Wetland Assessment Using Unmanned Aerial Vehicle (Uav) Photogrammetry

Boon, M. A.; Greenfield, Richard; Tesfamichael, Solomon G.

doi:10.5194/isprs-archives-xli-b1-781-2016

Cited by 20 publications

(13 citation statements)

References 17 publications

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“…UAVs are an emerging technology that benefits environmental monitoring by bridging the constraints in complex, dynamic, limited-access environments that historically have been challenging to survey [91]. Many contemporary studies focus on confirming and demonstrating the ability of various platforms to achieve sufficient horizontal/positional accuracy, and evaluating the efficacy of derivatives for wetland mapping [92][93][94]. In general, high accuracy and ultrahigh-resolution imagery are achievable from most UAVs [92].…”

Section: Unmanned Aerial Vehiclesmentioning

confidence: 99%

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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%

“…Many contemporary studies focus on confirming and demonstrating the ability of various platforms to achieve sufficient horizontal/positional accuracy, and evaluating the efficacy of derivatives for wetland mapping [92][93][94]. In general, high accuracy and ultrahigh-resolution imagery are achievable from most UAVs [92].…”

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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“…Boon et al [21] delineated wetlands and assessed wetland vegetation health in South Africa based on a true-colour 1.8-cm spatial resolution UAS-orthoimage in combination with height data from 3D point clouds (3.8-cm resolution in the wetland area and 29-cm resolution in the surrounding area). They found that the inclusion of height data significantly enhanced wetland delineation and classification.…”

Section: Introductionmentioning

confidence: 99%

Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation

2017

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Abstract:Monitoring of aquatic vegetation is an important component in the assessment of freshwater ecosystems. Remote sensing with unmanned aircraft systems (UASs) can provide sub-decimetre-resolution aerial images and is a useful tool for detailed vegetation mapping. In a previous study, non-submerged aquatic vegetation was successfully mapped using automated classification of spectral and textural features from a true-colour UAS-orthoimage with 5-cm pixels. In the present study, height data from a digital surface model (DSM) created from overlapping UAS-images has been incorporated together with the spectral and textural features from the UAS-orthoimage to test if classification accuracy can be improved further. We studied two levels of thematic detail: (a) Growth forms including the classes of water, nymphaeid, and helophyte; and (b) dominant taxa including seven vegetation classes. We hypothesized that the incorporation of height data together with spectral and textural features would increase classification accuracy as compared to using spectral and textural features alone, at both levels of thematic detail. We tested our hypothesis at five test sites (100 m × 100 m each) with varying vegetation complexity and image quality using automated object-based image analysis in combination with Random Forest classification. Overall accuracy at each of the five test sites ranged from 78% to 87% at the growth-form level and from 66% to 85% at the dominant-taxon level. In comparison to using spectral and textural features alone, the inclusion of height data increased the overall accuracy significantly by 4%-21% for growth-forms and 3%-30% for dominant taxa. The biggest improvement gained by adding height data was observed at the test site with the most complex vegetation. Height data derived from UAS-images has a large potential to efficiently increase the accuracy of automated classification of non-submerged aquatic vegetation, indicating good possibilities for operative mapping.

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“…Another limitation to this study stems from the use of only three spectral bands when using RGB images. In some cases, RGB sensors can be sufficient, such as wetland ecotypes segmentation and wetland delineation [11,17]. However, RGB sensors can fail to provide the necessary data when target objects are spectrally similar to non-target objects across the visible wavelengths, for example, in the case between lichen and bright logs.…”

Section: Discussionmentioning

confidence: 99%

Leveraging AI to Estimate Caribou Lichen in UAV Orthomosaics from Ground Photo Datasets

Richardson

Leblanc

Lovitt

et al. 2021

Drones

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Relating ground photographs to UAV orthomosaics is a key linkage required for accurate multi-scaled lichen mapping. Conventional methods of multi-scaled lichen mapping, such as random forest models and convolutional neural networks, heavily rely on pixel DN values for classification. However, the limited spectral range of ground photos requires additional characteristics to differentiate lichen from spectrally similar objects, such as bright logs. By applying a neural network to tiles of a UAV orthomosaics, additional characteristics, such as surface texture and spatial patterns, can be used for inferences. Our methodology used a neural network (UAV LiCNN) trained on ground photo mosaics to predict lichen in UAV orthomosaic tiles. The UAV LiCNN achieved mean user and producer accuracies of 85.84% and 92.93%, respectively, in the high lichen class across eight different orthomosaics. We compared the known lichen percentages found in 77 vegetation microplots with the predicted lichen percentage calculated from the UAV LiCNN, resulting in a R2 relationship of 0.6910. This research shows that AI models trained on ground photographs effectively classify lichen in UAV orthomosaics. Limiting factors include the misclassification of spectrally similar objects to lichen in the RGB bands and dark shadows cast by vegetation.

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Wetland Assessment Using Unmanned Aerial Vehicle (Uav) Photogrammetry

Cited by 20 publications

References 17 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

Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation

Leveraging AI to Estimate Caribou Lichen in UAV Orthomosaics from Ground Photo Datasets

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