【作者】 张旭；

【导师】 高瑞忠；

【作者基本信息】 内蒙古农业大学 ， 土木水利（专业学位）， 2022， 硕士

【摘要】 植被不仅是生态系统的重要组成部分,亦是流域下垫面的主要影响因素之一,明确植被的生长态势及趋势、分布特征以及驱动要素,对区域内生态修复、土地荒漠化治理以及水资源持续利用具有重要的意义。本文考虑植被、水文、地形等因素,基于Landsat系列卫星、气象、水文及DEM等数据,设计土壤、物探、地下水等试验方案,开展一系列测试工作获取数据信息,利用像元二分模型反演1991～2020年近30a吉兰泰盐湖流域植被覆盖度,分析分布规律及演化特征,以逐像元Slope趋势分析、Hurst指数和变异系数剖析30a植被覆盖度未来趋势性、稳定性以及持续性,运用Peason相关系数、地理探测器模型,解析要素与植被之间的响应关系,定性定量分析水文、地形、气候等要素对植被覆盖度的分布的影响,提取驱动要素的现状和变化特征,量化表征各驱动要素对植被覆盖度驱动程度,主要结论如下:(1)1991～2020年,植被覆盖度值最大行政区域为布古图苏木,均值为0.9319,滨河街道植被覆盖度最小,平均为0.3010;未来情景下,吉兰泰盐湖盆地流域植被覆盖度上升与下降趋势分布较为分散,5月、6月、7月、8月、9月植被覆盖度呈现上升趋势,年际植被覆盖度呈现下降趋势,但下降速率缓慢;流域Hurst指数在0.16～0.74之间,其中呈现反持续性的区域共计17514.65km~2,占总面积85.79%,呈现持续性的总面积为2510.41km~2,占总体面积的14.21%;空间稳定性主要以不稳定为主,变异系数值在0%～539%之间,整体表现为不稳定状态。(2)在进行植被与气象要素响应关系时,7月和9月降水与植被呈现正相关关系,其他生长期表现为负相关;6月、8月和9月气温与植被显现为正相关关系,5月和7月气温与植被表现为负相关关系;5月、6月、7月、8月以及年际蒸散发与植被相关关系为正相关,9月为负相关;年际植被与相对湿度数据相关系数平均值为0.076,表现为正相关关系;讨论水文地形要素对植被生长影响规律时,植被生长趋势随高程、坡度以及地下水这三类要素增大而增强;按坡向提取来看,东北方向植被覆盖度数值最高,东南方向植被覆盖度较低。(3)植被覆盖度空间分异的综合驱动机制:各要素对植被解释程度由大到小依次为:降水(0.316)>地下水(0.313)>土壤类型(0.294)>高程(0.203)>蒸散发(0.196)>相对湿度(0.182)>气温(0.081)>坡度(0.026)>坡向(0.024)>土地利用类型(0.019),降水与地下水分布对植被覆盖度解释程度较大;交互作用下,研究区内各要素呈现双因子增强与非线性增强,双因子增强为主要交互作用;风险探测分析中,各驱动要素最适宜植被生长区间分别为:气温(7℃～10℃)、降水(47.9mm～55.8mm)、蒸散发(703mm～807.9mm)、相对湿度(21%～27%)、高程数据(2890.75m～3159.00m)、土壤类型(半淋溶土)、土地分类(耕地)、坡度(>12°)、坡向(东北方向)、地下水分布(1361.54m～1371.65m)。更多还原

【Abstract】 Vegetation is not only an important part of the ecosystem,but also one of the main influencing factors of the underlying surface of the watershed,and it is of great significance to clarify the growth trend and trend,distribution characteristics and driving factors of vegetation in the region,which is of great significance for ecological restoration,land desertification control and sustainable utilization of water resources in the region.This paper considers vegetation,hydrology,topography and other factors,and designs soil,geophysical and groundwater experiments based on Landsat series satellite,meteorology,hydrology and DEM data.A series of work was carried out to obtain data information,and the vegetation coverage of nearly 30A Jilantai salt lake basin in 1991～2020 was inverted by using the pixel binary model,and the distribution law and evolution characteristics were analyzed,The future trend,stability and sustainability of vegetation cover in 30a were analyzed by cell-by-cell Slope trend analysis,Hurst index and coefficient of variation,Using the Peason correlation coefficient and geographic detector model,the response relationship between features and vegetation was analyzed,The influence of hydrology,topography,climate and other factors on the distribution of vegetation coverage was qualitatively and quantitatively analyzed,the status quo and change characteristics of the driving factors were extracted,and the driving degree of each driving factor on vegetation coverage was quantitatively characterized.The main conclusions are as follows:(1)From 1991 to 2020,the administrative area with the largest vegetation cover value was Bugutusum,with an average value of 0.9319,and the riverfront street had the smallest vegetation cover,with an average of 0.3010.Under the future trend,the upward and downward trend of vegetation coverage in the basin of Gelantai Salt Lake Basin is relatively scattered,and the vegetation coverage in May,June,July,August and September shows an upward trend,and the interannual vegetation coverage shows a downward trend,but the downward trend is slow.In the spatial sustainability analysis of the basin of the Gelantai Salt Lake Basin,the value of the Hurst index was between 0.16 and 0.74,of which the total area of antisustainance was 17514.65km~2,accounting for 85.79%of the total area,and the total area of persistence was 2510.41 km~2,accounting for 14.21%of the total area.The spatial stability is mainly based on instability,and the coefficient of variation value is between0%～539%,and the overall study area is unstable.(2)When the relationship between fractional vegetation cover and meteorological element response was carried out,rainfall and vegetation in July and September showed a positive correlation,and other growth periods showed negative correlation;In June,August and September,the temperature and vegetation showed a positive correlation,and the temperature and vegetation in May and July showed a negative correlation;The correlation between May,June,July,August and interannual evapotranspiration and fractional vegetation cover was positively correlated,and September was negatively correlated;The average value of interannual vegetation and relative humidity data was 0.076,showing a positive correlation.When discussing the influence of hydrological topographic elements on vegetation growth,the vegetation growth trend increased with the increase of the three types of elements:elevation,slope and groundwater.According to the aspect extraction,the fractional vegetation cover value is the highest in the northeast direction and the low fractional vegetation cover in the southeast direction.(3)Comprehensive driving mechanism of spatial differentiation of vegetation cover:the degree of interpretation of vegetation by each feature is from largest to smallest:Precipitation(0.316)>Groundwater distributionr(0.313)>Soil Type(0.294)>Elevation(0.203)>Evapotranspiration(0.196)>Relative Humidity(0.182)>Temperature(0.081)>Slope(0.026)>Aspect(0.024)>Land Use Type(0.019),Rainfall and groundwater distribution are more driven by fractional vegetation cover.Under the interaction,the elements in the study area showed two-factor enhancement and nonlinear enhancement,and two-factor enhancement was the main interaction.In the risk detection analysis,the most suitable vegetation growth intervals for each driving factor are:Air temperature(7°C～10°C),precipitation(47.9mm～55.8mm),evapotranspiration(703mm～807.9mm),relative humidity(21%～27%),elevation data(2890.75m～3159m),soil-type(semi-leached soil),land-classification(arable land),slope(>12°),aspect(northeast),groundwater distribution(1361.54m～1371.65m).更多还原