UAS-SfM for Coastal Research: Geomorphic Feature Extraction and Land Cover Classification from High-Resolution Elevation and Optical Imagery

Sturdivant, Emily J.; Lentz, Erika E.; Thieler, E. Robert; Farris, Amy S.; Weber, Kathryn M.; Remsen, David; Miner, Simon; Henderson, Rachel E.

doi:10.3390/rs9101020

Cited by 57 publications

(46 citation statements)

References 43 publications

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“…Presently, the accuracy of UAS-based photogrammetry remains largely dependant on both the acquisition of GCPs and on robust flight geometry (Table 3; [11,16,[39][40][41][42][43][44]). In terms of field effort, the first issue translates into time-consuming georeferencing of stable ground points [44] and/or ad-hoc deployment and surveying of optical targets.…”

Section: Experiments Originalitymentioning

confidence: 99%

“…Results show how these three techniques-mobile laser scanning, photogrammetry based on ground control points, and photogrammetry based on kinematic ground control points-deliver accurate (i.e., root mean square errors < 15 cm) 3D reconstruction of beach-to-dune transition areas, the latter being performed at lower survey and logistic costs, and with enhanced spatial coverage capabilities. This study opens the gate for exploring longer (hundreds of kilometers) shoreline dynamics with ground-control-point-free air and ground mapping techniques.coverage [11]. Indeed, spatial coverage of airborne LiDAR is unmatched [4].…”

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confidence: 99%

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Corridor Mapping of Sandy Coastal Foredunes with UAS Photogrammetry and Mobile Laser Scanning

Nahon

Molina²,

Blázquez³

et al. 2019

Remote Sensing

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Recurrent monitoring of sandy beaches and of the dunes behind them is needed to improve the scientific knowledge on their dynamics as well as to develop sustainable management practices of those valuable landforms. Unmanned Aircraft Systems (UAS) are sought as a means to fulfill this need, especially leveraged by photogrammetric and LiDAR-based mapping methods and technology. The present study compares different strategies to carry UAS photogrammetric corridor mapping over linear extensions of sandy shores. In particular, we present results on the coupling of a UAS with a mobile laser scanning system, operating simultaneously in Cap Ferret, SW France. This aerial-terrestrial tandem enables terrain reconstruction with kinematic ground control points, thus largely avoiding the deployment of surveyed ground control points on the non-stable sandy ground. Results show how these three techniques-mobile laser scanning, photogrammetry based on ground control points, and photogrammetry based on kinematic ground control points-deliver accurate (i.e., root mean square errors < 15 cm) 3D reconstruction of beach-to-dune transition areas, the latter being performed at lower survey and logistic costs, and with enhanced spatial coverage capabilities. This study opens the gate for exploring longer (hundreds of kilometers) shoreline dynamics with ground-control-point-free air and ground mapping techniques.coverage [11]. Indeed, spatial coverage of airborne LiDAR is unmatched [4]. Nonetheless, it involves technological, operational and logistic costs that prevent fast acquisition of pre-and post-storm data for instance. On the other hand, UAS photogrammetric surveys [10,12] are easier to perform, and incorporating post-processed or real-time differential GNSS processing techniques (PPK/RTK) and low-cost, low-to-medium tactical grade Inertial Measurement Units (IMU) implies a reduction in the use of surveyed Ground Control Points (GCPs) [13,14]. However, concerning linear coastal landforms, GCPs remain largely necessary to mitigate accuracy degradation along the corridor, i.e., "bowl effects" [15,16]. This constraint hampers the use of UAS over hundreds of kilometers of shore, as for instance, to survey long sandy beaches after storms [17].Over the last decade, Mobile Laser Scanning (MLS) has emerged as a credible alternative for surveying coastal beaches and foredunes [18][19][20][21]. MLS mainly consist of one or several terrestrial laser scanners mounted on a moving vehicle together with a medium-to-high grade GNSS or IMU/GNSS system. Along the vehicle path, scanners collect 3D point clouds with an accuracy comparable to airborne LiDAR standards at reduced technological and logistic costs. Typical achieved point cloud densities are over 100 points per m 2 , therefore offering a valuable opportunity to study foredune growth with high resolution [20]. Yet, from a coverage standpoint, steep dune slopes create shadow areas and leave large portions of the dune top unscanned. This limitation gives rise to one of the core value proposit...

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Section: Experiments Originalitymentioning

confidence: 99%

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confidence: 99%

Corridor Mapping of Sandy Coastal Foredunes with UAS Photogrammetry and Mobile Laser Scanning

Nahon

Molina²,

Blázquez³

et al. 2019

Remote Sensing

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“…There is a growing trend in studies of coastal and fluvial systems for using automated methods to extract information from time-series of imagery from fixed camera installations [10][11][12][13][14][15][16], UAVs [17][18][19], and other aerial platforms [20]. Fixed camera installations are designed for generating time-series of images for the assessment of geomorphic changes in dynamic environments.…”

Section: The Growing Use Of Image Classification In the Geosciencesmentioning

confidence: 99%

Landscape Classification with Deep Neural Networks

Buscombe

Ritchie

2018

Geosciences

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Abstract:The application of deep learning, specifically deep convolutional neural networks (DCNNs), to the classification of remotely-sensed imagery of natural landscapes has the potential to greatly assist in the analysis and interpretation of geomorphic processes. However, the general usefulness of deep learning applied to conventional photographic imagery at a landscape scale is, at yet, largely unproven. If DCNN-based image classification is to gain wider application and acceptance within the geoscience community, demonstrable successes need to be coupled with accessible tools to retrain deep neural networks to discriminate landforms and land uses in landscape imagery. Here, we present an efficient approach to train/apply DCNNs with/on sets of photographic images, using a powerful graphical method called a conditional random field (CRF), to generate DCNN training and testing data using minimal manual supervision. We apply the method to several sets of images of natural landscapes, acquired from satellites, aircraft, unmanned aerial vehicles, and fixed camera installations. We synthesize our findings to examine the general effectiveness of transfer learning to landscape-scale image classification. Finally, we show how DCNN predictions on small regions of images might be used in conjunction with a CRF for highly accurate pixel-level classification of images.

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“…An overview of the followed methodological workflow is given below ( Figure 5). The methodology is organized into four steps: (i) data acquisition and creation of tc-RGB and multispectral orthomosaics using the UAS-SfM (structure-from-motion) framework [10,30], (ii) orthomosaics preprocessing, (iii) object-based image analysis, and (iv) accuracy assessment. After UAS and in situ data acquisition, the divers' photos passed quality control and were interpreted to define the class that is depicted.…”

Section: Methodological Workflowmentioning

confidence: 99%

Comparison of True-Color and Multispectral Unmanned Aerial Systems Imagery for Marine Habitat Mapping Using Object-Based Image Analysis

Παπακωνσταντίνου¹,

Stamati²,

Topouzelis³

2020

Remote Sensing

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The use of unmanned aerial systems (UAS) over the past years has exploded due to their agility and ability to image an area with high-end products. UAS are a low-cost method for close remote sensing, giving scientists high-resolution data with limited deployment time, accessing even the most inaccessible areas. This study aims to produce marine habitat mapping by comparing the results produced from true-color RGB (tc-RGB) and multispectral high-resolution orthomosaics derived from UAS geodata using object-based image analysis (OBIA). The aerial data was acquired using two different types of sensors-one true-color RGB and one multispectral-both attached to a UAS, capturing images simultaneously. Additionally, divers' underwater images and echo sounder measurements were collected as in situ data. The produced orthomosaics were processed using three scenarios by applying different classifiers for the marine habitat classification. In the first and second scenario, the k-nearest neighbor (k-NN) and fuzzy rules were applied as classifiers, respectively. In the third scenario, fuzzy rules were applied in the echo sounder data to create samples for the classification process, and then the k-NN algorithm was used as the classifier. The in situ data collected were used as reference and training data. Additionally, these data were used for the calculation of the overall accuracy of the OBIA process in all scenarios. The classification results of the three scenarios were compared. Using tc-RGB instead of multispectral data provides better accuracy in detecting and classifying marine habitats when applying the k-NN as the classifier. In this case, the overall accuracy was 79%, and the Kappa index of agreement (KIA) was equal to 0.71, which illustrates the effectiveness of the proposed approach. The results showed that sub-decimeter resolution UAS data revealed the sub-bottom complexity to a large extent in relatively shallow areas as they provide accurate information that permits the habitat mapping in extreme detail. The produced habitat datasets are ideal as reference data for studying complex coastal environments using satellite imagery.

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UAS-SfM for Coastal Research: Geomorphic Feature Extraction and Land Cover Classification from High-Resolution Elevation and Optical Imagery

Cited by 57 publications

References 43 publications

Corridor Mapping of Sandy Coastal Foredunes with UAS Photogrammetry and Mobile Laser Scanning

Corridor Mapping of Sandy Coastal Foredunes with UAS Photogrammetry and Mobile Laser Scanning

Landscape Classification with Deep Neural Networks

Comparison of True-Color and Multispectral Unmanned Aerial Systems Imagery for Marine Habitat Mapping Using Object-Based Image Analysis

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