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.
Highlights
Global losses of wetland areas, increasing anthropogenic pressures on their resources and the uncertainty of future climate change effects urgently call for more sustainable and adaptive conservation and management strategies which, in turn, require better understanding of wetland ecosystem properties and spatio-temporal variation [1,2,3]
Remote sensing offers the benefits of capturing extensive study areas at the same state of plant phenology or inundation, spectral sensitivity of sensors to variation in wetland surface composition, and greater cost efficiency of repeated data collection compared to field surveys [9,10,11]
Key Summary Points from the Reviewed Studies. It is evident from the reviewed studies that object-based image analysis (OBIA) framework offers several important advantages to wetland analyses in various regions of the world, and its use as a flexible, multi-scale approach will likely actively continue in these complex and diverse environments
Summary
Global losses of wetland areas, increasing anthropogenic pressures on their resources and the uncertainty of future climate change effects urgently call for more sustainable and adaptive conservation and management strategies which, in turn, require better understanding of wetland ecosystem properties and spatio-temporal variation [1,2,3]. Research abilities to detect and characterize wetland ecosystems and to monitor their dynamics are often constrained by the limited on-site access, the risk of disturbing vulnerable habitats and species, and high surface complexity caused by fine-scale variation in topography, hydrologic properties and vegetation [4,5,6] These challenges make it difficult to obtain sufficiently large, representative and repeated samples from the field surveys in wetlands and to generalize ecological information across broader landscapes [7,8]. Several studies reported the improvement in wetland classification accuracy with OBIA compared to pixel-based methods up to 31% [22,24,29,30] It remains unclear whether the 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 [28].
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