长江流域资源与环境 >> 2019, Vol. 28 >> Issue (06): 1314-1323.doi: 10.11870/cjlyzyyhj201906007

• 自然资源 • 上一篇    下一篇

基于MODIS数据的安徽省植被水分利用效率时空变化及影响因素

王  芳1,2,张  运1,2,3*,黄  静1,2,汤  志1,2,何  好1,2,王银银1   

  1. (1. 安徽师范大学地理与旅游学院, 安徽 芜湖 241003;2. 资源环境与地理信息工程安徽省工程技术研究中心,安徽 芜湖 241003;3. 自然灾害过程与防控研究安徽省省级重点实验室,安徽 芜湖 241003)
  • 出版日期:2019-06-20 发布日期:2019-06-20

Spatio-Temporal Variations in Vegetation Water Use Efficiency and Their Influencing Factors in Anhui Province Based on MODIS Data

WANG Fang1,2,  ZHANG Yun1,2,3, HUANG Jing1,2, TANG Zhi1,2, HE Hao1,2,WANG Yin-yin1   

  1. (1. School of Geography and Tourism, Anhui Normal University, Wuhu 241003, China;2. Engineering Technology Research Center of Resources Environment and GIS, Anhui Province, Wuhu 241003, China;3.Anhui Key Laboratory of Natural Disaster Process and Prevention, Anhui Province, Wuhu 241003, China)
  • Online:2019-06-20 Published:2019-06-20

摘要: 水分利用效率是衡量生态系统碳水循环耦合程度的重要指标。基于MODIS数据、土地覆盖类型数据和气象数据,估算安徽省植被水分利用效率(WUE),采用趋势分析法和相关分析法对安徽省2000~2014年植被WUE的时空格局、变化趋势及影响因素进行研究。研究表明:(1)不同植被类型的WUE年均值差异明显,常绿阔叶林和常绿针叶林的WUE均值较高,分别达到1.66和1.69 gC?mm-1?m-2,而耕地的年均WUE最低,各植被类型的年均WUE按照“常绿针叶林>常绿阔叶林>灌木>草地>落叶阔叶林>针阔混交林>耕地”的顺序递减。植被年均WUE具有较强的空间分异性规律,整体上呈现南北高中间低的趋势,植被WUE的高值区主要分布在大别山区和皖南山区,分布范围与常绿针叶林、常绿阔叶林的分布范围基本一致。(2)安徽省2000~2014年植被WUE年内变化呈现出“增加-减小-增加-减小”的M状“双峰型”趋势,具有明显的季节差异,呈现出春季>秋季>夏季>冬季的特征,各季节植被WUE的均值分别占植被WUE的32.58%、24.91%、29.27%、13.24%。(3)安徽省植被WUE动态变化受到降水影响显著的区域占比3.88%;气温显著影响的区域占比2.19%;降水显著影响的地区主要分布在林地范围内,温度显著影响的地区则位于耕地范围内,降水和气温综合显著影响所占面积最小,为0.11%;而植被WUE受气温和降水影响均不显著占比为93.82%;整体上,安徽省大部分地区的植被WUE变化主要受非气候因素影响。

Abstract: Water use efficiency(WUE) is an important index to measure the coupling degree of the carbon and water cycles. Based on MODIS data, land cover type data and meteorological data, the vegetation water use efficiency WUE of Anhui Province was estimated. The temporal and spatial patterns, changing trends and influencing factors of vegetation WUE in Anhui Province from 2000 to 2014 were explored by trend analysis and correlation analysis. The results showed that 1) Annual WUE of different vegetation types showed significant differences and average WUE of evergreen broad-leaved forest and evergreen coniferous forest was higher, which reached 1.66 gC?mm-1?m-2 and 1.69 gC?mm-1?m-2 respectively, while the annual average WUE of cultivated land was the lowest. The annual WUE of each vegetation type decreased following the order of that: evergreen coniferous forest>evergreen broad-leaved forest>shrub, sparse forest>grassland>deciduous broad-leaved forest>coniferous broad-leaved mixed forest>cultivated land. Annual average WUE of vegetation had a strong spatial variability in Anhui Province, where it was higher in the north and south of the province but lower in the middle part. The high value area of vegetation WUE was mainly distributed in Dabie mountain area and southern mountainous area in Anhui Province, and the distribution range was basically consistent with the distribution range of evergreen coniferous forest and evergreen broad-leaved forest. 2) Between 2000 and 2014, the variation of vegetation WUE showed an M-shaped “double-peak” trend of “increase-decrease-increase-decrease” with obviously seasonal variation, displayed the sequence of spring>autumn>summer>winter. Besides, the average WUE of vegetation in each season accounted for 32.58%, 24.91%, 29.27% and 13.24% of the total vegetation WUE respectively. 3) The dynamic change of vegetation WUE in Anhui province resulting from non-climate factors made up 93.82% of the total area of vegetation in the province, followed by precipitation factor(3.88%) and temperature factor(2.19%), while the combination of precipitation and temperature had the smallest impact on WUE(0.11%). Besides, the temperature significantly affects the area within the cultivated land, while the precipitation significantly affects the area within the forrest land mainly. Due to the influence of human, non-climate factors had a greater impact on vegetation WUE changes.

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