基于Landsat时间序列数据的重庆市热力景观格局演变分析
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摘要
城市热岛效应是伴随城市化而产生的生态环境问题,研究城市热力景观格局的演变有助于掌握城市热岛效应变化的机制与规律,为城市产业合理布局、城市生态环境改善提供科学的决策支持。以重庆市主城九区为研究对象,基于2001年Landsat7 ETM+、2007年Landsat5 TM以及2014年Landsat8 OLI_TIRS三期遥感影像数据,利用辐射传输方程法和Jiménez-Mu?oz et al.(2014)的分裂窗算法反演地表温度,并在此基础上,计算热力景观格局指数,研究重庆市热力景观格局的演变过程。结果表明,(1)2001—2014年,重庆市主城九区热岛和强热岛景观类型范围不断扩大,热岛效应明显增强,渝中区热岛和强热岛所占百分比最高,江北区、南岸区、九龙坡区和大渡口区的增长速度较快。(2)热力景观类型中热岛面积的增加主要由正常区斑块转化而来,而强热岛面积的增加主要由正常区和热岛斑块转化而来,跨越正常区的斑块转化较难。(3)在斑块类型水平上,热岛与强热岛斑块优势度增大。2001—2014年热岛斑块密度减小3.33,平均斑块面积增大6倍;强热岛斑块密度减少0.65,平均斑块面积增大5倍。热岛和强热岛斑块变得大而集中,破碎度减小。热岛间的连通性与强热岛斑块间的连通性越来越高,连通性指数分别增大了3.51和8.41,强热岛斑块形状的复杂程度逐期变大。在景观水平上,2001—2014年重庆市的斑块数量和斑块密度减小,平均斑块面积增大,热力景观破碎化程度逐渐降低,斑块连通性指数高,均大于99.5。聚合度和均匀度指数分别增大14.85和0.09,像素间聚合成斑块的程度变大且斑块类型面积越来越均匀。由此可见,随着城市的发展,重庆市的热环境问题越来越严重,利用热力景观格局指数分析城市热环境,可了解城市热力景观格局的演变趋势,为热岛的缓解提供理论依据。
Urban heat island effect is one of the ecological environment issues in the process of urbanization. The study of the dynamic changes of the urban thermal landscape pattern is helpful to find out the mechanisms of urban heat island and provide decision support for urban planning and urban ecological environment improving. In this study, Landsat7 ETM+(2001), Landsat5 TM(2007) and Landsat8 OLI_TIRS(2014) were used to derive land surface temperature based on radiative transfer equation and Jiménez-Mu?oz's split window algorithm(2014). Then the thermal landscape pattern indices were calculated to analyze the evolution of the thermal landscape pattern in Chongqing City. The results showed that,(1) From 2001 to 2014, heat island and strong heat island expanded, and the heat island effect was significantly enhanced. Heat island and strong heat island in Yuzhong district had the highest percentage, and there was a faster growth rate in Jiangbei, Nanan, Jiulongpo and Dadukou district than which in other areas.(2) The increased heat island area was mainly converted from the normal area, and the increased strong heat island area was mainly converted from the normal area and the heat island. Patch conversion across normal area was generally difficult.(3)At the patch-class level, the preponderance of heat island and strong heat island were increased. From 2001 to 2014, the density of heat island patch decreased by 3.33, while the average patch area increased 6 times. Similarly, the density of strong heat island patch decreased by 0.65, while the average patch area increased 5 times. The heat island and strong heat island patches were larger and more concentrated than before, and the fragmentation was decreased. The connectivity between the heat island and strong heat island increased by 3.51 and 8.41, respectively. The shapes of the strong heat island patches became more complicated. At the landscape level, the patch number and patch density was decreased from 2001 to 2014, while the average patch size was increased, and the thermal landscape fragmentation degree of Chongqing city was gradually reduced in this period. The patch connectivity index was higher than 99.5. The aggregation index and the evenness index had increased by 14.85 and 0.09, respectively. The aggregation of the pixels was enhanced and the area of the patch type became more uniform. We can conclude that the thermal environment of Chongqing became more and more serious with the city's development. Our developed method for analyzing urban thermal environment using thermal landscape pattern index and findings in this study are helpful to understand the evolution trend of urban thermal landscape pattern, and provide a theoretical basis for the mitigation of urban heat island.
Urban heat island effect is one of the ecological environment issues in the process of urbanization. The study of the dynamic changes of the urban thermal landscape pattern is helpful to find out the mechanisms of urban heat island and provide decision support for urban planning and urban ecological environment improving. In this study, Landsat7 ETM+(2001), Landsat5 TM(2007) and Landsat8 OLI_TIRS(2014) were used to derive land surface temperature based on radiative transfer equation and Jiménez-Mu?oz's split window algorithm(2014). Then the thermal landscape pattern indices were calculated to analyze the evolution of the thermal landscape pattern in Chongqing City. The results showed that,(1) From 2001 to 2014, heat island and strong heat island expanded, and the heat island effect was significantly enhanced. Heat island and strong heat island in Yuzhong district had the highest percentage, and there was a faster growth rate in Jiangbei, Nanan, Jiulongpo and Dadukou district than which in other areas.(2) The increased heat island area was mainly converted from the normal area, and the increased strong heat island area was mainly converted from the normal area and the heat island. Patch conversion across normal area was generally difficult.(3)At the patch-class level, the preponderance of heat island and strong heat island were increased. From 2001 to 2014, the density of heat island patch decreased by 3.33, while the average patch area increased 6 times. Similarly, the density of strong heat island patch decreased by 0.65, while the average patch area increased 5 times. The heat island and strong heat island patches were larger and more concentrated than before, and the fragmentation was decreased. The connectivity between the heat island and strong heat island increased by 3.51 and 8.41, respectively. The shapes of the strong heat island patches became more complicated. At the landscape level, the patch number and patch density was decreased from 2001 to 2014, while the average patch size was increased, and the thermal landscape fragmentation degree of Chongqing city was gradually reduced in this period. The patch connectivity index was higher than 99.5. The aggregation index and the evenness index had increased by 14.85 and 0.09, respectively. The aggregation of the pixels was enhanced and the area of the patch type became more uniform. We can conclude that the thermal environment of Chongqing became more and more serious with the city's development. Our developed method for analyzing urban thermal environment using thermal landscape pattern index and findings in this study are helpful to understand the evolution trend of urban thermal landscape pattern, and provide a theoretical basis for the mitigation of urban heat island.
引文
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HUA L Z,WANG M.2012.Temporal and spatial characteristics of urban heat island of an Estuary City[J].Journal of Computers,7(12):3082-3087.
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LI Y Y,ZHANG H,KAINZ W,et al.2012.Monitoring patterns of urban heat islands of the fast-growing Shanghai metropolis,China:using time-series of Landsat TM/ETM+data[J].International Journal of Applied Earth Observation and Geoinformation,19:127-138.
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SOBRINO J A,LI Z L,STOLL M P,et al.1996.Multi-channel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data[J].International Journal of Remote Sensing,17(11):2089-2114.
SOBRINO J A,JIMNEZ-MUANOZ J C,PAOLINI L.2004.Land surface temperature retrieval from LANDSAT TM5[J].Remote Sensing of Environment,90(4):434-440.
STREUTKER D R.2003.Satellite-measured growth of the urban heat island of Houston,Texas[J].Remote Sensing of Environment,2003,85:282-289.
XIAN G,GRANE M.2006.An analysis of urban thermal characteristics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data[J].Remote Sensing of Environment,104(2):147-156.
XU L Y,XIE X D,LI S.2013.Correlation analysis of the urban heat island effect and the spatial and temporal distribution of atmospheric particulates using TM images in Beijing[J].Environmental Pollution,178:102-114.
WENG Q H,LU D S,SCHUBRING J.2004.Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies[J].Remote Sensing of Environment,89(4):467-483.
WENG Q H,LIU H,LU D S.2007.Assessing the effects of land use and land cover patterns on thermal conditions using landscape metrics in city of Indianapolis,United States[J].Urban Ecosystems,10(2):203-219.
白杨,孟浩,王敏,等.2013.上海市城市热岛景观格局演变特征研究[J].环境科学与技术,36(3):196-201.
陈云浩,李晓兵,史培军,等.2002.上海城市热环境的空间格局分析[J].地理科学,22(3):317-322.
戴晓燕,张利权,过仲阳,等.2009.上海城市热岛效应形成机制及空间格局[J].生态学报,29(7):3995-4004.
郭广猛,杨青生.2004.利用MODIS数据反演地表温度的研究[J].遥感技术与应用,19(1):35-36.
胡德勇,乔琨,王兴玲,等.2015.单窗算法结合Landsat 8热红外数据反演地表温度[J].遥感学报,19(6):964-976.
黄聚聪,赵小锋,唐立娜,等.2012.城市化进程中城市热岛景观格局演变的时空特征-以厦门市为例[J].生态学报,32(2):622-631.
李鉴清,张庆国,朱雅莉,等.2012.合肥市地表温度反演及热力景观格局动态变化研究[J].安徽农业大学学报,39(4):629-636.
李瑶,潘竟虎.2015.基于Landsat8劈窗算法与混合光谱分解的城市热岛效应空间格局分析-以兰州中心城区为例[J].干旱区地理,38(1):111-119.
林荣平,祁新华,叶士琳.2017.沿海河谷盆地城市热岛时空特征及驱动机制[J].生态学报,37(1):294-304.
路广,韩美,王敏,等.2017.近代黄河三角洲植被覆盖度时空变化分析[J].生态环境学报,26(3):422-428.
毛克彪,施建成,覃志豪,等.2006.一个针对ASTER数据同时反演地表温度和比辐射率的四通道算法[J].遥感学报,10(4):593-599.
孟丹,李小娟,宫辉力,等.2010.北京地区热力景观格局及典型城市景观的热环境效应[J].生态学报,30(13):3491-3500.
覃志豪.2004.陆地卫星TM6波段范围内地表比辐射率的估计[J].国土资源遥感,3(61):29-41.
覃志豪,高懋芳,秦晓敏.2005.农业旱灾监测中的地表温度遥感反演方法-以MODIS数据为例[J].自然灾害学报,14(4):64-71.
唐泽,郑海峰,任志彬,等.2017.城市地表热力景观格局时空演变-以长春市为例[J].生态学报,37(10):3264-3273.
陶康华,陈云浩,周巧兰,等.1999.热力景观在城市生态规划中的应用[J].城市研究,(1):20-22.
史作民,陈涛.1996.城市化及其对城市生态环境影响研究进展[J].生态学杂志,15(1):35-41.
王倩倩,覃志豪,王斐.2012.基于多源遥感数据反演地表温度的单窗算法[J].地理与地理信息科学,28(3):24-26,62.
徐涵秋.2015.新型Landsat 8卫星影像的反射率和地表温度反演[J].地球物理学报,58(3):741-747.
杨槐.2014.从Landsat 8影像反演地表温度的劈窗算法研究[J].测绘地理信息,39(4):73-77.
杨英宝,苏伟忠,江南.2006.南京市热岛效应时空特征的遥感分析[J].遥感技术与应用,21(6):488-492.
张好,徐涵秋,李乐,等.2014.成都市热岛效应与城市空间发展关系分析[J].地球信息科学学报,16(1):70-78.
DAI X Y,GUO Z,ZHANG L Q,et al.2010.Spatio-temporal exploratory analysis of urban surface temperature field in Shanghai,China[J].Stochastic Environmental Research and Risk Assessment,24(2):247-257.
GILLESPIE A,ROKUGAWA S,MATSUNAGA T,et al.1998.Atemperature and emissivity separation algorithm for advanced spaceborne thermal emission and reflection radiometer(ASTER)images[J].IEEE Transactions on Geoscience and Remote Sensing,36(4):1113-1126.
GROVER A,SINGH R B.2015.Analysis of urban heat island(uhi)in relation to normalized difference vegetation index(ndvi):a comparative study of delhi and Mumbai[J].Environments,2:125-138.
HUA L Z,WANG M.2012.Temporal and spatial characteristics of urban heat island of an Estuary City[J].Journal of Computers,7(12):3082-3087.
JIMéNEZ-MU?OZ J C,SOBRINO J A.2003.A generalized single-channel method for retrieving land surface temperature from remote sensing data[J].Journal of Geophysical Research Atmospheres,108(D22):2015-2023.
JIMéNEZ-MU?OZ J C,SOBRINO J A,SKOKOVIC D,et al.2014.Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data[J].IEEE Geoscience and Remote Sensing Letters,11(10):1840-1843.
LI Y Y,ZHANG H,KAINZ W,et al.2012.Monitoring patterns of urban heat islands of the fast-growing Shanghai metropolis,China:using time-series of Landsat TM/ETM+data[J].International Journal of Applied Earth Observation and Geoinformation,19:127-138.
QIN Z,KAMIELI A,and BERLINER P.2001.A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region[J].Remote Sensing,22(18):3719-3746.
ROZENSTEIN O,QIN Z H,DERIMIAN Y,et al.2014.Derivation of land surface temperature for landsat-8 TIRS using a split window algorithm[J].Sensors,14(4):5768-5780.
SOBRINO J A,LI Z L,STOLL M P,et al.1996.Multi-channel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data[J].International Journal of Remote Sensing,17(11):2089-2114.
SOBRINO J A,JIMNEZ-MUANOZ J C,PAOLINI L.2004.Land surface temperature retrieval from LANDSAT TM5[J].Remote Sensing of Environment,90(4):434-440.
STREUTKER D R.2003.Satellite-measured growth of the urban heat island of Houston,Texas[J].Remote Sensing of Environment,2003,85:282-289.
XIAN G,GRANE M.2006.An analysis of urban thermal characteristics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data[J].Remote Sensing of Environment,104(2):147-156.
XU L Y,XIE X D,LI S.2013.Correlation analysis of the urban heat island effect and the spatial and temporal distribution of atmospheric particulates using TM images in Beijing[J].Environmental Pollution,178:102-114.
WENG Q H,LU D S,SCHUBRING J.2004.Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies[J].Remote Sensing of Environment,89(4):467-483.
WENG Q H,LIU H,LU D S.2007.Assessing the effects of land use and land cover patterns on thermal conditions using landscape metrics in city of Indianapolis,United States[J].Urban Ecosystems,10(2):203-219.
白杨,孟浩,王敏,等.2013.上海市城市热岛景观格局演变特征研究[J].环境科学与技术,36(3):196-201.
陈云浩,李晓兵,史培军,等.2002.上海城市热环境的空间格局分析[J].地理科学,22(3):317-322.
戴晓燕,张利权,过仲阳,等.2009.上海城市热岛效应形成机制及空间格局[J].生态学报,29(7):3995-4004.
郭广猛,杨青生.2004.利用MODIS数据反演地表温度的研究[J].遥感技术与应用,19(1):35-36.
胡德勇,乔琨,王兴玲,等.2015.单窗算法结合Landsat 8热红外数据反演地表温度[J].遥感学报,19(6):964-976.
黄聚聪,赵小锋,唐立娜,等.2012.城市化进程中城市热岛景观格局演变的时空特征-以厦门市为例[J].生态学报,32(2):622-631.
李鉴清,张庆国,朱雅莉,等.2012.合肥市地表温度反演及热力景观格局动态变化研究[J].安徽农业大学学报,39(4):629-636.
李瑶,潘竟虎.2015.基于Landsat8劈窗算法与混合光谱分解的城市热岛效应空间格局分析-以兰州中心城区为例[J].干旱区地理,38(1):111-119.
林荣平,祁新华,叶士琳.2017.沿海河谷盆地城市热岛时空特征及驱动机制[J].生态学报,37(1):294-304.
路广,韩美,王敏,等.2017.近代黄河三角洲植被覆盖度时空变化分析[J].生态环境学报,26(3):422-428.
毛克彪,施建成,覃志豪,等.2006.一个针对ASTER数据同时反演地表温度和比辐射率的四通道算法[J].遥感学报,10(4):593-599.
孟丹,李小娟,宫辉力,等.2010.北京地区热力景观格局及典型城市景观的热环境效应[J].生态学报,30(13):3491-3500.
覃志豪.2004.陆地卫星TM6波段范围内地表比辐射率的估计[J].国土资源遥感,3(61):29-41.
覃志豪,高懋芳,秦晓敏.2005.农业旱灾监测中的地表温度遥感反演方法-以MODIS数据为例[J].自然灾害学报,14(4):64-71.
唐泽,郑海峰,任志彬,等.2017.城市地表热力景观格局时空演变-以长春市为例[J].生态学报,37(10):3264-3273.
陶康华,陈云浩,周巧兰,等.1999.热力景观在城市生态规划中的应用[J].城市研究,(1):20-22.
史作民,陈涛.1996.城市化及其对城市生态环境影响研究进展[J].生态学杂志,15(1):35-41.
王倩倩,覃志豪,王斐.2012.基于多源遥感数据反演地表温度的单窗算法[J].地理与地理信息科学,28(3):24-26,62.
徐涵秋.2015.新型Landsat 8卫星影像的反射率和地表温度反演[J].地球物理学报,58(3):741-747.
杨槐.2014.从Landsat 8影像反演地表温度的劈窗算法研究[J].测绘地理信息,39(4):73-77.
杨英宝,苏伟忠,江南.2006.南京市热岛效应时空特征的遥感分析[J].遥感技术与应用,21(6):488-492.
张好,徐涵秋,李乐,等.2014.成都市热岛效应与城市空间发展关系分析[J].地球信息科学学报,16(1):70-78.