基于Landsat-8影像的西安市地表温度遥感反演与影响因子研究
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摘要
基于西安市的Landsat-8数据,采用单通道算法进行了地表温度(LST)的反演,并通过实测LST值对反演精度进行了评价,同时分析了LST与不同地表类型、归一化植被指数(NDVI)以及归一化建筑指数(NDBI)之间的关系.结果表明,单通道算法反演LST精度较高,反演结果能较好地反演西安市LST的空间分布特征;不同地表类型对应的LST有明显的差异,城镇建设用地LST最高,农用地LST最低;通过在东西方向/南北方向剖面线上分别对NDVI、NDBI与LST进行线性拟合,发现NDVI与LST呈负相关线性关系, NDBI与LST呈正相关线性关系,进一步说明植被对地表起到了一定的降温作用,城镇建筑则加剧了城市热岛效应.
Based on Landsat-8 image of Xi'an, the single-channel algorithm was used to retrieve the land surface temperature(LST) and the retrieval accuracy was validated by the simultaneously measured surface temperature. The relationships between various surface types, normalized difference vegetation index(NDVI), normalized difference built-up index(NDBI) and LST were analyzed respectively. The results indicated that the single-channel algorithm manifested relatively high retrieval accuracy, and the retrieved surface temperature could effectively reflect the spatial distribution of LST in Xi'an. Distinct LST differences were detected between various surface types, where the LST of urban development land was the highest, while agricultural land was the lowest. The linear fitting of NDVI and NDBI with LST in both the east-west and north-south profile lines demonstrated that NDVI was negatively correlated with the LST, while NDBI was positively correlated, suggesting that vegetation had a cooling effect on the land surface, while urban constructions could markedly exacerbate the effect of urban heat island.
Based on Landsat-8 image of Xi'an, the single-channel algorithm was used to retrieve the land surface temperature(LST) and the retrieval accuracy was validated by the simultaneously measured surface temperature. The relationships between various surface types, normalized difference vegetation index(NDVI), normalized difference built-up index(NDBI) and LST were analyzed respectively. The results indicated that the single-channel algorithm manifested relatively high retrieval accuracy, and the retrieved surface temperature could effectively reflect the spatial distribution of LST in Xi'an. Distinct LST differences were detected between various surface types, where the LST of urban development land was the highest, while agricultural land was the lowest. The linear fitting of NDVI and NDBI with LST in both the east-west and north-south profile lines demonstrated that NDVI was negatively correlated with the LST, while NDBI was positively correlated, suggesting that vegetation had a cooling effect on the land surface, while urban constructions could markedly exacerbate the effect of urban heat island.
引文
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[13]历华,柳钦火,邹杰.基于MODIS数据的长株潭地区NDBI和NDVI与地表温度的关系研究[J].地理科学,2009, 29(2):262-267.
[14]武鹏飞,王茂军,张学霞.基于归一化建筑指数的北京市城市热岛效应分布特征[J].生态环境学报, 2009,18(4):1325-1331.
[15] Jiménez-Mu?oz J C, Sobrino C J. A generalized singlechannel method for retrieving land surface temperature from remote sensing data[J]. Journal of Geophysical Research, 2003, 108(22):4688.
[16]覃志豪.用陆地卫星TM6数据演算地表温度的单窗算法[J].地理学报, 2001, 56(4):456-466.
[17]杨丽萍,王乐,孙晓辉,等.基于遥感的西安市热力景观格局演变[J].水土保持研究, 2017, 24(1):250-256.
[18]覃志豪,李文娟,徐斌,等.陆地卫星TM6波段范围内地表比辐射率的估计[J].国土资源遥感, 2004, 16(3):28-32.
[19]宋挺,段峥,刘军志,等. Landsat 8数据地表温度反演算法对比[J].遥感学报, 2015, 19(3):451-464.
[20] Rouse J W, Haas R H, Schell J A, et al. Monitoring vegetation systems in the great plain with ERTS[C]//Proceedings of the Third Earth Resource Technology Satellite-1 Symposium. Greenbel:National Aeronautics and Space Administration SP-351, 1974.
[21]查勇,倪绍祥,杨山.一种利用TM图像自动提取城镇用地信息的有效方法[J].遥感学报, 2003, 7(1):37-40.
[22]李璇琼,卢正,李永树.兰州市热岛效应特征及驱动机制研究[J].兰州大学学报:自然科学版, 2016, 52(1):69-74.
[2]周坚华,孙天纵.城市遥感[M].上海:上海科学技术文献出版社, 1994.
[3]陈云海,穆亚超,颉耀文.基于Landsat遥感影像的黑河干流中游湿地信息提取[J].兰州大学学报:自然科学版, 2016, 52(5):587-592.
[4]宋挺,段峥,刘军志,等.基于Landsat-8数据和劈窗算法的地表温度反演及城市热岛效应研究[J].环境监控与预警, 2014, 6(5):4-14.
[5] United States Geological Survey. Landsat 8 reprocessing to begin February 3, 2014[EB/OL].(2014-02-19)[2017-12-27]. https://www.usgs.gov/land-resources/nli/landsat.
[6] Barsi J A, Schott J R, Hook S J, et al. Landsat-8 thermal infrared sensor(TIRS)vicarious radiometric calibration[J]. Remote Sensing, 2014, 6(11):11607-11626.
[7] Montanaro M, Gerace A, Lunsford A, et al. Stray light artifacts in imagery from the Landsat 8 thermal infrared sensor[J]. Remote Sensing, 2014, 6(11):10435-10456.
[8]徐涵秋. Landsat 8热红外数据定标参数的变化及其对地表温度反演的影响[J].遥感学报, 2016, 20(2):229-235.
[9] Jiménez-Mu?oz J C, Sobrino J A, Skokovic D, et al.Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(10):1840-1843.
[10]宫阿都,陈云浩,李京,等.北京市城市热岛与土地利用/覆盖变化的关系研究[J].中国图象图形学报,2007, 12(8):1476-1482.
[11] Dousset B, Gourmelon F. Satellite multi-sensor data analysis of urban surface temperature and landcover[J].Journal of Photogrammetry and Remote Sensing, 2003,58(1/2):43-54.
[12]张佳华,侯英雨,李贵才,等.北京城市及周边热岛日变化及季节特征的卫星遥感研究与影响因子分析[J].中国科学D辑:地球科学, 2005, 35(增刊1):187-194.
[13]历华,柳钦火,邹杰.基于MODIS数据的长株潭地区NDBI和NDVI与地表温度的关系研究[J].地理科学,2009, 29(2):262-267.
[14]武鹏飞,王茂军,张学霞.基于归一化建筑指数的北京市城市热岛效应分布特征[J].生态环境学报, 2009,18(4):1325-1331.
[15] Jiménez-Mu?oz J C, Sobrino C J. A generalized singlechannel method for retrieving land surface temperature from remote sensing data[J]. Journal of Geophysical Research, 2003, 108(22):4688.
[16]覃志豪.用陆地卫星TM6数据演算地表温度的单窗算法[J].地理学报, 2001, 56(4):456-466.
[17]杨丽萍,王乐,孙晓辉,等.基于遥感的西安市热力景观格局演变[J].水土保持研究, 2017, 24(1):250-256.
[18]覃志豪,李文娟,徐斌,等.陆地卫星TM6波段范围内地表比辐射率的估计[J].国土资源遥感, 2004, 16(3):28-32.
[19]宋挺,段峥,刘军志,等. Landsat 8数据地表温度反演算法对比[J].遥感学报, 2015, 19(3):451-464.
[20] Rouse J W, Haas R H, Schell J A, et al. Monitoring vegetation systems in the great plain with ERTS[C]//Proceedings of the Third Earth Resource Technology Satellite-1 Symposium. Greenbel:National Aeronautics and Space Administration SP-351, 1974.
[21]查勇,倪绍祥,杨山.一种利用TM图像自动提取城镇用地信息的有效方法[J].遥感学报, 2003, 7(1):37-40.
[22]李璇琼,卢正,李永树.兰州市热岛效应特征及驱动机制研究[J].兰州大学学报:自然科学版, 2016, 52(1):69-74.
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