基于地表参数变化的延河流域地表温度时空演变分析

doi:10.6046/zrzyyg.2023038

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摘要

地表温度是地表能量平衡与陆面过程的重要参数,与地表参数变化关系密切。该文以黄土高原的延河流域为例,基于2015年、2018年和2020年Landsat OLI/TIRS影像,在利用大气校正法反演地表温度的基础上,提取影像归一化建筑指数(normalized difference built-up index,NDBI)、归一化植被指数(normalized differential vegetation index,NDVI)和归一化水汽指数(normalized difference moisture index,NDMI),分析地表温度与地表参数和土地利用类型的关系,以及地表温度时空变化特征。结果表明,地表温度的反演值与验证值在2015年、2018年和2020年的相关系数分别达0.569,0.675和0.632,均大于0.5,具有一定的准确性和可行性。从地表温度时空变化特征上看,低温区、次中温区、次高温区面积有所减少,而中温区面积增长较大,高温区面积略有增长,地表温度有向中、高温增长的趋势; 从与土地利用类型的关系来看,下垫面覆盖类型的地表温度均呈现为水域<林地<草地<耕地<建设用地; 从与地表参数的定量关系来看,延河流域地表温度变化与地表参数变化存在显著相关性,NDBI与地表温度变化呈正相关性,NDVI和NDMI与地表温度变化呈负相关性; 从与地理环境因素的关系来看,地表温度随着海拔的升高而降低。在不同坡度上地表温度也体现差异性,其中平坡地表温度最高,坡度越陡温度越低。不同坡向地表温度具有显著差异,其中阳坡地表温度明显大于阴坡地表温度。研究结果可为复杂地区的地表水热环境研究提供参考。

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	李威洋
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关键词 ：地表温度, 遥感, 温度转移矩阵, 相关性分析, 延河流域

Abstract：

Surface temperature, a significant parameter of surface energy balance and land surface processes, is closely associated with the changes in surface parameters. With the Yanhe River basin in the Loess Plateau as a study area, this study derived the surface temperatures through inversion using the atmospheric correction method based on the Landsat OLI/TIRS images of 2015, 2018, and 2020. Moreover, by extracting the normalized difference build-up index (NDBI), normalized differential vegetation index (NDVI), and normalized difference moisture index (NDMI), this study analyzed the relationships of surface temperatures with surface parameters and land use types, as well as the spatio-temporal variations of surface temperatures. The results demonstrate that the correlation coefficients between the inverted and verified values of surface temperatures in 2015, 2018, and 2020 were 0.569, 0.675, and 0.632, respectively, all exceeding 0.5, suggesting certain accuracy and feasibility. Concerning the spatio-temporal variations of surface temperatures, the low-, sub-medium-, and sub-high-temperature zones exhibited decreased areas, whereas medium- and high-temperature zones manifested significantly and slightly increased areas, respectively, suggesting that the surface temperatures tended to increase towards medium and high temperatures. In terms of the relationship with land use types, the surface temperatures of underlying surface cover types increased in the order of water area, forest land, grassland, cultivated land, and construction land. The quantitative relationship reveals significant correlations between changes in surface temperatures and surface parameters of the Yanhe River basin. Specifically, the changes in surface temperatures were positively correlated with the NDBI but negatively correlated with the NDVI and the NDMI. From the perspective of geographical environment factors, surface temperatures decreased with increasing altitude. Different slopes exhibited distinct surface temperatures, which were the highest on flat slopes and lower on steeper slopes. Additionally, different slope aspects manifested significantly different surface temperatures, which were significantly higher on sunny slopes compared to shady slopes. The findings of this study will serve as a reference for exploring surface water thermal environments in complex areas.

Key words： surface temperature remote sensing temperature transfer matrix correlation analysis Yanhe River basin

收稿日期: 2023-02-22 出版日期: 2024-06-14

ZTFLH:

TP79

基金资助:西部青年学者项目“黄土丘陵区地形微生境分类评价与潜在植物群落分布模拟”(XAB2020YN04);国家自然科学基金项目“黄土丘陵区地形微气候环境与植物功能性状响应”(41501055)

通讯作者: 史海静(1983-),女,博士,副研究员,主要从事数字水土保持与大数据分析的科研工作。Email: shihaijingcn@nwafu.edu.cn。

作者简介: 李威洋(2002-),男,本科,主要从事遥感定量反演研究。Email: liweiyang@nwafu.edu.cn。

引用本文:

李威洋, 史海静, 聂玮廷, 杨鑫源. 基于地表参数变化的延河流域地表温度时空演变分析[J]. 自然资源遥感, 2024, 36(2): 229-238.
LI Weiyang, SHI Haijing, NIE Weiting, YANG Xinyuan. Analyzing the spatio-temporal evolution of surface temperatures in the Yanhe River basin based on the changes in surface parameters. Remote Sensing for Natural Resources, 2024, 36(2): 229-238.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2023038 或 https://www.gtzyyg.com/CN/Y2024/V36/I2/229

Fig.1 研究区位置

等级	划分依据^①
高温区	$T S > μ + S t d$
次高温区	$μ + 0.5 S t d < T S ≤ μ + S t d$
中温区	$μ - 0.5 S t d < T S ≤ μ + 0.5 S t d$
次中温区	$μ - S t d < T S ≤ μ - 0.5 S t d$
低温区	$T S < μ - S t d$

Tab.1 温度等级划分依据

Fig.2 ArcMap随机生成样本点

Tab.2 2015—2020年延河流域温度变化

Fig.3 不同年份延河流域温度等级分布

Tab.3 2015—2018年温度转移矩阵

Tab.4 2018—2020年温度转移矩阵

Tab.5 各土地类型在不同年份的温度评分情况

年份	回归分析^①
2015年	$L S T = 22.6651 e 0.993 N D B I R 2 = 0.495$	$L S T = - 34.4369 N D M I + 54.508 R 2 = 0.491$	$L S T = - 24.0171 N D V I + 53.895 R 2 = 0.449$
2018年	$L S T = 16.261 e 0.431 N D B I R 2 = 0.207$	$L S T = - 8.8673 N D M I + 24.661 R 2 = 0.203$	$L S T = - 6.4295 N D V I + 25.464 R 2 = 0.195$
2020年	$L S T = 13.224 e 1.363 N D B I R 2 = 0.489$	$L S T = - 34.78 N D M I + 43.726 R 2 = 0.473$	$L S T = - 23.9976 N D V I + 44.660 R 2 = 0.469$

Tab.6 NDBI,NDMI,NDVI与LST的定量关系

Fig.4 不同年份NDBI变化

Fig.5 不同年份NDVI变化

Fig.6 不同年份NDMI变化

Fig.7 延河流域地形因子分级分类图

Tab.7 2015年LST与地形因素分类统计

Tab.8 2018年LST与地形因素分类统计

Tab.9 2020年LST与地形因素分类统计

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