城市非渗透表面信息提取与应用研究
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摘要
城市作为人口集聚的增长极和经济发展的动力源，历来是学者研究的重点区域。虽然目前城市面积仅占全球陆地面积的很小部分，却集中了人类半数以上的人口。城市区域的快速扩展会对社会-经济-环境生态系统的可持续发展产生难以估量的影响，这些影响理应引起人们的警示和思考，并采取必要措施减少城市发展引起的消极影响，以保证人类社会的可持续发展。
非渗透表面，又被称为非透水表面，是指水分无法透过其深入土壤的人造表层，如道路、停车场、建筑物屋顶等，它是城市和村镇地表的重要组成部分。非渗透表面不仅是城市化程度的重要表征指标，而且是城镇环境质量的表征指标之一。准确提取和监测非渗透表面密度信息以及建立非渗透表面特征数据库对全球变化和人地交互关系的研究具有重要意义。
自获取首幅陆地遥感影像以来，遥感影像以其多波段、信息连续和获取方便等优点，逐渐成为非渗透表面密度信息提取的主要数据源，各种提取方法和理论模型也应运而生。目前，基于遥感影像的城市非渗透表面信息提取方法可归纳为像元尺度的非渗透表面信息提取法和亚像元尺度非渗透表面信息提取法。前者主要应用传统分类方法研究和提取城市非渗透表面信息，后者则基于像元分解技术，按密度大小定义并研究城市非渗透表面信息。
本研究以烟台市区为研究区域，以1989、1995、1999、2000、2006和2009年获取的Landsat TM/ETM+遥感影像数据为主要数据源，以研究区中巴地球资源卫星影像、谷歌地球影像、土地利用图以及行政区域矢量图等为辅助资料，经过几何校正、辐射校正等影像预处理手段，利用植被-非渗透表面-土壤模型和归一化线性光谱混合模型对研究区进行非渗透表面密度信息提取，研究其空间变化模式，并从两个方面展开对非渗透表面密度的应用研究：以多时相非渗透表面密度信息为决策判断条件，利用决策树分类模型对研究区进行土地覆被分类，与传统分类方法进行精度对比，并对人类活动影响的城市用地的动态变化模式加以研究，以求更为准确的监测研究区的城市用地变化情况；与此同时，对研究区城市非渗透表面密度/归一化差值植被指数与地表温度的相关关系进行初步的探索，为非渗透表面密度和归一化差值植被指数作为地表温度的表征指数的可靠性提供例证。
研究结果表明：（1）将植被-非渗透表面-土壤模型与归一化光谱线性分解模型结合起来提取烟台市区城市非渗透表面密度的准确度较高，得到的非渗透表面密度信息可以作为后续研究的基础数据源；
（2）对2009年烟台市非渗透表面密度进行密度分级后发现烟台市的城市低密度区和中密度区仍占据较大区域范围，高密度城市区域分布范围较小，烟台市区总体上仍处于低密度分散发展阶段；
（3）土地覆被分类结果显示融入非渗透表面密度信息的决策树模型的土地覆被分类精度高于作为对比的最大似然法和人工神经网络分类法的分类精度。对研究区进行城市用地变化监测发现自1989年至2009年，由于人类活动影响的不断深入，烟台市区城市用地面积扩展了约35.83km2，主要转自农用地、裸地和植被覆被区域；
（4）非渗透表面密度与地表温度呈显著的线性正相关关系，非渗透表面密度可以作为地表温度的表征指数；归一化差值植被指数受季节影响较为显著，与地表温度的线性关系不明显，无法作为地表温度的表征指数。对于本研究区而言，利用非渗透表面密度作为地表温度的表征指数较为适宜。
Urban area has been subjected to the attention of researchers since it is the mostpowerful source of economic development. Although now urban area covers a smallfraction of the earth land area, it has had profound impact on the development of riverbasin and even the global environment. High-speed urban expansion has producedincalculable damage to the sustainable development of the environmental andsocial-economic-ecological systems, and these repercussions should be directed moreattention by decision makers to make informed decisions which could balance thepositive and negative aspects of urban sprawl to ensure sustainable development.
Impervious surface refers to the artificial surface through which water cannotpenetrate into the soil, such as roads, parking lots, buildings, roofs, etc. In recent years,impervious surface became an indicator of the urbanization degree; at the same time,impervious surface is also an important indicator of urban environmental quality. It isof significance to extract and monitor impervious surface area for environmentalmonitoring and urban planning.
The extraction methods of urban impervious surface based on remotely sensedimages can be divided into per-pixel classification and sub-pixel classification.Per-pixel classification utilizes traditional methods such as maximum likelihoodmethod to monitor and extract the urban impervious surface area information inremote sensing images; sub-pixel classification algorithms decompose pixels,defining the impervious surface information depending on the density of impervioussurface.
Jiaodong Peninsula is the largest peninsula and is one of the most developedeconomical areas in China. Yantai is one of the most representative mid-sized coastalcities in China with150years history since it opened the harbor for internationalbusiness in May1861. Now it is the largest fishing seaport and the second largestindustrial city in Shandong province. With the help of multitemporal remote sensingimages, it is of great practical significance to take Yantai city as the case study area toextract and monitor Yantai city’s impervious surface information. However, until nowthere is no research studying the impervious surface information extraction in Yantaicity.
In this study, Yantai City, Jiaodong peninsula, was chosen as the case study area,the Landsat TM/ETM+remotely sensed images of1989,1995,1999,2000,2006and2009were selected as the main data sources, and China-Brazil Earth ResourcesSatellite images, digital elevation model data, land use maps and vector administrativedata were chosen as the auxiliary data. Image resolution and unity, imageenhancement, registration, geometric correction, minimum noise fraction, pixel purityindex transform were performed to the images. After that, the vegetation-impervioussurface area-soil (V-I-S) model and normalized spectral mixture analysis (NSMAS)model were used to extract the impervious surface area density component diagram tostudy the urban developing dynamics of Yantai city. Then the percent impervioussurface area was introduced into the decision tree classification method as thedecision rules to classify the land covers of the case study area in order to improve theaccuracy of monitoring the land cover change in Yantai city; and it was also used toexplore the relationships of percent impervious surface area and surface temperaturepreliminarily.
The results showed that:
(1) It is of high accuracy to combine vegetation-impermeable surface area-soil modeland the normalized spectral mixture analysis model for the extraction of theimpervious surface density of Yantai city, and the density information can be used asthe foundation data for the further researches;
(2) The low-density and medium density area of Yantai city still cover a large urbanareas, generally Yantai city is still in the low-density development stage.
(3) The accuracies of decision tree classification methods taking the three phases ofthe Yantai urban impervious surface density as decision rules were highest among thethree classification methods (Impervious surface area-decision tree method, maximumlikelihood method and artificial neural network classification method as comparison).Statistics of the classification results found that from1989to2009, the extendingimpervious area of Yantai City accounted to approximately35.83km2, and wastransferred from agricultural land, bare land and vegetation land cover areas;
(4) The study of the relationships between percent impervious surface and landsurface temperature, normalized difference vegetation index and land surface temperature extracting from the four different seasons of1995,2000,2006and2009.The results demonstrated that there was a strong linear relationship between landsurface temperature and percent impervious surface area for the four seasons, whilethe linear relationship between land surface temperature and normalized differencevegetation index is much less strong and is seasonal effected. The results suggest thatpercent impervious surface area provides an alternative parameter to the typicalnormalized difference vegetation index for analyzing land surface temperaturequantitatively in urbanized environments.
非渗透表面，又被称为非透水表面，是指水分无法透过其深入土壤的人造表层，如道路、停车场、建筑物屋顶等，它是城市和村镇地表的重要组成部分。非渗透表面不仅是城市化程度的重要表征指标，而且是城镇环境质量的表征指标之一。准确提取和监测非渗透表面密度信息以及建立非渗透表面特征数据库对全球变化和人地交互关系的研究具有重要意义。
自获取首幅陆地遥感影像以来，遥感影像以其多波段、信息连续和获取方便等优点，逐渐成为非渗透表面密度信息提取的主要数据源，各种提取方法和理论模型也应运而生。目前，基于遥感影像的城市非渗透表面信息提取方法可归纳为像元尺度的非渗透表面信息提取法和亚像元尺度非渗透表面信息提取法。前者主要应用传统分类方法研究和提取城市非渗透表面信息，后者则基于像元分解技术，按密度大小定义并研究城市非渗透表面信息。
本研究以烟台市区为研究区域，以1989、1995、1999、2000、2006和2009年获取的Landsat TM/ETM+遥感影像数据为主要数据源，以研究区中巴地球资源卫星影像、谷歌地球影像、土地利用图以及行政区域矢量图等为辅助资料，经过几何校正、辐射校正等影像预处理手段，利用植被-非渗透表面-土壤模型和归一化线性光谱混合模型对研究区进行非渗透表面密度信息提取，研究其空间变化模式，并从两个方面展开对非渗透表面密度的应用研究：以多时相非渗透表面密度信息为决策判断条件，利用决策树分类模型对研究区进行土地覆被分类，与传统分类方法进行精度对比，并对人类活动影响的城市用地的动态变化模式加以研究，以求更为准确的监测研究区的城市用地变化情况；与此同时，对研究区城市非渗透表面密度/归一化差值植被指数与地表温度的相关关系进行初步的探索，为非渗透表面密度和归一化差值植被指数作为地表温度的表征指数的可靠性提供例证。
研究结果表明：（1）将植被-非渗透表面-土壤模型与归一化光谱线性分解模型结合起来提取烟台市区城市非渗透表面密度的准确度较高，得到的非渗透表面密度信息可以作为后续研究的基础数据源；
（2）对2009年烟台市非渗透表面密度进行密度分级后发现烟台市的城市低密度区和中密度区仍占据较大区域范围，高密度城市区域分布范围较小，烟台市区总体上仍处于低密度分散发展阶段；
（3）土地覆被分类结果显示融入非渗透表面密度信息的决策树模型的土地覆被分类精度高于作为对比的最大似然法和人工神经网络分类法的分类精度。对研究区进行城市用地变化监测发现自1989年至2009年，由于人类活动影响的不断深入，烟台市区城市用地面积扩展了约35.83km2，主要转自农用地、裸地和植被覆被区域；
（4）非渗透表面密度与地表温度呈显著的线性正相关关系，非渗透表面密度可以作为地表温度的表征指数；归一化差值植被指数受季节影响较为显著，与地表温度的线性关系不明显，无法作为地表温度的表征指数。对于本研究区而言，利用非渗透表面密度作为地表温度的表征指数较为适宜。
Urban area has been subjected to the attention of researchers since it is the mostpowerful source of economic development. Although now urban area covers a smallfraction of the earth land area, it has had profound impact on the development of riverbasin and even the global environment. High-speed urban expansion has producedincalculable damage to the sustainable development of the environmental andsocial-economic-ecological systems, and these repercussions should be directed moreattention by decision makers to make informed decisions which could balance thepositive and negative aspects of urban sprawl to ensure sustainable development.
Impervious surface refers to the artificial surface through which water cannotpenetrate into the soil, such as roads, parking lots, buildings, roofs, etc. In recent years,impervious surface became an indicator of the urbanization degree; at the same time,impervious surface is also an important indicator of urban environmental quality. It isof significance to extract and monitor impervious surface area for environmentalmonitoring and urban planning.
The extraction methods of urban impervious surface based on remotely sensedimages can be divided into per-pixel classification and sub-pixel classification.Per-pixel classification utilizes traditional methods such as maximum likelihoodmethod to monitor and extract the urban impervious surface area information inremote sensing images; sub-pixel classification algorithms decompose pixels,defining the impervious surface information depending on the density of impervioussurface.
Jiaodong Peninsula is the largest peninsula and is one of the most developedeconomical areas in China. Yantai is one of the most representative mid-sized coastalcities in China with150years history since it opened the harbor for internationalbusiness in May1861. Now it is the largest fishing seaport and the second largestindustrial city in Shandong province. With the help of multitemporal remote sensingimages, it is of great practical significance to take Yantai city as the case study area toextract and monitor Yantai city’s impervious surface information. However, until nowthere is no research studying the impervious surface information extraction in Yantaicity.
In this study, Yantai City, Jiaodong peninsula, was chosen as the case study area,the Landsat TM/ETM+remotely sensed images of1989,1995,1999,2000,2006and2009were selected as the main data sources, and China-Brazil Earth ResourcesSatellite images, digital elevation model data, land use maps and vector administrativedata were chosen as the auxiliary data. Image resolution and unity, imageenhancement, registration, geometric correction, minimum noise fraction, pixel purityindex transform were performed to the images. After that, the vegetation-impervioussurface area-soil (V-I-S) model and normalized spectral mixture analysis (NSMAS)model were used to extract the impervious surface area density component diagram tostudy the urban developing dynamics of Yantai city. Then the percent impervioussurface area was introduced into the decision tree classification method as thedecision rules to classify the land covers of the case study area in order to improve theaccuracy of monitoring the land cover change in Yantai city; and it was also used toexplore the relationships of percent impervious surface area and surface temperaturepreliminarily.
The results showed that:
(1) It is of high accuracy to combine vegetation-impermeable surface area-soil modeland the normalized spectral mixture analysis model for the extraction of theimpervious surface density of Yantai city, and the density information can be used asthe foundation data for the further researches;
(2) The low-density and medium density area of Yantai city still cover a large urbanareas, generally Yantai city is still in the low-density development stage.
(3) The accuracies of decision tree classification methods taking the three phases ofthe Yantai urban impervious surface density as decision rules were highest among thethree classification methods (Impervious surface area-decision tree method, maximumlikelihood method and artificial neural network classification method as comparison).Statistics of the classification results found that from1989to2009, the extendingimpervious area of Yantai City accounted to approximately35.83km2, and wastransferred from agricultural land, bare land and vegetation land cover areas;
(4) The study of the relationships between percent impervious surface and landsurface temperature, normalized difference vegetation index and land surface temperature extracting from the four different seasons of1995,2000,2006and2009.The results demonstrated that there was a strong linear relationship between landsurface temperature and percent impervious surface area for the four seasons, whilethe linear relationship between land surface temperature and normalized differencevegetation index is much less strong and is seasonal effected. The results suggest thatpercent impervious surface area provides an alternative parameter to the typicalnormalized difference vegetation index for analyzing land surface temperaturequantitatively in urbanized environments.
引文
[1] Yuan F, Kali E S, Brian C L, Marvin E B. Land cover classification and change analysis of theTwin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing [J].Remote Sensing of Environment,2005,98:317–328.
[2] UNFPA. The state of world population2007: Unleashing the potential of urban growth [Z].United Nations Population Fund: United Nations Publications,2007.
[3] Herold M, Goldstein N C and Clarke K C, The spatiotemporal form of urban growth:measurement, analysis and modeling. Remote Sensing of Environment [J],2003,86：286–302.
[4] Liu X and Lathrop R G Jr. Urban change detection based on an artificial neural network [J].International Journal of Remote Sensing,2002,23:2513–2518.
[5] Reid W V. Biodiversity hotspots [J]. Trends in Ecology and Evolution,1998,13:275-280.
[6] Ricketts T and Imhoff M. Biodiversity, urban areas, and agriculture locating priority ecoregionsfor conservation [J]. URL: http://www.consecol.org/vol8/iss2/art1/,2003.
[7] Arnold C L Jr. and Gibbons C J. Impervious surface coverage: The emergence of a keyenvironmental indicator [J]. Journal of the American Planning Association,1996,62:243–258.
[8] Brun S E and Band L E. Simulating runoff behavior in an urbanizing watershed. Computers [J],Environment and Urban Systems,2000,24:5–22.
[9] Hurd J D and Civco D L. Temporal characterization of impervious surfaces for the State ofConnecticut [C]. Denver, Colorado, ASPRS Annual Conference Proceedings, May2004(Unpaginated CD-ROM).
[10] Weng Q. Modeling urban growth effect on surface runoff with the integration of remotesensing and GIS [J]. Environmental Management,2001,28:737–748.
[11] Patrick L, Thomas E, Claudia K. Settlement detection and impervious surface estimation inthe Mekong Delta using optical and SAR remote sensing data [J]. Remote Sensing ofEnvironment,2011,115:3007-3019.
[12] Yang L, Huang C, Homer C G, Wylie B K and Coan M J. An approach for mappinglarge-scale impervious surfaces: Synergistic use of Landsat-7ETM+and high spatial resolutionimagery [J]. Canadian Journal of Remote Sensing,2003,29:230–240.
[13] Cablk M E and Minor T B. Detecting and discriminating impervious cover with highresolution IKONOS data using principal component analysis and morphological operators [J].International Journal of Remote Sensing,2003,24:4627–4645.
[14] Goetz S J, Wright R K, Smith A J, Zinecker E and Schaub E. IKONOS imagery for resourcemanagement: Tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlanticregion [J]. Remote Sensing of Environment,2003,88:195–208.
[15] Hu X and Weng Q. Estimating impervious surfaces from medium spatial resolution imageryusing the self-organizing map and multi-layer perceptron neural networks [J]. Remote Sensingof Environment,2009,113(10):2089–2102.
[16] Hu X and Weng Q. Impervious surface area extraction from IKONOS imagery using anobject-based fuzzy method [J]. Geocarto International,2011,26(1):3–20.
[17] Wu C. Quantifying high-resolution impervious surfaces using spectral mixture analysis [J].International Journal of Remote Sensing,2009,30(11),2915–2932.
[18] Weng Q. Remote sensing of impervious surfaces in the urban areas: Requirements, methods,and trends [J]. Remote Sensing of Environment. doi:10.1016/j. rse.2011.02.030.
[19] Hodgson M E, Jensen J R Tullis J A, Riordan K D and Archer C M. Synergistic use of Lidarand color aerial photography for mapping urban parcel imperviousness [J]. PhotogrammetricEngineering and Remote Sensing,2003,69:973–980.
[20] Dougherty M, Dymond R L, Goetz S J, Jantz C A and Goulet N.Evaluation of impervioussurface estimates in a rapidly urbanizing watershed. Photogrammetric Engineering and RemoteSensing [J],2004,70:1275–1284.
[21] Jennings D B, Jarnagin S T and Ebert C W. A modeling approach for estimating watershedimpervious surface area from national land cover data92. Photogrammetric Engineering andRemote Sensing [J],2004,70:1295–1307.
[22] Bauer M E, Loffelholz B C and Wilson B. Estimating and mapping impervious surface areaby regression analysis of Landsat imagery [C]. Remote sensing of impervious surfaces. BocaRaton, Florida, Q Weng,2007:3–19.
[23] Carlson T N and Arthur S T. The impact of land use–land cover changes due to urbanizationon surface microclimate and hydrology: A satellite perspective [J]. Global and PlanetaryChange,25:49–65.
[24] Gillies R R, Box J B, Symanzik J and Rodemaker E J. Effects of urbanization on the aquaticfauna of the Line Creek watershed, Atlanta—A satellite perspective [J]. Remote Sensing ofEnvironment,2003,86:411–422.
[25] Boegh E, Poulsen R N, Butts M, Abrahamsen P, Dellwik E, Hansen S. Remote sensing basedevapotranspiration and runoff modeling of agricultural, forest and urban flux sites in Denmark:From field to macro-scale [J]. Journal of Hydrology,2009,377(3–4),300–316.
[26] Elvidge C, Tuttle B T, Sutton P C, Baugh K E, Howard A T, Milesi C. Global distribution anddensity of constructed impervious surfaces [J]. Sensors,2007,7:1962–1979.
[27] Lu D, Tian H, Zhou G and Ge H. Regional mapping of human settlements in southeasternChina with multisensor remotely sensed data [J]. Remote Sensing of Environment,2008,112(9):3668–3679.
[28] Powell S L, Cohen W B, Yang Z, Pierce J D and Alberti M. Quantification of impervioussurface in the Snohomish Water Resources Inventory Area of Western Washington from1972–2006[J]. Remote Sensing of Environment,2008,112:1895–1908.
[29] Elmore A J and Guinn S M. Synergistic use of Landsat Multispectral Scanner with GIRASland-cover data to retrieve impervious surface area for the Potomac River Basin in1975[J].Remote Sensing of Environment,2010,114:2384–2391.
[30] Carlson T N. Analysis and prediction of surface runoff in an urbanizing watershed usingsatellite imagery [J]. Journal of the American Water Resources Association,2004,40(4):1087–1098.
[31] Civco D L, Hurd J D, Wilson E H, Arnold C L and Prisloe M P Jr. Quantifying and describingurbanizing landscapes in the northeast United States [J]. Photogrammetric Engineering andRemote Sensing,2002,68(10):1083–1090.
[32] Bauer M E, Heinert N J, Doyle J K and Yuan F. Impervious surface mapping and changemonitoring using Landsat remote sensing [J]. Denver, Colorado, ASPRS Annual ConferenceProceedings, May2004(Unpaginated CD ROM).
[33] Chabaeva A A, Civco D L and Prisloe S. Development of a population density and land usebased regression model to calculate the amount of imperviousness [J]. Denver, Colorado,ASPRS Annual Conference Proceedings, May2004(Unpaginated CD ROM).
[34] Yuan F, Wu C and Bauer M E. Comparison of spectral analysis techniques for impervioussurface estimation using Landsat imagery [J]. Photogrammetric Engineering and RemoteSensing,2008,74(8):1045–1055.
[35] Yang L, Huang C, Homer C G, Wylie B K and Coan M J. An approach for mappinglarge-scale impervious surfaces: Synergistic use of Landsat-7ETM+and high spatial resolutionimagery [J]. Canadian Journal of Remote Sensing,2003,29:230–240.
[36] Ward D, Phinn S R and Murray A T. Monitoring growth in rapidly urbanizing areas usingremotely sensed data [J]. The Professional Geographer,2003,53:371–386.
[37] Madhavan B B, Kubo S, Kurisaki N and Sivakumar T. Appraising the anatomy and spatialgrowth of the Bangkok Metropolitan area using a vegetation–impervious–soil model throughremote sensing [J]. International Journal of Remote Sensing,2001,22:789–806.
[38] Phinn S, Stanford M, Scarth P, Murray A T and Shyy P T. Monitoring the composition ofurban environments based on the vegetation–impervious surface–soil (VIS) model by subpixelanalysis techniques [J]. International Journal of Remote Sensing,2002,23:4131–4153.
[39] Wu C and Murray A T. Estimating impervious surface distribution by spectral mixtureanalysis [J]. Remote Sensing of Environment,2003,84:493–505.
[40] Lu D and Weng Q. Use of impervious surface in urban land use classification [J]. RemoteSensing of Environment,2006,102(1–2):146–160.
[41] Powell R L, Roberts D A, Dennison P E and Hess L L. Sub-pixel mapping of urban land coverusing multiple endmember spectral mixture analysis: Manaus, Brazil [J]. Remote Sensing ofEnvironment,2007,106(2):253–267.
[42] Weng Q and Hu X. Medium spatial resolution satellite imagery for estimating and mappingurban impervious surfaces using LSMA and ANN [J]. IEEE Transaction on Geosciences andRemote Sensing,2008,46(8):2397–2406.
[43] Weng Q, Hu X and Liu H. Estimating impervious surfaces using linear spectral mixtureanalysis with multi-temporal ASTER images [J]. International Journal of Remote Sensing,2009,30(18):4807–4830.
[44] Yang L, Jiang L, Lin H and Liao, M. Quantifying sub-pixel urban impervious surface throughfusion of optical and InSAR imagery [J]. GIScience and Remote Sensing,2009,46(2):161–171.
[45] Wu C. Normalized spectral mixture analysis for monitoring urban composition using ETM+imagery [J]. Remote Sensing of Environment,2004,93:480–492.
[46]钱乐祥,崔海山.运用归一化光谱混合模型分析城市地表组成[J].国土资源遥感,2006,2:64-68.
[47] Franke J, Roberts D A, Halligan K and Menz G. Hierarchical Multiple Endmember SpectralMixture Analysis (MESMA) of hyperspectral imagery for urban environments [J]. RemoteSensing of Environment,2009,113:1712–1723.
[48] Yang F, Matsushita B and Fukushima T. A pre-screened and normalized multiple endmemberspectral mixture analysis for mapping impervious surface area in Lake Kasumigaura Basin,Japan [J]. ISPRS Journal of Photogrammetry and Remote Sensing,2010,65:79–490.
[49]李明杰,钱乐祥,吴志峰等.广州市海珠区高密度城区扩展SLEUTH模型模拟[J].地理学报,2010,10(65):1163-1172.
[50] Tan Q, Liu Z and Li X. Mapping urban surface imperviousness using SPOT multispectralsatellite images [J]. Proceedings of IEEE Geoscience and Remote Sensing Symposium(IGARSS2009), III-346-III-348, Cape Town. doi:10.1109/IGARSS.2009.5417773.
[51] Cablk M E and Minor T B. Detecting and discriminating impervious cover with highresolution IKONOS data using principal component analysis and morphological operators [J].International Journal of Remote Sensing,2003,24:4627–4645.
[52] Mohapatra R P and Wu C. Subpixel imperviousness estimation with IKONOS imagery: AnArtificial Neural Network approach [C]. Remote Sensing of Impervious Surfaces. Boca Raton,Q Weng,2007:1-33.
[53] Lu D, Hetrick S and Moran E. Impervious surface mapping with Quickbird imagery [J].International Journal of Remote Sensing,2010,32(9):2519-2533
[54] Zhou Y Y and Wang Y Q. Extraction of impervious, surface areas from high spatial resolutionimagery by multiple agent segmentation and classification [J]. Photogrammetric Engineeringand Remote Sensing,2008,74(7):857–868.
[55] Xinyang Yu, Anding Zhang, Xiyong Hou, Mingjie Li and Yingxiao Xia. Multi-temporalremote sensing of land cover change and urban sprawl in the coastal city of Yantai, China [J].International Journal of Digital Earth, DOI:10.1080/17538947.2011.653995
[56]中国城市统计年鉴[Z].国家统计局,中国统计出版社,2010:13.
[57] Landsat7Science Data Users Handbook. National Aeronautics and Space Administration[C],URL: http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_toc.html,2006.
[58] Gyanesh Chander, Brian L. Markham, Dennis L. Helder. Summary of current radiometriccalibration coefficients for Landsat MSS, TM, ETM+, and EO-1ALI sensors [J]. Remotesensing of environment,2009,113:893–903.
[59]Boardman J W and Kruse F A. Automated spectral analysis: a geological example usingAVIRIS data, north Grapevine Mountains [C]. Nevada: in Proceedings, ERIM Tenth ThematicConference on Geologic Remote Sensing.
[60] Boardman J W, Kruse F A and Green R O. Summares of the5nd Annual JPL AirborneGeoscience Workshop [M], JPL Publication95-1Vol.1. Mapping target signatures via partialunmixing of AVIRIS data,1995:23-26.
[61]罗红霞,龚健雅.线性和非线性光谱混合模型模拟土壤、植被混合光谱的效果分析[J].测绘通报,2005,5:6-10.
[62] Ridd M K. Exploring a V-I-S (vegetation–impervious surface–soil) model for urbanecosystem analysis through remote sensing: comparative anatomy for cities [J]. InternationalJournal of Remote Sensing,1995,16:2165-2185.
[63] Rashed T, Weeks J R, Gadalla M S. Revealing the anatomy of cities through spectral mixtureanalysis of multispectral satellite imagery: a case study of the greater Cairo region, Egypt [J].Geocarto International,2001,16(4):5-15.
[64] Lu D, Weng Q. Spectral mixture analysis of the urban landscape in Indianapolis city withLandsat ETM+imagery [J]. Photogram metric Engineering&Remote Sensing,2004,70(9):1053-1062.
[65] Wu C. Normalized spectral mixture analysis for monitoring urban composition using ETM+imagery [J]. Remote Sensing of Environment,2004,93(4):480-492.
[66]张自宾,武文波,金卓.基于决策规则的遥感影像土地利用信息提取[J].测绘科学,2008,33(10):200-203.
[67]孙艳玲,杨小唤,王新生等.基于决策树和MODIS数据的土地利用分类[J].资源科学,2007,29(9):169-174.
[68] DeFries R, Hansen M, Townshend J. Global Discrimination of Land Cover Types fromMetrics Derived from AVHRR Pathfinder Data. Remote Sensing of Environment [J].1995,54(3):209-222.
[69] Ruth D, Matthew H, John T. Global discrimination of land cover types from metrics derivedfrom AVHRR pathfinder data [J]. Remote Sensing of Environment,1995,54(3):209-222.
[70] Ruth D, Matthew H, John T. Global Land Cover Classifications at8km Spatial Resolution:The Use of Training Data Derived from Landsat Imagery in Decision Tree Classifiers [J]. NT. J.Remote Sensing,1998,19(16):3141-3168.
[71] Rick L, Andrea W. Rule-Based Classification Systems Using Classsification and RegressionTree (CART) Analysis [J]. Photogrammetry and Remote Sensing,2001,67(10):1137-1142.
[72] Homer C, Huang C Q, Yang L M. Development of2001National Land Cover Database forthe United States [J]. Photogrammetric Engineering and Remote Sensing,2004,70(7):829-840.
[73]王建,董光荣,李文君等.利用遥感信息决策树方法分层提取荒漠化土地类型的研究探讨[J].中国沙漠,2000,20(3):243-247.
[74]都金康,黄永胜,冯学智等.SPOT卫星影像的水体提取方法及分类研究[J].遥感学报,2001,5(3):214-219.
[75]赵萍,冯学智,林广发. SPOT卫星影像居民地信息自动提取的决策树方法研究[J].遥感学报,2003,7(4):309-316.
[76]李彤,吴骅.采用决策树分类技术对北京市土地覆盖现状进行研究[J].遥感技术与应用，2004,19(6):485-487.
[77]王大鹏,王周龙,崔青春等.基于决策树的龙口市土地利用/覆盖分类研究[J].水土保持研究,2008,15(1):114-116.
[78]柯新利,边馥苓.基于C5.0决策树算法的元胞自动机土地利用变化模拟模型[J].长江流域资源与环境,2010,19(4):403-408.
[79]李爽,丁圣彦,钱乐祥.决策树分类法及其在土地覆盖分类中的应用[J].遥感技术与应用,2002,17(1):6-11.
[80]申文明,王文杰,罗海江等.基于决策树分类技术的遥感影像分类方法研究[J].遥感技术与应用,2007,22(3):333-338.
[81]徐晓桃,韩涛,颉耀文.基于单时相MODIS数据的土地覆盖三种分类方法对比研究[J].干旱地区农业研究,2008,26(3):253-258.
[82]郭健,张继贤,张永红等.多时相MODIS影像土地覆盖分类比较研究[J].测绘学报,2009,38(1):88-92.
[83]Volgamann J E, Sohl T and Howard S M. Regional characterization of land cover usingmultiple sources of data [J]. Engineering and Remote Sensing,1998,64(1):45-57.
[84] Pielke R, Lee T J, Copeland J H, Eastman J L, et al. The use of USGS Provided Data toimprove weather and climate simulations [J]. Ecological Applications,1997,7(1):3-21.
[85] Jensen J R,2004. Introductory Digital Image Processing,3rd ed., Prentice Hall.
[86] Carlson T N, Gillies R R and Schmugge T J. An interpretation of methodologies for indirectmeasurement of soil water content and fractional vegetation cover [J]. Agricultural and ForestMeteorology,1995,77:191-205.
[87] Gallo K P and Owen T W. Satellite based adjustments for the urban heat island temperaturebias [J]. Journal of Applied Meteorology,1999,38:806-813.
[88] Gillies R R and Carlson T N. Thermal remote sensing of surface soil water content with partialvegetation cover for incorporation into climate models [J]. Journal of Applied Meteorology,1995,34:745-756.
[89] Gillies R R, Carlson T N, Cui J, Kustas W P and Humes K S. A verification of the “triangle”method for obtaining surface soil water content and energy fluxes from remote measurementsof the Normalized Difference Vegetation Index (NDVI) and surface radiant temperature [J].International Journal of Remote Sensing,1997,18:3145-3166.
[90] Goward S N, Xue Y and Czajkowski K P. Evaluating land surface moisture conditions fromthe remotely sensed temperature/vegetation index measurements: an exploration with thesimplified simple biosphere model [J]. Remote Sensing of Environment,2002,79:225-242.
[91] Arthur S T, Carlson T N and Ripley D A J. Land use dynamics of Chester County,Pennsylvania, from a satellite remote sensing perspective [J]. Geocarto International,2000,15(1):25-35.
[92] Bauer M E, Heinert N J, Doyle J K and Yuan F. Impervious surface mapping and changemonitoring using satellite remote sensing [C]. Proceedings, American Society ofPhotogrammetry and Remote Sensing Annual Conference. May24–28,2004. unpaginated CDROM,10pp.
[93] Civco D L, Hurd J D, Wilson E H, Arnold C L and Prisloe Jr. M P. Quantifying and describingurbanizing landscapes in the northeast United States [J]. Photogrammetric Engineering andRemote Sensing,2002,68(10):1083-1090.
[94] Weng Q. A remote sensing-GIS evaluation of urban expansion and its impact on surfacetemperature in the Zhujiang Delta, China [J]. International Journal of Remote Sensing,2001,22(10):1999-2014.
[95] Yang L, Huang C, Homer C G, Wylie B K and Coan MJ. An approach for mapping large-areaimpervious surfaces: Synergistic use of Landsat7ETM+and high spatial resolution imagery[J]. Canadian Journal of Remote Sensing,2003,29(2):230-240.
[96] Chander G and Markham B. Revised Landsat-5TM radiometric calibration procedures andpostcalibration dynamic ranges [J]. IEEE Transactions on Geoscience and Remote Sensing,2003,41(11):2674-2677.
[97] Barsi J A, Schott J R, Palluconi F D and Hook S J. Validation of a web-based atmosphericcorrection tool for single thermal band instruments [C]. Proceedings, SPIE, vol.5882. Paper58820E. Bellingham, WA. Pp:7.
[98] Snyder W C, Wan Z, Zhang Y and Feng Y Z. Classification based emissivity for land surfacetemperature measurement from space [J]. International Journal of Remote Sensing,1998,19:2753-2774.
[99] Owen T W, Carlson T N and Gillies R R. An assessment of satellite remotely sensed landcover parameters in quantitatively describing the climatic effect of urbanization [J].International Journal of Remote Sensing,1998,19:1663-1681.
[100] Weng Q, Lu D and Schubring J. Estimation of land surface temperature–vegetationabundance relationship for urban heat island studies [J]. Remote Sensing of Environment,2004,89:467-483.
[101] Yuan F and Bauer M E. Comparison of impervious surface area and normalized differencevegetation index as indicators of surface urban heat island effects in Landsat imagery [J].Remote Sensing of Environment,2007,106:375-386.
[2] UNFPA. The state of world population2007: Unleashing the potential of urban growth [Z].United Nations Population Fund: United Nations Publications,2007.
[3] Herold M, Goldstein N C and Clarke K C, The spatiotemporal form of urban growth:measurement, analysis and modeling. Remote Sensing of Environment [J],2003,86：286–302.
[4] Liu X and Lathrop R G Jr. Urban change detection based on an artificial neural network [J].International Journal of Remote Sensing,2002,23:2513–2518.
[5] Reid W V. Biodiversity hotspots [J]. Trends in Ecology and Evolution,1998,13:275-280.
[6] Ricketts T and Imhoff M. Biodiversity, urban areas, and agriculture locating priority ecoregionsfor conservation [J]. URL: http://www.consecol.org/vol8/iss2/art1/,2003.
[7] Arnold C L Jr. and Gibbons C J. Impervious surface coverage: The emergence of a keyenvironmental indicator [J]. Journal of the American Planning Association,1996,62:243–258.
[8] Brun S E and Band L E. Simulating runoff behavior in an urbanizing watershed. Computers [J],Environment and Urban Systems,2000,24:5–22.
[9] Hurd J D and Civco D L. Temporal characterization of impervious surfaces for the State ofConnecticut [C]. Denver, Colorado, ASPRS Annual Conference Proceedings, May2004(Unpaginated CD-ROM).
[10] Weng Q. Modeling urban growth effect on surface runoff with the integration of remotesensing and GIS [J]. Environmental Management,2001,28:737–748.
[11] Patrick L, Thomas E, Claudia K. Settlement detection and impervious surface estimation inthe Mekong Delta using optical and SAR remote sensing data [J]. Remote Sensing ofEnvironment,2011,115:3007-3019.
[12] Yang L, Huang C, Homer C G, Wylie B K and Coan M J. An approach for mappinglarge-scale impervious surfaces: Synergistic use of Landsat-7ETM+and high spatial resolutionimagery [J]. Canadian Journal of Remote Sensing,2003,29:230–240.
[13] Cablk M E and Minor T B. Detecting and discriminating impervious cover with highresolution IKONOS data using principal component analysis and morphological operators [J].International Journal of Remote Sensing,2003,24:4627–4645.
[14] Goetz S J, Wright R K, Smith A J, Zinecker E and Schaub E. IKONOS imagery for resourcemanagement: Tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlanticregion [J]. Remote Sensing of Environment,2003,88:195–208.
[15] Hu X and Weng Q. Estimating impervious surfaces from medium spatial resolution imageryusing the self-organizing map and multi-layer perceptron neural networks [J]. Remote Sensingof Environment,2009,113(10):2089–2102.
[16] Hu X and Weng Q. Impervious surface area extraction from IKONOS imagery using anobject-based fuzzy method [J]. Geocarto International,2011,26(1):3–20.
[17] Wu C. Quantifying high-resolution impervious surfaces using spectral mixture analysis [J].International Journal of Remote Sensing,2009,30(11),2915–2932.
[18] Weng Q. Remote sensing of impervious surfaces in the urban areas: Requirements, methods,and trends [J]. Remote Sensing of Environment. doi:10.1016/j. rse.2011.02.030.
[19] Hodgson M E, Jensen J R Tullis J A, Riordan K D and Archer C M. Synergistic use of Lidarand color aerial photography for mapping urban parcel imperviousness [J]. PhotogrammetricEngineering and Remote Sensing,2003,69:973–980.
[20] Dougherty M, Dymond R L, Goetz S J, Jantz C A and Goulet N.Evaluation of impervioussurface estimates in a rapidly urbanizing watershed. Photogrammetric Engineering and RemoteSensing [J],2004,70:1275–1284.
[21] Jennings D B, Jarnagin S T and Ebert C W. A modeling approach for estimating watershedimpervious surface area from national land cover data92. Photogrammetric Engineering andRemote Sensing [J],2004,70:1295–1307.
[22] Bauer M E, Loffelholz B C and Wilson B. Estimating and mapping impervious surface areaby regression analysis of Landsat imagery [C]. Remote sensing of impervious surfaces. BocaRaton, Florida, Q Weng,2007:3–19.
[23] Carlson T N and Arthur S T. The impact of land use–land cover changes due to urbanizationon surface microclimate and hydrology: A satellite perspective [J]. Global and PlanetaryChange,25:49–65.
[24] Gillies R R, Box J B, Symanzik J and Rodemaker E J. Effects of urbanization on the aquaticfauna of the Line Creek watershed, Atlanta—A satellite perspective [J]. Remote Sensing ofEnvironment,2003,86:411–422.
[25] Boegh E, Poulsen R N, Butts M, Abrahamsen P, Dellwik E, Hansen S. Remote sensing basedevapotranspiration and runoff modeling of agricultural, forest and urban flux sites in Denmark:From field to macro-scale [J]. Journal of Hydrology,2009,377(3–4),300–316.
[26] Elvidge C, Tuttle B T, Sutton P C, Baugh K E, Howard A T, Milesi C. Global distribution anddensity of constructed impervious surfaces [J]. Sensors,2007,7:1962–1979.
[27] Lu D, Tian H, Zhou G and Ge H. Regional mapping of human settlements in southeasternChina with multisensor remotely sensed data [J]. Remote Sensing of Environment,2008,112(9):3668–3679.
[28] Powell S L, Cohen W B, Yang Z, Pierce J D and Alberti M. Quantification of impervioussurface in the Snohomish Water Resources Inventory Area of Western Washington from1972–2006[J]. Remote Sensing of Environment,2008,112:1895–1908.
[29] Elmore A J and Guinn S M. Synergistic use of Landsat Multispectral Scanner with GIRASland-cover data to retrieve impervious surface area for the Potomac River Basin in1975[J].Remote Sensing of Environment,2010,114:2384–2391.
[30] Carlson T N. Analysis and prediction of surface runoff in an urbanizing watershed usingsatellite imagery [J]. Journal of the American Water Resources Association,2004,40(4):1087–1098.
[31] Civco D L, Hurd J D, Wilson E H, Arnold C L and Prisloe M P Jr. Quantifying and describingurbanizing landscapes in the northeast United States [J]. Photogrammetric Engineering andRemote Sensing,2002,68(10):1083–1090.
[32] Bauer M E, Heinert N J, Doyle J K and Yuan F. Impervious surface mapping and changemonitoring using Landsat remote sensing [J]. Denver, Colorado, ASPRS Annual ConferenceProceedings, May2004(Unpaginated CD ROM).
[33] Chabaeva A A, Civco D L and Prisloe S. Development of a population density and land usebased regression model to calculate the amount of imperviousness [J]. Denver, Colorado,ASPRS Annual Conference Proceedings, May2004(Unpaginated CD ROM).
[34] Yuan F, Wu C and Bauer M E. Comparison of spectral analysis techniques for impervioussurface estimation using Landsat imagery [J]. Photogrammetric Engineering and RemoteSensing,2008,74(8):1045–1055.
[35] Yang L, Huang C, Homer C G, Wylie B K and Coan M J. An approach for mappinglarge-scale impervious surfaces: Synergistic use of Landsat-7ETM+and high spatial resolutionimagery [J]. Canadian Journal of Remote Sensing,2003,29:230–240.
[36] Ward D, Phinn S R and Murray A T. Monitoring growth in rapidly urbanizing areas usingremotely sensed data [J]. The Professional Geographer,2003,53:371–386.
[37] Madhavan B B, Kubo S, Kurisaki N and Sivakumar T. Appraising the anatomy and spatialgrowth of the Bangkok Metropolitan area using a vegetation–impervious–soil model throughremote sensing [J]. International Journal of Remote Sensing,2001,22:789–806.
[38] Phinn S, Stanford M, Scarth P, Murray A T and Shyy P T. Monitoring the composition ofurban environments based on the vegetation–impervious surface–soil (VIS) model by subpixelanalysis techniques [J]. International Journal of Remote Sensing,2002,23:4131–4153.
[39] Wu C and Murray A T. Estimating impervious surface distribution by spectral mixtureanalysis [J]. Remote Sensing of Environment,2003,84:493–505.
[40] Lu D and Weng Q. Use of impervious surface in urban land use classification [J]. RemoteSensing of Environment,2006,102(1–2):146–160.
[41] Powell R L, Roberts D A, Dennison P E and Hess L L. Sub-pixel mapping of urban land coverusing multiple endmember spectral mixture analysis: Manaus, Brazil [J]. Remote Sensing ofEnvironment,2007,106(2):253–267.
[42] Weng Q and Hu X. Medium spatial resolution satellite imagery for estimating and mappingurban impervious surfaces using LSMA and ANN [J]. IEEE Transaction on Geosciences andRemote Sensing,2008,46(8):2397–2406.
[43] Weng Q, Hu X and Liu H. Estimating impervious surfaces using linear spectral mixtureanalysis with multi-temporal ASTER images [J]. International Journal of Remote Sensing,2009,30(18):4807–4830.
[44] Yang L, Jiang L, Lin H and Liao, M. Quantifying sub-pixel urban impervious surface throughfusion of optical and InSAR imagery [J]. GIScience and Remote Sensing,2009,46(2):161–171.
[45] Wu C. Normalized spectral mixture analysis for monitoring urban composition using ETM+imagery [J]. Remote Sensing of Environment,2004,93:480–492.
[46]钱乐祥,崔海山.运用归一化光谱混合模型分析城市地表组成[J].国土资源遥感,2006,2:64-68.
[47] Franke J, Roberts D A, Halligan K and Menz G. Hierarchical Multiple Endmember SpectralMixture Analysis (MESMA) of hyperspectral imagery for urban environments [J]. RemoteSensing of Environment,2009,113:1712–1723.
[48] Yang F, Matsushita B and Fukushima T. A pre-screened and normalized multiple endmemberspectral mixture analysis for mapping impervious surface area in Lake Kasumigaura Basin,Japan [J]. ISPRS Journal of Photogrammetry and Remote Sensing,2010,65:79–490.
[49]李明杰,钱乐祥,吴志峰等.广州市海珠区高密度城区扩展SLEUTH模型模拟[J].地理学报,2010,10(65):1163-1172.
[50] Tan Q, Liu Z and Li X. Mapping urban surface imperviousness using SPOT multispectralsatellite images [J]. Proceedings of IEEE Geoscience and Remote Sensing Symposium(IGARSS2009), III-346-III-348, Cape Town. doi:10.1109/IGARSS.2009.5417773.
[51] Cablk M E and Minor T B. Detecting and discriminating impervious cover with highresolution IKONOS data using principal component analysis and morphological operators [J].International Journal of Remote Sensing,2003,24:4627–4645.
[52] Mohapatra R P and Wu C. Subpixel imperviousness estimation with IKONOS imagery: AnArtificial Neural Network approach [C]. Remote Sensing of Impervious Surfaces. Boca Raton,Q Weng,2007:1-33.
[53] Lu D, Hetrick S and Moran E. Impervious surface mapping with Quickbird imagery [J].International Journal of Remote Sensing,2010,32(9):2519-2533
[54] Zhou Y Y and Wang Y Q. Extraction of impervious, surface areas from high spatial resolutionimagery by multiple agent segmentation and classification [J]. Photogrammetric Engineeringand Remote Sensing,2008,74(7):857–868.
[55] Xinyang Yu, Anding Zhang, Xiyong Hou, Mingjie Li and Yingxiao Xia. Multi-temporalremote sensing of land cover change and urban sprawl in the coastal city of Yantai, China [J].International Journal of Digital Earth, DOI:10.1080/17538947.2011.653995
[56]中国城市统计年鉴[Z].国家统计局,中国统计出版社,2010:13.
[57] Landsat7Science Data Users Handbook. National Aeronautics and Space Administration[C],URL: http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_toc.html,2006.
[58] Gyanesh Chander, Brian L. Markham, Dennis L. Helder. Summary of current radiometriccalibration coefficients for Landsat MSS, TM, ETM+, and EO-1ALI sensors [J]. Remotesensing of environment,2009,113:893–903.
[59]Boardman J W and Kruse F A. Automated spectral analysis: a geological example usingAVIRIS data, north Grapevine Mountains [C]. Nevada: in Proceedings, ERIM Tenth ThematicConference on Geologic Remote Sensing.
[60] Boardman J W, Kruse F A and Green R O. Summares of the5nd Annual JPL AirborneGeoscience Workshop [M], JPL Publication95-1Vol.1. Mapping target signatures via partialunmixing of AVIRIS data,1995:23-26.
[61]罗红霞,龚健雅.线性和非线性光谱混合模型模拟土壤、植被混合光谱的效果分析[J].测绘通报,2005,5:6-10.
[62] Ridd M K. Exploring a V-I-S (vegetation–impervious surface–soil) model for urbanecosystem analysis through remote sensing: comparative anatomy for cities [J]. InternationalJournal of Remote Sensing,1995,16:2165-2185.
[63] Rashed T, Weeks J R, Gadalla M S. Revealing the anatomy of cities through spectral mixtureanalysis of multispectral satellite imagery: a case study of the greater Cairo region, Egypt [J].Geocarto International,2001,16(4):5-15.
[64] Lu D, Weng Q. Spectral mixture analysis of the urban landscape in Indianapolis city withLandsat ETM+imagery [J]. Photogram metric Engineering&Remote Sensing,2004,70(9):1053-1062.
[65] Wu C. Normalized spectral mixture analysis for monitoring urban composition using ETM+imagery [J]. Remote Sensing of Environment,2004,93(4):480-492.
[66]张自宾,武文波,金卓.基于决策规则的遥感影像土地利用信息提取[J].测绘科学,2008,33(10):200-203.
[67]孙艳玲,杨小唤,王新生等.基于决策树和MODIS数据的土地利用分类[J].资源科学,2007,29(9):169-174.
[68] DeFries R, Hansen M, Townshend J. Global Discrimination of Land Cover Types fromMetrics Derived from AVHRR Pathfinder Data. Remote Sensing of Environment [J].1995,54(3):209-222.
[69] Ruth D, Matthew H, John T. Global discrimination of land cover types from metrics derivedfrom AVHRR pathfinder data [J]. Remote Sensing of Environment,1995,54(3):209-222.
[70] Ruth D, Matthew H, John T. Global Land Cover Classifications at8km Spatial Resolution:The Use of Training Data Derived from Landsat Imagery in Decision Tree Classifiers [J]. NT. J.Remote Sensing,1998,19(16):3141-3168.
[71] Rick L, Andrea W. Rule-Based Classification Systems Using Classsification and RegressionTree (CART) Analysis [J]. Photogrammetry and Remote Sensing,2001,67(10):1137-1142.
[72] Homer C, Huang C Q, Yang L M. Development of2001National Land Cover Database forthe United States [J]. Photogrammetric Engineering and Remote Sensing,2004,70(7):829-840.
[73]王建,董光荣,李文君等.利用遥感信息决策树方法分层提取荒漠化土地类型的研究探讨[J].中国沙漠,2000,20(3):243-247.
[74]都金康,黄永胜,冯学智等.SPOT卫星影像的水体提取方法及分类研究[J].遥感学报,2001,5(3):214-219.
[75]赵萍,冯学智,林广发. SPOT卫星影像居民地信息自动提取的决策树方法研究[J].遥感学报,2003,7(4):309-316.
[76]李彤,吴骅.采用决策树分类技术对北京市土地覆盖现状进行研究[J].遥感技术与应用，2004,19(6):485-487.
[77]王大鹏,王周龙,崔青春等.基于决策树的龙口市土地利用/覆盖分类研究[J].水土保持研究,2008,15(1):114-116.
[78]柯新利,边馥苓.基于C5.0决策树算法的元胞自动机土地利用变化模拟模型[J].长江流域资源与环境,2010,19(4):403-408.
[79]李爽,丁圣彦,钱乐祥.决策树分类法及其在土地覆盖分类中的应用[J].遥感技术与应用,2002,17(1):6-11.
[80]申文明,王文杰,罗海江等.基于决策树分类技术的遥感影像分类方法研究[J].遥感技术与应用,2007,22(3):333-338.
[81]徐晓桃,韩涛,颉耀文.基于单时相MODIS数据的土地覆盖三种分类方法对比研究[J].干旱地区农业研究,2008,26(3):253-258.
[82]郭健,张继贤,张永红等.多时相MODIS影像土地覆盖分类比较研究[J].测绘学报,2009,38(1):88-92.
[83]Volgamann J E, Sohl T and Howard S M. Regional characterization of land cover usingmultiple sources of data [J]. Engineering and Remote Sensing,1998,64(1):45-57.
[84] Pielke R, Lee T J, Copeland J H, Eastman J L, et al. The use of USGS Provided Data toimprove weather and climate simulations [J]. Ecological Applications,1997,7(1):3-21.
[85] Jensen J R,2004. Introductory Digital Image Processing,3rd ed., Prentice Hall.
[86] Carlson T N, Gillies R R and Schmugge T J. An interpretation of methodologies for indirectmeasurement of soil water content and fractional vegetation cover [J]. Agricultural and ForestMeteorology,1995,77:191-205.
[87] Gallo K P and Owen T W. Satellite based adjustments for the urban heat island temperaturebias [J]. Journal of Applied Meteorology,1999,38:806-813.
[88] Gillies R R and Carlson T N. Thermal remote sensing of surface soil water content with partialvegetation cover for incorporation into climate models [J]. Journal of Applied Meteorology,1995,34:745-756.
[89] Gillies R R, Carlson T N, Cui J, Kustas W P and Humes K S. A verification of the “triangle”method for obtaining surface soil water content and energy fluxes from remote measurementsof the Normalized Difference Vegetation Index (NDVI) and surface radiant temperature [J].International Journal of Remote Sensing,1997,18:3145-3166.
[90] Goward S N, Xue Y and Czajkowski K P. Evaluating land surface moisture conditions fromthe remotely sensed temperature/vegetation index measurements: an exploration with thesimplified simple biosphere model [J]. Remote Sensing of Environment,2002,79:225-242.
[91] Arthur S T, Carlson T N and Ripley D A J. Land use dynamics of Chester County,Pennsylvania, from a satellite remote sensing perspective [J]. Geocarto International,2000,15(1):25-35.
[92] Bauer M E, Heinert N J, Doyle J K and Yuan F. Impervious surface mapping and changemonitoring using satellite remote sensing [C]. Proceedings, American Society ofPhotogrammetry and Remote Sensing Annual Conference. May24–28,2004. unpaginated CDROM,10pp.
[93] Civco D L, Hurd J D, Wilson E H, Arnold C L and Prisloe Jr. M P. Quantifying and describingurbanizing landscapes in the northeast United States [J]. Photogrammetric Engineering andRemote Sensing,2002,68(10):1083-1090.
[94] Weng Q. A remote sensing-GIS evaluation of urban expansion and its impact on surfacetemperature in the Zhujiang Delta, China [J]. International Journal of Remote Sensing,2001,22(10):1999-2014.
[95] Yang L, Huang C, Homer C G, Wylie B K and Coan MJ. An approach for mapping large-areaimpervious surfaces: Synergistic use of Landsat7ETM+and high spatial resolution imagery[J]. Canadian Journal of Remote Sensing,2003,29(2):230-240.
[96] Chander G and Markham B. Revised Landsat-5TM radiometric calibration procedures andpostcalibration dynamic ranges [J]. IEEE Transactions on Geoscience and Remote Sensing,2003,41(11):2674-2677.
[97] Barsi J A, Schott J R, Palluconi F D and Hook S J. Validation of a web-based atmosphericcorrection tool for single thermal band instruments [C]. Proceedings, SPIE, vol.5882. Paper58820E. Bellingham, WA. Pp:7.
[98] Snyder W C, Wan Z, Zhang Y and Feng Y Z. Classification based emissivity for land surfacetemperature measurement from space [J]. International Journal of Remote Sensing,1998,19:2753-2774.
[99] Owen T W, Carlson T N and Gillies R R. An assessment of satellite remotely sensed landcover parameters in quantitatively describing the climatic effect of urbanization [J].International Journal of Remote Sensing,1998,19:1663-1681.
[100] Weng Q, Lu D and Schubring J. Estimation of land surface temperature–vegetationabundance relationship for urban heat island studies [J]. Remote Sensing of Environment,2004,89:467-483.
[101] Yuan F and Bauer M E. Comparison of impervious surface area and normalized differencevegetation index as indicators of surface urban heat island effects in Landsat imagery [J].Remote Sensing of Environment,2007,106:375-386.