scholarly journals Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases

2020 ◽  
Vol 12 (14) ◽  
pp. 2276
Author(s):  
Laura Alonso ◽  
Juan Picos ◽  
Guillermo Bastos ◽  
Julia Armesto
Keyword(s):  
Open Access ◽  
Point Cloud ◽  
Castanea Sativa ◽  
Point Clouds ◽  
Coefficient Of Determination ◽  
National Database ◽  
Tree Level ◽  
Tree Plantations ◽  
Support Vector ◽  
Individual Trees

Highly fragmented land property hinders the planning and management of single species tree plantations. In such situations, acquiring information about the available resources is challenging. This study aims to propose a method to locate and characterize tree plantations in these cases. Galicia (Northwest of Spain) is an area where property is extremely divided into small parcels. European chestnut (Castanea sativa) plantations are an important source of income there; however, it is often difficult to obtain information about them due to their small size and scattered distribution. Therefore, we selected a Galician region with a high presence of chestnut plantations as a case study area in order to locate and characterize small plantations using open-access data. First, we detected the location of chestnut plantations applying a supervised classification for a combination of: Sentinel-2 images and the open-access low-density Light Detection and Ranging (LiDAR) point clouds, obtained from the untapped open-access LiDAR Spanish national database. Three classification algorithms were used: Random Forest (RF), Support Vector Machine (SVM), and XGBoost. We later characterized the plots at the tree-level using the LiDAR point-cloud. We detected individual trees and obtained their height applying a local maxima algorithm to a point-cloud-derived Canopy Height Model (CHM). We also calculated the crown surface of each tree by applying a method based on two-dimensional (2D) tree shape reconstruction and canopy segmentation to a projection of the LiDAR point cloud. Chestnut plantations were detected with an overall accuracy of 81.5%. Individual trees were identified with a detection rate of 96%. The coefficient of determination R2 value for tree height estimation was 0.83, while for the crown surface calculation it was 0.74. The accuracy achieved with these open-access databases makes the proposed procedure suitable for acquiring knowledge about the location and state of chestnut plantations as well as for monitoring their evolution.

scholarly journals A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR

2021 ◽  
Vol 13 (8) ◽  
pp. 1442
Author(s):  
Kaisen Ma ◽  
Yujiu Xiong ◽  
Fugen Jiang ◽  
Song Chen ◽  
Hua Sun
Keyword(s):  
Point Cloud ◽  
Local Maximum ◽  
Window Size ◽  
Point Clouds ◽  
High Density ◽  
Watershed Algorithm ◽  
Canopy Area ◽  
Individual Trees ◽  
Optimal Window ◽  
Cloud Density

Detecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract tree structural parameters. The method involves the following key steps: (1) unmanned aerial vehicle (UAV)-scanned, high-density laser point clouds were classified, and a vegetation point cloud density model (VPCDM) was established by analyzing the spatial density distribution of the classified vegetation point cloud in the plane projection; and (2) a local maximum algorithm with an optimal window size was used to detect tree seed points and to extract tree heights, and an improved watershed algorithm was used to extract the tree crowns. The proposed method was tested at three sites with different canopy coverage rates in a pine-dominated forest in northern China. The results showed that (1) the kappa coefficient between the proposed VPCDM and the commonly used CHM was 0.79, indicating that performance of the VPCDM is comparable to that of the CHM; (2) the local maximum algorithm with the optimal window size could be used to segment individual trees and obtain optimal single-tree segmentation accuracy and detection rate results; and (3) compared with the original watershed algorithm, the improved watershed algorithm significantly increased the accuracy of canopy area extraction. In conclusion, the proposed VPCDM may provide an innovative data segmentation model for light detection and ranging (LiDAR)-based high-density point clouds and enhance the accuracy of parameter extraction.

scholarly journals VALIDATION OF A UAV-DERIVED POINT CLOUD BY SEMANTIC CLASSIFICATION AND COMPARISON WITH TLS DATA

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 83-90
Author(s):  
M. Franzini ◽  
V. Casella ◽  
P. Marchese ◽  
M. Marini ◽  
G. Della Porta ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
Aerial Survey ◽  
Gradual Transition ◽  
Support Vector ◽  
Uniform Density ◽  
Mutual Distance ◽  
Semantic Classification ◽  
Cost Efficient

Abstract. Recent years showed a gradual transition from terrestrial to aerial survey thanks to the development of UAV and sensors for it. Many sectors benefited by this change among which geological one; drones are flexible, cost-efficient and can support outcrops surveying in many difficult situations such as inaccessible steep and high rock faces. The experiences acquired in terrestrial survey, with total stations, GNSS or terrestrial laser scanner (TLS), are not yet completely transferred to UAV acquisition. Hence, quality comparisons are still needed. The present paper is framed in this perspective aiming to evaluate the quality of the point clouds generated by an UAV in a geological context; data analysis was conducted comparing the UAV product with the homologous acquired with a TLS system. Exploiting modern semantic classification, based on eigenfeatures and support vector machine (SVM), the two point clouds were compared in terms of density and mutual distance. The UAV survey proves its usefulness in this situation with a uniform density distribution in the whole area and producing a point cloud with a quality comparable with the more traditional TLS systems.

scholarly journals A Single Point-Based Multilevel Features Fusion and Pyramid Neighborhood Optimization Method for ALS Point Cloud Classification

2019 ◽  
Vol 9 (5) ◽  
pp. 951 ◽  
Author(s):  
Yong Li ◽  
Guofeng Tong ◽  
Xiance Du ◽  
Xiang Yang ◽  
Jianjun Zhang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Single Point ◽  
Point Clouds ◽  
Optimization Method ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Angle Distribution ◽  
Features Fusion ◽  
Cloud Classification ◽  
Point Cloud Classification

3D point cloud classification has wide applications in the field of scene understanding. Point cloud classification based on points can more accurately segment the boundary region between adjacent objects. In this paper, a point cloud classification algorithm based on a single point multilevel features fusion and pyramid neighborhood optimization are proposed for a Airborne Laser Scanning (ALS) point cloud. First, the proposed algorithm determines the neighborhood region of each point, after which the features of each single point are extracted. For the characteristics of the ALS point cloud, two new feature descriptors are proposed, i.e., a normal angle distribution histogram and latitude sampling histogram. Following this, multilevel features of a single point are constructed by multi-resolution of the point cloud and multi-neighborhood spaces. Next, the features are trained by the Support Vector Machine based on a Gaussian kernel function, and the points are classified by the trained model. Finally, a classification results optimization method based on a multi-scale pyramid neighborhood constructed by a multi-resolution point cloud is used. In the experiment, the algorithm is tested by a public dataset. The experimental results show that the proposed algorithm can effectively classify large-scale ALS point clouds. Compared with the existing algorithms, the proposed algorithm has a better classification performance.

scholarly journals Nondestructive Determination of Nitrogen, Phosphorus and Potassium Contents in Greenhouse Tomato Plants Based on Multispectral Three-Dimensional Imaging

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5295 ◽  
Author(s):  
Guoxiang Sun ◽  
Yongqian Ding ◽  
Xiaochan Wang ◽  
Wei Lu ◽  
Ye Sun ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Point Cloud ◽  
Prediction Models ◽  
Three Dimensional ◽  
Point Clouds ◽  
Support Vector ◽  
3D Point Cloud ◽  
Tomato Plants ◽  
Nondestructive Determination ◽  
Cloud Models

Measurement of plant nitrogen (N), phosphorus (P), and potassium (K) levels are important for determining precise fertilization management approaches for crops cultivated in greenhouses. To accurately, rapidly, stably, and nondestructively measure the NPK levels in tomato plants, a nondestructive determination method based on multispectral three-dimensional (3D) imaging was proposed. Multiview RGB-D images and multispectral images were synchronously collected, and the plant multispectral reflectance was registered to the depth coordinates according to Fourier transform principles. Based on the Kinect sensor pose estimation and self-calibration, the unified transformation of the multiview point cloud coordinate system was realized. Finally, the iterative closest point (ICP) algorithm was used for the precise registration of multiview point clouds and the reconstruction of plant multispectral 3D point cloud models. Using the normalized grayscale similarity coefficient, the degree of spectral overlap, and the Hausdorff distance set, the accuracy of the reconstructed multispectral 3D point clouds was quantitatively evaluated, the average value was 0.9116, 0.9343 and 0.41 cm, respectively. The results indicated that the multispectral reflectance could be registered to the Kinect depth coordinates accurately based on the Fourier transform principles, the reconstruction accuracy of the multispectral 3D point cloud model met the model reconstruction needs of tomato plants. Using back-propagation artificial neural network (BPANN), support vector machine regression (SVMR), and gaussian process regression (GPR) methods, determination models for the NPK contents in tomato plants based on the reflectance characteristics of plant multispectral 3D point cloud models were separately constructed. The relative error (RE) of the N content by BPANN, SVMR and GPR prediction models were 2.27%, 7.46% and 4.03%, respectively. The RE of the P content by BPANN, SVMR and GPR prediction models were 3.32%, 8.92% and 8.41%, respectively. The RE of the K content by BPANN, SVMR and GPR prediction models were 3.27%, 5.73% and 3.32%, respectively. These models provided highly efficient and accurate measurements of the NPK contents in tomato plants. The NPK contents determination performance of these models were more stable than those of single-view models.

scholarly journals Machine Learning-Based Supervised Classification of Point Clouds Using Multiscale Geometric Features

2021 ◽  
Vol 10 (3) ◽  
pp. 187
Author(s):  
Muhammed Enes Atik ◽  
Zaide Duran ◽  
Dursun Zafer Seker
Keyword(s):  
Machine Learning ◽  
Point Cloud ◽  
Supervised Classification ◽  
Point Clouds ◽  
Support Vector ◽  
Geometric Features ◽  
Mathematical Tool ◽  
City Area ◽  
3D Point Clouds

3D scene classification has become an important research field in photogrammetry, remote sensing, computer vision and robotics with the widespread usage of 3D point clouds. Point cloud classification, called semantic labeling, semantic segmentation, or semantic classification of point clouds is a challenging topic. Machine learning, on the other hand, is a powerful mathematical tool used to classify 3D point clouds whose content can be significantly complex. In this study, the classification performance of different machine learning algorithms in multiple scales was evaluated. The feature spaces of the points in the point cloud were created using the geometric features generated based on the eigenvalues of the covariance matrix. Eight supervised classification algorithms were tested in four different areas from three datasets (the Dublin City dataset, Vaihingen dataset and Oakland3D dataset). The algorithms were evaluated in terms of overall accuracy, precision, recall, F1 score and process time. The best overall results were obtained for four test areas with different algorithms. Dublin City Area 1 was obtained with Random Forest as 93.12%, Dublin City Area 2 was obtained with a Multilayer Perceptron algorithm as 92.78%, Vaihingen was obtained as 79.71% with Support Vector Machines and Oakland3D with Linear Discriminant Analysis as 97.30%.

scholarly journals Comparison of Backpack, Handheld, Under-Canopy UAV, and Above-Canopy UAV Laser Scanning for Field Reference Data Collection in Boreal Forests

2020 ◽  
Vol 12 (20) ◽  
pp. 3327 ◽  
Author(s):  
Eric Hyyppä ◽  
Xiaowei Yu ◽  
Harri Kaartinen ◽  
Teemu Hakala ◽  
Antero Kukko ◽  
...  
Keyword(s):  
Data Collection ◽  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Tree Height ◽  
Tree Level ◽  
Stem Volume ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Individual Trees

In this work, we compared six emerging mobile laser scanning (MLS) technologies for field reference data collection at the individual tree level in boreal forest conditions. The systems under study were an in-house developed AKHKA-R3 backpack laser scanner, a handheld Zeb-Horizon laser scanner, an under-canopy UAV (Unmanned Aircraft Vehicle) laser scanning system, and three above-canopy UAV laser scanning systems providing point clouds with varying point densities. To assess the performance of the methods for automated measurements of diameter at breast height (DBH), stem curve, tree height and stem volume, we utilized all of the six systems to collect point cloud data on two 32 m-by-32 m test sites classified as sparse (n = 42 trees) and obstructed (n = 43 trees). To analyze the data collected with the two ground-based MLS systems and the under-canopy UAV system, we used a workflow based on our recent work featuring simultaneous localization and mapping (SLAM) technology, a stem arc detection algorithm, and an iterative arc matching algorithm. This workflow enabled us to obtain accurate stem diameter estimates from the point cloud data despite a small but relevant time-dependent drift in the SLAM-corrected trajectory of the scanner. We found out that the ground-based MLS systems and the under-canopy UAV system could be used to measure the stem diameter (DBH) with a root mean square error (RMSE) of 2–8%, whereas the stem curve measurements had an RMSE of 2–15% that depended on the system and the measurement height. Furthermore, the backpack and handheld scanners could be employed for sufficiently accurate tree height measurements (RMSE = 2–10%) in order to estimate the stem volumes of individual trees with an RMSE of approximately 10%. A similar accuracy was obtained when combining stem curves estimated with the under-canopy UAV system and tree heights extracted with an above-canopy flying laser scanning unit. Importantly, the volume estimation error of these three MLS systems was found to be of the same level as the error corresponding to manual field measurements on the two test sites. To analyze point cloud data collected with the three above-canopy flying UAV systems, we used a random forest model trained on field reference data collected from nearby plots. Using the random forest model, we were able to estimate the DBH of individual trees with an RMSE of 10–20%, the tree height with an RMSE of 2–8%, and the stem volume with an RMSE of 20–50%. Our results indicate that ground-based and under-canopy MLS systems provide a promising approach for field reference data collection at the individual tree level, whereas the accuracy of above-canopy UAV laser scanning systems is not yet sufficient for predicting stem attributes of individual trees for field reference data with a high accuracy.

scholarly journals IMPROVING GPS TRAJECTORIES USING 3D CITY MODELS AND KINEMATIC POINT CLOUDS

2019 ◽  
Vol IV-4/W9 ◽  
pp. 35-42 ◽  
Author(s):  
Y. Dehbi ◽  
L. Lucks ◽  
J. Behmann ◽  
L. Klingbeil ◽  
L. Plümer
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Urban Environments ◽  
Support Vector ◽  
Mobile Mapping ◽  
General Applicability ◽  
Data Set ◽  
Building Models

Abstract. Accurate and robust positioning of vehicles in urban environments is of high importance for many applications (e.g. autonomous driving or mobile mapping). In the case of mobile mapping systems, a simultaneous mapping of the environment using laser scanning and an accurate positioning using GNSS is targeted. This requirement is often not guaranteed in shadowed cities where GNSS signals are usually disturbed, weak or even unavailable. Both, the generated point clouds and the derived trajectory are consequently imprecise. We propose a novel approach which incorporates prior knowledge, i.e. 3D building model of the environment, and improves the point cloud and the trajectory. The key idea is to benefit from the complementarity of both GNSS and 3D building models. The point cloud is matched to the city model using a point-to-plane ICP. An informed sampling of appropriate matching points is enabled by a pre-classification step. Support vector machines (SVMs) are used to discriminate between facade and remaining points. Local inconsistencies are tackled by a segment-wise partitioning of the point cloud where an interpolation guarantees a seamless transition between the segments. The full processing chain is implemented from the detection of facades in the point clouds, the matching between them and the building models and the update of the trajectory estimate. The general applicability of the implemented method is demonstrated on an inner city data set recorded with a mobile mapping system.

scholarly journals CLASSIFICATION OF MOBILE TERRESTRIAL LIDAR POINT CLOUD IN URBAN AREA USING LOCAL DESCRIPTORS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 1117-1122 ◽  
Author(s):  
M. Zaboli ◽  
H. Rastiveis ◽  
A. Shams ◽  
B. Hosseiny ◽  
W. A. Sarasua
Keyword(s):  
Urban Area ◽  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Point Clouds ◽  
Automated Analysis ◽  
Support Vector ◽  
Terrestrial Lidar ◽  
Local Descriptors ◽  
3D Point Clouds

Abstract. Automated analysis of three-dimensional (3D) point clouds has become a boon in Photogrammetry, Remote Sensing, Computer Vision, and Robotics. The aim of this paper is to compare classifying algorithms tested on an urban area point cloud acquired by a Mobile Terrestrial Laser Scanning (MTLS) system. The algorithms were tested based on local geometrical and radiometric descriptors. In this study, local descriptors such as linearity, planarity, intensity, etc. are initially extracted for each point by observing their neighbor points. These features are then imported to a classification algorithm to automatically label each point. Here, five powerful classification algorithms including k-Nearest Neighbors (k-NN), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Multilayer Perceptron (MLP) Neural Network, and Random Forest (RF) are tested. Eight semantic classes are considered for each method in an equal condition. The best overall accuracy of 90% was achieved with the RF algorithm. The results proved the reliability of the applied descriptors and RF classifier for MTLS point cloud classification.

scholarly journals A Comparison of ALS and Dense Photogrammetric Point Clouds for Individual Tree Detection in Radiata Pine Plantations

2021 ◽  
Vol 13 (17) ◽  
pp. 3536
Author(s):  
Irfan A. Iqbal ◽  
Jon Osborn ◽  
Christine Stone ◽  
Arko Lucieer
Keyword(s):  
Forest Inventory ◽  
Point Cloud ◽  
Point Clouds ◽  
Tree Level ◽  
Point Cloud Data ◽  
Individual Tree ◽  
Detection Rates ◽  
Cloud Data ◽  
Tree Detection ◽  
Structural Metrics

Digital aerial photogrammetry (DAP) has emerged as a potentially cost-effective alternative to airborne laser scanning (ALS) for forest inventory methods that employ point cloud data. Forest inventory derived from DAP using area-based methods has been shown to achieve accuracy similar to that of ALS data. At the tree level, individual tree detection (ITD) algorithms have been developed to detect and/or delineate individual trees either from ALS point cloud data or from ALS- or DAP-based canopy height models. An examination of the application of ITDs to DAP-based point clouds has not yet been reported. In this research, we evaluate the suitability of DAP-based point clouds for individual tree detection in the Pinus radiata plantation. Two ITD algorithms designed to work with point cloud data are applied to dense point clouds generated from small- and medium-format photography and to an ALS point cloud. Performance of the two ITD algorithms, the influence of stand structure on tree detection rates, and the relationship between tree detection rates and canopy structural metrics are investigated. Overall, we show that there is a good agreement between ALS- and DAP-based ITD results (proportion of false negatives for ALS, SFP, and MFP was always lower than 29.6%, 25.3%, and 28.6%, respectively, whereas, the proportion of false positives for ALS, SFP, and MFP was always lower than 39.4%, 30.7%, and 33.7%, respectively). Differences between small- and medium-format DAP results were minor (for SFP and MFP, differences between recall, precision, and F-score were always less than 0.08, 0.03, and 0.05, respectively), suggesting that DAP point cloud data is robust for ITD. Our results show that among all the canopy structural metrics, the number of trees per hectare has the greatest influence on the tree detection rates.

scholarly journals Monitoring Selective Logging in a Pine-Dominated Forest in Central Germany with Repeated Drone Flights Utilizing A Low Cost RTK Quadcopter

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 11 ◽  
Author(s):  
Christian Thiel ◽  
Marlin M. Müller ◽  
Christian Berger ◽  
Felix Cremer ◽  
Clémence Dubois ◽  
...  
Keyword(s):  
Low Cost ◽  
Three Dimensional ◽  
Point Clouds ◽  
Selective Logging ◽  
Ease Of Use ◽  
Tree Level ◽  
Unmanned Aerial Systems ◽  
3D Point Clouds ◽  
Individual Trees ◽  
New Generation

There is no doubt that unmanned aerial systems (UAS) will play an increasing role in Earth observation in the near future. The field of application is very broad and includes aspects of environmental monitoring, security, humanitarian aid, or engineering. In particular, drones with camera systems are already widely used. The capability to compute ultra-high-resolution orthomosaics and three-dimensional (3D) point clouds from UAS imagery generates a wide interest in such systems, not only in the science community, but also in industry and agencies. In particular, forestry sciences benefit from ultra-high-structural and spectral information as regular tree level-based monitoring becomes feasible. There is a great need for this kind of information as, for example, due to the spring and summer droughts in Europe in the years 2018/2019, large quantities of individual trees were damaged or even died. This study focuses on selective logging at the level of individual trees using repeated drone flights. Using the new generation of UAS, which allows for sub-decimeter-level positioning accuracies, a change detection approach based on bi-temporal UAS acquisitions was implemented. In comparison to conventional UAS, the effort of implementing repeated drone flights in the field was low, because no ground control points needed to be surveyed. As shown in this study, the geometrical offset between the two collected datasets was below 10 cm across the site, which enabled a direct comparison of both datasets without the need for post-processing (e.g., image matching). For the detection of logged trees, we utilized the spectral and height differences between both acquisitions. For their delineation, an object-based approach was employed, which was proven to be highly accurate (precision = 97.5%; recall = 91.6%). Due to the ease of use of such new generation, off-the-shelf consumer drones, their decreasing purchase costs, the quality of available workflows for data processing, and the convincing results presented here, UAS-based data can and should complement conventional forest inventory practices.

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