Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

Unmanned aerial vehicle laser scanning (UAVLS) systems present a relatively new means of remote sensing and are increasingly applied in the field of forest ecology and management. However, one of the most essential parameters in forest inventory, tree diameter at breast height (DBH), cannot be directly extracted from aerial point cloud data due to the limitations of scanning angle and canopy obstruction. Therefore, in this study DBH-UAVLS point cloud estimation models were established using a generalized nonlinear mixed-effects (NLME) model. The experiments were conducted using Larix olgensis as the subject species, and a total of 8364 correctly delineated trees from UAVLS data within 118 plots across 11 sites were used for DBH modeling. Both tree- and plot-level metrics were obtained using light detection and ranging (LiDAR) and were used as the models’ independent predictors. The results indicated that the addition of site-level random effects significantly improved the model fitting. Compared with nonparametric modeling approaches (random forest and k-nearest neighbors) and uni- or multivariable weighted nonlinear least square regression through leave-one-site-out cross-validation, the NLME model with local calibration achieved the lowest root mean square error (RMSE) values (1.94 cm) and the most stable prediction across different sites. Using the site in a random-effects model improved the transferability of LiDAR-based DBH estimation. The best linear unbiased predictor (BLUP), used to conduct local model calibration, led to an improvement in the models’ performance as the number of field measurements increased. The research provides a baseline for unmanned aerial vehicle (UAV) small-scale forest inventories and might be a reasonable alternative for operational forestry.

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Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)

Canadian Journal of Forest Research ◽

10.1139/cjfr-2019-0418 ◽

2020 ◽

Vol 50 (10) ◽

pp. 1012-1024

Author(s):

Meimei Wang ◽

Jiayuan Lin

Keyword(s):

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Laser Scanning ◽

Tree Height ◽

Absolute Error ◽

Coefficient Of Determination ◽

Surface Model ◽

Individual Tree ◽

Aerial Vehicle ◽

The Mean

Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. Object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.

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Performance Analysis in Object Diameter Estimation based on Single and Multiple Non-Destructive and Discrete Infrared Sensors

Jurnal Teknologi ◽

10.11113/jt.v67.2028 ◽

2014 ◽

Vol 67 (1) ◽

Author(s):

Norashikin M. Thamrin ◽

Norhashim Mohd. Arshad ◽

Ramli Adnan ◽

Rosidah Sam ◽

Noorfazdli Abd. Razak ◽

...

Keyword(s):

Unmanned Aerial Vehicle ◽

Laser Scanning ◽

Pass Rate ◽

Small Scale ◽

Infrared Sensors ◽

Scanning Method ◽

Average Percentage ◽

Multiple Sensors ◽

Localization And Mapping ◽

Aerial Vehicle

In Simultaneous Localization and Mapping (SLAM) technique, recognizing and marking the landmarks in the environment is very important. Therefore, in a commercial farm, rows of trees, borderline of rows as well as the trees and other features are mostly used by the researchers in realizing the automation process in this field. In this paper, the detection of the tree based on its diameter is focused. There are few techniques available in determining the size of the tree trunk inclusive of the laser scanning method as well as image-based measurements. However, those techniques require heavy computations and equipments which become constraints in a lightweight unmanned aerial vehicle implementation. Therefore, in this paper, the detection of an object by using a single and multiple infrared sensors on a non-stationary automated vehicle platform is discussed. The experiments were executed on different size of objects in order to investigate the effectiveness of this proposed method. This work is initially tested on the ground, based in the lab environment by using an omni directional vehicle which later will be adapted on a small-scale unmanned aerial vehicle implementation for tree diameter estimation in the agriculture farm. In the current study, comparing multiple sensors with single sensor orientation showed that the average percentage of the pass rate in the pole recognition for the former is relatively more accurate than the latter with 93.2 percent and 74.2 percent, respectively.

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UNMANNED AERIAL VEHICLE LASER SCANNING FOR EROSION MONITORING IN ALPINE GRASSLAND

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-w5-405-2019 ◽

2019 ◽

Vol IV-2/W5 ◽

pp. 405-412 ◽

Cited By ~ 2

Author(s):

A. Mayr ◽

M. Bremer ◽

M. Rutzinger ◽

C. Geitner

Keyword(s):

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Laser Scanning ◽

Test Site ◽

Satellite System ◽

Alpine Grassland ◽

Proof Of Concept ◽

Aerial Vehicle ◽

Geomorphological Changes ◽

Global Navigation Satellite

Abstract. With this contribution we assess the potential of unmanned aerial vehicle (UAV) based laser scanning for monitoring shallow erosion in Alpine grassland. A 3D point cloud has been acquired by unmanned aerial vehicle laser scanning (ULS) at a test site in the subalpine/alpine elevation zone of the Dolomites (South Tyrol, Italy). To assess its accuracy, this point cloud is compared with (i) differential global navigation satellite system (GNSS) reference measurements and (ii) a terrestrial laser scanning (TLS) point cloud. The ULS point cloud and an airborne laser scanning (ALS) point cloud are rasterized into digital surface models (DSMs) and, as a proof-of-concept for erosion quantification, we calculate the elevation difference between the ULS DSM from 2018 and the ALS DSM from 2010. For contiguous spatial objects of elevation change, the volumetric difference is calculated and a land cover class (bare earth, grassland, trees), derived from the ULS reflectance and RGB colour, is assigned to each change object. In this test, the accuracy and density of the ALS point cloud is mainly limiting the detection of geomorphological changes. Nevertheless, the plausibility of the results is confirmed by geomorphological interpretation and documentation in the field. A total eroded volume of 672&thinsp;m3 is estimated for the test site (48&thinsp;ha). Such volumetric estimates of erosion over multiple years are a key information for improving sustainable soil management. Based on this proof-of-concept and the accuracy analysis, we conclude that repeated ULS campaigns are a well-suited tool for erosion monitoring in Alpine grassland.

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The Feasibility of Modelling the Crown Profile of Larix olgensis Using Unmanned Aerial Vehicle Laser Scanning Data

Sensors ◽

10.3390/s20195555 ◽

2020 ◽

Vol 20 (19) ◽

pp. 5555 ◽

Cited By ~ 1

Author(s):

Ying Quan ◽

Mingze Li ◽

Zhen Zhen ◽

Yuanshuo Hao ◽

Bin Wang

Keyword(s):

Unmanned Aerial Vehicle ◽

Laser Scanning ◽

Low Cost ◽

Pearson Correlation ◽

Point Clouds ◽

Larix Olgensis ◽

Individual Tree ◽

Profile Fitting ◽

Aerial Vehicle ◽

Individual Trees

Unmanned aerial vehicle (UAV) laser scanning, as an emerging form of near-ground light detection and ranging (LiDAR) remote sensing technology, is widely used for crown structure extraction due to its flexibility, convenience, and high point density. Herein, we evaluated the feasibility of using a low-cost UAV-LiDAR system to extract the fine-scale crown profile of Larix olgensis. Specifically, individual trees were isolated from LiDAR point clouds and then stratified from the point clouds of segmented individual tree crowns at 0.5 m intervals to obtain the width percentiles of each layer as profile points. Four equations (the parabola, Mitscherlich, power, and modified beta equations) were then applied to model the profiles of the entire and upper crown. The results showed that a region-based hierarchical cross-section analysis algorithm can successfully delineate 77.4% of the field-measured trees in high-density (>2400 trees/ha) forest stands. The crown profile generated with the 95th width percentile was adequate when compared with the predicted value of the existing field-based crown profile model (the Pearson correlation coefficient (ρ) was 0.864, root mean square error (RMSE) = 0.3354 m). The modified beta equation yielded slightly better results than the other equations for crown profile fitting and explained 85.9% of the variability in the crown radius for the entire crown and 87.8% of this variability for the upper crown. Compared with the cone and 3D convex hull volumes, the crown volumes predicted by our profile models had significantly smaller errors. The results revealed that the crown profile can be well described by using UAV-LiDAR, providing a novel way to obtain crown profile information without destructive sampling and showing the potential of the use of UAV-LiDAR in future forestry investigations and monitoring.

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Individual Tree Detection in a Eucalyptus Plantation Using Unmanned Aerial Vehicle (UAV)-LiDAR

Remote Sensing ◽

10.3390/rs12050885 ◽

2020 ◽

Vol 12 (5) ◽

pp. 885 ◽

Cited By ~ 4

Author(s):

Juan Picos ◽

Guillermo Bastos ◽

Daniel Míguez ◽

Laura Alonso ◽

Julia Armesto

Keyword(s):

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Single Point ◽

Tree Height ◽

Individual Tree ◽

Eucalyptus Plantation ◽

Local Maxima ◽

Eucalypt Plantations ◽

Aerial Vehicle ◽

Precision Forestry

The present study addresses the tree counting of a Eucalyptus plantation, the most widely planted hardwood in the world. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) was used for the estimation of Eucalyptus trees. LiDAR-based estimation of Eucalyptus is a challenge due to the irregular shape and multiple trunks. To overcome this difficulty, the layer of the point cloud containing the stems was automatically classified and extracted according to the height thresholds, and those points were horizontally projected. Two different procedures were applied on these points. One is based on creating a buffer around each single point and combining the overlapping resulting polygons. The other one consists of a two-dimensional raster calculated from a kernel density estimation with an axis-aligned bivariate quartic kernel. Results were assessed against the manual interpretation of the LiDAR point cloud. Both methods yielded a detection rate (DR) of 103.7% and 113.6%, respectively. Results of the application of the local maxima filter to the canopy height model (CHM) intensely depends on the algorithm and the CHM pixel size. Additionally, the height of each tree was calculated from the CHM. Estimates of tree height produced from the CHM was sensitive to spatial resolution. A resolution of 2.0 m produced a R2 and a root mean square error (RMSE) of 0.99 m and 0.34 m, respectively. A finer resolution of 0.5 m produced a more accurate height estimation, with a R2 and a RMSE of 0.99 and 0.44 m, respectively. The quality of the results is a step toward precision forestry in eucalypt plantations.

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Assessment of Individual Tree Detection and Canopy Cover Estimation using Unmanned Aerial Vehicle based Light Detection and Ranging (UAV-LiDAR) Data in Planted Forests

Remote Sensing ◽

10.3390/rs11080908 ◽

2019 ◽

Vol 11 (8) ◽

pp. 908 ◽

Cited By ~ 18

Author(s):

Xiangqian Wu ◽

Xin Shen ◽

Lin Cao ◽

Guibin Wang ◽

Fuliang Cao

Keyword(s):

Unmanned Aerial Vehicle ◽

Forest Inventory ◽

Canopy Cover ◽

Light Detection And Ranging ◽

Estimation Methods ◽

Lidar Data ◽

Tree Crown ◽

Polynomial Fitting ◽

Individual Tree ◽

Aerial Vehicle

Canopy cover is a key forest structural parameter that is commonly used in forest inventory, sustainable forest management and maintaining ecosystem services. Recently, much attention has been paid to the use of unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) due to the flexibility, convenience, and high point density advantages of this method. In this study, we used UAV-based LiDAR data with individual tree segmentation-based method (ITSM), canopy height model-based method (CHMM), and a statistical model method (SMM) with LiDAR metrics to estimate the canopy cover of a pure ginkgo (Ginkgo biloba L.) planted forest in China. First, each individual tree within the plot was segmented using watershed, polynomial fitting, individual tree crown segmentation (ITCS) and point cloud segmentation (PCS) algorithms, and the canopy cover was calculated using the segmented individual tree crown (ITSM). Second, the CHM-based method, which was based on the CHM height threshold, was used to estimate the canopy cover in each plot. Third, the canopy cover was estimated using the multiple linear regression (MLR) model and assessed by leave-one-out cross validation. Finally, the performance of three canopy cover estimation methods was evaluated and compared by the canopy cover from the field data. The results demonstrated that, the PCS algorithm had the highest accuracy (F = 0.83), followed by the ITCS (F = 0.82) and watershed (F = 0.79) algorithms; the polynomial fitting algorithm had the lowest accuracy (F = 0.77). In the sensitivity analysis, the three CHM-based algorithms (i.e., watershed, polynomial fitting and ITCS) had the highest accuracy when the CHM resolution was 0.5 m, and the PCS algorithm had the highest accuracy when the distance threshold was 2 m. In addition, the ITSM had the highest accuracy in estimation of canopy cover (R2 = 0.92, rRMSE = 3.5%), followed by the CHMM (R2 = 0.94, rRMSE = 5.4%), and the SMM had a relative low accuracy (R2 = 0.80, rRMSE = 5.9%).The UAV-based LiDAR data can be effectively used in individual tree crown segmentation and canopy cover estimation at plot-level, and CC estimation methods can provide references for forest inventory, sustainable management and ecosystem assessment.

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Spatial ETL for 3D Building Modelling Based on Unmanned Aerial Vehicle Data in Semi-Urban Areas

Remote Sensing ◽

10.3390/rs12121972 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1972 ◽

Cited By ~ 1

Author(s):

Urška Drešček ◽

Mojca Kosmatin Fras ◽

Jernej Tekavec ◽

Anka Lisec

Keyword(s):

Unmanned Aerial Vehicle ◽

Spatial Data ◽

Urban Areas ◽

Point Cloud ◽

Process Model ◽

Laser Scanning ◽

3D City Models ◽

Aerial Vehicle ◽

Spatial Etl ◽

City Models

This paper provides the innovative approach of using a spatial extract, transform, load (ETL) solution for 3D building modelling, based on an unmanned aerial vehicle (UAV) photogrammetric point cloud. The main objective of the paper is to present the holistic workflow for 3D building modelling, emphasising the benefits of using spatial ETL solutions for this purpose. Namely, despite the increasing demands for 3D city models and their geospatial applications, the generation of 3D city models is still challenging in the geospatial domain. Advanced geospatial technologies provide various possibilities for the mass acquisition of geospatial data that is further used for 3D city modelling, but there is a huge difference in the cost and quality of input data. While aerial photogrammetry and airborne laser scanning involve high costs, UAV photogrammetry has brought new opportunities, including for small and medium-sized companies, by providing a more flexible and low-cost source of spatial data for 3D modelling. In our data-driven approach, we use a spatial ETL solution to reconstruct a 3D building model from a dense image matching point cloud which was obtained beforehand from UAV imagery. The results are 3D building models in a semantic vector format consistent with the OGC CityGML standard, Level of Detail 2 (LOD2). The approach has been tested on selected buildings in a simple semi-urban area. We conclude that spatial ETL solutions can be efficiently used for 3D building modelling from UAV data, where the data process model developed allows the developer to easily control and manipulate each processing step.

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COMPARISON AND TIME SERIES ANALYSIS OF LANDSLIDE DISPLACEMENT MAPPED BY AIRBORNE, TERRESTRIAL AND UNMANNED AERIAL VEHICLE BASED PLATFORMS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-w5-421-2019 ◽

2019 ◽

Vol IV-2/W5 ◽

pp. 421-428 ◽

Cited By ~ 3

Author(s):

J. Pfeiffer ◽

T. Zieher ◽

M. Rutzinger ◽

M. Bremer ◽

V. Wichmann

Keyword(s):

Time Series ◽

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

Image Correlation ◽

Landslide Monitoring ◽

Displacement Vectors ◽

Aerial Vehicle ◽

Displacement Patterns

Abstract. Slow moving deep-seated gravitational slope deformations are threatening infrastructure and economic wellbeing in mountainous areas. Accelerating landslides may end up in a catastrophic slope failure in terms of rapid rock avalanches. Continuous landslide monitoring enables the identification of critical acceleration thresholds, which are required in natural hazard management. Among many existing monitoring methods, laser scanning is a cost effective method providing 3D data for deriving three dimensional and areawide displacement vectors at certain morphological structures travelling on top of the landslide. Comparing displacements between selected observation periods allows the spatial interpretation of landslide acceleration or deceleration. This contribution presents five laser scanning datasets of the active Reissenschuh landslide (Tyrol, Austria) acquired by airborne laser scanning (ALS), terrestrial laser scanning (TLS) and Unmanned aerial vehicle Laser Scanning (ULS) sensors. Three observation periods with acquisition dates between 2008 and 2018 are used to derive area-wide displacement vectors. To ensure a most suitable displacement derivation between ALS, TLS and ULS platforms, an analysis investigating point cloud features within varying search radii is carried out, in order to identify a neighbourhood where common surfaces are represented platform independent or differences between the platforms are minimized. Consequent displacement vector estimation is done by ICP-Matching using morphological structures within the high resolution TLS and ULS point cloud. Displacements from the lower resolution ALS point cloud and TLS point cloud were determined using a modified version of the well-known image correlation (IMCORR) method working with point cloud derived shaded relief images combined with digital terrain models (DTM). The interplatform compatible analyses of the multi-temporal laser scanning data allows to quantify the area-wide displacement patterns of the landslide. Furthermore, changes of these displacement patterns over time are assessed area-wide. Spatially varying areas of landslide acceleration and deceleration in the order of &plusmn;15&thinsp;cm&thinsp;a&minus;1 between 2008 and 2017 and an area wide acceleration of up to 20&thinsp;cm&thinsp;a&minus;1 between 2016 and 2018 are identified. Continuing the existing time series with future ULS acquisitions may enable a more complete and detailed displacement monitoring using entirely represented objects within the point clouds.

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ACCURACY OF SNOW DEPTH ESTIMATION IN MOUNTAIN AND PRAIRIE ENVIRONMENTS BY AN UNMANNED AERIAL VEHICLE

10.5194/tc-2016-9 ◽

2016 ◽

Cited By ~ 2

Author(s):

Phillip Harder ◽

Michael Schirmer ◽

John Pomeroy ◽

Warren Helgason

Keyword(s):

Unmanned Aerial Vehicle ◽

Snow Depth ◽

Laser Scanning ◽

Low Cost ◽

Depth Estimation ◽

Snow Accumulation ◽

Small Scale ◽

Canadian Prairie ◽

Aerial Vehicle ◽

Mean Square Errors

Abstract. The quantification of the spatial distribution of snow is crucial to predict and assess snow as a water resource and understand land-atmosphere interactions in cold regions. Typical remote sensing approaches to quantify snow depth have focused on terrestrial and airborne laser scanning and recently airborne (manned and unmanned) photogrammetry. In this study photography from a small unmanned aerial vehicle (UAV) was used to generate digital surface models (DSMs) and orthomosaics for snowcovers at a cultivated agricultural Canadian Prairie and a sparsely-vegetated Rocky Mountain alpine ridgetop site using Structure from Motion (SfM). The ability of this method to quantify snow depth, changes in depth and its spatial variability was assessed for different terrain types over time. Root mean square errors in snow depth estimation from the DSMs were 8.8 cm for a short prairie grain stubble surface, 13.7 cm for a tall prairie grain stubble surface and 8.5 cm for an alpine mountain surface. This technique provided meaningful information on maximum snow accumulation and snow-covered area depletion at all sites, while temporal changes in snow depth could also be quantified at the alpine site due to the deeper snowpack and consequent higher signal-to noise-ratio. The application of SfM to UAV photographs can estimate snow depth in areas with snow depth > 30 cm – this restricts its utility for studies of the ablation of shallow, windblown snowpacks. Accuracy varied with surface characteristics, sunlight and wind speed during the flight, with the most consistent performance found for wind speeds < 6 m s−1, clear skies, high sun angles and surfaces with negligible vegetation cover. Relative to surfaces having greater contrast and more identifiable features, snow surfaces present unique challenges when applying SfM to imagery collected by a small UAV for the generation of DSMs. Regardless, the low cost, deployment mobility and the capability of repeat-on-demand flights that generate DSMs and orthomosaics of unprecedented spatial resolution provide exciting opportunities to quantify previously unobservable small-scale variability in snow depth and its dynamics.

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