京津冀地区植被时空动态及定量归因

doi:10.12082/dqxxkx.2019.180578

摘要/Abstract

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。

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

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.

Key words: spatial distribution, NDVI, spatial heterogeneity, quantitative attribution, geodetector, Beijing-Tianjin-Hebei region

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]. 地球信息科学学报, 2019, 21(5): 767-780.DOI:10.12082/dqxxkx.2019.180578

Shijie YAN, Huan WANG, Kewei JIAO. Spatiotemporal Dynamics of NDVI in the Beijing-Tianjin-Hebei Region based on MODIS Data and Quantitative Attribution[J]. Journal of Geo-information Science, 2019, 21(5): 767-780.DOI:10.12082/dqxxkx.2019.180578

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需要的数据	数据来源	计算方法
MODIS中国NDVI月合成数据(2006.01-2015.12)	中国科学院计算机网络信息中心国际科学数据镜像网站(http://www.gscloud.cn)
气象数据(降水、平均温度、最高温度、最低温度、风速、日照时数、湿度和水汽压)	国家气象信息中心(http://data.cma.cn)	其中水汽压数据由湿度数据计算得到
标准化降水蒸散指数(SPEI)	由常规气象数据计算得到	SPEI采用Beguería 等^[42]开发的R语言扩展包计算(http://cran.r-project.org/web/packages/SPEI)
30m、1000mDEM数据	中国科学院资源环境科学数据中心(http://www.resdc.cn)
植被类型数据(1:100万)^[41]	中国科学院资源环境科学数据中心(http://www.resdc.cn)
地貌类型数据(1:100万)	中国科学院资源环境科学数据中心(http://www.resdc.cn)
土地利用数据(2005, 2010, 2015)	中国科学院资源环境科学数据中心(http://www.resdc.cn)

判断依据	交互作用类型
q(X1∩X2)<Min(q(X1), q(X2))	非线性减弱
Min(q(X1), q(X2))<q(X1∩X2)<Max(q(X1), q(X2))	单因子非线性减弱
q(X1∩X2)>Max(q(X1), q(X2))	双因子增强
q(X1∩X2)=q(X1)+q(X2)	独立
q(X1∩X2)>q(X1)+q(X2)	非线性增强

	春	夏	秋	冬	年
主导交互作用1	降水∩水汽压	湿度∩土地利用	土地利用∩水汽压	土地利用∩平均温度	土地利用∩降水
q值	0.565	0.634	0.539	0.434	0.582
主导交互作用2	降水∩湿度	湿度∩水汽压	土地利用∩湿度	土地利用∩海拔	降水∩海拔
q值	0.505	0.631	0.535	0.404	0.543
主导交互作用3	土地利用∩水汽压	湿度∩平均温度	土地利用∩海拔	土地利用∩水汽压	降水∩水汽压
q值	0.472	0.627	0.531	0.395	0.532

	平原	台地	丘陵	小起伏山地	中起伏山地	大起伏山地
均值	0.671	0.550	0.591	0.691	0.733	0.750
变率/a	0.0052	0.0069	0.0074	0.0089	0.0087	0.0081
变率显著性	显著	显著	极显著	极显著	极显著	极显著

	平原	台地	丘陵	小起伏山地	中起伏山地	大起伏山地
主导交互作用1	日照时数∩水汽压	湿度∩日照时数	平均温度∩湿度	降水∩土地利用	湿度∩土地利用	土地利用∩降水
q值	0.586	0.755	0.760	0.634	0.587	0.570
主导交互作用2	日照时数∩湿度	湿度∩干旱指数	降水∩土地利用	湿度∩土地利用	降水∩土地利用	土地利用∩湿度
q值	0.575	0.744	0.758	0.606	0.579	0.513
主导交互作用3	降水∩湿度	湿度∩土地利用	日照时数∩湿度	湿度∩平均温度	湿度∩日照时数	降水∩海拔
q值	0.539	0.743	0.750	0.596	0.502	0.485