Identifying tree crown areas in undulating eucalyptus plantations using JSEG multi-scale segmentation and unmanned aerial vehicle near-infrared imagery

Kang, Jun; Wang, Li; Fang, Chen; Niu, Zheng

doi:10.1080/01431161.2016.1253900

Cited by 20 publications

(13 citation statements)

References 30 publications

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“…Such methods utilize morphological operations and image segmentation. Kang et al [158] successfully utilized NIR imagery for segmenting individual trees with overall accuracy about 90%. However, methods based only on image analysis can be affected e.g., by different light conditions in one image caused by local topography or varying forest structure.…”

Section: Individual Tree Segmentationmentioning

confidence: 99%

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

Surový

Kuželka

2019

Forests

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In recent decades, remote sensing techniques and the associated hardware and software have made substantial improvements. With satellite images that can obtain sub-meter spatial resolution, and new hardware, particularly unmanned aerial vehicles and systems, there are many emerging opportunities for improved data acquisition, including variable temporal and spectral resolutions. Combined with the evolution of techniques for aerial remote sensing, such as full wave laser scanners, hyperspectral scanners, and aerial radar sensors, the potential to incorporate this new data in forest management is enormous. Here we provide an overview of the current state-of-the-art remote sensing techniques for large forest areas thousands or tens of thousands of hectares. We examined modern remote sensing techniques used to obtain forest data that are directly applicable to decision making issues, and we provided a general overview of the types of data that can be obtained using remote sensing. The most easily accessible forest variable described in many works is stand or tree height, followed by other inventory variables like basal area, tree number, diameters, and volume, which are crucial in decision making process, especially for thinning and harvest planning, and timber transport optimization. Information about zonation and species composition are often described as more difficult to assess; however, this information usually is not required on annual basis. Counts of studies on forest health show an increasing trend in the last years, mostly in context of availability of new sensors as well as increased forest vulnerability caused by climate change; by virtue to modern sensors interesting methods were developed for detection of stressed or damaged trees. Unexpectedly few works focus on regeneration and seedlings evaluation; though regenerated stands should be regularly monitored in order to maintain forest cover sustainability.

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Section: Individual Tree Segmentationmentioning

confidence: 99%

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

Surový

Kuželka

2019

Forests

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“…Depending on the spatial resolution of the imagery, seedling detection is akin to individual-tree detection in mature forests, which has been well studied using satellites [7], piloted aircraft [4,[8][9][10][11][12][13][14][15] and, more recently, UAVs [16][17][18][19][20][21][22][23]. Given a fixed spatial resolution, the accuracy with which individual trees are detected tends to improve with crown size [11].…”

Section: Introductionmentioning

confidence: 99%

Detection of Coniferous Seedlings in UAV Imagery

Feduck

McDermid

Castilla

2018

Forests

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Rapid assessment of forest regeneration using unmanned aerial vehicles (UAVs) is likely to decrease the cost of establishment surveys in a variety of resource industries. This research tests the feasibility of using UAVs to rapidly identify coniferous seedlings in replanted forest-harvest areas in Alberta, Canada. In developing our protocols, we gave special consideration to creating a workflow that could perform in an operational context, avoiding comprehensive wall-to-wall surveys and complex photogrammetric processing in favor of an efficient sampling-based approach, consumer-grade cameras, and straightforward image handling. Using simple spectral decision rules from a red, green, and blue (RGB) camera, we documented a seedling detection rate of 75.8 % (n = 149), on the basis of independent test data. While moderate imbalances between the omission and commission errors suggest that our workflow has a tendency to underestimate the seedling density in a harvest block, the plot-level associations with ground surveys were very high (Pearson's r = 0.98; n = 14). Our results were promising enough to suggest that UAVs can be used to detect coniferous seedlings in an operational capacity with standard RGB cameras alone, although our workflow relies on seasonal leaf-off windows where seedlings are visible and spectrally distinct from their surroundings. In addition, the differential errors between the pine seedlings and spruce seedlings suggest that operational workflows could benefit from multiple decision rules designed to handle diversity in species and other sources of spectral variability.

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“…The capability of UAS imaging systems to generate ultra-high-resolution imagery and 3D data holds great potential for the development of forestry applications. This is expressed in a large number of recent publications, such as [6,[20][21][22][23][24][25][26][27][28][29][30][31][32]. According to [6], these publications focus on the estimation of dendrometric parameters, including height measurements, tree species classification, quantification of spatial gaps in forests, forest fire-related issues, forest health monitoring, and forest disease mapping.…”

Section: Uas Sfm Data-based Tree Detectionmentioning

confidence: 99%

Monitoring Selective Logging in a Pine-Dominated Forest in Central Germany with Repeated Drone Flights Utilizing A Low Cost RTK Quadcopter

Thiel

Müller

Berger

et al. 2020

Drones

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There is no doubt that unmanned aerial systems (UAS) will play an increasing role in Earth observation in the near future. The field of application is very broad and includes aspects of environmental monitoring, security, humanitarian aid, or engineering. In particular, drones with camera systems are already widely used. The capability to compute ultra-high-resolution orthomosaics and three-dimensional (3D) point clouds from UAS imagery generates a wide interest in such systems, not only in the science community, but also in industry and agencies. In particular, forestry sciences benefit from ultra-high-structural and spectral information as regular tree level-based monitoring becomes feasible. There is a great need for this kind of information as, for example, due to the spring and summer droughts in Europe in the years 2018/2019, large quantities of individual trees were damaged or even died. This study focuses on selective logging at the level of individual trees using repeated drone flights. Using the new generation of UAS, which allows for sub-decimeter-level positioning accuracies, a change detection approach based on bi-temporal UAS acquisitions was implemented. In comparison to conventional UAS, the effort of implementing repeated drone flights in the field was low, because no ground control points needed to be surveyed. As shown in this study, the geometrical offset between the two collected datasets was below 10 cm across the site, which enabled a direct comparison of both datasets without the need for post-processing (e.g., image matching). For the detection of logged trees, we utilized the spectral and height differences between both acquisitions. For their delineation, an object-based approach was employed, which was proven to be highly accurate (precision = 97.5%; recall = 91.6%). Due to the ease of use of such new generation, off-the-shelf consumer drones, their decreasing purchase costs, the quality of available workflows for data processing, and the convincing results presented here, UAS-based data can and should complement conventional forest inventory practices.Drones 2020, 4, 11 2 of 26 Remote Sensing. The title is based on the idea that, after the implementation and operationalization of airborne and spaceborne platforms, UAS emerged as a new source of valuable Earth observation data. These data are capable of closing the gap between in-situ and far range remote sensing data, and thus allow for new developments of data scaling approaches [2]. Most UAS are equipped with optical camera systems providing imagery with ultra-high (i.e., centimeter scale) spatial resolution. Well-established photogrammetric processing chains, often summarized as structure from motion (SfM), exist to compute three-dimensional (3D) point clouds and orthomosaics based on overlapping UAS imagery [3]. Their high flexibility and accessibility (data acquisition at almost any time), scalability (choice of spatial resolution and areal coverage), and ease of use has led to an increased use of UAS for many applications and to...

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Identifying tree crown areas in undulating eucalyptus plantations using JSEG multi-scale segmentation and unmanned aerial vehicle near-infrared imagery

Cited by 20 publications

References 30 publications

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

Detection of Coniferous Seedlings in UAV Imagery

Monitoring Selective Logging in a Pine-Dominated Forest in Central Germany with Repeated Drone Flights Utilizing A Low Cost RTK Quadcopter

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