Next Article in Journal
Assessing the Impact of Tides and Atmospheric Fronts on Submesoscale Physical and Bio-Optical Distributions near a Coastal Convergence Zone
Previous Article in Journal
Relationship between Light Use Efficiency and Photochemical Reflectance Index Corrected Using a BRDF Model at a Subtropical Mixed Forest
Previous Article in Special Issue
Satellite Observations of Wind Wake and Associated Oceanic Thermal Responses: A Case Study of Hainan Island Wind Wake
Open AccessArticle

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

Department of Marine Sciences, Marine Remote Sensing Group, University of the Aegean, University Hill, 81100 Mytilene, Greece
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 554; https://doi.org/10.3390/rs12030554 (registering DOI)
Received: 18 December 2019 / Revised: 30 January 2020 / Accepted: 4 February 2020 / Published: 7 February 2020
(This article belongs to the Special Issue Applications of Remote Sensing in Coastal Areas)
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. View Full-Text
Keywords: coastal remote sensing; habitat mapping; unmanned aerial vehicle (UAV); unmanned aircraft system (UAS); drone; object-based image analysis (OBIA); UAS data acquisition coastal remote sensing; habitat mapping; unmanned aerial vehicle (UAV); unmanned aircraft system (UAS); drone; object-based image analysis (OBIA); UAS data acquisition
Show Figures

Graphical abstract

MDPI and ACS Style

Papakonstantinou, A.; Stamati, C.; Topouzelis, K. Comparison of True-Color and Multispectral Unmanned Aerial Systems Imagery for Marine Habitat Mapping Using Object-Based Image Analysis. Remote Sens. 2020, 12, 554.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop