The potential of Unmanned Aerial Systems: A tool towards precision classification of hard-to-distinguish vegetation types?

Komárek, Jan; Klouček, Tomáš; Prošek, Jiří

doi:10.1016/j.jag.2018.05.003

Cited by 53 publications

(38 citation statements)

References 46 publications

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“…More recently, researchers have also turned to modified off-the-shelf RGB and near-infrared (NIR) cameras, in pursuit of more accurate vegetation mapping [34][35][36][37][38][39]. This provides an advantage for vegetation mapping, leading to an increase in the classification accuracy of up to 15% [40].…”

Section: Platform and Sensor Choicementioning

confidence: 99%

“…Capabilities for multi-sensor combinations to better inform processes has been a particularly useful benefit of UAS. For example, to explore the impact that these sensors may have on the accuracy of vegetation mapping, Komárek et al [40] showed that identification accuracy of aquatic vegetation increased from 60-68% (using only visible) to 63-71% (combining visible + thermal). Replacing the visible with multispectral data further improved the accuracy interval to 74-81%.…”

Section: Platform and Sensor Choicementioning

confidence: 99%

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Current Practices in UAS-based Environmental Monitoring

et al. 2020

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With the increasing role that unmanned aerial systems (UAS) are playing in data collection for environmental studies, two key challenges relate to harmonizing and providing standardized guidance for data collection, and also establishing protocols that are applicable across a broad range of environments and conditions. In this context, a network of scientists are cooperating within the framework of the Harmonious Project to develop and promote harmonized mapping strategies and disseminate operational guidance to ensure best practice for data collection and interpretation. The culmination of these efforts is summarized in the present manuscript. Through this synthesis study, we identify the many interdependencies of each step in the collection and processing chain, and outline approaches to formalize and ensure a successful workflow and product development. Given the number of environmental conditions, constraints, and variables that could possibly be explored from UAS platforms, it is impractical to provide protocols that can be applied universally under all scenarios. However, it is possible to collate and systematically order the fragmented knowledge on UAS collection and analysis to identify the best practices that can best ensure the streamlined and rigorous development of scientific products.

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Section: Platform and Sensor Choicementioning

confidence: 99%

Section: Platform and Sensor Choicementioning

confidence: 99%

Current Practices in UAS-based Environmental Monitoring

et al. 2020

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“…Dense macroalgae exhibits textural differences that are generally distinguishable from dense eelgrass, whereas sparse eelgrass can easily be mistaken for sparse macroalgae, especially in areas where eelgrass is mixed with non-eelgrass SAV. The omission of sparse eelgrass is a common issue in classification of optical imagery (e.g., Barrell and Grant 2015) , and it may be possible to reduce this issue by: obtaining finer resolution imagery at lower flight altitudes; using pixel-based instead of object-based classifications in cases where there is minimal non-eelgrass SAV mixing (Duffy et al 2018); or through the use of multispectral sensors mounted on UAS to utilize the spectral differences between eelgrass and macroalgae (O'Neill et al 2011;Kom arek et al 2018). Cloud cover was not amongst the top predictors of mapping confidence as we hypothesized.…”

Section: Additional Influences On Mapping Outcomementioning

confidence: 99%

Mapping with confidence; delineating seagrass habitats using Unoccupied Aerial Systems (UAS)

Nahirnick

Reshitnyk

Campbell

et al. 2018

Remote Sens Ecol Conserv

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There is growing interest in the use of Unoccupied Aerial Systems (UAS) for mapping and monitoring of seagrass habitats. UAS provide flexibility with timing of imagery capture, are relatively inexpensive, and obtain very high spatial resolution imagery compared to imagery acquired from sensors mounted on satellite or piloted aircraft. However, research to date has focused on UAS applications for exposed intertidal areas or clear tropical waters. In contrast, submerged seagrass meadows in temperate regions are subject to high cloud cover and water column turbidity, which may limit the application of UAS imagery for coastal habitat mapping. To test the constraints on UAS seagrass mapping, we examined the effects of five environmental conditions at the time of UAS image acquisition (sun angle, tidal height, cloud cover, Secchi depth and wind speed) and five site characteristics (eelgrass patchiness and density, presence and density of non‐eelgrass submerged aquatic vegetation, sediment tone, eelgrass deep edge and site exposure) at 26 eelgrass (Zostera marina) monitoring sites in British Columbia, Canada. Eelgrass was delineated in UAS orthomosaics using object‐based image analysis, combining image segmentation with manual classification. Each site was ranked according to the analysts’ confidence in the delineated eelgrass. Robust Linear Regression revealed sun angle and ‘theoretical visibility’ (an aggregate of tidal height, Secchi depth, and eelgrass deep edge conditions) to be the most important variables affecting mapping confidence. In general, ideal environmental conditions to obtain high confidence eelgrass mapping included: (1) sun angles below 40°; (2) positive theoretical visibility with Secchi depths >5 m; (3) cloud cover conditions of <10% or >90%; and (4) wind speeds less than 5 km h−1. Additionally, high mapping confidence was achieved for sites with dense, continuous, and homogeneous eelgrass meadows. The results of this analysis will guide implementation of UAS mapping technologies in coastal temperate regions.

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“…The method is particularly useful for cases where individual objects, such as trees, are comprised of multiple pixels. This characteristic has made GEOBIA a common choice for UAS image analysis of vegetation [36]. A variety of classification methods can be implemented in a GEOBIA workflow.…”

Section: Geographic Object-based Image Analysismentioning

confidence: 99%

UAS-GEOBIA Approach to Sapling Identification in Jack Pine Barrens after Fire

White

Bomber

Hupy

et al. 2018

Drones

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Jack pine (pinus banksiana) forests are unique ecosystems controlled by wildfire. Understanding the traits of revegetation after wildfire is important for sustainable forest management, as these forests not only provide economic resources, but also are home to specialized species, like the Kirtland Warbler (Setophaga kirtlandii). Individual tree detection of jack pine saplings after fire events can provide information about an environment's recovery. Traditional satellite and manned aerial sensors lack the flexibility and spatial resolution required for identifying saplings in early post-fire analysis. Here we evaluated the use of unmanned aerial systems and geographic object-based image analysis for jack pine sapling identification in a region burned during the 2012 Duck Lake Fire in the Upper Peninsula of Michigan. Results of this study indicate that sapling identification accuracies can top 90%, and that accuracy improves with the inclusion of red and near infrared spectral bands. Results also indicated that late season imagery performed best when discriminating between young (<5 years) jack pines and herbaceous ground cover in these environments.Drones 2018, 2, 40 2 of 15 as a valid and low-cost method to generate both orthomosaics and digital surface models (DSMs) derived from 2D image sequences [5,6]. In their study of SfM derived IDT, Reference [7] performed ITD using UAS-SfM derived canopy height models based on algorithms designed for LiDAR data processing. The authors achieved the most accurate results for smoothing window size (SWS) at 3 × 3 irrespective of the fixed window size (FWS). In their assessment of models utilizing SWS and FWS of 3 × 3, the authors achieved a statistical F-scores greater than 0.80. Reference [8] reconstructed poplar saplings using digital photographs and terrestrial LiDAR (T-LiDAR), finding that T-LiDAR was more accurate at 3D construction than digital photographs, but at a much higher cost. Reference [9] examined the potential contribution hyperspectral imagery makes to IDT, achieving accuracies between 40% and 95% in tree detection. A comparison of LiDAR and SfM technology by Reference [10] indicated achieved accuracies of 96% and 80%, respectively, and the authors concluded that the technologies were capable of producing equally acceptable results for plot level estimates. These studies indicate that photogrammetric methods can provide accurate results for identifying tree crowns; however, none of these studies addressed sapling identification in natural environments. Additionally, processing photogrammetric datasets like SfM and LiDAR are computationally intensive for large areas. Finally, Reference [11] developed a land cover classification using multi-view data using a conditional random field (CRF) model, leading to accuracy improvements between 6% and 16.4% for a variety of classification methods. While these methods show promise of integrating multiple image view points for constructing classifications, we posit that there is still a need to develop robust low-cost (...

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The potential of Unmanned Aerial Systems: A tool towards precision classification of hard-to-distinguish vegetation types?

Cited by 53 publications

References 46 publications

Current Practices in UAS-based Environmental Monitoring

Current Practices in UAS-based Environmental Monitoring

Mapping with confidence; delineating seagrass habitats using Unoccupied Aerial Systems (UAS)

UAS-GEOBIA Approach to Sapling Identification in Jack Pine Barrens after Fire

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