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

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.

A Study on Wetland Cover Map Formulation and Evaluation Using Unmanned Aerial Vehicle High-Resolution Images

“…Although endmember extraction was used as an effective underlying data sampling technique in hyperspectral studies, it is difficult to obtain pure pixels of images from classifications within a similar spectrum, confirming that only some characteristic arrangements are possible. Similar data, such as for plants, suggest that there is a limit to detecting a complex target spectrum [75]. The endmember technique is considered particularly difficult to apply to multispectral images.…”

Identification of Urban Green Space Types and Estimation of Above-Ground Biomass Using Sentinel-1 and Sentinel-2 Data

“…Automatic information acquisition and repeatable observations can be used to monitor UGS, by employing remote sensing image classification technology based on pixels or objects [16][17][18]. In addition, the object-oriented method focuses on the relationship between pixels and their neighbors, which can provide higher accuracy in UGS classification results, as an alternative to the traditional pixel-based image classification method, that can result in the "salt and pepper effect" [19][20][21]. Likewise, it is inappropriate, as well as ecologically destructive, to rely on the traditional vegetation harvesting method, i.e., by harvesting-drying-weighing, for AGB monitoring in environmentally complex and easily disturbed UGSs [22].…”