Detection of Coniferous Seedlings in UAV Imagery

Feduck, Corey; McDermid, Gregory J.; Castilla, Guillermo

doi:10.3390/f9070432

Cited by 57 publications

(56 citation statements)

References 34 publications

(43 reference statements)

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“…Several studies evidence the contribute of UAVs in the detection of forests regeneration. Feduck et al [157] analysed the ability of UAV-based RGB imagery to detect coniferous seedlings in replanted forest-harvest areas, in leaf-off conditions, obtaining a detection rate of 75.8% (n = 149). In Puliti et al [158], UAV data was used for modelling tree density and canopy height, in young boreal forests stands under regeneration.…”

Section: Other Applicationsmentioning

confidence: 99%

“…Apart from forest applications with more incidence towards tree development and its status, other applications in forestry contexts were explored using UAVs for: forest canopy assessment (canopy cover [125], canopy gaps [152][153][154], LAI [7,155], foliage clumping [7] and leaf angle distribution [156]), regeneration of forests [126,127,157,158], assessment of soil disturbances in post-harvest areas [159][160][161], monitoring of logging operations [162] and tree-stump detection [163]. Most of the studies rely in the use of RGB sensors mounted on rotary-wing UAVs (apart from the multispectral sensor used in [155]), except for canopy gaps [152][153][154] in which a fixed-wing UAVs were used.…”

Section: Other Applicationsmentioning

confidence: 99%

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Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

et al. 2020

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Currently, climate change poses a global threat, which may compromise the sustainability of agriculture, forestry and other land surface systems. In a changing world scenario, the economic importance of Remote Sensing (RS) to monitor forests and agricultural resources is imperative to the development of agroforestry systems. Traditional RS technologies encompass satellite and manned aircraft platforms. These platforms are continuously improving in terms of spatial, spectral, and temporal resolutions. The high spatial and temporal resolutions, flexibility and lower operational costs make Unmanned Aerial Vehicles (UAVs) a good alternative to traditional RS platforms. In the management process of forests resources, UAVs are one of the most suitable options to consider, mainly due to: (1) low operational costs and high-intensity data collection; (2) its capacity to host a wide range of sensors that could be adapted to be task-oriented; (3) its ability to plan data acquisition campaigns, avoiding inadequate weather conditions and providing data availability on-demand; and (4) the possibility to be used in real-time operations. This review aims to present the most significant UAV applications in forestry, identifying the appropriate sensors to be used in each situation as well as the data processing techniques commonly implemented.

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Section: Other Applicationsmentioning

confidence: 99%

Section: Other Applicationsmentioning

confidence: 99%

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

et al. 2020

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“…We think that tree matching is possible to be executed in real-time if SLAM algorithms [61][62][63] are used for 3D tree rendering instead of structure-from-motion algorithms [57,64,65]. Furthermore, accurate geo-referencing of tree stems could help improve species identification when analysis is executed data fusing spectral information with locational data [66][67][68].…”

Section: Discussionmentioning

confidence: 99%

Accurate Geo-Referencing of Trees with No or Inaccurate Terrestrial Location Devices

2019

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Accurate and precise location of trees from data acquired under-the-canopy is challenging and time-consuming. However, current forestry practices would benefit tremendously from the knowledge of tree coordinates, particularly when the information used to position them is acquired with inexpensive sensors. Therefore, the objective of our study is to geo-reference trees using point clouds created from the images acquired below canopy. We developed a procedure that uses the coordinates of the trees seen from above canopy to position the same trees seen below canopy. To geo-reference the trees from above canopy we captured images with an unmanned aerial vehicle. We reconstructed the trunk with photogrammetric point clouds built with a structure-from-motion procedure from images recorded in a circular pattern at multiple locations throughout the stand. We matched the trees segmented from below canopy with the trees extracted from above canopy using a non-rigid point-matching algorithm. To ensure accuracy, we reduced the number of matching trees by dividing the trees segmented from above using a grid with 50 m cells. Our procedure was implemented on a 7.1 ha Douglas-fir stand from Oregon USA. The proposed procedure is relatively fast, as approximately 600 trees were mapped in approximately 1 min. The procedure is sensitive to the point density, directly impacting tree location, as differences larger than 2 m between the coordinates of the tree top and the bottom part of the stem could lead to matching errors larger than 1 m. Furthermore, the larger the number of trees to be matched the higher the accuracy is, which could allow for misalignment errors larger than 2 m between the locations of the trees segmented from above and below.needed. Stand level information is particularly needed for intermediate cuts, when the decision is taken considering all the trees not only a portion of them. Although total height of each tree in a stand can be relatively easy estimated from point clouds, lidar or photogrammetric, there are at least two attributes that are difficult to estimate accurately and precisely for all trees: dbh and location. Many algorithms have been developed to estimate dbh from above canopy data (e.g., point clouds or images) with significant success [4][5][6][7]. However, while expected accuracy is in many instances achieved, the precision of estimating dbh from above canopy is limited, which restricts its operational usage. The effort placed in estimation of tree location mirror the one for tree size, but the success was clearly less impressive, as the accuracy is still measured in meters [8].Point clouds play a significant role in forest operations, particularly in road design and road maintenance [9,10]. Point clouds generated without an active sensor, such as lidar, or stereopsis are sometimes labeled photogrammetric point clouds [11] or phodar [12]. Photogrammetric point clouds (PPC) made inroads in harvesting, but they were employed similarly with aerial laser scanning, as the applications were based on t...

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“…The aims of the papers were to test the feasibility of using UAVs to rapidly identify coniferous seedlings in replanted forest-harvest using an efficient sampling-based approach, consumer-grade cameras, and straightforward image handling, such as in [1]. The tree characteristics monitored were tree height, crown width, prediction of diameter at breast height (DBH), and tree age with low cost, high efficiency, and high precision in [2].…”

Section: Overview Of Contributionsmentioning

confidence: 99%

“…Various data processing methods were used in this Special Issue. Image analysis workflow, where a three-step, object-based process consisting of image segmentation, automated classification using a classification and regression tree (CART) machine- Seven papers in this special issue reported results from UAV platform using RGB cameras [1][2][3][4][5][6][7], three paper used multispectral cameras [6,8,9], while one paper used a hyperspectral camera [6].…”

Section: Overview Of Contributionsmentioning

confidence: 99%

Editorial for the Special Issue “Forestry Applications of Unmanned Aerial Vehicles (UAVs)”

Matese

2020

Forests

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Unmanned aerial vehicles (UAVs) are new platforms that have been increasingly used in the last few years for forestry applications that benefit from the added value of flexibility, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. This special issue (SI) collects nine papers reporting research on different forestry applications using UAV imagery. The special issue covers seven Red-Green-Blue (RGB) sensor papers, three papers on multispectral imagery, and one further paper on hyperspectral data acquisition system. Several data processing and machine learning methods are presented. The special issue provides an overview regarding potential applications to provide forestry characteristics in a timely, cost-efficient way. With the fast development of sensors technology and image processing algorithms, the forestry potential applications will growing fast, but future work should consider the consistency and repeatability of these novel techniques.

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Detection of Coniferous Seedlings in UAV Imagery

Cited by 57 publications

References 34 publications

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

Accurate Geo-Referencing of Trees with No or Inaccurate Terrestrial Location Devices

Editorial for the Special Issue “Forestry Applications of Unmanned Aerial Vehicles (UAVs)”

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