Object-Based Image Analysis in Wetland Research: A Review

Abstract

Abstract:The applications of object-based image analysis (OBIA) in remote sensing studies of wetlands have been growing over recent decades, addressing tasks from detection and delineation of wetland bodies to comprehensive analyses of within-wetland cover types and their change. Compared to pixel-based approaches, OBIA offers several important benefits to wetland analyses related to smoothing of the local noise, incorporating meaningful non-spectral features for class separation and accounting for landscape hierarchy of wetland ecosystem organization and structure. However, there has been little discussion on whether unique challenges of wetland environments can be uniformly addressed by OBIA across different types of data, spatial scales and research objectives, and to what extent technical and conceptual aspects of this framework may themselves present challenges in a complex wetland setting. This review presents a synthesis of 73 studies that applied OBIA to different types of remote sensing data, spatial scale and research objectives. It summarizes the progress and scope of OBIA uses in wetlands, key benefits of this approach, factors related to accuracy and uncertainty in its applications and the main research needs and directions to expand the OBIA capacity in the future wetland studies. Growing demands for higher-accuracy wetland characterization at both regional and local scales together with advances in very high resolution remote sensing and novel tasks in wetland restoration monitoring will likely continue active exploration of the OBIA potential in these diverse and complex environments.

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

“…Random Forest classifications performed well, confirming the widely reported effectiveness of the RF algorithm for land cover classification (e.g., [15,58,64,65]), and particularly for mapping wetlands in tropical environments (e.g., [73,89,92]). Overall, OOB accuracy was above 80% for most models, above 90% for a selection of optimized models, and as high as 99% for model M1, which integrated spectral, SAR and topographic data, and images from multiple years and seasons.…”

“…Individual class accuracies varied according to the ecology of the dominant species, phenology, and disturbance, which in turn affect reflectance and backscatter characteristics [89]. Perennial wetlands such as Papyrus Swamps and Forested Wetlands, which occur as homogenous plant communities, generally achieved higher accuracies and consistency (stability) between classification models compared to seasonally inundated herbaceous wetlands.…”

Remote Sensing for Wetland Mapping and Historical Change Detection at the Nisqually River Delta

“…The object-based segmentation approach works well with hierarchical analysis in wetland environments, as it is suited for multi-level processing where discrete land cover types can be identified using expert knowledge [29]. Rule-based hierarchical classifications require initial set-up time, but the benefit of using ancillary data and expert knowledge can improve results over other statistical models [27].…”

Assessing the Utility of Uav-Borne Hyperspectral Image and Photogrammetry Derived 3d Data for Wetland Species Distribution Quick Mapping

“…To this end, satellite and airborne remote sensing data has been widely used for wetland species classification (Dronova, 2015;Fabian Ewald Fassnacht et al, 2016). Hyperspectral sensors provides narrow-band and contiguous spectral data, which allows better examination and discrimination of vegetation types (Adam, Mutanga, & Rugege, 2010).…”