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Surface moisture estimation in urban areas.

机译:估计城市地区的地面湿度。

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摘要

Surface moisture is an important parameter because it modifies urban microclimate and surface layer meteorology. The primary objectives of this paper are: 1) to analyze the impact of surface roughness from buildings on surface moisture in urban areas; and 2) to quantify the impact of surface roughness resulting from urban trees on surface moisture. To achieve the objectives, two hypotheses were tested: 1) the distribution of surface moisture is associated with the structural complexity of buildings in urban areas; and 2) The distribution and change of surface moisture is associated with the distribution and vigor of urban trees. The study area is Indianapolis, Indiana, USA. In the part of the morphology of urban trees, Warren Township was selected due to the limitation of tree inventory data. To test the hypotheses, the research design was made to extract the aerodynamic parameters, such as frontal areas, roughness length and displacement height of buildings and trees from Terrestrial and Airborne LiDAR data, then to input the aerodynamic parameters into the urban surface energy balance model. The methodology was developed for comparing the impact of aerodynamic parameters from LiDAR data with the parameters that were derived empirically from land use and land cover data.;The analytical procedures are discussed below: 1) to capture the spatial and temporal variation of surface moisture, daily and hourly Land Surface Temperature (LST) were downscaled from 4 km to 1 km, and 960 m to 30 m, respectively, by regression between LST and various components that impact LST; 2) to estimate surface moisture, namely soil moisture and evapotranspiration (ET), land surfaces were classified into soil, vegetation, and impervious surfaces, using Linear Spectral Mixture Analysis (LSMA); 3) aerodynamic parameters of buildings and trees were extracted from Airborne and Terrestrial LiDAR data; 4) the Temperature-Vegetation-Index (TVX) method, and the Two-Source-Energy-Balance (TSEB) model was used to estimate soil moisture and ET and fractional cover of urban landscape was used to estimate soil moisture and ET over vegetation, soil, and impervious surfaces.;The results of this dissertation showed the general trend of daily and hourly soil moisture and ET over vegetation, soil, and impervious surfaces in summer, fall, and winter seasons, and the response of soil moisture and ET to precipitations over three types of land covers. In summer, hourly soil moisture fluctuates yet stable; during frequent precipitation, hourly ET over soil and impervious surface show similar patterns, while vegetation surfaces yielded lower ET, which indicated that the distribution and change of surface moisture is associated with the distribution and vigor of urban trees. From fall to winter, the general trend of soil moisture and ET were found decreased, and response to precipitations becomes weaker in winter. The spatial distribution of ET shows that the central urban area has higher ET than regular impervious surfaces and the city's average, which indicate the distribution of surface moisture is associated with the structural complexity of buildings. The two hypotheses were supported, and the methodology is tested to be effective in surface moisture estimation in urban areas. The results suggested future studies on the impact of anthropogenic heat on surface moisture and the data integrity issue in multiple data source in urban areas.
机译:地表水分是一个重要参数,因为它改变了城市的微气候和表层气象。本文的主要目的是:1)分析建筑物表面粗糙度对市区表面水分的影响; 2)量化城市树木造成的表面粗糙度对表面湿度的影响。为了实现这一目标,检验了两个假设:1)表面水分的分布与城市建筑结构的复杂性有关; 2)地表水分的分布和变化与城市树木的分布和活力有关。研究区域是美国印第安纳州印第安纳波利斯。在城市树木的形态学部分中,由于树木清单数据的限制,选择了沃伦镇。为了验证假设,进行了研究设计,以从地面和机载LiDAR数据中提取建筑物,树木的额叶面积等空气动力学参数,然后将其输入到城市表面能平衡模型中。开发该方法的目的是将LiDAR数据中的空气动力学参数的影响与从土地利用和土地覆盖数据中经验得出的参数进行比较。;分析程序讨论如下:1)捕获地表水分的时空变化,通过将LST与影响LST的各个组成部分之间的回归,将每日和每小时的陆地表面温度(LST)分别从4 km降为1 km,将960 m降为30 m。 2)为了估计表面湿度,即土壤湿度和蒸散量(ET),使用线性光谱混合分析(LSMA)将土地表面分为土壤,植被和不透水表面; 3)从机载和地面LiDAR数据中提取建筑物和树木的空气动力学参数; 4)利用温度-植被指数(TVX)方法和两源能量平衡(TSEB)模型估算土壤水分和ET,使用城市景观的分数覆盖率估算植被上的土壤水分和ET ;结果表明,夏季,秋季和冬季,植被,土壤和不透水表面上的日,小时土壤水分和ET的总体趋势以及土壤水分和ET的响应三种类型的土地覆盖的降水量。夏季,每小时土壤水分波动但稳定。在频繁降水期间,土壤和不透水表面的每小时ET表现出相似的模式,而植被表面的ET较低,这表明表面水分的分布和变化与城市树木的分布和活力有关。从秋季到冬季,发现土壤水分和ET的总体趋势下降,并且冬季对降水的响应变弱。 ET的空间分布表明,中心城区的ET比常规的不透水表面和城市平均水平高,这表明地表水分的分布与建筑物的结构复杂性有关。支持这两个假设,并且对该方法进行了测试,可有效估算城市地区的表面湿度。结果提示了关于人为热量对表面水分的影响以及城市中多个数据源中数据完整性问题的未来研究。

著录项

  • 作者

    Jiang, Yitong.;

  • 作者单位

    Indiana State University.;

  • 授予单位 Indiana State University.;
  • 学科 Geography.;Remote sensing.;Geographic information science and geodesy.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 175 p.
  • 总页数 175
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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