Remote sensing monitoring and analysis based on multi-sourced data of mangroves in Pearl Bay, Guangxi

LI Meina; TIAN Yuqing; YIN Ping; DUAN Xiaoyong; ZHANG Jialin; LI Xue; ZHANG Yao

doi:10.16028/j.1009-2722.2024.244

2025 Vol. 41, No. 4

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Article navigation > Marine Geology Frontiers > 2025 > 41(4): 82-92

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LI Meina, TIAN Yuqing, YIN Ping, DUAN Xiaoyong, ZHANG Jialin, LI Xue, ZHANG Yao. Remote sensing monitoring and analysis based on multi-sourced data of mangroves in Pearl Bay, Guangxi[J]. Marine Geology Frontiers, 2025, 41(4): 82-92. doi: 10.16028/j.1009-2722.2024.244

Citation:

LI Meina, TIAN Yuqing, YIN Ping, DUAN Xiaoyong, ZHANG Jialin, LI Xue, ZHANG Yao. Remote sensing monitoring and analysis based on multi-sourced data of mangroves in Pearl Bay, Guangxi[J]. Marine Geology Frontiers, 2025, 41(4): 82-92. doi: 10.16028/j.1009-2722.2024.244

Remote sensing monitoring and analysis based on multi-sourced data of mangroves in Pearl Bay, Guangxi

1.
Geology Research Center for Coast and Shelf, China Geological Survey (CGS); Qingdao Institute of Marine Geology, CGS, Qingdao 266237, China
2.
Zhoushan Marine Geohazards Field Observation and Research Station, CGS, Qingdao 266237, China
3.
Engineering Technology Innovation Center for Marine Methane Monitoring, Ministry of Natural Resources, Qingdao 266237, China
4.
Observation and Research Station of Zhejiang Coastal Urban Geological Security, Ministry of Natural Resources, Hangzhou 310007, China
5.
Zhejiang Institute of Marine Geological Survey, Zhoushan 316000, China
6.
College of Marine Geosciences, Ocean University of China, Qingdao 266100, China
7.
School of Earth Sciences, China University of Geosciences,Wuhan 430074, China

More Information

Corresponding author: YIN Ping, pingyin@fio.org.cn

Received Date: 21 October 2024

Publish Date: 28 April 2025

Abstract

Abstract

To understand the spatiotemporal evolution of mangroves in Pearl Bay, Guangxi over the past 30 years, we used Landsat TM/OLI satellite remote sensing images as the data source and combined them with Sentinel-2 satellite remote sensing images. Based on the spectral characteristics of the ground objects, we constructed a decision tree classification rule. Through the decision tree classification method based on tasseled cap transformation, we extracted the spatial distribution information of mangroves in 1995, 2000, 2005, 2010, 2015 and 2023, calculated the dynamic degree and landscape pattern index, and analyzed the dynamic spatiotemporal evolution of mangroves in the study area from 1995 to 2023. Results indicate that over the past nearly 30 years, mangrove forests in Pearl Bay have changed significantly in terms of area composition and spatial location, presenting an overall trend of decreasing first and then increasing. The period with the highest dynamic degree is 2005-2010, during which the mangrove area increased to 836.21 hectares, with a dynamic degree of 3.59%. The calculation of the landscape pattern index shows that the fragmentation and landscape differentiation degree generally exhibit a trend of increasing first and then decreasing, while the aggregation degree is relatively stable. The spatial evolution of the mangroves are related to natural factors such as precipitation, temperature, sediment deposition, and pest damage, and are closely related to human activities such as pond culture.
- mangrove /
- remote sensing monitoring /
- space-time evolution /
- landscape pattern index /
- driving factor

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References

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