Use of Unmanned Aerial Vehicles for Monitoring Recovery of Forest Vegetation on Petroleum Well Sites

Hird, Jennifer N.; Montaghi, Alessandro; McDermid, Gregory J.; Kariyeva, Jahan; Moorman, Brian J.; Nielsen, Scott E.; McIntosh, Anne C.S.

doi:10.3390/rs9050413

Cited by 46 publications

(19 citation statements)

References 60 publications

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“…However, the LiDAR sensors come with equipment costs and operational difficulties that some may wish to avoid. Consumer-grade cameras mounted on UAVs provide an attractive low-cost source of vegetation information over disturbed and regenerating forests [25,26].…”

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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Section: Introductionmentioning

confidence: 99%

Detection of Coniferous Seedlings in UAV Imagery

Feduck

McDermid

Castilla

2018

Forests

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“…Currently, airborne Lidar is the most accurate remote sensing system for obtaining specific stand-level forest biophysical properties [1,5]. However, because of cost limitations, repeat surveys using airborne Lidar data are often difficult [6]. Therefore, we need alternative approaches to obtain forest biophysical properties.…”

Section: Introductionmentioning

confidence: 99%

Forest Structure Estimation from a UAV-Based Photogrammetric Point Cloud in Managed Temperate Coniferous Forests

Ota

Ogawa

Mizoue

et al. 2017

Forests

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Abstract:Here, we investigated the capabilities of a lightweight unmanned aerial vehicle (UAV) photogrammetric point cloud for estimating forest biophysical properties in managed temperate coniferous forests in Japan, and the importance of spectral information for the estimation. We estimated four biophysical properties: stand volume (V), Lorey's mean height (H L ), mean height (H A ), and max height (H M ). We developed three independent variable sets, which included a height variable, a spectral variable, and a combined height and spectral variable. The addition of a dominant tree type to the above data sets was also tested. The model including a height variable and dominant tree type was the best for all biophysical property estimations. The root-mean-square errors (RMSEs) for the best model for V, H L , H A , and H M , were 118.30, 1.13, 1.24, and 1.24, respectively. The model including a height variable alone yielded the second highest accuracy. The respective RMSEs were 131.74, 1.21, 1.31, and 1.32. The model including a spectral variable alone yielded much lower estimation accuracy than that including a height variable. Thus, a lightweight UAV photogrammetric point cloud could accurately estimate forest biophysical properties, and a spectral variable was not necessarily required for the estimation. The dominant tree type improved estimation accuracy.

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“…RGB imagery represents the most rudimentary remote sensing option for UAV use, but the near-universal application of RGB sensors in the literature analysed highlights their significant utility (and accessibility) for restoration monitoring. For example, low-altitude high-resolution RGB imagery has been used to accurately estimate tree height, crown diameter and biomass in forested areas [14,[43][44][45], and has been employed to determine the presence or absence of target plant species [46][47][48][49]. One particularly attractive feature of RGB imagery is its capacity to be stitched into large orthomosaics using, for example, Structure from Motion (SfM) techniques [50].…”

Section: A Need To Realise the Full Potential Of Uav Platforms And Sementioning

confidence: 99%

Methodological Ambiguity and Inconsistency Constrain Unmanned Aerial Vehicles as A Silver Bullet for Monitoring Ecological Restoration

et al. 2019

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The last decade has seen an exponential increase in the application of unmanned aerial vehicles (UAVs) to ecological monitoring research, though with little standardisation or comparability in methodological approaches and research aims. We reviewed the international peer-reviewed literature in order to explore the potential limitations on the feasibility of UAV-use in the monitoring of ecological restoration, and examined how they might be mitigated to maximise the quality, reliability and comparability of UAV-generated data. We found little evidence of translational research applying UAV-based approaches to ecological restoration, with less than 7% of 2133 published UAV monitoring studies centred around ecological restoration. Of the 48 studies, > 65% had been published in the three years preceding this study. Where studies utilised UAVs for rehabilitation or restoration applications, there was a strong propensity for single-sensor monitoring using commercially available RPAs fitted with the modest-resolution RGB sensors available. There was a strong positive correlation between the use of complex and expensive sensors (e.g., LiDAR, thermal cameras, hyperspectral sensors) and the complexity of chosen image classification techniques (e.g., machine learning), suggesting that cost remains a primary constraint to the wide application of multiple or complex sensors in UAV-based research. We propose that if UAV-acquired data are to represent the future of ecological monitoring, research requires a) consistency in the proven application of different platforms and sensors to the monitoring of target landforms, organisms and ecosystems, underpinned by clearly articulated monitoring goals and outcomes; b) optimization of data analysis techniques and the manner in which data are reported, undertaken in cross-disciplinary partnership with fields such as bioinformatics and machine learning; and c) the development of sound, reasonable and multi-laterally homogenous regulatory and policy framework supporting the application of UAVs to the large-scale and potentially trans-disciplinary ecological applications of the future.

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Use of Unmanned Aerial Vehicles for Monitoring Recovery of Forest Vegetation on Petroleum Well Sites

Cited by 46 publications

References 60 publications

Detection of Coniferous Seedlings in UAV Imagery

Detection of Coniferous Seedlings in UAV Imagery

Forest Structure Estimation from a UAV-Based Photogrammetric Point Cloud in Managed Temperate Coniferous Forests

Methodological Ambiguity and Inconsistency Constrain Unmanned Aerial Vehicles as A Silver Bullet for Monitoring Ecological Restoration

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