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Article

Comparison of Image Endmember- and Object-Based Classification of Very-High-Spatial-Resolution Unmanned Aircraft System (UAS) Narrow-Band Images for Mapping Riparian Forests and Other Land Covers

1
Department of Geography, College of Geosciences, Texas A & M University (TAMU), 3147 TAMU, College Station, TX 77843-3147, USA
2
Center for Geospatial Science, Applications and Technology (GEOSAT), Texas A & M University, College Station, TX 77843, USA
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School of Engineering and Information Technology, Spatial Information Management, Carinthia University of Applied Sciences, 9524 Villach, Austria
4
SIENA (Spatial Informatics for ENvironmental Applications), Carinthia University of Applied Sciences, 9524 Villach, Austria
*
Author to whom correspondence should be addressed.
Current address: Sandia National Laboratories, Water Power Technologies, Albuquerque, NM 87185, USA.
Academic Editor: Giuseppe Modica
Land 2022, 11(2), 246; https://doi.org/10.3390/land11020246
Received: 15 December 2021 / Revised: 23 January 2022 / Accepted: 27 January 2022 / Published: 7 February 2022
(This article belongs to the Section Land – Observation and Monitoring)
Riparian forests are critical for carbon storage, biodiversity, and river water quality. There has been an increasing use of very-high-spatial-resolution (VHR) unmanned aircraft systems (UAS)-based remote sensing for riparian forest mapping. However, for improved riparian forest/zone monitoring, restoration, and management, an enhanced understanding of the accuracy of different classification methods for mapping riparian forests and other land covers at high thematic resolution is necessary. Research that compares classification efficacies of endmember- and object-based methods applied to VHR (e.g., UAS) images is limited. Using the Sequential Maximum Angle Convex Cone (SMACC) endmember extraction algorithm (EEA) jointly with the Spectral Angle Mapper (SAM) classifier, and a separate multiresolution segmentation/object-based classification method, we map riparian forests/land covers and compare the classification accuracies accrued via the application of these two approaches to narrow-band, VHR UAS orthoimages collected over two river reaches/riparian areas in Austria. We assess the effect of pixel size on classification accuracy, with 7 and 20 cm pixels, and evaluate performance across multiple dates. Our findings show that the object-based classification accuracies are markedly higher than those of the endmember-based approach, where the former generally have overall accuracies of >85%. Poor endmember-based classification accuracies are likely due to the very small pixel sizes, as well as the large number of classes, and the relatively small number of bands used. Object-based classification in this context provides for effective riparian forest/zone monitoring and management. View Full-Text
Keywords: remote sensing; unmanned aircraft systems; UAS; endmember; endmember-based classification; object-based classification; riparian; forest; land cover remote sensing; unmanned aircraft systems; UAS; endmember; endmember-based classification; object-based classification; riparian; forest; land cover
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MDPI and ACS Style

Filippi, A.M.; Güneralp, İ.; Castillo, C.R.; Ma, A.; Paulus, G.; Anders, K.-H. Comparison of Image Endmember- and Object-Based Classification of Very-High-Spatial-Resolution Unmanned Aircraft System (UAS) Narrow-Band Images for Mapping Riparian Forests and Other Land Covers. Land 2022, 11, 246. https://doi.org/10.3390/land11020246

AMA Style

Filippi AM, Güneralp İ, Castillo CR, Ma A, Paulus G, Anders K-H. Comparison of Image Endmember- and Object-Based Classification of Very-High-Spatial-Resolution Unmanned Aircraft System (UAS) Narrow-Band Images for Mapping Riparian Forests and Other Land Covers. Land. 2022; 11(2):246. https://doi.org/10.3390/land11020246

Chicago/Turabian Style

Filippi, Anthony M., İnci Güneralp, Cesar R. Castillo, Andong Ma, Gernot Paulus, and Karl-Heinrich Anders. 2022. "Comparison of Image Endmember- and Object-Based Classification of Very-High-Spatial-Resolution Unmanned Aircraft System (UAS) Narrow-Band Images for Mapping Riparian Forests and Other Land Covers" Land 11, no. 2: 246. https://doi.org/10.3390/land11020246

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