A Review of Unoccupied Aerial Vehicle Use in Wetland Applications: Emerging Opportunities in Approach, Technology, and Data

Dronova, Iryna; Kislik, Chippie; Dinh, Zack; Kelly, Maggi

doi:10.3390/drones5020045

Cited by 28 publications

(15 citation statements)

References 175 publications

(692 reference statements)

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“…The investigation of UAV data for wetland applications remains an ongoing, highly active area of research. Available literature suggests promise for PPR wetland applications (and beyond), facilitating per basin analysis over small study areas [103].…”

Section: Unmanned Aerial Vehiclesmentioning

confidence: 99%

Remote Sensing of Wetlands in the Prairie Pothole Region of North America

Montgomery

Mahoney

Brisco³

et al. 2021

Remote Sensing

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The Prairie Pothole Region (PPR) of North America is an extremely important habitat for a diverse range of wetland ecosystems that provide a wealth of socio-economic value. This paper describes the ecological characteristics and importance of PPR wetlands and the use of remote sensing for mapping and monitoring applications. While there are comprehensive reviews for wetland remote sensing in recent publications, there is no comprehensive review about the use of remote sensing in the PPR. First, the PPR is described, including the wetland classification systems that have been used, the water regimes that control the surface water and water levels, and the soil and vegetation characteristics of the region. The tools and techniques that have been used in the PPR for analyses of geospatial data for wetland applications are described. Field observations for ground truth data are critical for good validation and accuracy assessment of the many products that are produced. Wetland classification approaches are reviewed, including Decision Trees, Machine Learning, and object versus pixel-based approaches. A comprehensive description of the remote sensing systems and data that have been employed by various studies in the PPR is provided. A wide range of data can be used for various applications, including passive optical data like aerial photographs or satellite-based, Earth-observation data. Both airborne and spaceborne lidar studies are described. A detailed description of Synthetic Aperture RADAR (SAR) data and research are provided. The state of the art is the use of multi-source data to achieve higher accuracies and hybrid approaches. Digital Surface Models are also being incorporated in geospatial analyses to separate forest and shrub and emergent systems based on vegetation height. Remote sensing provides a cost-effective mechanism for mapping and monitoring PPR wetlands, especially with the logistical difficulties and cost of field-based methods. The wetland characteristics of the PPR dictate the need for high resolution in both time and space, which is increasingly possible with the numerous and increasing remote sensing systems available and the trend to open-source data and tools. The fusion of multi-source remote sensing data via state-of-the-art machine learning is recommended for wetland applications in the PPR. The use of such data promotes flexibility for sensor addition, subtraction, or substitution as a function of application needs and potential cost restrictions. This is important in the PPR because of the challenges related to the highly dynamic nature of this unique region.

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Section: Unmanned Aerial Vehiclesmentioning

confidence: 99%

Remote Sensing of Wetlands in the Prairie Pothole Region of North America

Montgomery

Mahoney

Brisco³

et al. 2021

Remote Sensing

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“…Power consumption and battery capacity remain the primary drawbacks of the current technology, however, development of this technology is rapid. Mapping can also become more area-efficient when using fixed-wing unmanned aircraft instead of the multi-copters used in this study [24]. Detailed UAS monitoring should be focused on the areas of interest predicted to face significant changes in restoration or areas that can be used to represent the site as a whole.…”

Section: Uas Mapping and Sfm Processing Experiencesmentioning

confidence: 99%

“…Unmanned aircraft systems (UAS), in particular, allow photogrammetric mapping at a centimetre-level spatial resolution [23]. UAS mapping is an automated process providing quick results and flexibility, particularly in environments where conventional field surveys are laborious, such as wetlands [24,25]. The photogrammetric elevation model is produced using UAS data with a structure-frommotion (SfM) machine learning algorithm which combines the neighboring images and determines depth information for each pixel when the images have sufficient overlap [26].…”

Section: Introductionmentioning

confidence: 99%

Unmanned Aircraft System (UAS) Structure-From-Motion (SfM) for Monitoring the Changed Flow Paths and Wetness in Minerotrophic Peatland Restoration

et al. 2022

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Peatland restoration aims to achieve pristine water pathway conditions to recover dispersed wetness, water quality, biodiversity and carbon sequestration. Restoration monitoring needs new methods for understanding the spatial effects of restoration in peatlands. We introduce an approach using high-resolution data produced with an unmanned aircraft system (UAS) and supported by the available light detection and ranging (LiDAR) data to reveal the hydrological impacts of elevation changes in peatlands due to restoration. The impacts were assessed by analyzing flow accumulation and the SAGA Wetness Index (SWI). UAS campaigns were implemented at two boreal minerotrophic peatland sites in degraded and restored states. Simultaneously, the control campaigns mapped pristine sites to reveal the method sensitivity of external factors. The results revealed that the data accuracy is sufficient for describing the primary elevation changes caused by excavation. The cell-wise root mean square error in elevation was on average 48 mm when two pristine UAS campaigns were compared with each other, and 98 mm when each UAS campaign was compared with the LiDAR data. Furthermore, spatial patterns of more subtle peat swelling and subsidence were found. The restorations were assessed as successful, as dispersing the flows increased the mean wetness by 2.9–6.9%, while the absolute changes at the pristine sites were 0.4–2.4%. The wetness also became more evenly distributed as the standard deviation decreased by 13–15% (a 3.1–3.6% change for pristine). The total length of the main flow routes increased by 25–37% (a 3.1–8.1% change for pristine), representing the increased dispersion and convolution of flow. The validity of the method was supported by the field-determined soil water content (SWC), which showed a statistically significant correlation (R2 = 0.26–0.42) for the restoration sites but not for the control sites, possibly due to their upslope catchment areas being too small. Despite the uncertainties related to the heterogenic soil properties and complex groundwater interactions, we conclude the method to have potential for estimating changed flow paths and wetness following peatland restoration.

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“…In particular, unmanned aerial vehicles (UAVs-henceforth referred to as drones) are relatively inexpensive, flexible and convenient platforms that are widely accessible and can collect optical, thermal, multispectral and/or elevation data. Drones have the capacity to collect very high resolution data (up to 0.55 cm; Dronova et al, 2021), potentially enabling high accuracy and precision in coastal wetland monitoring. While studies frequently monitor on unitemporal or bitemporal scales (e.g., Kelly et al, 2011;Meng et al, 2017), drones allow flexibility in monitoring frequency, and there is scope for exploring the benefits of multitemporal surveys for rehabilitation monitoring (Ridge and Johnston, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Second, we produce accurate classifications on a species-level. Third, as image resolution is known to be important for accurate detection of fine-scale details (Dronova et al, 2021), we investigate what resolution is necessary for accurate saltmarsh rehabilitation monitoring. Fourth, in assessing the trajectory of blue carbon growth at a rehabilitation site (as detailed in Sadat-Noori et al, 2021), we develop an understanding of temporal, spatial, environmental and biological aspects of blue carbon restoration.…”

Section: Introductionmentioning

confidence: 99%

Blue carbon ecosystem monitoring using remote sensing reveals wetland restoration pathways

Lanceman

Sadat‐Noori²,

Gaston³

et al. 2022

Front. Environ. Sci.

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In an era of climate and biodiversity crises, ecosystem rehabilitation is critical to the ongoing wellbeing of humans and the environment. Coastal ecosystem rehabilitation is particularly important, as these ecosystems sequester large quantities of carbon (known in marine ecosystems as “blue carbon”) thereby mitigating climate change effects while also providing ecosystem services and biodiversity benefits. The recent formal accreditation of blue carbon services is producing a proliferation of rehabilitation projects, which must be monitored and quantified over time and space to assess on-ground outcomes. Consequently, remote sensing techniques such as drone surveys, and machine learning techniques such as image classification, are increasingly being employed to monitor wetlands. However, few projects, if any, have tracked blue carbon restoration across temporal and spatial scales at an accuracy that could be used to adequately map species establishment with low-cost methods. This study presents an open-source, user-friendly workflow, using object-based image classification and a random forest classifier in Google Earth Engine, to accurately classify 4 years of multispectral and photogrammetrically derived digital elevation model drone data at a saltmarsh rehabilitation site on the east coast of Australia (Hunter River estuary, NSW). High classification accuracies were achieved, with >90% accuracy at 0.1 m resolution. At the study site, saltmarsh colonised most suitable areas, increasing by 142% and resulting in 56 tonnes of carbon sequestered, within a 4-year period, providing insight into blue carbon regeneration trajectories. Saltmarsh growth patterns were species-specific, influenced by species’ reproductive and dispersal strategies. Our findings suggested that biotic factors and interactions were important in influencing species’ distributions and succession trajectories. This work can help improve the efficiency and effectiveness of restoration planning and monitoring at coastal wetlands and similar ecosystems worldwide, with the potential to apply this approach to other types of remote sensing imagery and to calculate other rehabilitation co-benefits. Importantly, the method can be used to calculate blue carbon habitat creation following tidal restoration of coastal wetlands.

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A Review of Unoccupied Aerial Vehicle Use in Wetland Applications: Emerging Opportunities in Approach, Technology, and Data

Cited by 28 publications

References 175 publications

Remote Sensing of Wetlands in the Prairie Pothole Region of North America

Remote Sensing of Wetlands in the Prairie Pothole Region of North America

Unmanned Aircraft System (UAS) Structure-From-Motion (SfM) for Monitoring the Changed Flow Paths and Wetness in Minerotrophic Peatland Restoration

Blue carbon ecosystem monitoring using remote sensing reveals wetland restoration pathways

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