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首页> 外文期刊>Advances in space research >Quantitative analysis of spatial distribution of land surface temperature (LST) in relation Ecohydrological, terrain and socio- economic factors based on Landsat data in mountainous area
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Quantitative analysis of spatial distribution of land surface temperature (LST) in relation Ecohydrological, terrain and socio- economic factors based on Landsat data in mountainous area

机译：基于山地地区山地地区地区陆地上的生态水质，地形，地形和社会经济因素的地表温度（LST）空间分布的定量分析

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摘要

Land surface temperature (LST) is considered as one of the most significantly effective factors on the regional climate and ecology, playing an important role in connecting surface energy and water exchange. In mountainous regions, LST reveals lots of inconsistencies due to the effect of such factors as topography, vegetation, solar radiation, etc. We sought to investigate the the temporal and spatial variation LST in different years and its relationship with effective factors in 5 dimensions using Multiple statistical methods, the sepidan region in northwest Iran. The multi-factorial land use, topographic (elevation, slope, aspect), biophysical indices (normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized difference built up index (NDBI), and modified of normalized difference water index (MNDWI)), socio-economic (fossil fuel CO_2 emissions (FFCOE) and road density(RD)), and climate (temperature and solar radiation) was studied in the current research. To this end, Images of July 1998 and 2017 were extracted from Thematic Mapper (TM5) and Operational Land Imager/Thermal infrared sensors (OLI/TIRS8). Moreover, ordinary least squares regression (OLS), Best subset regression, and Hierarchical Partitioning Analysis (HP) were used to investigate the relationship between LST and relevant effective factors. The results indicated that the temperature range varied from 10 to 53 °C in the time period mentioned. The highest amount of LST was observed in barren land use and the lowest one was found in garden lands. An negative correlation was found between LST and elevation. On the other hand, the highest value of the Laps rate of surface temperature was observed in the southern aspects and the lowest one was observed in the western aspects. Furthermore, the highest and lowest values of lase rate were found in slopes less than 10°, and in 50 to 60-degree slopes, respectively. The results of the OLS correlation indicated a negative correlation between LST and NDVI, NDMI, and MNDWI, and a positive correlation of LST with climatic and socio-economic indicators. LST's highest and lowest correlations were found to be with vegetation (R~2 = 0.95) and road density (R~2 = 0.1). Finally, while in 1998 temperature and vegetation were identified as the most influential factors on LST, it was the elevation that was found to be the most effective factor on LST in 2017 with the effective rate of 82.72%. This study offers a valuable viewpoint on the temporal and spatial variations of LST, their complexity, and the environmental factors that affect them. The viewpoint could, therefore, be used for prospective studies on the analysis of the ecosystem's reaction to climate changes.
机译：陆地表面温度（LST）被认为是区域气候和生态学中最具显着有效因素之一，在连接表面能量和水交换中发挥着重要作用。在山区，LST由于这种因素作为地形，植被，太阳辐射等的影响，我们寻求调查不同年份的时间和空间变化及5维度的有效因素的关系多种统计方法，伊朗西北地区的绵羊地区。多因素土地使用，地形（海拔，斜坡，方面），生物物理指数（归一化差异植被指数（NDVI），归一化差分湿度指数（NDMI），归一化差异建立指数（NDBI），并修改了归一化差水目前研究在目前研究了指数（MNDWI）），社会经济（化石燃料CO_2排放（FFCOE）和道路密度（RD））和气候（温度和太阳辐射）。为此，从主题映射器（TM5）和运营陆地成像器/热红外传感器（OLI / TIRS8）中提取了1998年7月和2017年7月的图像。此外，使用普通的最小二乘回归（OLS），最佳子集回归和分层分区分析（HP）来研究LST与相关有效因素之间的关系。结果表明，温度范围在提到的时间段内从10至53℃变化。在贫瘠的土地使用中观察到最高量的LST，并且在花园土地上发现最低的LST。在LST和高度之间发现了负相关性。另一方面，在南部方面观察到表面温度的滞留率的最高值，并且在西方方面观察到最低的表面温度。此外，在小于10°的斜率下发现了最高和最低值的液体率值，分别在50至60度斜坡中。 OLS相关结果表明LST和NDVI，NDMI和MNDWI之间的负相关，以及LST与气候和社会经济指标的正相关。 LST的最高和最低的相关性被发现是植被（R〜2 = 0.95）和道路密度（R〜2 = 0.1）。最后，虽然在1998年的温度和植被中被确定为最有影响力的因素，但它是2017年LST中最有效的因素的升高，其有效率为82.72％。本研究提供了关于LST的时间和空间变化，它们的复杂性和影响它们的环境因素的有价值的观点。因此，该观点可以用于对生态系统对气候变化的反应分析的前瞻性研究。

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