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YUAN Yixin, WEN Qingke, XU Jinyong, et al. Remote sensing monitoring of mangrove forest changes from 1990 to 2020 in Guangdong-Hong Kong-Macao Greater Bay Area. [J]. National Remote Sensing Bulletin 27(6):1496-1510(2023)
粤港澳大湾区规划建设成为国际竞争力一流湾区和世界级城市群，资源集约利用、生态环境优美是国际湾区全面建成的重要方面之一。粤港澳大湾区拥有丰富的滨海红树林湿地，在消浪减风、保护生物多样性、净化海水及固碳等方面发挥重要生态作用。湾区红树林经历过人类开发活动的破坏，也受到了湿地保护政策指引下的良好恢复，但由于调查手段不一及监测时效性限制，目前仍缺乏标准一致的数据以科学客观地阐明粤港澳大湾区红树林湿地的历史变化与最新现状。本文利用现状监测和动态更新相结合的方法，构建了1990年—2020年期间共计4个时段标准一致、前后可比的红树林分布与变化数据库；利用高性能云计算GEE平台（Google Earth Engine），提出了一种红树林现状快速更新方法。研究表明：（1）利用GEE平台可快速提取红树林初步分类结果作为动态更新的参考底图，大大提高了目视解译动态更新方法的速度，为红树林高效管理提供及时的数据支持。（2）粤港澳大湾区过去30年的红树林面积净增长10.21 km,2,，红树林总体得到了较好的恢复和保护，但是在1990年 —2000年期间，红树林是净减少的时期，净减少4.60 km,2,，主要是被新增养殖坑塘、人工用地占用；2000年以来，粤港澳大湾区红树林面积持续增加，红树林公园和自然保护区的建设对红树林存量面积起到了积极作用。（3）虽然红树林自然分布多见于潮间带，但是粤港澳大湾区2010年之后通过建设红树林公园恢复的红树林，有向内陆方向延伸建设的态势。
The Guangdong-Hong Kong-Macao Greater Bay Area is a developing competitive international bay area and world-class city cluster. One of the most important aspects of such a comprehensive development goal is efficient and eco-friendly resource usage. Coastal mangrove wetlands are vast in this area, where mangroves play an important ecological role in reducing waves and wind, thereby protecting biodiversity, purifying the sea, and sequestering carbon. The mangrove forests in the bay area were damaged by human activities but have been well-restored under the guidance of the wetland protection policy. However, a consistent and standard dataset for scientifically and objectively clarifying the historical changes in and latest status quo of mangrove wetlands at the regional scale is lacking owing to inconsistent investigation methods and limitations in timely monitoring. By utilizing status quo monitoring, combined with the dynamic updating method, this study constructs a standard and consistent database that is scientifically comparable across different years. Specifically, this study proposes a dynamic updating method based on a high-performance cloud computing platform, namely, Google Earth Engine (GEE), for last-time-period updating, which largely improves the updating efficiency. Using satellite remote sensing images, this study constructs a long-term mangrove distribution series for 1990, 2000, 2010, 2018, and 2020. In addition, this study quantifies the mangrove changes during the four time periods. Results show that (1) an efficient mangrove dynamic updating method can be designed utilizing the GEE platform, making timely and constant mangrove database construction and yearly updating at the regional scale feasible. The timely database can contribute to the efficient management of mangroves by corresponding departments. (2) Over the past three decades, the mangrove forests in the Guangdong-Hong Kong-Macao Greater Bay Area were well-restored and protected, with the total area increasing by 10.21 km,2, from 1990 to 2020. However, during the period of 1990—2000, the area decreased by 4.60 km,2, owing to the occupation of newly built fish/shrimp ponds and artificial construction. Since 2000, the mangrove area has increased steadily owing to the construction of mangrove parks and nature reserves. (3) Although natural-growing mangrove forests are mostly found in intertidal zones, the newly planted mangroves, restored as mangrove parks, demonstrated a tendency to extend inland slightly after 2010.
红树林变化监测动态更新Google Earth Engine（GEE）随机森林遥感
mangrovechange detectiondynamic updating methodGoogle Earth Engine (GEE)random forest methodremote sensing
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