scholarly journals Identification of Linear Vegetation Elements in a Rural Landscape Using LiDAR Point Clouds

2019 ◽  
Vol 11 (3) ◽  
pp. 292 ◽  
Author(s):  
Chris Lucas ◽  
Willem Bouten ◽  
Zsófia Koma ◽  
W. Kissling ◽  
Arie Seijmonsbergen
Keyword(s):  
Regional Planning ◽  
Point Cloud ◽  
Agricultural Land ◽  
3D Structure ◽  
Point Clouds ◽  
Bare Soil ◽  
Agricultural Landscapes ◽  
Rural Landscape ◽  
Conservation Strategies ◽  
Rural Landscapes

Modernization of agricultural land use across Europe is responsible for a substantial decline of linear vegetation elements such as tree lines, hedgerows, riparian vegetation, and green lanes. These linear objects have an important function for biodiversity, e.g., as ecological corridors and local habitats for many animal and plant species. Knowledge on their spatial distribution is therefore essential to support conservation strategies and regional planning in rural landscapes but detailed inventories of such linear objects are often lacking. Here, we propose a method to detect linear vegetation elements in agricultural landscapes using classification and segmentation of high-resolution Light Detection and Ranging (LiDAR) point data. To quantify the 3D structure of vegetation, we applied point cloud analysis to identify point-based and neighborhood-based features. As a preprocessing step, we removed planar surfaces such as grassland, bare soil, and water bodies from the point cloud using a feature that describes to what extent the points are scattered in the local neighborhood. We then applied a random forest classifier to separate the remaining points into vegetation and other. Subsequently, a rectangularity-based region growing algorithm allowed to segment the vegetation points into 2D rectangular objects, which were then classified into linear objects based on their elongatedness. We evaluated the accuracy of the linear objects against a manually delineated validation set. The results showed high user’s (0.80), producer’s (0.85), and total accuracies (0.90). These findings are a promising step towards testing our method in other regions and for upscaling it to broad spatial extents. This would allow producing detailed inventories of linear vegetation elements at regional and continental scales in support of biodiversity conservation and regional planning in agricultural and other rural landscapes.

scholarly journals INVESTIGATING FULLY CONVOLUTIONAL NETWORK TO SEMANTIC LABELLING OF BATHYMETRIC POINT CLOUD

2020 ◽  
Vol V-2-2020 ◽  
pp. 657-663
Author(s):  
S. Daniel ◽  
V. Dupont
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
3D Structure ◽  
Point Clouds ◽  
Training Data ◽  
Strong Impact ◽  
Convolutional Network ◽  
Real Time Processing ◽  
Time Processing ◽  
Fully Convolutional Network

Abstract. The benefit of autonomous vehicles in hydrography is largely based on the ability of these platforms to carry out survey campaigns in a fully autonomous manner. One solution is to have real-time processing onboard the survey vessel. To meet this real-time processing goal, deep learning based-models are favored. Although Artificial Intelligence (AI) is booming, the main studies have been devoted to optical images and more recently, to LIDAR point clouds. However, little attention has been paid to the underwater environment. In this paper, we present an investigation into the adaptation of deep neural network to multi-beam echo-sounder (MBES) point cloud in order to classify sea-bottom morphology. More precisely, the paper investigates whether fully convolutional network can be trained while using the native 3D structure of the point cloud. A preprocessing approach is provided in order to overcome the lack of adequate training data. The results reported from the test data sets show the level of complexity related to natural, underwater terrain features where a classification accuracy no better than 65% can be reached when 2 micro topographic classes are used. Point density and resolution have a strong impact on the seabed morphology thereby affecting the classification scheme.

Towards the automatic 3D characterization of forest plots

2020 ◽  
Author(s):  
Carlos Cabo ◽  
Celestino Ordoñez ◽  
Covadonga Prendes ◽  
Stefan Doerr ◽  
Jose V. Roces-Diaz ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
3D Structure ◽  
Tree Height ◽  
Point Clouds ◽  
Forest Plot ◽  
Multiscale Approach ◽  
Plot Structure

<p>Ground-based point clouds (from laser scanning or photogrammetry, and from static or mobile devices) give very detailed 3D information of forest plots. Also, if this information is complemented with data gathered from aerial vehicles, some parts of the forest structure that are not visible from the terrain can be represented (e.g. treetops). However, the heterogeneity of the point clouds, the complexity of some forest plots and the limitations of some data gathering/processing techniques lead to some occlusions and misrepresentations of the features in the plot. Therefore, complete automation of very detailed characterizations of all the items/features/structures in a forest plot is, most of the times, not possible yet.</p><p>On one hand, single trees (or small groups of them) can be modelled in detail from dense point clouds (e.g. using quantitative structure models), but this processes usually require  complete absence of leaves and  intense and/or active operator labouring. On the other hand, many methods automate the location of the trees in a plot and the estimation of basic parameters, like the diameters and, sometimes, the total tree height.</p><p>We are developing a fully automatic method that lies in between some very accurate but labour-intensive single-tree models, and the mere location and diameter calculation of the trees in a plot. Our method is able to automatically detect and locate the trees in a plot and calculate diameters, but it is also able to characterize the 3D tree structure: stem model, inclination and curvature; inclination and location of the main branches (in some cases); and tree crown individualization and diameter estimation. In addition, our method also classifies the points on understory vegetation.</p><p>Our method relies on the integration of algorithms that have been developed by our team, and includes the development of new modules. The first step consists in an initial classification of the point cloud using a multiscale approach based on local shapes. As a result, the point cloud is preliminarily classified into three classes: stems, branches and leaves, and ground. After that, a series of geometric operations lead to the final 3D characterization of the plot structure: (i) stem axes and section modelling (from the pre-classified points on the stems), (ii) distance points-closest stem axis and tree individualization, (iii) extraction and characterization of the main branches, and (iv) final classification of the points laying on stems, main branches, rest of the canopy, understory and ground.</p><p>We are testing the algorithm in several forest plots with coniferous and broadleaf trees. Initial results show values of completeness and correctness for tree detection and point classification over 90%.</p><p>Currently, there are already several cross-cutting projects using our method´s results as inputs: (i) Automatic calculation of taper functions (use: diameters along the stem and tree height), (ii) wood quality based on shape (use: diameters along the stem and insertion of main branches), and (iii) wildfire behaviour models (use: fuel classification and 3D structure to adapt the data to the format of the existing 3D fuel standard models).</p>

scholarly journals On-Ground Vineyard Reconstruction Using a LiDAR-Based Automated System

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1102 ◽  
Author(s):  
Hugo Moreno ◽  
Constantino Valero ◽  
José María Bengochea-Guevara ◽  
Ángela Ribeiro ◽  
Miguel Garrido-Izard ◽  
...  
Keyword(s):  
Point Cloud ◽  
3D Structure ◽  
Point Clouds ◽  
Light Detection And Ranging ◽  
Sensor Data ◽  
Automated System ◽  
Light Detection ◽  
3D Point Clouds ◽  
High Sampling ◽  
Rtk Gps

Crop 3D modeling allows site-specific management at different crop stages. In recent years, light detection and ranging (LiDAR) sensors have been widely used for gathering information about plant architecture to extract biophysical parameters for decision-making programs. The study reconstructed vineyard crops using light detection and ranging (LiDAR) technology. Its accuracy and performance were assessed for vineyard crop characterization using distance measurements, aiming to obtain a 3D reconstruction. A LiDAR sensor was installed on-board a mobile platform equipped with an RTK-GNSS receiver for crop 2D scanning. The LiDAR system consisted of a 2D time-of-flight sensor, a gimbal connecting the device to the structure, and an RTK-GPS to record the sensor data position. The LiDAR sensor was facing downwards installed on-board an electric platform. It scans in planes perpendicular to the travel direction. Measurements of distance between the LiDAR and the vineyards had a high spatial resolution, providing high-density 3D point clouds. The 3D point cloud was obtained containing all the points where the laser beam impacted. The fusion of LiDAR impacts and the positions of each associated to the RTK-GPS allowed the creation of the 3D structure. Although point clouds were already filtered, discarding points out of the study area, the branch volume cannot be directly calculated, since it turns into a 3D solid cluster that encloses a volume. To obtain the 3D object surface, and therefore to be able to calculate the volume enclosed by this surface, a suitable alpha shape was generated as an outline that envelops the outer points of the point cloud. The 3D scenes were obtained during the winter season when only branches were present and defoliated. The models were used to extract information related to height and branch volume. These models might be used for automatic pruning or relating this parameter to evaluate the future yield at each location. The 3D map was correlated with ground truth, which was manually determined, pruning the remaining weight. The number of scans by LiDAR influenced the relationship with the actual biomass measurements and had a significant effect on the treatments. A positive linear fit was obtained for the comparison between actual dry biomass and LiDAR volume. The influence of individual treatments was of low significance. The results showed strong correlations with actual values of biomass and volume with R2 = 0.75, and when comparing LiDAR scans with weight, the R2 rose up to 0.85. The obtained values show that this LiDAR technique is also valid for branch reconstruction with great advantages over other types of non-contact ranging sensors, regarding a high sampling resolution and high sampling rates. Even narrow branches were properly detected, which demonstrates the accuracy of the system working on difficult scenarios such as defoliated crops.

scholarly journals OPACITY-BASED EDGE HIGHLIGHTING FOR TRANSPARENT VISUALIZATION OF 3D SCANNED POINT CLOUDS

2020 ◽  
Vol V-2-2020 ◽  
pp. 373-380
Author(s):  
K. Kawakami ◽  
K. Hasegawa ◽  
L. Li ◽  
H. Nagata ◽  
M. Adachi ◽  
...  
Keyword(s):  
Real World ◽  
Point Cloud ◽  
Large Scale ◽  
3D Structure ◽  
Point Clouds ◽  
High Quality ◽  
3D Images ◽  
3D Structures ◽  
3D Objects ◽  
Scale Point

Abstract. The recent development of 3D scanning technologies has made it possible to quickly and accurately record various 3D objects in the real world. The 3D scanned data take the form of large-scale point clouds, which describe complex 3D structures of the target objects and the surrounding scenes. The complexity becomes significant in cases that a scanned object has internal 3D structures, and the acquired point cloud is created by merging the scanning results of both the interior and surface shapes. To observe the whole 3D structure of such complex point-based objects, the point-based transparent visualization, which we recently proposed, is useful because we can observe the internal 3D structures as well as the surface shapes based on high-quality see-through 3D images. However, transparent visualization sometimes shows us too much information so that the generated images become confusing. To address this problem, in this paper, we propose to combine “edge highlighting” with transparent visualization. This combination makes the created see-through images quite understandable because we can highlight the 3D edges of visualized shapes as high-curvature areas. In addition, to make the combination more effective, we propose a new edge highlighting method applicable to 3D scanned point clouds. We call the method “opacity-based edge highlighting,” which appropriately utilizes the effect of transparency to make the 3D edge regions look clearer. The proposed method works well for both sharp (high-curvature) and soft (low-curvature) 3D edges. We show several experiments that demonstrate our method’s effectiveness by using real 3D scanned point clouds.

scholarly journals Monitoring Traditional Rural Landscapes. The Case of Italy

2019 ◽  
Vol 11 (21) ◽  
pp. 6107 ◽  
Author(s):  
Mauro Agnoletti ◽  
Francesca Emanueli ◽  
Federica Corrieri ◽  
Martina Venturi ◽  
Antonio Santoro
Keyword(s):  
Land Use ◽  
Monitoring System ◽  
Biological Diversity ◽  
National Level ◽  
Agricultural Landscapes ◽  
Rural Landscape ◽  
Convention On Biological Diversity ◽  
The European Union ◽  
Rural Landscapes ◽  
National Monitoring

The importance of rural landscapes is recognized at both the international and national level. The Food and Agriculture Organization (FAO) has established a program called Globally Important Agricultural Heritage Systems (GIAHS) and agricultural landscapes are also listed in the UNESCO World Heritage List. The World Bank and the Convention on Biological Diversity also have departments working on this topic, while landscape has been included in the Common Agricultural Policy of the European Union 2020–2027. One of the most important tools for landscape management, conservation and valorization is the development of a monitoring system, suited to control not only dynamics, but also the effectiveness of the policies affecting rural landscape. A research project of the Italian Ministry of Agricultural, Food and Forestry Policies has identified 123 areas scattered in the entire Italian territory, with an average size of 1300 ha, in order to establish a national monitoring system for traditional rural landscapes. As a result of this national survey, the Ministry decided to establish the National Register of Historical Rural Landscapes, that is also the Italian list for potential application to GIAHS. These landscapes are characterized by a long history, presence of traditional practices, typical foods, complex landscape mosaics and high biocultural diversity. Detailed land use maps have been produced for each area, and among other data, the average number of land use types (19.6 ha) and the average patch size (2.7 ha) detected, confirm the fine grain of these landscapes characterized by high complexity and diversity of the landscape structure. A second survey was carried out five years later, in order to create a national monitoring system based on fixed study areas. The paper shows that in the last five years no major changes occurred, and even in the 33 areas where transformations are considered significant (i.e., >5% of the surface of the area), the characteristic features of the historical landscape are still well preserved. This confirms the resilience of these systems despite climatic and socioeconomic pressures.

scholarly journals Fee Simple Failures: Rural Landscapes and Race

2021 ◽  
pp. 1695
Author(s):  
Jessica Shoemaker
Keyword(s):  
Rural Communities ◽  
Land Tenure ◽  
Agricultural Land ◽  
Rural Landscape ◽  
Environmental Crisis ◽  
Property Law ◽  
Private Land ◽  
Real Property ◽  
Rural Landscapes ◽  
Rural Places

Property law’s roots are rural. America pursued an early agrarian vision that understood real property rights as instrumental to achieving a country of free, engaged citizens who cared for their communities and stewarded their physical place in it. But we have drifted far from this ideal. Today, American agriculture is industrialized, and rural communities are in decline. The fee simple ownership form has failed every agrarian objective but one: the maintenance of white landownership. For it was also embedded in the original American experiment that land ownership would be racialized for the benefit of its white citizens, through acts of colonialism, slavery, and explicit race-based exclusion in property law. Today, rather than undoing this racialized legacy, modern property rules only further concentrate and homogenize rural landownership. Agricultural landownership remains almost entirely— 98 percent—white. This is a critical racial justice issue that converges directly with our impending environmental crisis and the decline of rural communities more generally. This Article builds on work of rural sociologists and farm advocates who demonstrate, again and again, that despite a pervasive narrative of rural places dying for want of population and agricultural systems too far gone for reform, the reality is a crowd of emerging farmers—and farmers of color in particular— clamoring for access. Existing policy efforts to support beginning farmers have focused primarily on supporting a few private land transactions within existing systems. This Article brings property theory to the table for the first time, arguing that property law itself is not only responsible for the original racialized distributions of agricultural land but also actively perpetuates both ongoing racialized disparities and the currently industrialized and depopulated rural landscape. This Article deconstructs our most fundamental land-tenure choice—the fee simple itself—and calls on our collective legal imagination to develop more adaptive, inclusive, and dynamic land-tenure designs rooted in these otherwise overlooked rural places.

scholarly journals A novel algorithm of ship structure modeling and target identification based on point cloud for automation in bulk cargo terminals

2021 ◽  
pp. 002029402199280
Author(s):  
Yang Miao ◽  
Changan Li ◽  
Zhan Li ◽  
Yipeng Yang ◽  
Xinghu Yu
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
Feature Matching ◽  
3D Structure ◽  
Point Clouds ◽  
Container Terminals ◽  
Identification Algorithm ◽  
Spherical Coordinate ◽  
3D Point Clouds ◽  
2D Images

Achieving port automation of machinery at bulk terminals is a challenging problem due to the volatile operation environments and complexity of bulk loading compared to the situations in container terminals. In order to facilitate port automation, we present a method of hull modeling (reconstruction of hull’s structure) and operation target (cargo holds under loading) identification based on 3D point cloud collected by Laser Measurement System mounted on the ship loader. In the hull modeling algorithm, we incrementally register pairs of point clouds and reconstruct the 3D structure of bulk ship’s hull blocks in details through process of encoder data of the loader, FPFH feature matching and ICP algorithm. In the identification algorithm, we project real-time point clouds of the operation zone to spherical coordinate and transforms the 3D point clouds to 2D images for fast and reliable identification of operation target. Our method detects and complements four edges of the operation target through process of the 2D images and estimates both posture and size of operation target in the bulk terminal based on the complemented edges. Experimental trials show that our algorithm allows us to achieve the reconstruction of hull blocks and real-time identification of operation target with high accuracy and reliability.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

scholarly journals 3D Thermal Monitoring of Jointed Rock Masses through Infrared Thermography and Photogrammetry

2021 ◽  
Vol 13 (5) ◽  
pp. 957
Author(s):  
Guglielmo Grechi ◽  
Matteo Fiorucci ◽  
Gian Marco Marmoni ◽  
Salvatore Martino
Keyword(s):  
Surface Temperature ◽  
Infrared Thermography ◽  
Point Cloud ◽  
Engineering Geology ◽  
Point Clouds ◽  
Jointed Rock ◽  
Geometric Accuracy ◽  
Rock Masses ◽  
Natural Systems ◽  
Temperature Distributions

The study of strain effects in thermally-forced rock masses has gathered growing interest from engineering geology researchers in the last decade. In this framework, digital photogrammetry and infrared thermography have become two of the most exploited remote surveying techniques in engineering geology applications because they can provide useful information concerning geomechanical and thermal conditions of these complex natural systems where the mechanical role of joints cannot be neglected. In this paper, a methodology is proposed for generating point clouds of rock masses prone to failure, combining the high geometric accuracy of RGB optical images and the thermal information derived by infrared thermography surveys. Multiple 3D thermal point clouds and a high-resolution RGB point cloud were separately generated and co-registered by acquiring thermograms at different times of the day and in different seasons using commercial software for Structure from Motion and point cloud analysis. Temperature attributes of thermal point clouds were merged with the reference high-resolution optical point cloud to obtain a composite 3D model storing accurate geometric information and multitemporal surface temperature distributions. The quality of merged point clouds was evaluated by comparing temperature distributions derived by 2D thermograms and 3D thermal models, with a view to estimating their accuracy in describing surface thermal fields. Moreover, a preliminary attempt was made to test the feasibility of this approach in investigating the thermal behavior of complex natural systems such as jointed rock masses by analyzing the spatial distribution and temporal evolution of surface temperature ranges under different climatic conditions. The obtained results show that despite the low resolution of the IR sensor, the geometric accuracy and the correspondence between 2D and 3D temperature measurements are high enough to consider 3D thermal point clouds suitable to describe surface temperature distributions and adequate for monitoring purposes of jointed rock mass.

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

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