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Remote sensing monitoring of mangrove forest changes from 1990 to 2020 in Guangdong-Hong Kong-Macao Greater Bay Area

  • role： First author第一作者
  • Affiliation：

    National Engineering Research Centerfor Geomatics (NCG), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：yuanyixin19@mails.ucas.ac.cn
  • Introduction：袁艺馨，研究方向为资源环境遥感。E-mail： yuanyixin19@mails.ucas.ac.cn
YUAN Yixin1,  
  • role： Corresponding author通信作者
  • Affiliation：

    National Engineering Research Centerfor Geomatics (NCG), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：wenqk@aircas.ac.cn
  • Introduction：温庆可，研究方向为准实时土地资源遥感应用。E-mail：wenqk@aircas.ac.cn
WEN Qingke1*,  
  • Affiliation：

    National Engineering Research Centerfor Geomatics (NCG), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

XU Jinyong1,  
  • Affiliation：

    Remote Sensing Department of Water Ecology and Environment, Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment, Beijing 100094, China

WANG Chen2,  
  • Affiliation：

    National Engineering Research Centerfor Geomatics (NCG), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

ZHAO Xiaoli1,  
  • Affiliation：

    National Engineering Research Centerfor Geomatics (NCG), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

LIU Shuo1,  
  • Affiliation：

    National Engineering Research Centerfor Geomatics (NCG), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

XIE Rui1

Resümee

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 km2 from 1990 to 2020. However, during the period of 1990—2000, the area decreased by 4.60 km2 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.

Schlüsselwort

mangrove;Change Detection;dynamic updating method;Google Earth Engine (GEE);random forest method;remote sensing

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