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.

Algal blooms have become major public health and ecosystem vitality concerns globally. The prevalence of blooms has increased due to warming water and additional nutrient inputs into aquatic systems. In response, various remotely-sensed methods of detection, analysis, and forecasting have been developed. Satellite imaging has proven successful in the identification of various inland and coastal blooms at large spatial and temporal scales, and airborne platforms offer higher spatial and often spectral resolution at targeted temporal frequencies. Unmanned aerial vehicles (UAVs) have recently emerged as another tool for algal bloom detection, providing users with on-demand high spatial and temporal resolution at lower costs. However, due to the challenges of processing images of water, payload costs and limitations, and a lack of standardized methods, UAV-based algal bloom studies have not gained critical traction. This literature review explores the current state of this field, and highlights opportunities that could promote its growth. By understanding the technical parameters required to identify algal blooms with airborne platforms, and comparing these capabilities to current UAV technology, such knowledge will assist managers, researchers, and public health officials in utilizing UAVs to monitor and predict blooms at greater spatial and temporal precision, reducing exposure to potentially toxic events.

Recent developments in technology and data processing for Unoccupied Aerial Vehicles (UAVs) have revolutionized the scope of ecosystem monitoring, providing novel pathways to fill the critical gap between limited-scope field surveys and limited-customization satellite and piloted aerial platforms. These advances are especially ground-breaking for supporting management, restoration, and conservation of landscapes with limited field access and vulnerable ecological systems, particularly wetlands. This study presents a scoping review of the current status and emerging opportunities in wetland UAV applications, with particular emphasis on ecosystem management goals and remaining research, technology, and data needs to even better support these goals in the future. Using 122 case studies from 29 countries, we discuss which wetland monitoring and management objectives are most served by this rapidly developing technology, and what workflows were employed to analyze these data. This review showcases many ways in which UAVs may help reduce or replace logistically demanding field surveys and can help improve the efficiency of UAV-based workflows to support longer-term monitoring in the face of wetland environmental challenges and management constraints. We also highlight several emerging trends in applications, technology, and data and offer insights into future needs.