| 京津冀地区植被时空动态及定量归因 |
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| 引用本文： | 阎世杰,王欢,焦珂伟.京津冀地区植被时空动态及定量归因[J].地球信息科学,2019,21(5):767-780. |
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| 作者姓名： | 阎世杰 王欢 焦珂伟 |
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| 作者单位： | 1. 中国科学院遥感与数字地球研究所数字地球重点实验室, 北京1000942. 中国科学院大学, 北京1000493. 中国科学院地理科学与资源研究所, 北京1001014. 中国科学院沈阳应用生态研究所, 沈阳110016 |
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| 基金项目： | 中国科学院A类先导专项子课题(XDA19030501);新疆自治区重大科技专项(2018A03004) |
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| 摘 要： | 作为气候变化的敏感指示器,植被的物候、生长、空间分布格局等特征及其动态变化主要取决于气候环境中的水热条件,因此在气候变化背景下,气候-植被关系成为了全球变化研究的前沿和热点问题。本文综合平均温度、降水、水汽压、湿度、日照时数、SPEI等气候因子,坡度、坡向海拔等地形因子及人为活动因子,应用地理探测器方法针对2006-2015年京津冀地区不同季节NDVI、不同地貌类型区、不同植被类型区生长季NDVI的定量归因研究,揭示了过去10年间植被时空分布格局,及植被对气候、非气候因素响应的季节差异与区域差异,以期为生态工程的建设与修复提供参考意义。趋势分析表明：①2006-2015年京津冀地区NDVI呈现增加趋势,但存在显著的空间差异,如山地生长季NDVI的增长速率大于平原、台地、丘陵等地;②基于地理探测器的定量归因结果表明,降水是年尺度上NDVI空间分布的主导因子（解释力39.4%）,土地利用与降水的交互作用对NDVI的影响最为明显（q=58.2%）;③NDVI对气候因子的响应存在季节性及区域性差异,水汽压是春季NDVI空间分布的主导因子,湿度是夏、秋两季的主导因子,土地利用是冬季的主导因子;④影响因子对生长季NDVI的解释力因不同地貌类型区、不同植被类型区而差异显著。
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| 关 键 词： | NDVI 空间分布 空间异质性 定量归因 地理探测器 京津冀地区 |
Spatiotemporal Dynamics of NDVI in the Beijing-Tianjin-Hebei Region based on MODIS Data and Quantitative Attribution |
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| Shijie YAN,Huan WANG,Kewei JIAO.Spatiotemporal Dynamics of NDVI in the Beijing-Tianjin-Hebei Region based on MODIS Data and Quantitative Attribution[J].Geo-information Science,2019,21(5):767-780. |
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| Authors: | Shijie YAN Huan WANG Kewei JIAO |
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| Institution: | 1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China2. University of Chinese Academy of Sciences, Beijing 100049, China3. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China4. Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China |
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| Abstract: | Vegetation is a sensitive indicator of global climatic changes, and hydrothermal conditions are the main abiotic factors that determine the phenology, spatial pattern, and dynamics of vegetation. Thus, against the background of a changing climate, the climate-vegetation relationship is a hot topic in current global change research. Using geodetector, this study integrated climatic factors (e.g., average temperature, precipitation, water vapor pressure, humidity, sunshine hours, standardized precipitation evapotranspiration index), topographic factors (e.g., slope and elevation), and anthropogenic factors to determine the dominant factors that influenced the normalized difference vegetation index (NDVI) in the Beijing-Tianjin-Hebei region from 2006 to 2015. Different seasons, geomorphological types, and vegetation types were considered during the quantitative attribution analysis. This study revealed the temporal and spatial pattern of vegetation, and the response of vegetation to climate and non-climate factors over the past 10 years, and provided a reference for the construction and restoration of ecological engineering. Trend analysis showed that the NDVI increased during this period, albeit with differences on different spatial scales. In montane regions, the NDVI increased more rapidly than in plains, terraces, and hills. In different vegetation-covered areas, the NDVI increased most rapidly in broadleaf forest, followed by shrubland and coniferous forest. Based on the results of the quantitative distribution analysis, at the temporal scale of one year, precipitation was the dominant factor driving NDVI and explained 39.4% of the spatial distribution, while the interaction of rainfall and land use was the dominant interaction factor, with a q value of 0.582. We observed seasonal and regional differences in the response of NDVI to climatic factors. In the four seasons, vapour pressure was the dominant factor for the spatial distribution of NDVI; humidity is the dominant factor in summer and autumn; and in winter, land use was the dominant factor for NDVI distribution. The explanatory power of the influencing factors on NDVI in the growing season differed in diverse geomorphological types. In montane areas, with increasing elevation, the q value of average temperature decreased. The explanatory power of impacting factors on NDVI of the growing season was different among diverse vegetation types. For different vegetation types, the explanatory power of precipitation on the spatial distribution of NDVI in the growing season was greater than that of mean temperature, with the q value ranked as following grassland > cultivated vegetation > shrubland > broadleaf forest >coniferous forest. In coniferous forest distributed areas, the explanatory power of single factors was insignificant; however, the interaction between two factors can greatly enhance the q value, and the interaction between moisture factors and topographic factors was the dominant factor. |
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| Keywords: | spatial distribution NDVI spatial heterogeneity quantitative attribution geodetector Beijing-Tianjin-Hebei region |
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