长江科学院院报 ›› 2024, Vol. 41 ›› Issue (6): 58-68.DOI: 10.11988/ckyyb.20230041

• 水土保持与生态修复 • 上一篇    下一篇

2001—2021年巢湖流域植被覆盖时空变化及驱动分析

王亚琼1, 高曼莉1, 罗劲松1, 徐莹梅1, 徐伟1, 卢毅敏2,3   

  1. 1.安徽林业职业技术学院 资源与环境系,合肥 230000;
    2.福州大学 数字中国研究院(福建),福州 350108;
    3.福州大学 空间数据挖掘与信息共享教育部重点实验室,福州 350108
  • 收稿日期:2023-01-15 修回日期:2023-05-05 出版日期:2024-06-01 发布日期:2024-06-03
  • 通讯作者: 卢毅敏(1973-),男,福建仙游人,副研究员,博士,主要从事资源环境模型与系统模拟。E-mail: luym@lreis.ac.cn
  • 作者简介:王亚琼(1992-),女,河南周口人,讲师,硕士,主要从事空间数据挖掘与地理知识工程。E-mail: wyq920506@163.com
  • 基金资助:
    国家重点研发科技专项(2017YFB0503500);安徽省高校自然科学研究项目(2023AH052985)

Spatio-temporal Variations of Vegetation Coverage and Driving Forces in Chaohu Lake Basin from 2001 to 2021

WANG Ya-qiong1, GAO Man-li1, LUO Jin-song1, XU Ying-mei1, XU Wei1, LU Yi-min2,3   

  1. 1. Department of Resources and Environment, Anhui Vocational and Technical College of Forestry, Hefei 230000,China;
    2. The Academy of Digital China, Fuzhou University,Fuzhou 350108,China;
    3. Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education,Fuzhou University,Fuzhou 350108,China
  • Received:2023-01-15 Revised:2023-05-05 Online:2024-06-01 Published:2024-06-03

摘要: 基于2001—2021年Landsat遥感数据,研究巢湖流域植被覆盖多年变化,利用时空地理加权模型探讨地形、气候及人类活动的驱动影响。结果表明:①流域内平均植被覆盖度0.75,近20 a总体呈增加趋势,增速0.12%/a;2009年前,呈下降趋势,2009年后,呈增加趋势。②流域内89.79%的区域植被覆盖度高于0.6,不同土地利用的植被覆盖度为林地>旱作农田>稀疏植被>灌溉农田>城市建成区>湿地,湿地受蓄水排涝功能的影响,植被覆盖度相对较低。③近20 a,34.78%的区域植被覆盖度发生变化,以增加趋势为主,面积占比62.73%,集中在合肥市老城区、山区林地、沿江北岸平原地带以及环巢湖湿地区域,政策上的生态环境保护和流域治理对植被覆盖产生正影响成效显著;减少趋势发生在城市外围和部分耕地区域,体现城镇化建设、耕地种植模式等人类活动对植被覆盖的负影响,对于引江济淮(派河—东淝河)段施工沿线和柘皋河流域植被覆盖度减少,应值得关注。④2010年前,地形因素的影响逐年降低,气候作用逐年增加,2010年后,地形因素持续增加并成为主要因素。⑤人类活动对植被覆盖的影响程度整体呈增强趋势,生态环境得到改善,增强幅度为0.013%/(10 a)。

关键词: 植被覆盖度, 时空分析, 驱动影响, Landsat遥感数据, 时空地理加权模型, 巢湖流域

Abstract: Based on Landsat remote sensing data from 2001 to 2021, we investigated the vegetation cover changes in Chaohu Lake Basin, and further delved into the driving effects of terrain, climate and human activities by using Geographical and Temporal Weighted Regression (GTWR). The findings reveal several key points: 1) The average vegetation coverage within the basin stood at 0.75, exhibiting a consistent upward trajectory over the past two decades, with an annual growth rate of 0.12%. Notably, a declining trend prevailed before 2009, followed by a subsequent upswing. 2) A substantial majority (89.79%) of the area exhibited vegetation coverage exceeding 0.6, with forested land boasting the highest coverage, followed by dry farmland, sparse vegetation, irrigated farmland, urban built-up area, and wetland in descending order. The low vegetation cover in wetlands can be attributed to water management functions such as storage and drainage. 3) Over the past two decades, 34.78% of the region witnessed changes in vegetation coverage, predominantly characterized by an increasing trend encompassing 62.73% of the total area. These changes were notably concentrated in the historical urban core of Hefei, mountainous forest regions, plains along the northern river bank, and wetlands surrounding Chaohu Lake. Policies of ecological conservation and watershed governance exerted an evident positive influence on vegetation coverage. Conversely, diminishing trends were observed in the periphery of the city and in some cultivated land areas, reflecting adverse impacts of urbanization and cultivation practices. Particular attention should be paid to the declining vegetation coverage along the construction line of Paihe River to Dongfei River segment of the Water Diversion Project from Yangtze River to Huaihe River, as well as the Zhegao River basin. 4) Prior to 2010, the influence of topographical factors exhibited a declining trend, while climatic effects gradually intensified. Conversely, after 2010, topographical factors intensified and emerged as the predominant driving force of vegetation changes. 5) Human activities exerted an overall increasing influence on vegetation cover, contributing to ecological environment at an enhancement rate of 0.013% per decade.

Key words: vegetation coverage, spatio-temporal analysis, driving effect, Landsat remote sensing data, geographical and temporal weighted regression, Chaohu Lake Basin

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