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An implementation of Meta's 2023 foundation artificial intelligence model, Segment Anything (SAM) is tested and used to assist in mapping changes in the extent of riparian woodland using publicly available archival aerial imagery along three gravel bed, meandering, river reaches in rural settings in the UK. Using visual prompts in interactive mode, this newly applied approach is shown to deliver substantial time savings over manual digitisation techniques and, for the type of imagery and the small‐scale deployed, potentially greater accuracy. When applied to high‐resolution (25 cm) aerial imagery SAM appears to be a practical and useful method for examining vegetation and landform change in a manner that has previously only been feasible through detailed field studies. The extent of riparian wood increased by 37–46% between 1999 and 2022 along all three reaches with extension occurring in three main situations: lateral expansion of existing woodland patches along stable or near stable banks; localised bankside establishment of trees transplanted under flood conditions; and progressive colonisation of point bars that developed through channel migration. Considering these factors, important conditions for the establishment, survival and expansion of riparian wood are discussed and likely differences in species distribution according to the geomorphic context are highlighted.
An implementation of Meta's 2023 foundation artificial intelligence model, Segment Anything (SAM) is tested and used to assist in mapping changes in the extent of riparian woodland using publicly available archival aerial imagery along three gravel bed, meandering, river reaches in rural settings in the UK. Using visual prompts in interactive mode, this newly applied approach is shown to deliver substantial time savings over manual digitisation techniques and, for the type of imagery and the small‐scale deployed, potentially greater accuracy. When applied to high‐resolution (25 cm) aerial imagery SAM appears to be a practical and useful method for examining vegetation and landform change in a manner that has previously only been feasible through detailed field studies. The extent of riparian wood increased by 37–46% between 1999 and 2022 along all three reaches with extension occurring in three main situations: lateral expansion of existing woodland patches along stable or near stable banks; localised bankside establishment of trees transplanted under flood conditions; and progressive colonisation of point bars that developed through channel migration. Considering these factors, important conditions for the establishment, survival and expansion of riparian wood are discussed and likely differences in species distribution according to the geomorphic context are highlighted.
River and lake health assessment (RLHA) is an important approach to alleviating the conflict between protecting river and lake ecosystems and fostering socioeconomic development, aiming for comprehensive protection, governance, and management. Vegetation, a key component of the riparian zone, supports and maintains river and lake health (RLH) by providing a range of ecological functions. While research on riparian zone vegetation is ongoing, these studies have not yet been synthesized from the perspective of integrating RLHA with the ecological functions of riparian zone vegetation. In this paper, based on the bibliometric method, the relevant literature studies on the topics of RLHA and unmanned aerial vehicle (UAV) remote sensing of vegetation were screened and counted, and the keywords were highlighted, respectively. Based on the connotation of RLH, this paper categorizes the indicators of RLHA into five aspects: water space: the critical area from the river and lake water body to the land in the riparian zone; water resources: the amount of water in the river and lake; water environment: the quality of water in the river and lake; water ecology:aquatic organisms in the river and lake; and water services:the function of ecosystem services in the river and lake. Based on these five aspects, this paper analyzes the key role of riparian zone vegetation in RLHA. In this paper, the key roles of riparian zone vegetation in RLHA are summarized as follows: stabilizing riverbanks, purifying water quality, regulating water temperature, providing food, replenishing groundwater, providing biological habitats, and beautifying human habitats. This paper analyzes the application of riparian zone vegetation ecological functions in RLH, summarizing the correlation between RLHA indicators and these ecological functions. Moreover, this paper analyzes the advantages of UAV remote sensing technology in the quantitative monitoring of riparian zone vegetation. This analysis is based on the high spatial and temporal resolution characteristics of UAV remote sensing technology and focuses on monitoring the ecological functions of riparian zone vegetation. On this basis, this paper summarizes the content and indicators of UAV quantitative remote sensing monitoring of riparian zone vegetation for RLHA. It covers several aspects: delineation of riparian zone extent, identification of vegetation types and distribution, the influence of vegetation on changes in the river floodplain, vegetation cover, plant diversity, and the impact of vegetation distribution on biological habitat. This paper summarizes the monitoring objects involved in monitoring riparian zones, riparian zone vegetation, river floodplains, and biological habitats, and summarizes the monitoring indicators for each category. Finally, this paper analyzes the challenges of UAV quantitative remote sensing for riparian zone vegetation at the current stage, including the limitations of UAV platforms and sensors, and the complexity of UAV remote sensing data information. This paper envisages the future application prospects of UAV quantitative remote sensing for riparian zone vegetation, including the development of hardware and software such as UAV platforms, sensors, and data technologies, as well as the development of integrated air-to-ground monitoring systems and the construction of UAV quantitative remote sensing platforms tailored to actual management applications.
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