scholarly journals Estimating Individual Conifer Seedling Height Using Drone-Based Image Point Clouds

Forests ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 924 ◽  
Author(s):  
Guillermo Castilla ◽  
Michelle Filiatrault ◽  
Gregory J. McDermid ◽  
Michael Gartrell
Keyword(s):  
Point Clouds ◽  
Image Point ◽  
Tree Seedlings ◽  
Seedling Height ◽  
Determination Method ◽  
Estimation Errors ◽  
Conifer Seedlings ◽  
Seedling Size ◽  
Height Estimates ◽  
Conifer Seedling

Research Highlights: This is the most comprehensive analysis to date of the accuracy of height estimates for individual conifer seedlings derived from drone-based image point clouds (DIPCs). We provide insights into the effects on accuracy of ground sampling distance (GSD), phenology, ground determination method, seedling size, and more. Background and Objectives: Regeneration success in disturbed forests involves costly ground surveys of tree seedlings exceeding a minimum height. Here we assess the accuracy with which conifer seedling height can be estimated using drones, and how height errors translate into counting errors in stocking surveys. Materials and Methods: We compared height estimates derived from DIPCs of different GSD (0.35 cm, 0.75 cm, and 3 cm), phenological state (leaf-on and leaf-off), and ground determination method (based on either the DIPC itself or an ancillary digital terrain model). Each set of height estimates came from data acquired in up to three linear disturbances in the boreal forest of Alberta, Canada, and included 22 to 189 surveyed seedlings, which were split into two height strata to assess two survey scenarios. Results: The best result (root mean square error (RMSE) = 24 cm; bias = −11 cm; R2 = 0.63; n = 48) was achieved for seedlings >30 cm with 0.35 cm GSD in leaf-off conditions and ground elevation from the DIPC. The second-best result had the same GSD and ground method but was leaf-on and not significantly different from the first. Results for seedlings ≤30 cm were unreliable (nil R2). Height estimates derived from manual softcopy interpretation were similar to the corresponding DIPC results. Height estimation errors hardly affected seedling counting errors (best balance was 8% omission and 6% commission). Accuracy and correlation were stronger at finer GSDs and improved with seedling size. Conclusions: Millimetric (GSD <1 cm) DIPC can be used for estimating the height of individual conifer seedlings taller than 30 cm.

scholarly journals Response of Oak and Maple Seed Germination and Seedling Growth to Different Manganese Fertilizers in a Cultured Substratum

Forests ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 547
Author(s):  
Kaile Mai ◽  
Roger A. Williams
Keyword(s):  
North America ◽  
Seedling Growth ◽  
Forest Ecosystems ◽  
Oak Forest ◽  
Red Oak ◽  
Seedling Height ◽  
Northern Red Oak ◽  
Red Maple ◽  
Basal Diameter ◽  
Seedling Size

Oak regeneration failures have been causing a slow decline in the occurrence of oak forest ecosystems in eastern North America. Accordingly, our study sought to determine a means of creating more vigorous and competitive oak seedlings by the addition of manganese (Mn) fertilizers. Seeds of northern red oak (Quercus rubra L.), chestnut oak (Quercus prinus L.), and red maple (Acer rubrum L.), one of oak’s major competitors in North America oak forest ecosystems, were sown in 0.7 liter pots that contained a growing medium mixture of peat moss, perlite, and sand in a ratio of 2:1:2, and germinated in a greenhouse. Three different chemical compound Mn fertilizer treatments—manganese chloride (0.16 mg L−1 Mn, MnCl2·4H2O), nanoparticle manganese in the form of manganese hydroxide (0.01 mg/L Mn, nanoparticle Mn(OH)2), and manganese hydroxide (0.01 mg L−1 Mn, Mn(OH)2)—and a treatment of Hoagland solution were applied to the planted seed. These treatments were compared to a control consisting of water, and treatments were applied twice a week over a 12 week period. Germination rates and seedling growth were measured over this period of time. At the end of 12 weeks seedlings were harvested, separated into roots, stem, and foliage for the purpose of biomass and nutrient analysis by seedling component. Northern red oak displayed a 100% germination success rate with MnCl2·4H2O and Mn(OH)2 treatments, while red maple germination was reduced with the MnCl2·4H2O and nanoparticle Mn(OH)2 treatments with only a 32% and 24% germination rate, respectively. The MnCl2·4H2O treatment produced the largest overall seedling size (basal diameter squared times the seedling height) of red maple with a 191.6% increase; however, the MnCl2·4H2O treatment produced the largest overall seedling size (basal diameter squared times the seedling height) of northern red oak and chestnut oak with an increase of 503.7% and 339.5%, respectively. The greatest increase in overall seedling size for northern red oak was with the Mn(OH)2 treatment at 507.2%, and 601.2% for chestnut oak with the nanoparticle Mn(OH)2 treatment. MnCl2·4H2O treatment significantly increased the oak foliar nitrogen (N) content. It appears that the application of Mn fertilizer can increase the germination and growth of these oak species while suppressing or having a lesser effect on red maple, thus creating a competitive advantage for oak over its competitor.

scholarly journals AN AUTOMATIC ALGORITHM FOR MINIMIZING ANOMALIES AND DISCREPANCIES IN POINT CLOUDS ACQUIRED BY LASER SCANNING TECHNIQUE

2016 ◽  
Vol XLI-B5 ◽  
pp. 779-783
Author(s):  
Fabiane Bordin ◽  
Luiz Gonzaga Jr ◽  
Fabricio Galhardo Muller ◽  
Mauricio Roberto Veronez ◽  
Marco Scaioni
Keyword(s):  
Laser Pulse ◽  
Laser Beam ◽  
Laser Scanning ◽  
Point Clouds ◽  
Intrinsic Properties ◽  
Estimation Errors ◽  
Scanning Technique ◽  
3D Data ◽  
Laser Pulse Intensity ◽  
Automatic Algorithm

Laser scanning technique from airborne and land platforms has been largely used for collecting 3D data in large volumes in the field of geosciences. Furthermore, the laser pulse intensity has been widely exploited to analyze and classify rocks and biomass, and for carbon storage estimation. In general, a laser beam is emitted, collides with targets and only a percentage of emitted beam returns according to intrinsic properties of each target. Also, due interferences and partial collisions, the laser return intensity can be incorrect, introducing serious errors in classification and/or estimation processes. To address this problem and avoid misclassification and estimation errors, we have proposed a new algorithm to correct return intensity for laser scanning sensors. Different case studies have been used to evaluate and validated proposed approach.

scholarly journals Detection of aspen in conifer-dominated boreal forests with seasonal multispectral drone image point clouds

Silva Fennica ◽  
2021 ◽  
Vol 55 (4) ◽  
Author(s):  
Alwin Hardenbol ◽  
Anton Kuzmin ◽  
Lauri Korhonen ◽  
Pasi Korpelainen ◽  
Timo Kumpula ◽  
...  
Keyword(s):  
Tree Species ◽  
Classification Accuracy ◽  
Boreal Forests ◽  
Growing Season ◽  
Point Clouds ◽  
Image Point ◽  
Detection Accuracy ◽  
Important Species ◽  
Linear Discriminant ◽  
European Aspen

Current remote sensing methods can provide detailed tree species classification in boreal forests. However, classification studies have so far focused on the dominant tree species, with few studies on less frequent but ecologically important species. We aimed to separate European aspen ( tremula L.), a biodiversity-supporting tree species, from the more common species in European boreal forests ( L., [L.] Karst., spp.). Using multispectral drone images collected on five dates throughout one thermal growing season (May–September), we tested the optimal season for the acquisition of mono-temporal data. These images were collected from a mature, unmanaged forest. After conversion into photogrammetric point clouds, we segmented crowns manually and automatically and classified the species by linear discriminant analysis. The highest overall classification accuracy (95%) for the four species as well as the highest classification accuracy for aspen specifically (user’s accuracy of 97% and a producer’s accuracy of 96%) were obtained at the beginning of the thermal growing season (13 May) by manual segmentation. On 13 May, aspen had no leaves yet, unlike birches. In contrast, the lowest classification accuracy was achieved on 27 September during the autumn senescence period. This is potentially caused by high intraspecific variation in aspen autumn coloration but may also be related to our date of acquisition. Our findings indicate that multispectral drone images collected in spring can be used to locate and classify less frequent tree species highly accurately. The temporal variation in leaf and canopy appearance can alter the detection accuracy considerably.PopulusPinus sylvestrisPicea abiesBetula

scholarly journals INTEGRATING SMARTPHONE IMAGES AND AIRBORNE LIDAR DATA FOR COMPLETE URBAN BUILDING MODELLING

2016 ◽  
Vol XLI-B5 ◽  
pp. 741-747 ◽  
Author(s):  
Shenman Zhang ◽  
Jie Shan ◽  
Zhichao Zhang ◽  
Jixing Yan ◽  
Yaolin Hou
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Image Point ◽  
Lidar Data ◽  
Building Model ◽  
Building Facades ◽  
Single Data ◽  
Airborne Lidar Data ◽  
Building Reconstruction

A complete building model reconstruction needs data collected from both air and ground. The former often has sparse coverage on building façades, while the latter usually is unable to observe the building rooftops. Attempting to solve the missing data issues in building reconstruction from single data source, we describe an approach for complete building reconstruction that integrates airborne LiDAR data and ground smartphone imagery. First, by taking advantages of GPS and digital compass information embedded in the image metadata of smartphones, we are able to find airborne LiDAR point clouds for the corresponding buildings in the images. In the next step, Structure-from-Motion and dense multi-view stereo algorithms are applied to generate building point cloud from multiple ground images. The third step extracts building outlines respectively from the LiDAR point cloud and the ground image point cloud. An automated correspondence between these two sets of building outlines allows us to achieve a precise registration and combination of the two point clouds, which ultimately results in a complete and full resolution building model. The developed approach overcomes the problem of sparse points on building façades in airborne LiDAR and the deficiency of rooftops in ground images such that the merits of both datasets are utilized.

scholarly journals Using ALS Data to Improve Co-Registration of Photogrammetry-Based Point Cloud Data in Urban Areas

2020 ◽  
Vol 12 (12) ◽  
pp. 1943
Author(s):  
Ranjith Gopalakrishnan ◽  
Daniela Ali-Sisto ◽  
Mikko Kukkonen ◽  
Pekka Savolainen ◽  
Petteri Packalen
Keyword(s):  
Standard Deviation ◽  
Urban Areas ◽  
Laser Scanning ◽  
Point Clouds ◽  
Image Point ◽  
Vertical Coordinate ◽  
Cloud Data ◽  
Suburban Areas ◽  
Aerial Photogrammetry ◽  
Forested Areas

Globally, urban areas are rapidly expanding and high-quality remote sensing products are essential to help guide such development towards efficient and sustainable pathways. Here, we present an algorithm to address a common problem in digital aerial photogrammetry (DAP)-based image point clouds: vertical mis-registration. The algorithm uses the ground as inferred from airborne laser scanning (ALS) data as a reference surface and re-aligns individual point clouds to this surface. We demonstrate the effectiveness of the proposed method for the city of Kuopio, in central Finland. Here, we use the standard deviation of the vertical coordinate values as a measure of the mis-registration. We show that such standard deviation decreased substantially (more than 1.0 m) for a large proportion (23.2%) of the study area. Moreover, it was shown that the method performed better in urban and suburban areas, compared to vegetated areas (parks, forested areas, and so on). Hence, we demonstrate that the proposed algorithm is a simple and effective method to improve the quality and usability of DAP-based point clouds in urban areas.

scholarly journals The Use of UAV in Cadastral Mapping of the Czech Republic

2021 ◽  
Vol 10 (6) ◽  
pp. 380
Author(s):  
Václav Šafář ◽  
Markéta Potůčková ◽  
Jakub Karas ◽  
Jan Tlustý ◽  
Eva Štefanová ◽  
...  
Keyword(s):  
Czech Republic ◽  
Real Estate ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Maximum Efficiency ◽  
Image Point ◽  
The Czech Republic ◽  
Main Challenge ◽  
Check Points

The main challenge in the renewal and updating of the Cadastre of Real Estate of the Czech Republic is to achieve maximum efficiency but to retain the required accuracy of all points in the register. The paper discusses the possibility of using UAV photogrammetry and laser scanning for cadastral mapping in the Czech Republic. Point clouds from images and laser scans together with orthoimages were derived over twelve test areas. Control and check points were measured using geodetic methods (RTK-GNSS and total stations). The accuracy of the detailed survey based on UAV technologies was checked on hundreds of points, mainly building corners and fence foundations. The results show that the required accuracy of 0.14 m was achieved on more than 80% and 98% of points in the case of the image point clouds and orthoimages and the case of the LiDAR point cloud, respectively. Nevertheless, the methods lack completeness of the performed survey that must be supplied by geodetic measurements. The paper also provides a comparison of the costs connected to traditional and UAV-based cadastral mapping, and it addresses the necessary changes in the organisational and technological processes in order to utilise the UAV based technologies.

scholarly journals INTEGRATING SMARTPHONE IMAGES AND AIRBORNE LIDAR DATA FOR COMPLETE URBAN BUILDING MODELLING

2016 ◽  
Vol XLI-B5 ◽  
pp. 741-747 ◽  
Author(s):  
Shenman Zhang ◽  
Jie Shan ◽  
Zhichao Zhang ◽  
Jixing Yan ◽  
Yaolin Hou
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Image Point ◽  
Lidar Data ◽  
Building Model ◽  
Building Facades ◽  
Single Data ◽  
Airborne Lidar Data ◽  
Building Reconstruction

A complete building model reconstruction needs data collected from both air and ground. The former often has sparse coverage on building façades, while the latter usually is unable to observe the building rooftops. Attempting to solve the missing data issues in building reconstruction from single data source, we describe an approach for complete building reconstruction that integrates airborne LiDAR data and ground smartphone imagery. First, by taking advantages of GPS and digital compass information embedded in the image metadata of smartphones, we are able to find airborne LiDAR point clouds for the corresponding buildings in the images. In the next step, Structure-from-Motion and dense multi-view stereo algorithms are applied to generate building point cloud from multiple ground images. The third step extracts building outlines respectively from the LiDAR point cloud and the ground image point cloud. An automated correspondence between these two sets of building outlines allows us to achieve a precise registration and combination of the two point clouds, which ultimately results in a complete and full resolution building model. The developed approach overcomes the problem of sparse points on building façades in airborne LiDAR and the deficiency of rooftops in ground images such that the merits of both datasets are utilized.

scholarly journals CO2 Enriched Air Increased Growth of Conifer Seedlings

1970 ◽  
Vol 46 (3) ◽  
pp. 229-230 ◽  
Author(s):  
C. W. Yeatman
Keyword(s):  
Light Intensity ◽  
Norway Spruce ◽  
Scots Pine ◽  
Dry Weight ◽  
White Spruce ◽  
Jack Pine ◽  
Tree Seedlings ◽  
Short Term ◽  
Conifer Seedlings

The dry weight of 3-week-old seedlings of white spruce, Norway spruce, jack pine and Scots pine was 30–80% greater than the control when grown in atmospheres enriched 3- to 5-fold with carbondioxide. Seedlings also responded positively to a difference in light intensity. CO2 enriched atmospheres might profitably be used for the short term propagation of tree seedlings grown in greenhouses.

scholarly journals AN AUTOMATIC ALGORITHM FOR MINIMIZING ANOMALIES AND DISCREPANCIES IN POINT CLOUDS ACQUIRED BY LASER SCANNING TECHNIQUE

2016 ◽  
Vol XLI-B5 ◽  
pp. 779-783
Author(s):  
Fabiane Bordin ◽  
Luiz Gonzaga Jr ◽  
Fabricio Galhardo Muller ◽  
Mauricio Roberto Veronez ◽  
Marco Scaioni
Keyword(s):  
Laser Pulse ◽  
Laser Beam ◽  
Laser Scanning ◽  
Point Clouds ◽  
Intrinsic Properties ◽  
Estimation Errors ◽  
Scanning Technique ◽  
3D Data ◽  
Laser Pulse Intensity ◽  
Automatic Algorithm

Laser scanning technique from airborne and land platforms has been largely used for collecting 3D data in large volumes in the field of geosciences. Furthermore, the laser pulse intensity has been widely exploited to analyze and classify rocks and biomass, and for carbon storage estimation. In general, a laser beam is emitted, collides with targets and only a percentage of emitted beam returns according to intrinsic properties of each target. Also, due interferences and partial collisions, the laser return intensity can be incorrect, introducing serious errors in classification and/or estimation processes. To address this problem and avoid misclassification and estimation errors, we have proposed a new algorithm to correct return intensity for laser scanning sensors. Different case studies have been used to evaluate and validated proposed approach.

scholarly journals HYBRID GEOREFERENCING, ENHANCEMENT AND CLASSIFICATION OF ULTRA-HIGH RESOLUTION UAV LIDAR AND IMAGE POINT CLOUDS FOR MONITORING APPLICATIONS

2020 ◽  
Vol V-2-2020 ◽  
pp. 727-734
Author(s):  
N. Haala ◽  
M. Kölle ◽  
M. Cramer ◽  
D. Laupheimer ◽  
G. Mandlburger ◽  
...  
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Image Texture ◽  
Image Point ◽  
Joint Orientation ◽  
3D Point Clouds ◽  
3D Data

Abstract. This paper presents a study on the potential of ultra-high accurate UAV-based 3D data capture by combining both imagery and LiDAR data. Our work is motivated by a project aiming at the monitoring of subsidence in an area of mixed use. Thus, it covers built-up regions in a village with a ship lock as the main object of interest as well as regions of agricultural use. In order to monitor potential subsidence in the order of 10 mm/year, we aim at sub-centimeter accuracies of the respective 3D point clouds. We show that hybrid georeferencing helps to increase the accuracy of the adjusted LiDAR point cloud by integrating results from photogrammetric block adjustment to improve the time-dependent trajectory corrections. As our main contribution, we demonstrate that joint orientation of laser scans and images in a hybrid adjustment framework significantly improves the relative and absolute height accuracies. By these means, accuracies corresponding to the GSD of the integrated imagery can be achieved. Image data can also help to enhance the LiDAR point clouds. As an example, integrating results from Multi-View Stereo potentially increases the point density from airborne LiDAR. Furthermore, image texture can support 3D point cloud classification. This semantic segmentation discussed in the final part of the paper is a prerequisite for further enhancement and analysis of the captured point cloud.

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