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

Anthony M. Filippi, İnci Güneralp, Cesar R. Castillo, Andong Ma, Gernot Paulus and Karl-Heinrich Anders
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Anthony M. Filippi: Department of Geography, College of Geosciences, Texas A & M University (TAMU), 3147 TAMU, College Station, TX 77843-3147, USA
İnci Güneralp: Department of Geography, College of Geosciences, Texas A & M University (TAMU), 3147 TAMU, College Station, TX 77843-3147, USA
Cesar R. Castillo: Department of Geography, College of Geosciences, Texas A & M University (TAMU), 3147 TAMU, College Station, TX 77843-3147, USA
Andong Ma: Department of Geography, College of Geosciences, Texas A & M University (TAMU), 3147 TAMU, College Station, TX 77843-3147, USA
Gernot Paulus: School of Engineering and Information Technology, Spatial Information Management, Carinthia University of Applied Sciences, 9524 Villach, Austria
Karl-Heinrich Anders: School of Engineering and Information Technology, Spatial Information Management, Carinthia University of Applied Sciences, 9524 Villach, Austria

Land, 2022, vol. 11, issue 2, 1-33

Abstract: 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.

Keywords: remote sensing; unmanned aircraft systems; UAS; endmember; endmember-based classification; object-based classification; riparian; forest; land cover (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
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