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Remote Sens. 2017, 9(3), 247; doi:10.3390/rs9030247

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

1
Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, SE-75007 Uppsala, Sweden
2
Department of Forest Resource Management, Swedish University of Agricultural Sciences, SE-90183 Umeå, Sweden
3
Department of Wildlife, Fish, and Environmental Studies, Swedish University of Agricultural Sciences, SE-90183 Umeå, Sweden
*
Author to whom correspondence should be addressed.
Academic Editors: Farid Melgani, Francesco Nex, Parth Sarathi Roy and Prasad S. Thenkabail
Received: 30 November 2016 / Revised: 17 February 2017 / Accepted: 1 March 2017 / Published: 7 March 2017
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
View Full-Text   |   Download PDF [2174 KB, uploaded 8 March 2017]   |  

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. View Full-Text
Keywords: digital surface model (DSM); drone; growth form; non-submerged aquatic vegetation; object-based image analysis (OBIA); Random Forest; remotely piloted aircraft system (RPAS); species identification; sub-decimetre spatial resolution; unmanned aerial vehicle (UAV); unmanned aircraft system (UAS) digital surface model (DSM); drone; growth form; non-submerged aquatic vegetation; object-based image analysis (OBIA); Random Forest; remotely piloted aircraft system (RPAS); species identification; sub-decimetre spatial resolution; unmanned aerial vehicle (UAV); unmanned aircraft system (UAS)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Husson, E.; Reese, H.; Ecke, F. Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation. Remote Sens. 2017, 9, 247.

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