洞庭湖湿地植被时空动态及其驱动力分析  

Spatiotemporal Patterns and Driving Forces of Vegetation Restoration and Degradation in Dongting Lake Wetland

作　　者：张雨田 石军南[2] 张怀清[1,3] 吴炳伦 Zhang Yutian;Shi Junnan;Zhang Huaiqing;Wu Binglun(Institute of Forest Resource Information Techniques,Chinese Academy of Forestry,Beijing 100091;Central South University of Forestry&Technology,Changsha 410004;Key Laboratory of Forestry Remote Sensing and Information System,National Forestry and Grassland Administration,Beijing 100091;Changsha General Survey of Natural Resources Center,China Geological Survey,Changsha 410600)

机构地区：[1]中国林业科学研究院资源信息研究所,北京100091 [2]中南林业科技大学林学院,长沙410004 [3]国家林业和草原局林业遥感与信息技术重点实验室,北京100091 [4]中国地质调查局长沙自然资源综合调查中心,长沙410600

出　　处：《林业科学》2024年第8期1-13,共13页Scientia Silvae Sinicae

基　　金：“十四五”国家重点研发计划项目(2023YFF1303701)。

摘　　要：【目的】探究洞庭湖湿地植被覆盖变化的长期时空格局及其对气候变化和人类活动的响应机制,为湿地生态系统保护提供决策依据。【方法】利用FSDAF(时空融合数据分析框架)算法融合Landsat和MODIS影像,获取洞庭湖湿地2000—2019年月尺度归一化差异植被指数(NDVI)时间序列,采用改进的STL时序分解方法分离洞庭湖湿地植被NDVI季节和趋势分量,在不同时间尺度下量化湿地植被覆盖对环境变化和人为干扰的响应。基于线性回归方法与高时空分辨率的NDVI季节和趋势分量数据对洞庭湖湿地植被进行时空动态分析,识别湿地植被在不同尺度的时空动态格局。应用基于偏相关的分析方法定量评估2000—2019年3个主要气候因子(温度、降水量和太阳辐射)和人为因素对趋势和季节性植被变化的贡献。【结果】1)2000—2019年,洞庭湖湿地植被NDVI季节和趋势分量变化呈现出空间分异格局,但总体呈“绿化”趋势,变化率分别为4.8×10^(−3)a^(−1)和0.4×10^(−3)a^(−1)。2)温度和太阳辐射与植被变化存在显著正相关关系,与植被变化的季节相关性大于趋势相关性。降水量与植被变化的相关性相对较低,且与水稻的NDVI变化呈负相关关系(趋势分量偏相关系数R=-0.27;季节分量偏相关系数R=−0.42)。3)2000—2019年,人为因素和气候变化对洞庭湖湿地植被变化的平均相对贡献率分别为58%和42%,其中人为因素对长期和季节性湿地植被生长与恢复的相对贡献率分别为55%和62%,气候变化对长期和季节性湿地植被退化的相对贡献率分别为53%和56%。【结论】人为因素促进植被生长是洞庭湖湿地植被增绿的主要动因;气候变化对湿地生态系统构成威胁,采取合适的生态保护与修复方案仍是未来实现洞庭湖湿地生态系统可持续发展的重要手段。【Objective】Wetland vegetation can purify the environment,regulate climate,and improve the soil,which is of great significance to the ecological security and stability of the wetland system.Wetland vegetation communities are,however,under serious threat from accelerated global warming and human disturbance.Probing the long-term spatial-temporal pattern of wetland vegetation cover change and its response to climate change and human activities are important for informing decisions on wetland protection.【Method】It is difficult to collect long-term,reliable optical observations with high spatiotemporal resolution in the Dongting Lake wetland due to its location in the subtropical monsoon climate zone and its frequent cloud and rainy conditions.Firstly,this study used the flexible spatiotemporal data analysis fusion(FSDAF)algorithm to fuse Landsat and MODIS images to obtain the monthly normalized difference vegetation index(NDVI)time series during the study period(2000—2019).To quantify the response of wetland vegetation cover to environmental changes and human disturbance at different time scales,the seasonal and trend components of wetland vegetation NDVI in Dongting Lake were separated by an improved seasonal-trend decomposition procedure based on the loess(STL)time series decomposition method.Based on the linear trend analysis and NDVI seasonal and trend component data with a high spatiotemporal resolution,the spatiotemporal dynamic patterns of wetland vegetation at different scales were identified.Finally,a partial correlation-based approach was used to quantitatively assess the contributions of three major climatic factors(i.e.,temperature,precipitation,and solar radiation)and anthropogenic factors to seasonal and trend vegetation changes from 2000 to 2019.【Result】1)From 2000 to 2019,the trend component and seasonal component of NDVI vegetation in Dongting Lake wetland showed spatial differentiation patterns,but overall exhibited a“greening”trend,with a change rate of 4.8×10^(−3)a^(−1)and 0

关 键 词：湿地植被 气候变化 时空融合 NDVI时间序列 洞庭湖湿地 

分 类 号：S758[农业科学—森林经理学]