Using LiDAR Point Clouds in Determination of the Scots Pine Stands Spatial Structure Meaning in the Conservation of Lichen Communities in “Bory Tucholskie” National Park

2019 ◽  
Vol 31 (1) ◽  
pp. 85-103
Author(s):  
Piotr Wężyk ◽  
Paweł Hawryło ◽  
Marta Szostak ◽  
Karolina Zięba-Kulawik ◽  
Monika Winczek ◽  
...  
Keyword(s):  
Spatial Structure ◽  
National Park ◽  
Laser Scanning ◽  
Ground Level ◽  
Point Clouds ◽  
Organic Layer ◽  
Active Protection ◽  
Scanning Lidar ◽  
Pine Stands ◽  
Biometric Features

Abstract The aim of the research carried out in 2018 and financed by the Forest Fund was the analysis of biometric features and parameters of pine stands in the area of the “Bory Tucholskie” National Park (PNBT), where a program of active protection of lichen was initiated in 2017. Environmental analyses were conducted in relation to selected biometric features of trees and stands using laser scanning (LiDAR), including ULS (Unmanned Laser Scanning; RIEGL VUX-1) and TLS (Terrestrial Laser Scanning; FARO FOCUS 3D; X130). Thanks to the application of LiDAR technology, the structure of pine stands was precisely determined by means of a series of descriptive statistics characterizing the 3D spatial structure of vegetation. Using the Trees Crown Model (CHM), the analysis of the volume of tree crowns and the volume of space under canopy was performed. For the analysed sub-compartments, GIS solar analyses were carried out for the solar energy reaching the canopy and the ground level due to active protection of lichen. Multispectral photos were obtained using a specialized RedEdge-M camera (MicaSense) mounted on the UAV multi rotor platform Typhoon H520 (Yuneec). Flights with a thermal camera were also performed in order to detect places on the ground with high temperature. Plant indices: NDVI, NDRE, GNDVI and GRVI were also calculated for sub-compartments. The data obtained in 2017 and 2018 were the basis for spatial and temporal analyses of 4-D changes in stands which were related to the removal of some trees and organic layer (litter, moss layer).

scholarly journals Comparison of Airborne Laser Scanning of Low and High Above Ground Level for Selected Infrastructure Objects

2018 ◽  
Vol 8 (2) ◽  
pp. 89-96 ◽  
Author(s):  
J. Siwiec
Keyword(s):  
Laser Scanning ◽  
New Technology ◽  
Ground Level ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Structural Elements ◽  
Transmission Tower ◽  
Deviation Analysis ◽  
Airborne Laser ◽  
Internal Coherence

Abstract Along with the development of the technology of drone construction (UAV - Unmanned Aerial Vehicles), the number of applications of these solutions in the industry also grew. The aim of the research is to check the accuracy of data obtained using the new technology of UAV scanning and to compare them with one that is widely spread - high-altitude airborne Lidar, in terms of quality and spectrum of applications in industry and infrastructure. The research involved two infrastructure objects: a reinforced concrete one-span bridge and Lattice transmission tower with powerlines. The density of measurement, internal and external cohesion of point clouds obtained from both methods were compared. Plane fitting and deviation analysis were used. The data of UAV origin in both cases provided a sufficient density, allowing the recognition of structural elements, and internal coherence and precision of measurements important in modeling. The study shows that UAV mounted scanning may be used in the same applications as Airborne Lidar, as well as in other tasks requiring greater precision.

scholarly journals LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain

2018 ◽  
Vol 8 (11) ◽  
pp. 2318 ◽  
Author(s):  
Qingyuan Zhu ◽  
Jinjin Wu ◽  
Huosheng Hu ◽  
Chunsheng Xiao ◽  
Wei Chen
Keyword(s):  
Autonomous Vehicles ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Normal Vector ◽  
Registration Method ◽  
High Noise ◽  
Key Points ◽  
Scanning Lidar ◽  
Multisensor Data

When 3D laser scanning (LIDAR) is used for navigation of autonomous vehicles operated on unstructured terrain, it is necessary to register the acquired point cloud and accurately perform point cloud reconstruction of the terrain in time. This paper proposes a novel registration method to deal with uneven-density and high-noise of unstructured terrain point clouds. It has two steps of operation, namely initial registration and accurate registration. Multisensor data is firstly used for initial registration. An improved Iterative Closest Point (ICP) algorithm is then deployed for accurate registration. This algorithm extracts key points and builds feature descriptors based on the neighborhood normal vector, point cloud density and curvature. An adaptive threshold is introduced to accelerate iterative convergence. Experimental results are given to show that our two-step registration method can effectively solve the uneven-density and high-noise problem in registration of unstructured terrain point clouds, thereby improving the accuracy of terrain point cloud reconstruction.

scholarly journals An Evaluation of the Effects of UAS Flight Parameters on Digital Aerial Photogrammetry Processing and Dense-Cloud Production Quality in a Scots Pine Forest

2021 ◽  
Vol 13 (6) ◽  
pp. 1121
Author(s):  
Raul Sampaio de Lima ◽  
Mait Lang ◽  
Niall G. Burnside ◽  
Miguel Villoslada Peciña ◽  
Tauri Arumäe ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Laser Scanning ◽  
Model Fitting ◽  
Ground Level ◽  
Point Clouds ◽  
Production Quality ◽  
Unmanned Aerial Systems ◽  
Aerial Photogrammetry ◽  
Wide Range ◽  
Scots Pine Forest

The application of unmanned aerial systems (UAS) in forest research includes a wide range of equipment, systems, and flight settings, creating a need for enhancing data acquisition efficiency and quality. Thus, we assessed the effects of flying altitude and lateral and longitudinal overlaps on digital aerial photogrammetry (DAP) processing and the ability of its products to provide point clouds for forestry inventory. For this, we used 18 combinations of flight settings for data acquisition, and a nationwide airborne laser scanning (ALS) dataset as reference data. Linear regression was applied for modeling DAP quality indicators and model fitting quality as the function of flight settings; equivalence tests compared DAP- and ALS-products. Most of DAP-Digital Terrain Models (DTM) showed a moderate to high agreement (R2 > 0.70) when fitted to ALS-based models; nine models had a regression slope within the 1% region of equivalence. The best DAP-Canopy Height Model (CHM) was generated using ALS-DTM with an R2 = 0.42 when compared with ALS-CHM, indicating reduced similarity. Altogether, our results suggest that the optimal combination of flight settings should include a 90% lateral overlap, a 70% longitudinal overlap, and a minimum altitude of 120 m above ground level, independent of the availability of an ALS-derived DTM for height normalization. We also provided insights into the effects of flight settings on DAP outputs for future applications in similar forest stands, emphasizing the benefits of overlaps for comprehensive scene reconstruction and altitude for canopy surface detection.

scholarly journals GEOMETRY-BASED POINT CLOUD CLASSIFICATION USING HEIGHT DISTRIBUTIONS

2020 ◽  
Vol V-2-2020 ◽  
pp. 259-266
Author(s):  
F. Politz ◽  
M. Sester ◽  
C. Brenner
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Ground Level ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Training Data ◽  
Mountainous Regions ◽  
Dense Image ◽  
The Mean ◽  
Point Cloud Classification

Abstract. Semantic segmentation is one of the main steps in the processing chain for Airborne Laser Scanning (ALS) point clouds, but it is also one of the most labour intensive steps, as it requires many labelled examples to train a classifier. National mapping agencies (NMAs) have to acquire nationwide ALS data every couple of years for their duties. Having point clouds cover different terrains such as flat or mountainous regions, a classifier often requires a refinement using additional data from those specific terrains. In this study, we present an algorithm, which is able to classify point clouds of similar terrain types without requiring any additional training data and which is still able to achieve overall F1-Scores of over 90% in most setups. Our algorithm uses up to two height distributions within a single cell in a rasterized point cloud. For each distribution, the empirical mean and standard deviation are calculated, which are the input for a Convolutional Neural Network (CNN) classifier. Consequently, our approach only requires the geometry of point clouds, which enables also the usage of the same network structure for point clouds from other sensor systems such as Dense Image Matching. Since the mean ground level varies with the observed area, we also examined five different normalisation methods for our input in order to reduce the ground influence on the point clouds and thus increase its transferability towards other datasets. We test our trained networks on four different tests sets with the classes’ ground, building, water, non-ground and bridge.

Evaluation of the expediency of using thermal i magery data for decryption exogenous processes and vegetation

2018 ◽  
Vol 933 (3) ◽  
pp. 52-62
Author(s):  
V.S. Tikunov ◽  
I.A. Rylskiy ◽  
S.B. Lukatzkiy
Keyword(s):  
Spatial Data ◽  
Laser Scanning ◽  
Data Extraction ◽  
Spectral Band ◽  
Information Provision ◽  
Lidar Data ◽  
Thermal Imagery ◽  
Scanning Lidar ◽  
Field Works ◽  
Object Features

Innovative methods of aerial surveys changed approaches to information provision of projecting dramatically in last years. Nowadays there are several methods pretending to be the most efficient for collecting geospatial data intended for projecting – airborne laser scanning (LIDAR) data, RGB aerial imagery (forming 3D pointclouds) and orthoimages. Thermal imagery is one of the additional methods that can be used for projecting. LIDAR data is precise, it allows us to measure relief even under the vegetation, or to collect laser re-flections from wires, metal constructions and poles. Precision and completeness of the DEM, produced from LIDAR data, allows to define relief microforms. Airborne imagery (visual spectrum) is very widespread and can be easily depicted. Thermal images are more strange and less widespread, they use different way of image forming, and spectral features of ob-jects can vary in specific ways. Either way, the additional spectral band can be useful for achieving additional spatial data and different object features, it can minimize field works. Here different aspects of thermal imagery are described in comparison with RGB (visual) images, LIDAR data and GIS layers. The attempt to estimate the feasibility of thermal imag-es for new data extraction is made.

scholarly journals Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks

2021 ◽  
Vol 13 (11) ◽  
pp. 2135
Author(s):  
Jesús Balado ◽  
Pedro Arias ◽  
Henrique Lorenzo ◽  
Adrián Meijide-Rodríguez
Keyword(s):  
Laser Scanning ◽  
Ambient Noise ◽  
Point Clouds ◽  
Density Variation ◽  
Density Reduction ◽  
Manual Intervention ◽  
Point Density ◽  
Disturbance Analysis ◽  
Street Furniture

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.

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.

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

2021 ◽  
Vol 13 (3) ◽  
pp. 507
Author(s):  
Tasiyiwa Priscilla Muumbe ◽  
Jussi Baade ◽  
Jenia Singh ◽  
Christiane Schmullius ◽  
Christian Thau
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Laser Scanning ◽  
Spatial Scales ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Parameter Extraction ◽  
Airborne Lidar ◽  
Special Focus ◽  
Vegetation Parameter

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

scholarly journals Revealing Changes in the Stem Form and Volume Allocation in Diverse Boreal Forests Using Two-Date Terrestrial Laser Scanning

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 835
Author(s):  
Ville Luoma ◽  
Tuomas Yrttimaa ◽  
Ville Kankare ◽  
Ninni Saarinen ◽  
Jiri Pyörälä ◽  
...  
Keyword(s):  
Tree Growth ◽  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Stem Volume ◽  
Accurate Information ◽  
Stem Form ◽  
Monitoring Period ◽  
Significant Difference

Tree growth is a multidimensional process that is affected by several factors. There is a continuous demand for improved information on tree growth and the ecological traits controlling it. This study aims at providing new approaches to improve ecological understanding of tree growth by the means of terrestrial laser scanning (TLS). Changes in tree stem form and stem volume allocation were investigated during a five-year monitoring period. In total, a selection of attributes from 736 trees from 37 sample plots representing different forest structures were extracted from taper curves derived from two-date TLS point clouds. The results of this study showed the capability of point cloud-based methods in detecting changes in the stem form and volume allocation. In addition, the results showed a significant difference between different forest structures in how relative stem volume and logwood volume increased during the monitoring period. Along with contributing to providing more accurate information for monitoring purposes in general, the findings of this study showed the ability and many possibilities of point cloud-based method to characterize changes in living organisms in particular, which further promote the feasibility of using point clouds as an observation method also in ecological studies.

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