青藏高原植被时空分布规律及其影响因素研究

发布时间：2018-04-10 17:21

本文选题：青藏高原 + 时空分布　；参考：《中国地质大学(北京)》2016年博士论文

【摘要】：近几十年中,很多研究通过简单的空间化、定量化、多尺度化以及时间序列分析的方法来探讨区域植被和环境因素的结构、功能以及相关变化过程,具体概括为两点:1)时间序列上主要是简单的一次线性统计分析;2)空间分布上主要从空间自相关性的角度出发,简单利用Moran's I系数或者半变异函数进行评价,分析不够深入具体,且在植被绿度遥感监测方面的应用较少,而且地表NDVI和气候变量的相关性的研究尚处在初级阶段,其影响程度随着地理位置的改变也不相同。可以说利用遥感手段综合、充分地从时间序列上和空间分布上,定量化地研究植被时空变化、空间异质性以及时空相关性的工作相对较少。本论文基于1982-2006年GIMMS AVHRR NDVI数据和2001-2010年MODIS NDVI数据利用一次分段线性回归方法、空间自相关分析、半变异函数分析、分维分析的方法来研究青藏高原植被在时间序列和空间分布上的变化,并借助MERRA气候数据(平均气温、降水总量、太阳总辐射)和中国气象站点日值气候资料(年均最小相对湿度、年均日最高气温、年均日最低气温、年总日照时数、年平均相对湿度、年平均气温、年平均风速以及年总降水量)综合评估近三十年的时空变化特征,主要完成的研究内容和结论如下:(1)利用线性回归方法分析时间序列数据,就植被整体、各植被类型以及区域植被变化方面展开研究,结果发现:整体植被绿度具有增长的趋向;具有较高绿度水平的植被类型在气候变化的影响下具有较高的敏感度;区域化研究中,人类对植被的影响非常显著。(2)鉴于时间序列数据中存在突变点情况,提出了使用分段线性回归的方法来监测植被变化,结果表明:该方法能够成功监测一次线性回归方法并不能监测到的突变情况。(3)开展三维空间维度变化分析、空间自相关分析、半变异函数分析和分维分析,结果发现:植被具有正的空间自相关性且表现出明显的高值和低值聚集区;结构性因子在空间总变异中占主导地位(70%),地貌特征和山脉走势导致植被空间分布主要沿东南-西北方向展布;区域的气候条件、地形地貌以及人类活动对植被空间分布的影响在逐年增强。(4)鉴于研究数据中存在的非平稳现象,提出采用低阶二次多项式拟合趋势的方法来达到去趋势的目的,结果表明:该方法成功地达到了预期目的。(5)研究全年MERRA数据和AVHRR数据以及生长季站点日值资料和MODIS数据的相关性,结果发现:在不同时间尺度不同空间尺度上,植被与气候变量的相关性是基本一致的;植被类型不同,其主要气候驱动变量也不同。
[Abstract]:In recent decades, many studies have explored the structure, functions and related processes of regional vegetation and environmental factors through simple spatial, quantitative, multi-scale and time series analysis.It is summed up specifically as two points and one) the time series is mainly a simple one-order linear statistical analysis.) the spatial distribution is mainly from the angle of spatial autocorrelation. The simple use of Moran's I coefficient or semi-variogram to evaluate, the analysis is not deep enough to be specific.Moreover, the application of remote sensing in vegetation greening monitoring is rare, and the correlation between surface NDVI and climate variables is still in the primary stage, and its influence degree varies with the change of geographical location.It can be said that the use of remote sensing means synthesis, fully from the time series and spatial distribution, quantitative study of space-time changes of vegetation, spatial heterogeneity and space-time correlation work is relatively less.This paper is based on GIMMS AVHRR NDVI data from 1982 to 2006 and MODIS NDVI data from 2001-2010 to 2001-2010 using a piecewise linear regression method, spatial autocorrelation analysis, semi-variable function analysis.The method of fractal dimension analysis is used to study the time series and spatial distribution of vegetation in Qinghai-Xizang Plateau, and with the help of MERRA climate data (mean temperature, total precipitation,Total solar radiation) and daily climatic data of meteorological stations in China (mean minimum relative humidity, annual maximum daily air temperature, average annual minimum daily temperature, annual total sunshine hours, annual mean relative humidity, annual average temperature,The main contents and conclusions of this study are as follows: 1) using linear regression method to analyze time series data.The study on vegetation types and regional vegetation changes shows that the overall vegetation greenery tends to increase; the vegetation types with higher green degree level have a higher sensitivity under the influence of climate change; in the regionalization research,In view of the existence of mutation points in time series data, a piecewise linear regression method is proposed to monitor vegetation change.The results show that this method can successfully monitor the mutation situation that can not be detected by the linear regression method. It can be used to analyze the dimensional change of three-dimensional space, spatial autocorrelation analysis, semi-variogram analysis and fractal dimension analysis.The results showed that the vegetation had positive spatial autocorrelation and showed obvious high and low value concentration areas.Structural factors play a dominant role in the total spatial variation, geomorphological characteristics and mountain trends lead to the spatial distribution of vegetation mainly along the southeast to northwest direction; regional climate conditions,The influence of landform and human activities on the spatial distribution of vegetation is increasing year by year. In view of the non-stationary phenomenon in the study data, the method of fitting the trend with low-order quadratic polynomials is proposed to achieve the purpose of removing the trend.The results show that the proposed method has successfully achieved the desired purpose. (5) the correlation between annual MERRA data and AVHRR data, as well as the daily data of growing season site and MODIS data is studied. The results show that: at different time scales and different spatial scales,The correlation between vegetation and climate variables is basically the same, and the main climate driving variables are different with different vegetation types.
【学位授予单位】：中国地质大学(北京)
【学位级别】：博士
【学位授予年份】：2016
【分类号】：Q948

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