Spatiotemporal disturbances and attribution analysis of mangrove in southern China from 1986 to 2020 based on time-series Landsat imagery

Kexin Long; Zhaojun Chen; Huaiqing Zhang; Meng Zhang

doi:10.1016/j.scitotenv.2023.169157

Spatiotemporal disturbances and attribution analysis of mangrove in southern China from 1986 to 2020 based on time-series Landsat imagery

Sci Total Environ. 2024 Feb 20:912:169157. doi: 10.1016/j.scitotenv.2023.169157. Epub 2023 Dec 6.

Authors

Kexin Long¹, Zhaojun Chen¹, Huaiqing Zhang², Meng Zhang³

Affiliations

¹ Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China; Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China; Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, China.
² Research Institute of Forest Resources Information Techniques, Chinese Academy of Forestry, Beijing 100091, China.
³ Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China; Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China; Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, China. Electronic address: mengzhang@csuft.edu.cn.

PMID: 38061141
DOI: 10.1016/j.scitotenv.2023.169157

Abstract

As one of the most productive ecosystems in the world, mangrove has a critical role to play in both the natural ecosystem and the human economic and social society. However, two thirds of the world's mangrove have been irreversibly damaged over the past 100 years, as a result of ongoing human activities and climate change. In this paper, adopting Landsat for the past 36 years as the data source, the detection of spatiotemporal changes of mangrove in southern China was carried out based on the Google Earth Engine (GEE) cloud platform using the LandTrendr algorithm. In addition, the attribution of mangrove disturbances was analyzed by a random forest algorithm. The results indicated the area of mangrove recovery (5174.64 hm²) was much larger than the area of mangrove disturbances (1625.40 hm²) over the 35-year period in the study area. The disturbances of mangrove in southern China were dominated by low and low-to-medium-level disturbances, with an area of 1009.89 hm², accounting for 57.50 % of the total disturbances. The mangrove recovery was also dominated by low and low-to-medium-level recovery, with an area of 3239.19 hm², accounting for 62.61 % of the total recovery area. Both human and natural factors interacted and influenced each other, together causing spatiotemporal disturbances of mangrove in southern China during 1986-2020. The mangrove disturbances in the Phase I (1986-2000) and Phase III (2011-2020) were characterized by human-induced (50.74 % and 58.86 %), such as construction of roads and aquaculture ponds. The mangrove disturbances in the Phase II (2001-2010) were dominated by natural factors (55.73 %), such as tides, flooding, and species invasions. It was also observed that the area of mangrove recovery in southern China increased dramatically from 1986 to 2020 due to the promulgation and implementation of the Chinese government's policy on mangrove protection, as well as increased human awareness of mangrove wetland protection.

Keywords: Attribution; Change detection; LandTrendr; Landsat; Mangrove; Random forest.