scholarly journals TREE CROWN MAPPING BASED ON UNMANNED AERIAL VEHICLE (UAV) TOWARDS A GREEN-SUSTAINABLE RESIDENTIAL

2021 ◽  
Vol 19 (16) ◽  
Author(s):  
Suzanah Abdullah ◽  
Mohd Fadzil Abdul Rashid ◽  
Khairul Nizam Tahar ◽  
Muhammad Ariffin Osoman
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Living Environment ◽  
Tree Planting ◽  
Tree Crown ◽  
Sustainable City ◽  
Tree Crowns ◽  
Greenhouse Gasses ◽  
Gis Technologies ◽  
Aerial Vehicle ◽  
Residential Neighbourhood

Tree crown plays a crucial role in creating urban characters and spatial arrangements of living environment towards a green-sustainable city. It provides the fundamental needs for human’s living quality and health conditions such as improving water quality, preserving energy, minimising greenhouse gasses, and beautification and comfortable purposes. Therefore, there is a need for urban planners to recognise its importance and plan for it wisely. This paper attempts to demonstrate a mapping tree crowns for a case of the residential neighbourhood using Unmanned Aerial Vehicle (UAV) based GIS technologies. Four main stages involved in a mapping tree crown process namely: flight planning, data acquisition, data processing and analyses and results. As a result, this paper able to show the capabilities of the technologies in measuring and mapping tree crowns for the residential neighbourhood. Moreover, it provides urban planners with informative scenario of the tree planting and clarifies its importance for future planning and benefits – in creating and promoting a green-sustainable and healthy living environment.

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

2019 ◽  
Vol 11 (8) ◽  
pp. 908 ◽  
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.

scholarly journals UNMANNED AERIAL VEHICLE (UAV) SURVEY-ASSISTED 3D MANGROVE TREE MODELING

2018 ◽  
Vol XLII-4/W9 ◽  
pp. 123-127
Author(s):  
G. A. Domingo ◽  
A. R. C. Claridades ◽  
M. E. A. Tupas
Keyword(s):  
Unmanned Aerial Vehicle ◽  
3D Visualization ◽  
Scientific Information ◽  
Three Dimensional ◽  
Surface Model ◽  
Tree Crown ◽  
Tree Model ◽  
Mangrove Tree ◽  
Aerial Vehicle ◽  
To Come

<p><strong>Abstract.</strong> 3D visualization is a tool that supports geospatial analysis through the application of scientific information. It enhances the quality of standard photography and can be used in many applications. Through this study, a 3D mangrove tree model is generated, as assisted by a tree crown derived from UAV images. The researchers explored different platforms namely: MeshLab, SketchUp (with 3D Tree Maker extension), and Clara.io, to come up with a more realistic three-dimensional (3D) model of a mangrove tree. From an Unmanned Aerial Vehicle (UAV) derived Digital Surface Model (DSM), an isolated tree crown was selected which was then used as an assisting tool in creating the final 3D mangrove tree model. A default tree object was modified according to the characteristics as described by the DSM. Additional branches and leaves were added to the existing tree object, and its shape was modified to conform to the tree crown. The resulting model may be used to more accurately depict objects in the area to be visualized, however an automation procedure is recommended for an easier and more effective generation of multiple tree models expected in an area.</p>

scholarly journals Characterization of peach tree crown by using high-resolution images from an unmanned aerial vehicle

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Yue Mu ◽  
Yuichiro Fujii ◽  
Daisuke Takata ◽  
Bangyou Zheng ◽  
Koji Noshita ◽  
...  
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Peach Tree ◽  
Tree Crown ◽  
Aerial Vehicle ◽  
High Resolution Images

scholarly journals Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests

2021 ◽  
Vol 13 (9) ◽  
pp. 1723
Author(s):  
Anton Kuzmin ◽  
Lauri Korhonen ◽  
Sonja Kivinen ◽  
Pekka Hurskainen ◽  
Pasi Korpelainen ◽  
...  
Keyword(s):  
Forest Management ◽  
Unmanned Aerial Vehicle ◽  
Classification Accuracy ◽  
Boreal Forests ◽  
Populus Tremula ◽  
Tree Level ◽  
Tree Crown ◽  
Individual Tree ◽  
European Aspen ◽  
Aerial Vehicle

European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests. Large-diameter aspens maintain the diversity of hundreds of species, many of which are threatened in Fennoscandia. Due to a low economic value and relatively sparse and scattered occurrence of aspen in boreal forests, there is a lack of information of the spatial and temporal distribution of aspen, which hampers efficient planning and implementation of sustainable forest management practices and conservation efforts. Our objective was to assess identification of European aspen at the individual tree level in a southern boreal forest using high-resolution photogrammetric point cloud (PPC) and multispectral (MSP) orthomosaics acquired with an unmanned aerial vehicle (UAV). The structure-from-motion approach was applied to generate RGB imagery-based PPC to be used for individual tree-crown delineation. Multispectral data were collected using two UAV cameras: Parrot Sequoia and MicaSense RedEdge-M. Tree-crown outlines were obtained from watershed segmentation of PPC data and intersected with multispectral mosaics to extract and calculate spectral metrics for individual trees. We assessed the role of spectral data features extracted from PPC and multispectral mosaics and a combination of it, using a machine learning classifier—Support Vector Machine (SVM) to perform two different classifications: discrimination of aspen from the other species combined into one class and classification of all four species (aspen, birch, pine, spruce) simultaneously. In the first scenario, the highest classification accuracy of 84% (F1-score) for aspen and overall accuracy of 90.1% was achieved using only RGB features from PPC, whereas in the second scenario, the highest classification accuracy of 86 % (F1-score) for aspen and overall accuracy of 83.3% was achieved using the combination of RGB and MSP features. The proposed method provides a new possibility for the rapid assessment of aspen occurrence to enable more efficient forest management as well as contribute to biodiversity monitoring and conservation efforts in boreal forests.

Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Unmanned Aerial Vehicle ◽  
Sensor Data ◽  
Planetary Gearbox ◽  
Aerial Vehicle

Results of Spatial Structure of Atmosphere Radiation in a Spectral Range (1.5–2) µm Research

2018 ◽  
pp. 7-13
Author(s):  
Anton M. Mishchenko ◽  
Sergei S. Rachkovsky ◽  
Vladimir A. Smolin ◽  
Igor V . Yakimenko
Keyword(s):  
Spatial Structure ◽  
Unmanned Aerial Vehicle ◽  
Wavelength Range ◽  
Spectral Range ◽  
Near Ir ◽  
Optoelectronic Systems ◽  
Aerial Vehicle

Results of experimental studying radiation spatial structure of atmosphere background nonuniformities and of an unmanned aerial vehicle being the detection object are presented. The question on a possibility of its detection using optoelectronic systems against the background of a cloudy field in the near IR wavelength range is also considered.

Positioning the Angle of Attack Sensor on an Unmanned Aerial Vehicle Wing

2016 ◽  
Vol 3 (1) ◽  
pp. 25-33
Author(s):  
Amir Birjandi ◽  
◽  
Valentin Guerry ◽  
Eric Bibeau ◽  
Hamidreza Bolandhemmat ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Angle Of Attack ◽  
Aerial Vehicle
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