High-resolution, low-altitude aerial photography in physical geography

Barrell, Jeffrey; Grant, Jon E.

doi:10.1177/0309133315578943

Cited by 27 publications

(13 citation statements)

References 74 publications

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“…resolutions in this study, it may be possible to obtain the distributions of seagrass coupled with those of the feeding trails. Although seagrass monitoring based on UAV survey is increasing [41][42][43][44], few case studies on monitoring small species such as Halophila ovalis dominated in this study site [74]. The simultaneous monitoring of the feeding trails and seagrass will be a future task.…”

Section: Plos Onementioning

confidence: 99%

“…Some of these applications use a photogrammetric approach that provides a flat and undistorted field of view [35,[38][39][40]. This approach is increasingly being utilized in the context of seagrass mapping [41][42][43][44]. However, few applications in high-turbidity waters have been reported [44] because the performance of photogrammetry in such areas is affected by optical properties of water including turbidity and sunlight reflection [45][46][47].…”

Section: Introductionmentioning

confidence: 99%

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Deep neural networks based automated extraction of dugong feeding trails from UAV images in the intertidal seagrass beds

et al. 2021

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Dugongs (Dugong dugon) are seagrass specialists distributed in shallow coastal waters in tropical and subtropical seas. The area and distribution of the dugongs’ feeding trails, which are unvegetated winding tracks left after feeding, have been used as an indicator of their feeding ground utilization. However, current ground-based measurements of these trails require a large amount of time and effort. Here, we developed effective methods to observe the dugongs’ feeding trails using unmanned aerial vehicle (UAV) images (1) by extracting the dugong feeding trails using deep neural networks. Furthermore, we demonstrated two applications as follows; (2) extraction of the daily new feeding trails with deep neural networks and (3) estimation the direction of the feeding trails. We obtained aerial photographs from the intertidal seagrass bed at Talibong Island, Trang Province, Thailand. The F1 scores, which are a measure of binary classification model’s accuracy taking false positives and false negatives into account, for the method (1) were 89.5% and 87.7% for the images with ground sampling resolutions of 1 cm/pixel and 0.5 cm/pixel, respectively, while the F1 score for the method (2) was 61.9%. The F1 score for the method (1) was high enough to perform scientific studies on the dugong. However, the method (2) should be improved, and there remains a need for manual correction. The mean area of the extracted daily new feeding trails from September 12–27, 2019, was 187.8 m2 per day (n = 9). Total 63.9% of the feeding trails was estimated to have direction within a range of 112.5° and 157.5°. These proposed new methods will reduce the time and efforts required for future feeding trail observations and contribute to future assessments of the dugongs’ seagrass habitat use.

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Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep neural networks based automated extraction of dugong feeding trails from UAV images in the intertidal seagrass beds

et al. 2021

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“…In comparison, it is more difficult to delineate clusters of dense vegetation or invertebrates, with >1 species often being present in a single cluster (e.g. Barrell & Grant 2015, Michez et al 2016. Because counts tend to be difficult to obtain within such clusters, scientists tend to focus on constructing distribution maps that are as precise as possible, rather than obtaining an exact count, which would require appropriate machine-learning methods to obtain high-quality results (Kaneko et al 2014, Barrell & Grant 2015, Michez et al 2016.…”

Section: Ecosystems Habitats Wildlife and Plant Speciesmentioning

confidence: 99%

Importance of machine learning for enhancing ecological studies using information-rich imagery

Dujon¹,

Schofield²

2019

Endang. Species. Res.

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There is increasing demand for efficient ways to process large volumes of data from visual-based remote-technology, such as unmanned aerial vehicles (UAVs) in ecology and conservation, with machine learning methods representing a promising avenue to address varying user demands. Here, we evaluated current trends in how machine learning and UAVs are used to process imagery data for detecting animals and vegetation across habitats, placing emphasis on their utility for endangered species. We reviewed 213 publications that used UAVs at 256 study sites, of which just 89 (42%) used machine learning to assess the visual data. We evaluated geographical and temporal trends and identified how each technology is used at a global scale. We also identified the most commonly encountered machine-learning methods, including potential reasons for their limited use in ecology and possible solutions. Thirteen out of the 17 habitats defined by the International Union for Conservation of Nature (IUCN) habitat classification scheme were monitored using UAVs, while 12 habitats were monitored using both UAVs and machine learning. Our results show that, while machine learning is already being used across many habitat types, it is primarily restricted to more uniform habitats at present. Out of 173 plant and animal species monitored using UAV surveys, 30 were of conservation concern, with machine learning being used to assess UAV imagery data for 9 of these species. In conclusion, we anticipate that the joint use of UAVs and machine learning for ecological research and conservation will expand as machine learning methods become more accessible.

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“…Additionally, seagrass blades may fold over at low tide or when there is a fast current, increasing the likelihood of overestimating seagrass density. While recent studies have provided roadmaps of ideal environmental conditions for RPAS surveys of seagrass (e.g., Joyce et al 2018;Nahirnick et al 2019b;Tait et al 2019;Yang et al 2020), studies evaluating the efficacy of surveys have generally focused on one-time occurrences in tropical regions (e.g., Ellis et al 2020) or portions of a meadow (Barrell and Grant 2015;Duffy et al 2018;Ellis et al 2020;Konar and Iken 2018;Krause et al 2021). In temperate regions, high environmental variability, including rapid shifts in cloud cover, wind, and chemistry of estuarine waters (e.g., tannins), challenges RPAS surveys (Nahirnick et al 2019a(Nahirnick et al , 2019b.…”

Section: Introductionmentioning

confidence: 99%

The reproducibility of remotely piloted aircraft systems to monitor seasonal variation in submerged seagrass and estuarine habitats

Prystay

Adams

Favaro

et al. 2023

FACETS

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Seasonal variation in seagrass growth and senescence affects the provision of ecosystem services and restoration efforts, requiring seasonal monitoring. Remotely piloted aircraft systems (RPAS) enable frequent high-resolution surveys at full-meadow scales. However, the reproducibility of RPAS surveys is challenged by varying environmental conditions, which are common in temperate estuarine systems. We surveyed three eelgrass ( Zostera marina) meadows in Newfoundland, Canada, using an RPAS equipped with a three-color band (red, green, blue [RGB]) camera, to evaluate the seasonal reproducibility of RPAS surveys and assess the effects of flight altitude (30–115 m) on classification accuracy. Habitat percent cover was estimated using supervised image classification and compared to corresponding estimates from snorkel quadrat surveys. Our results revealed inconsistent misclassification due to environmental variability and low spectral separability between habitats. This rendered differentiating between model misclassification versus actual changes in seagrass cover infeasible. Conflicting estimates in seagrass and macroalgae percent cover compared to snorkel estimates could not be corrected by decreasing the RPAS altitude. Instead, higher altitude surveys may be worth the trade-off of lower image resolution to avoid environmental conditions shifting mid-survey. We conclude that RPAS surveys using RGB imagery alone may be insufficient to discriminate seasonal changes in estuarine subtidal vegetated habitats.

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High-resolution, low-altitude aerial photography in physical geography

Cited by 27 publications

References 74 publications

Deep neural networks based automated extraction of dugong feeding trails from UAV images in the intertidal seagrass beds

Deep neural networks based automated extraction of dugong feeding trails from UAV images in the intertidal seagrass beds

Importance of machine learning for enhancing ecological studies using information-rich imagery

The reproducibility of remotely piloted aircraft systems to monitor seasonal variation in submerged seagrass and estuarine habitats

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