuracy

In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and possibilities of penetration of the point cloud from the image-based approach, whilst referring to various types of land cover, was conducted based on Real Time Kinematic Global Navigation Satellite System (GNSS-RTK) measurements and was compared to archival airborne laser scanning data. The vertical accuracy of DTM was evaluated for uncovered and vegetation areas separately, providing information about the influence of the vegetation height on the results of the bare ground extraction and DTM generation. In uncovered and low vegetation areas (0–20 cm), the vertical accuracies of digital terrain models generated from different data sources were quite similar: for the UAV Laser Scanning (ULS) data, the RMSE was 0.11 m, and for the image-based data collected using the UAV platform, it was 0.14 m, whereas for medium vegetation (higher than 60 cm), the RMSE from these two data sources were 0.11 m and 0.36 m, respectively. A decrease in the accuracy of 0.10 m, for every 20 cm of vegetation height, was observed for photogrammetric data; and such a dependency was not noticed in the case of models created from the ULS data.

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