植物生态学报 ›› 2022, Vol. 46 ›› Issue (10): 1268-1279.DOI: 10.17521/cjpe.2022.0234

• 研究论文 • 上一篇    下一篇

沿海养殖池塘对红树林生态系统影响的时空变化监测与分析

姜玉峰1,2, 李晶1,2, 信瑞瑞1,2, 李艺1,2,*()   

  1. 1厦门大学环境与生态学院, 滨海湿地生态系统教育部重点实验室, 福建厦门 361102
    2南方海洋科学与工程广东省实验室(珠海), 广东珠海 511458
  • 收稿日期:2022-06-06 接受日期:2022-09-05 出版日期:2022-10-20 发布日期:2022-09-28
  • 通讯作者: *李艺(yili@xmu.edu.cn)
  • 基金资助:
    国家自然科学基金(41701205);中央高校基本科研业务费专项资金(20720190089);南方海洋科学与工程广东省实验室(珠海)创新团队项目(311021004)

Spatial-temporal dynamics of coastal aquaculture ponds and its impacts on mangrove ecosystems

JIANG Yu-Feng1,2, LI Jing1,2, XIN Rui-Rui1,2, LI Yi1,2,*()   

  1. 1Key Laboratory of Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, China
    2and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 511458, China
  • Received:2022-06-06 Accepted:2022-09-05 Online:2022-10-20 Published:2022-09-28
  • Contact: *LI Yi(yili@xmu.edu.cn)
  • Supported by:
    National Natural Science Foundation of China(41701205);Fundamental Research Funds for the Central Universities(20720190089);Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Innovative Team Project(311021004)

摘要:

随着沿海人类活动的日益加剧, 其对红树林生态系统健康和可持续发展的影响也逐渐凸显, 实现红树林周边典型人类活动时空动态变化监测对红树林生态系统的保护与修复意义重大。该研究基于Landsat多时相遥感数据和Google Earth Engine平台, 通过面向对象的机器学习方法, 融入水体季节波动信息作为分类特征, 获取了1990、2000、2010和2020年4个不同时期中国沿海红树林分布省区(包括广东、福建、浙江、台湾、广西及海南) 30 m分辨率的养殖池塘空间格局及其变化特征, 并进一步解析养殖池塘对红树林生态系统的影响。研究结果表明: (1)研究区域内4个时间节点的沿海养殖池塘面积总量分别为2 963、5 200、5 377及4 805 km2, 呈先增加后减少的趋势, 于2010-2020年间达到峰值。沿海养殖池塘面积变化趋势和达峰时间存在明显区域差异性, 其主要原因是红树林保护政策、养殖池塘规范管理和阶段性经济目标的区域差异化。(2)我国沿海养殖池塘集中分布在21°-24° N区域(广东和广西), 与红树林沿纬度的分布格局呈错峰分布。其中, 红树林与沿海养殖池塘集中分布区(21°-22° N)存在大量养殖池塘堤边生长红树林的特色格局, 此区域内两者交互作用最为紧密, 是探究人类活动对红树林生态系统影响的典型热点地区。(3)养殖池塘侵占红树林是造成红树林损失的最直接原因, 并导致红树林空间分布格局呈现局部破碎化或聚集化的极端发展趋势。该研究通过解析沿海养殖池塘的空间格局, 为精准评估红树林周边典型人类活动变化提供数据支撑, 为进一步监测红树林空间格局动态变化趋势和红树林优先修复区识别提供参考依据。

关键词: 沿海养殖池塘, 人类活动, 时空分析, 机器学习, 水体淹没频率

Abstract:

Aims With increasing anthropogenic activities in coastal areas, human disturbances have been identified as major causes of the decline of coastal mangroves and undemine the sustainable development. Monitoring the spatial-temporal dynamics of typical human activities in mangrove ecosystems and adjacent areas is critical in conservation and restoration of local mangrove ecosystems.

Methods We proposed an object-oriented machine learning method based on seasonal water fluctuations, using Landsat satellite imagery on Google Earth Engine platform. Inundation frequency was incorporated as a classification feature to obtain the spatial pattern of aquaculture ponds, which is concerned as the key driver of degradation and losses of mangroves. We revealed the dynamics of aquaculture ponds at a 30 m-resolution between 1990 and 2020 in China’s coastal regions with mangrove community detected, including Guangdong, Fujian, Zhejiang, Taiwan, Guangxi, and Hainan.

Important findings The total area of coastal aquaculture ponds in 1990 was about 2 963 km2, which increased to 5 200 km2 in 2000 and 5 377 km2 in 2010, and then decreased to 4 805 km2 in 2020. The maximum appeared between 2010 and 2020, but there was a significant regional variation in the changing pattern and peaking time of coastal aquaculture ponds. Coastal aquaculture ponds were mainly concentrated in the region of 21°-24° N (Guangdong and Guangxi). The spatial pattern of mangroves was shown as a staggered arrangement to that of aquaculture ponds. Our results also indicate a symbiotic relationship between aquaculture ponds and mangroves at latitude 21°-22° N, where a large number of mangroves grow along the edges of aquaculture ponds. This special distribution of mangroves and aquaculture ponds leads to a high level of interconnections between these two ecosystems, which can be recognized as the typical areas in exploring the impacts of human activities on mangrove ecosystems. The conversion of mangroves to aquaculture ponds was the primary cause of mangrove loss, which led to the extreme fragmentation and aggregation of mangrove patches in different areas. Our research on the spatial-temporal pattern of coastal aquaculture ponds provides an accurate dataset to assess the impacts of increasing human activities on mangrove ecosystems, and may contribute to the identification of priority restoration area.

Key words: coastal aquaculture ponds, human activities, spatial-temporal analysis, machine learning, inundation frequency