地理学报 ›› 2019, Vol. 74 ›› Issue (1): 76-86.doi: 10.11821/dlxb201901006

• 气候变化与地表过程 • 上一篇    下一篇

生态保护工程和气候变化对长江源区植被变化的影响量化

唐见1,2(),曹慧群1,2,陈进2()   

  1. 1. 长江科学院流域水环境研究所,武汉 430010
    2. 流域水资源与生态环境科学湖北省重点实验室,武汉 430010
  • 收稿日期:2017-08-17 出版日期:2019-01-18 发布日期:2019-01-18
  • 基金资助:
    国家自然科学基金项目(51609008);湖北省自然科学基金项目(2016CFA092);中央级公益性科研院所基本科研业务费项目(CKSF2015015/SH)

Effects of ecological conservation projects and climate variations on vegetation changes in the source region of the Yangtze River

TANG Jian1,2(),CAO Huiqun1,2,CHEN Jin2()   

  1. 1. Basin Water Environmental Research Department, Changjiang River Scientific Research Institute, Wuhan 430010, China
    2. Key Lab of Basin Water Resource and Eco-environmental Science in Hubei Province, Wuhan 430010, China
  • Received:2017-08-17 Online:2019-01-18 Published:2019-01-18
  • Supported by:
    National Natural Science Foundation of China, No.51609008;Natural Science Foundation of Hubei Province, No.2016CFA092;The Fundamental Research Funds for Central Public Welfare Research Institutes, No.CKSF2015015/SH

摘要:

分析长江源区生态保护工程和气候变化对植被变化的影响程度,对于长江源区生态工程的生态效益评估,以及区域植被适应性生态管理政策的制定具有重要意义。因此,本文基于1982-2015年的归一化植被指数数据(Normalized Difference Vegetation Index, NDVI)和气象数据,分析长江源区植被NDVI的时空变化规律,构建预测植被NDVI对气候因子响应的人工神经网络模型,在此基础上,在年和季节尺度上量化气候变化和生态保护工程对长江源区植被变化的影响程度。结果表明:① 在长江源区气候条件变化和生态保护工程影响下,长江源区植被退化得到遏制,植被生长呈好转趋势;② 海拔相对较低的通天河附近植被NDVI增加幅度较大,高海拔的沱沱河和当曲流域的植被NDVI增加幅度相对较小;③ 长江源区植被NDVI对气候因子响应存在1~2月的滞后性。构建的人工神经网络模型的拟合优度参数人工神经网模型具有较高的预测精度,可以用来模拟植被NDVI对气候因子的响应;④ 年尺度的植被NDVI增加受到生态保护工程的影响程度(58.5%)大于气候变化的影响程度(41.5%)。生长季生态保护工程对NDVI的影响程度(63.3%)大于气候变化对NDVI的影响程度(36.7%),而非生长季气候变化是影响长江源区植被生长的关键要素(52.8%)。研究结果有助于为长江源区植被生态系统恢复、管理和利用战略的科学制定提供决策依据。

关键词: 植被, 气候变化, 生态保护工程, 归一化植被指数, 人工神经网络, 长江源区

Abstract:

Quantitative research on the effects of ecological conservation projects and climate variations on vegetation changes is vital to the ecological benefit evaluation of ecological conservation projects, and has important implications for sustainable ecological rehabilitation management strategies in the source region of the Yangtze River. Based on the normalized difference vegetation index (NDVI) data and meteorological data during 1982-2015, this paper examines the temporal and spatial variations of NDVI; constructs back propagation artificial neural network (BPANN) model to simulate the responses of NDVI to climate factors; and quantifies the effects of ecological conservation projects and climate variations on vegetation changes at the annual and seasonal scales in the source region of the Yangtze River. The results indicate that: (1) Because of the effects of ecological conservation projects and climate variations, vegetation degradation curbed in the source region of the Yangtze River. (2) NDVI increased greatly near the Tongtian River located at relatively low altitudes. Minor increases in NDVI were observed near the Tuotuo and Dangqu rivers located at relatively high altitudes. (3) A time lag (about 1-2 months) existed between NDVI and major climate factors in the source region of the Yangtze River. The goodness of fit of the BPANN model shows that the simulation accuracy is relatively high. The model can be used to simulate the responses of NDVI to climate variations. (4) Ecological conservation projects exerted a slightly greater impact on NDVI changes than they did on climate variations at the yearly time scale (58.5% and 41.5%, respectively). During growing season, ecological conservation projects also exerted a slightly greater impact on NDVI changes than they did on climate variations (63.3% and 36.7%, respectively). During non-growth season, climate variations are the key factor affecting vegetation growth in the source region of the Yangtze River (52.8%). The research results provide a basis for scientific decision-making about the vegetation ecosystem rehabilitation, management and utilization strategies in the source region of the Yangtze River.

Key words: vegetation, ecological conservation projects, climate variation, normalized difference vegetation index, artificial neural network, the source region of the Yangtze River