Mapping and Classification of Ecologically Sensitive Marine Habitats Using Unmanned Aerial Vehicle (UAV) Imagery and Object-Based Image Analysis (OBIA)

Ventura, Daniele; Bonifazi, Andrea; Gravina, Maria Flavia; Belluscio, Andrea; Ardizzone, Giandomenico

doi:10.3390/rs10091331

Cited by 165 publications

(148 citation statements)

References 88 publications

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“…4). While the results presented here have specifically addressed UAS eelgrass mapping in a temperate environment, the findings would be generally applicable to other submerged marine habitats such as macroalgae, rocky and biogenic reefs, and other seagrass species in both temperate and tropical environments (Casella et al 2017;Ventura et al 2018).…”

Section: Improving Map Reliabilitymentioning

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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Section: Improving Map Reliabilitymentioning

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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“…Platforms, data processing, and analytic tools are easily available to practitioners of any experience level (Ventura et al. ). Data that were previously unattainable can now be readily produced with this technology.…”

Section: Discussionmentioning

confidence: 99%

“…Unmanned aircraft systems have revolutionized spatial ecology with the arrival of high-resolution data, low operating costs, and repeatability with userdetermined survey times (Anderson and Gaston 2013). Platforms, data processing, and analytic tools are easily available to practitioners of any experience level (Ventura et al 2018). Data that were previously unattainable can now be readily produced with this technology.…”

Section: Discussionmentioning

confidence: 99%

Use of Drones in Fishery Science

et al. 2019

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The use of unmanned aircraft systems (UASs), commonly referred to as drones, has rapidly expanded across many scientific disciplines. Like other fields, fisheries research would benefit significantly from broader use of this emerging technology but has lagged behind other disciplines. Like the implementation of satellite and aircraft‐based remote sensing technology in previous decades brought a greater understanding of large‐scale spatial patterns and processes, UAS technology has the potential to put those tools in the hands of individual researchers, allowing implementation at finer spatial and temporal scales for a fraction of the cost. Our goal is to provide a “how‐to” for fisheries researchers interested in using UAS technology. We outline the necessary steps for any UAS project from choosing the appropriate platform and sensors to data acquisition and analysis. We also present the current ways in which UASs are being used in a fisheries research context as well as potential future research directions.

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“…These processes require multitemporal data, either from satellites or from unmanned aerial systems (UAS). The availability of very high-resolution orthomosaics presents increasing interest, as it provides to scientists and relevant stakeholders detailed information for understanding coastal dynamics and implementing environmental policies [6,[10][11][12][13]. However, the use of high-resolution orthomosaics created from UAS data is expected to improve mapping accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, three classifications were performed using three different methods: (i) maximum likelihood in ArcGIS 10.4, (ii) extraction and classification of homogeneous objects (ECHO) in MultiSpec 3.4, and (iii) OBIA in eCognition Developer 8.7, with an overall accuracy of 78.8%, 80.9%, and 89.01%, respectively [16]. In a subsequent study, Ventura et al 2018 [12] referred to the island of Giglio in Central Italy, where they carried out habitat mapping in three different coastal environments. Using OBIA and the nearest neighbor algorithm as classifiers, four different classifications were applied to identify Posidonia Oceanica meadows, nurseries for juvenile fish, and biogenic reefs with overall accuracies of 85%, 84%, and 80%, respectively.…”

Section: Introductionmentioning

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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Mapping and Classification of Ecologically Sensitive Marine Habitats Using Unmanned Aerial Vehicle (UAV) Imagery and Object-Based Image Analysis (OBIA)

Cited by 165 publications

References 88 publications

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

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

Use of Drones in Fishery Science

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

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