Detection of forest canopy gaps from very high resolution aerial images

Nyamgeroh, Beryl B.; Groen, T.A.; Weir, M.J.C.; Dimov, Petar; Zlatanov, Tzvetan

doi:10.1016/j.ecolind.2018.08.011

Cited by 18 publications

(11 citation statements)

References 27 publications

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“…The traditional method is based on ground measurements at the stand level [21] and is effective to account for gap area and number, but not to accurately measure other key gap features (e.g., shape) and to adequately reflect the spatial distribution pattern; additionally, surveys over large areas are time consuming and labor intensive [22]. The method based on remote sensing satellite images or aerial photography is thought to be a better way to solve these problems so as to obtain measurements efficiently and accurately at a large scale [23]. However, even the resolution of conventional aerial or satellite images is commonly not high enough to permit accurate spatial quantifications of small canopy gaps or patches [24].…”

Section: Introductionmentioning

confidence: 99%

Canopy Gaps Improve Landscape Aesthetic Service by Promoting Autumn Color-Leaved Tree Species Diversity and Color-Leaved Patch Properties in Subalpine Forests of Southwestern China

Yuan

et al. 2021

Forests

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Background and Objectives: The landscape aesthetic service (LAS) is a crucial ecosystem service in subalpine forests of the mountain and ravine regions of the Tibetan Plateau, especially in autumn; it can provide important ecological and economic value, improving the livelihood of the local people. Canopy gap acts as a key role in the maintenance of species diversity and forest stability and ecosystem services. However, the mechanisms and interactions of forest stability maintained by gap formations and LAS via gap dynamics are not fully understood. The aim of this study was to explore the effects of canopy gaps on autumn color-leaved tree species diversity (ACTSD), color-leaved patch structure attributes (CPSA), LAS, and their interactions during the autumn viewing period, and to provide a fundamental basis for the management and protection of subalpine autumn color-leaved forests (SACF). Materials and Methods: We used very high-resolution images to quantify gap characteristics and examined the effects of canopy gaps on ACTSD, CPSA, and LAS in 21 SACF plots in southwestern China. We then used path analysis to determine the relationships between these factors. Results: The number of gaps and total gap area were the key gap characteristics affecting LAS; particularly, medium canopy gaps (51–100 m2) played a more important role. The path model showed that increasing the total gap area along with the number of medium canopy gaps had direct positive effects on ACTSD, color-leaved patch diversity, and total color-leaved patch area, thus improving the LAS. Conclusions: Canopy gaps indirectly improve LAS in autumn by significantly affecting ACTSD and CPSA. Our results suggest that forest managers may be able to manipulate the numbers and proportion of medium canopy gaps to protect and preserve ACTSD and color-leaved landscapes, promoting the LAS of SACF, and in turn, ensuring the coordinated development of economic, social, and ecological benefits for the underdeveloped rural montane areas of southwestern China.

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Section: Introductionmentioning

confidence: 99%

Canopy Gaps Improve Landscape Aesthetic Service by Promoting Autumn Color-Leaved Tree Species Diversity and Color-Leaved Patch Properties in Subalpine Forests of Southwestern China

Yuan

et al. 2021

Forests

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“…While some authors have mapped discrete canopy openings using high-resolution optical imagery [24,25], most recent studies have used light detection and ranging (LiDAR) [26][27][28][29][30], sometimes supplemented with optical data [31,32]. LiDAR is an active remote-sensing technology that is commonly mounted on terrestrial platforms, unmanned aerial vehicles, or piloted aircraft [33] for forest applications.…”

Section: Introductionmentioning

confidence: 99%

Comparison of LiDAR and Digital Aerial Photogrammetry for Characterizing Canopy Openings in the Boreal Forest of Northern Alberta

et al. 2019

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Forest canopy openings are a key element of forest structure, influencing a host of ecological dynamics. Light detection and ranging (LiDAR) is the de-facto standard for measuring three-dimensional forest structure, but digital aerial photogrammetry (DAP) has emerged as a viable and economical alternative. We compared the performance of LiDAR and DAP data for characterizing canopy openings and no-openings across a 1-km2 expanse of boreal forest in northern Alberta, Canada. Structural openings in canopy cover were delineated using three canopy height model (CHM) alternatives, from (i) LiDAR, (ii) DAP, and (iii) a LiDAR/DAP hybrid. From a point-based detectability perspective, the LiDAR CHM produced the best results (87% overall accuracy), followed by the hybrid and DAP models (47% and 46%, respectively). The hybrid and DAP CHMs experienced large errors of omission (9–53%), particularly with small openings up to 20m2, which are an important element of boreal forest structure. By missing these, DAP and hybrid datasets substantially under-reported the total area of openings across our site (152,470 m2 and 159,848 m2, respectively) compared to LiDAR (245,920 m2). Our results illustrate DAP’s sensitivity to occlusions, mismatched tie points, and other optical challenges inherent to using structure-from-motion workflows in complex forest scenes. These under-documented constraints currently limit the technology’s capacity to fully characterize canopy structure. For now, we recommend that operational use of DAP in forests be limited to mapping large canopy openings, and area-based attributes that are well-documented in the literature.

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“…In fact, due to geometric features of plant canopies can offer relevant indicators, individual canopy-related features interested farmers but the most accurate estimations for canopies all mostly based on destructive and costly labourintensive manual measurements (Gower et al, 1999;Jonckheere et al, 2004;Ma et al, 2017). To overcome these disadvantages, UAV-based imagery in conjunction with computer vision methodologies have become widely used on the research of tree extraction (Nyamgeroh et al, 2018;Durfee et al, 2019). Brede et al (2017) concluded that UAV-borne laser scanning(ULS) has the potential to perform comparable to Terrestrial Laser Scanning for estimating forest canopy height.…”

Section: Introductionmentioning

confidence: 99%

Automatic instance segmentation of orchard canopy in unmanned aerial vehicle imagery using deep learning

Zhang

Chen

et al. 2022

Front. Plant Sci.

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The widespread use of unmanned aerial vehicles (UAV) is significant for the effective management of orchards in the context of precision agriculture. To reduce the traditional mode of continuous spraying, variable target spraying machines require detailed information about tree canopy. Although deep learning methods have been widely used in the fields of identifying individual trees, there are still phenomena of branches extending and shadows preventing segmenting edges of tree canopy precisely. Hence, a methodology (MPAPR R-CNN) for the high-precision segment method of apple trees in high-density cultivation orchards by low-altitude visible light images captured is proposed. Mask R-CNN with a path augmentation feature pyramid network (PAFPN) and PointRend algorithm was used as the base segmentation algorithm to output the precise boundaries of the apple tree canopy, which addresses the over- and under-sampling issues encountered in the pixel labeling tasks. The proposed method was tested on another miniature map of the orchard. The average precision (AP) was selected to evaluate the metric of the proposed model. The results showed that with the help of training with the PAFPN and PointRend backbone head that AP_seg and AP_box score improved by 8.96% and 8.37%, respectively. It can be concluded that our algorithm could better capture features of the canopy edges, it could improve the accuracy of the edges of canopy segmentation results.

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Detection of forest canopy gaps from very high resolution aerial images

Cited by 18 publications

References 27 publications

Canopy Gaps Improve Landscape Aesthetic Service by Promoting Autumn Color-Leaved Tree Species Diversity and Color-Leaved Patch Properties in Subalpine Forests of Southwestern China

Canopy Gaps Improve Landscape Aesthetic Service by Promoting Autumn Color-Leaved Tree Species Diversity and Color-Leaved Patch Properties in Subalpine Forests of Southwestern China

Comparison of LiDAR and Digital Aerial Photogrammetry for Characterizing Canopy Openings in the Boreal Forest of Northern Alberta

Automatic instance segmentation of orchard canopy in unmanned aerial vehicle imagery using deep learning

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