scholarly journals The Use of an Unmanned Aerial Vehicle for Tree Phenotyping Studies

Separations ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 160
Author(s):  
Shara Ahmed ◽  
Catherine E. Nicholson ◽  
Paul Muto ◽  
Justin J. Perry ◽  
John R. Dean
Keyword(s):  
20Th Century ◽  
Unmanned Aerial Vehicle ◽  
Scots Pine ◽  
Tree Species ◽  
Principal Component ◽  
Silver Birch ◽  
Black Pine ◽  
Native Trees ◽  
Norway Maple ◽  
Aerial Vehicle

A strip of 20th-century landscape woodland planted alongside a 17th to mid-18th century ancient and semi-natural woodland (ASNW) was investigated by applied aerial spectroscopy using an unmanned aerial vehicle (UAV) with a multispectral image camera (MSI). A simple classification approach of normalized difference spectral index (NDSI), derived using principal component analysis (PCA), enabled the identification of the non-native trees within the 20th-century boundary. The tree species within this boundary, classified by NDSI, were further segmented by the machine learning segmentation method of k-means clustering. This combined innovative approach has enabled the identification of multiple tree species in the 20th-century boundary. Phenotyping of trees at canopy level using the UAV with MSI, across 8052 m2, identified black pine (23%), Norway maple (19%), Scots pine (12%), and sycamore (19%) as well as native trees (oak and silver birch, 27%). This derived data was corroborated by field identification at ground-level, over an area of 6785 m2, that confirmed the presence of black pine (26%), Norway maple (30%), Scots pine (10%), and sycamore (14%) as well as other trees (oak and silver birch, 20%). The benefits of using a UAV, with an MSI camera, for monitoring tree boundaries next to a new housing development are demonstrated.

scholarly journals Applied aerial spectroscopy: A case study on remote sensing of an ancient and semi-natural woodland

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260056
Author(s):  
Shara Ahmed ◽  
Catherine E. Nicholson ◽  
Paul Muto ◽  
Justin J. Perry ◽  
John R. Dean
Keyword(s):  
Tree Species ◽  
Low Cost ◽  
Principal Component ◽  
Ground Level ◽  
Empty Space ◽  
Silver Birch ◽  
Dead Trees ◽  
Aerial Vehicle ◽  
Woodland Regeneration

An area of ancient and semi-natural woodland (ASNW) has been investigated by applied aerial spectroscopy using an unmanned aerial vehicle (UAV) with multispectral image (MSI) camera. A novel normalised difference spectral index (NDSI) algorithm was developed using principal component analysis (PCA). This novel NDSI was then combined with a simple segmentation method of thresholding and applied for the identification of native tree species as well as the overall health of the woodland. Using this new approach allowed the identification of trees at canopy level, across 7.4 hectares (73,934 m2) of ASNW, as oak (53%), silver birch (37%), empty space (9%) and dead trees (1%). This UAV derived data was corroborated, for its accuracy, by a statistically valid ground-level field study that identified oak (47%), silver birch (46%) and dead trees (7.4%). This simple innovative approach, using a low-cost multirotor UAV with MSI camera, is both rapid to deploy, was flown around 100 m above ground level, provides useable high resolution (5.3 cm / pixel) data within 22 mins that can be interrogated using readily available PC-based software to identify tree species. In addition, it provides an overall oversight of woodland health and has the potential to inform a future woodland regeneration strategy.

Tree Species Identification Based on FCN Using the Visible Images Obtained from an Unmanned Aerial Vehicle

2020 ◽  
Vol 57 (10) ◽  
pp. 101001
Author(s):  
戴鹏钦 Dai Pengqin ◽  
丁丽霞 Ding Lixia ◽  
刘丽娟 Liu Lijuan ◽  
董落凡 Dong Luofan ◽  
黄依婷 Huang Yiting
Keyword(s):  
Species Identification ◽  
Unmanned Aerial Vehicle ◽  
Tree Species ◽  
Visible Images ◽  
Aerial Vehicle

scholarly journals Measurement of nitrogen content in rice by inversion of hyperspectral reflectance data from an unmanned aerial vehicle

2018 ◽  
Vol 48 (6) ◽  
Author(s):  
Du Wen ◽  
Xu Tongyu ◽  
Yu Fenghua ◽  
Chen Chunling
Keyword(s):  
Remote Sensing ◽  
Nitrogen Content ◽  
Unmanned Aerial Vehicle ◽  
Gaussian Process Regression ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Ground Truth ◽  
Hyperspectral Remote Sensing ◽  
Inversion Algorithm ◽  
Aerial Vehicle

ABSTRACT: The Nitrogen content of rice leaves has a significant effect on growth quality and crop yield. We proposed and demonstrated a non-invasive method for the quantitative inversion of rice nitrogen content based on hyperspectral remote sensing data collected by an unmanned aerial vehicle (UAV). Rice canopy albedo images were acquired by a hyperspectral imager onboard an M600-UAV platform. The radiation calibration method was then used to process these data and the reflectance of canopy leaves was acquired. Experimental validation was conducted using the rice field of Shenyang Agricultural University, which was classified into 4 fertilizer levels: zero nitrogen, low nitrogen, normal nitrogen, and high nitrogen. Gaussian process regression (GPR) was then used to train the inversion algorithm to identify specific spectral bands with the highest contribution. This led to a reduction in noise and a higher inversion accuracy. Principal component analysis (PCA) was also used for dimensionality reduction, thereby reducing redundant information and significantly increasing efficiency. A comparison with ground truth measurements demonstrated that the proposed technique was successful in establishing a nitrogen inversion model, the accuracy of which was quantified using a linear fit (R2=0.8525) and the root mean square error (RMSE=0.9507). These results support the use of GPR and provide a theoretical basis for the inversion of rice nitrogen by UAV hyperspectral remote sensing.

Content of some nutrients in Scots pine, silver birch and Norway maple in an urbanized environment

2015 ◽  
Author(s):  
Mirosław Wyszkowski ◽  
Mirosław Wyszkowski ◽  
Beata Modrzewska ◽  
Milena Kosiorek
Keyword(s):  
Scots Pine ◽  
Silver Birch ◽  
Norway Maple

Scots pine stands biomass assessment using 3D data from unmanned aerial vehicle imagery in the Chernobyl Exclusion Zone

2021 ◽  
Vol 295 ◽  
pp. 113319
Author(s):  
Dmytrii Holiaka ◽  
Hiroaki Kato ◽  
Vasyl Yoschenko ◽  
Yuichi Onda ◽  
Yasunori Igarashi ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Scots Pine ◽  
Exclusion Zone ◽  
3D Data ◽  
Biomass Assessment ◽  
Aerial Vehicle ◽  
Pine Stands ◽  
Scots Pine Stands

scholarly journals A Weather Monitoring Unmanned Aerial Vehicle

2019 ◽  
Vol 8 (3) ◽  
pp. 2886-2889
Keyword(s):  
Global Warming ◽  
Unmanned Aerial Vehicles ◽  
20Th Century ◽  
Unmanned Aerial Vehicle ◽  
Military Bases ◽  
Aerial Vehicles ◽  
Public Areas ◽  
Aerial Vehicle

The mankind has boomed in the 20th century, and this results the huge level of damage to the ecosystem. The technology which was developed for the convenience of the mankind has indirectly stressed in the environment and thus contributed knowingly to global warming. Therefore, it is required to monitor the environment so as to maintain the damage to it, and also to deal with the upcoming disasters by the means or proper supervision, one way of performing these activities is preferred by Unmanned Aerial Vehicles (UAVs). The UAVs can perform multiple of tasks at once, and it can be used for supervision in multiple fields such as military bases, house, colleges and other restricted, personal or public areas

Phosphorus forms in technosols afforested with N-fixing and non-N-fixing tree species 

2021 ◽  
Author(s):  
Katarzyna Sroka ◽  
Marcin Chodak ◽  
Marcin Pietrzykowski
Keyword(s):  
Scots Pine ◽  
Tree Species ◽  
Black Locust ◽  
Soil Samples ◽  
Silver Birch ◽  
Soil Horizon ◽  
Black Alder ◽  
Soil Substrate ◽  
Phosphorus Forms ◽  
Labile P

<p>Tree species capable of forming a symbiosis with N-fixing bacteria may affect P availability in reclaimed technosols. The objective of this study was to compare the effect of N-fixing tree species and non-N-fixing species on phosphorus forms in technosols developing from various materials. Soil samples were taken under black locust (Robinia pseudoaccaccia), black alder (Alnus glutinosa), silver birch (Betula pendula) and Scots pine (Pinus sylvestris) from two depths (0-5 cm and 5 – 20 cm). The soil substrates were fly ashes, sands and clays. In the soil samples measured were concentrations of total P (P<sub>t</sub>),  water soluble P (P<sub>H2O</sub>),  dilute salt-extractable P (P<sub>ex</sub>), microbial biomass P (P<sub>mic</sub>) and total labile P (P<sub>labil</sub>). Multifactor ANOVA revealed that tree species did not influence contents of P<sub>t</sub>, P<sub>ex</sub> and P<sub>H20</sub>. However, there was a statistically significant effect of soil substrate and soil horizon on these forms of P. The factors tree species, soil substrate and soil horizon had statistically significant effect on P<sub>mic </sub>content whereas content of P<sub>labil</sub> was affected by tree species and soil horizon. Multiple Range Tests by tree species showed that soils under Scots pine contained significantly less P<sub>mic </sub>than soils under other tree species studied. There were no significant differences in P<sub>mic</sub> between the soils under silver birch, black alder and black locust. The soils under Scots pine contained also significantly less P<sub>labil</sub> than the soils under black locust and silver birch. Our study included P forms that are considered labile (except P<sub>t</sub>). The obtained results indicated that the effect of N-fixing trees on these forms of P was weak. Instead we noticed that Scots pine had negative effect on some forms of labile P. </p><p>The study was financed by The National Science Centre, Poland, grant No. 2018/31/B/ST10/01626.</p>

Prospects of Using Unmanned Aerial Vehicle for Assessing Climate-Making Properties of Park Tree Species Using Kiev A.V. Fomin Botanical Garden as an Example

2018 ◽  
Vol 50 (4) ◽  
pp. 64-74
Author(s):  
Taras A. Kazantsev ◽  
Oxana A. Futornа ◽  
Nataliya B. Svietlova ◽  
Vladislava A. Badanina ◽  
Nataliya Yu. Taran
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Tree Species ◽  
Botanical Garden ◽  
Aerial Vehicle

scholarly journals DETERMINING GEOMETRIC PARAMETERS OF AGRICULTURAL TREES FROM LASER SCANNING DATA OBTAINED WITH UNMANNED AERIAL VEHICLE

2018 ◽  
Vol XLII-2 ◽  
pp. 407-410 ◽  
Author(s):  
E. Hadas ◽  
G. Jozkow ◽  
A. Walicka ◽  
A. Borkowski
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Laser Scanning ◽  
Principal Component ◽  
Point Clouds ◽  
Apple Orchard ◽  
Lidar Data ◽  
Local Minima ◽  
Alpha Shape ◽  
Aerial Vehicle ◽  
Individual Trees

The estimation of dendrometric parameters has become an important issue for agriculture planning and for the efficient management of orchards. Airborne Laser Scanning (ALS) data is widely used in forestry and many algorithms for automatic estimation of dendrometric parameters of individual forest trees were developed. Unfortunately, due to significant differences between forest and fruit trees, some contradictions exist against adopting the achievements of forestry science to agricultural studies indiscriminately.<br> In this study we present the methodology to identify individual trees in apple orchard and estimate heights of individual trees, using high-density LiDAR data (3200&amp;thinsp;points/m<sup>2</sup>) obtained with Unmanned Aerial Vehicle (UAV) equipped with Velodyne HDL32-E sensor. The processing strategy combines the alpha-shape algorithm, principal component analysis (PCA) and detection of local minima. The alpha-shape algorithm is used to separate tree rows. In order to separate trees in a single row, we detect local minima on the canopy profile and slice polygons from alpha-shape results. We successfully separated 92&amp;thinsp;% of trees in the test area. 6&amp;thinsp;% of trees in orchard were not separated from each other and 2&amp;thinsp;% were sliced into two polygons. The RMSE of tree heights determined from the point clouds compared to field measurements was equal to 0.09&amp;thinsp;m, and the correlation coefficient was equal to 0.96. The results confirm the usefulness of LiDAR data from UAV platform in orchard inventory.

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