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Long time-series remote sensing monitoring of mangrove forests in the Guangdong-Hong Kong-Macao Greater Bay Area

  • role： First author第一作者
  • Affiliation：

    Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China

  • Email：jiakai@mail.bnu.edu.cn
  • Introduction：贾凯，研究方向为湿地遥感、遥感长时序变化分析。E-mail： jiakai@mail.bnu.edu.cn
JIA Kai1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China

    Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China

  • Email：css@gdas.ac.cn
  • Introduction：陈水森，研究方向为河口海岸遥感、精准农业。E-mail： css@gdas.ac.cn
CHEN Shuisen13*,  
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

JIANG Weiguo2

résumé

With the explosive growth of remote sensing images, the contradiction between the refined requirements of the change processing analysis and the lack of local computing power has become increasingly prominent. The emergence of Google Earth Engine (GEE) geographic cloud platform has solved the pain points of the industry, where users are strained with computing power.The Guangdong-Hong Kong-Macao Greater Bay Area (GHM) is considered the study area, and an annual wetland classification data set from 1987 to 2020 is constructed with the support of GEE. It is analyzed for the temporal phase characteristics and spatial expansion process of mangroves, and the result reveals the positive effects of the establishment of protected areas and tidal flat afforestation on the mangrove protection and restoration by combining the accurate time point of change identified by the continuous long time series analysis.The imaging quality of optical images is greatly reduced because the study area is located in a tropical and subtropical cloudy and rainy area. To fully use the satellite remote sensing multitemporal features to effectively eliminate clouds/shadows, all Landsat images during the time period are collected to form a continuous time series. With the support of the GEE cloud platform to satisfy the requirements of computing power for the large amounts of data, the random forest algorithm is applied to obtain the annual wetland classification data set from 1987 to 2020. On this basis, the spatio-temporal characteristics of mangrove over long time are mined.According to the accuracy assessment, the averaged out-of-bag error of wetland data set is 6.61%±0.08%, and the average identification accuracy for mangroves is 89.64%±0.13%. In 2020, the Greater Bay Area has 2,174 ha of mangrove forests, and 81% of the mangrove forests is concentrated in Shenzhen Bay, Qi’ao Island, and Zhenhai Bay. The mangrove forests in the GHM experience steady development (1987—2003) and then rapid growth (2003—2020), the main increase is observed in Zhenhai Bay (40%) and Qi’ao Island (28%). The mangrove forests in Qi’ao Island and Zhenhai Bay are still in a period of rapid growth. However, Qi’ao Island has the fastest growth rate; it has doubled its area by 30 times since 2002. Shenzhen Bay has entered a stable period (2009—2020) after its early rapid growth (1987—2009). Shenzhen Bay became the only mangrove distribution area that formed a stable core area in the GHM due to the early establishment of the reserve. Although Zhenhai Bay has the largest area of mangrove forests, the ecosystem is more fragile because of the narrow width and the fragmented landscape.The establishment of nature reserves and tidal flat afforestation has played an important role in the growth of mangrove area. Integrated monitoring, such as satellites, drones, and ground monitoring, should be incorporated into the mangrove protection and restoration assessment. This study provides scientific evidence support for the implementation of the sustainable development strategy goals of the GHM and has a certain guiding role in the construction of coastal ecological barriers.

mots-clés

remote sensing;Mangrove forests;continuous long time series;Google Earth Engine;Guangzhou-Hong Kong-Macao Greater Bay Area;spatio-temporal information mining;spatial expansion processing

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