The Relationship between Urban Land Surface Material Fractions and Brightness Temperature Based on MESMA

The relationship between urban land surface material fractions (ULSMFs) and brightness temperature has long attracted attention in research on urban environments. In this paper, a multiple endmember spectral mixture analysis (MESMA) method was applied to extract vegetation-impervious surface-soil (V-I-S) fractions in each pixel, and the surface brightness temperature was derived by using the radiation in the upper atmosphere, on the basis of Landsat 8 images. Then, a clustering analysis, ternary triangular chart (TTC), and a multivariate statistical analysis were applied to ascertain the relationship between the fractions in each pixel and the land surface brightness temperature (LSBT). The hypsometric TTC, as well as the geographical distribution features of the LSBT, revealed that the changes in LSBT were associated with the high fractions of impervious surfaces (or vegetation), in addition to the temperature distribution differences across locations with varying land-cover types. The data fitting results showed that the comprehensive endmember fractions of V-I-S explained 98.6% of fluctuating LSBT, and the impervious surface fraction had a positive impact on the LSBT, whereas the fraction of vegetation had a negative impact on the LSBT.

[1]  Vicente Caselles,et al.  Validation of Landsat-7/ETM+ Thermal-Band Calibration and Atmospheric Correction With Ground-Based Measurements , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[2]  B. Turner,et al.  Remote sensing of the surface urban heat island and land architecture in Phoenix, Arizona: Combined effects of land composition and configuration and cadastral-demographic-economic factors , 2015 .

[3]  Hao Wu,et al.  Assessing the effects of land use spatial structure on urban heat islands using HJ-1B remote sensing imagery in Wuhan, China , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[4]  Andy P. Field,et al.  Discovering Statistics Using Ibm Spss Statistics , 2017 .

[5]  B. Markham,et al.  Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors , 2009 .

[6]  D. Roberts,et al.  A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper , 2004 .

[7]  Hanqiu Xu.  Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[8]  M. Ridd.  Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities , 1995 .

[9]  Qihao Weng,et al.  A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery , 2016 .

[10]  Wang Yu-feng.  Urban Heat Island Effect and Vegetation Cover Index Relation Using Landsat TM Image , 2006 .

[11]  D. Roberts,et al.  Burn severity mapping from Landsat MESMA fraction images and Land Surface Temperature , 2017 .

[12]  H. Fischer,et al.  Land surface temperature and emissivity estimation from passive sensor data: Theory and practice-current trends , 2002 .

[13]  Zhou Hong,et al.  The Surveying on Thermal Distribution in Urban Based on GIS and Remote Sensing , 2001 .

[14]  Xiaoling Chen,et al.  Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes , 2006 .

[15]  D. Roberts,et al.  Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data , 1993 .

[16]  D. Roberts,et al.  Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE , 2003 .

[17]  Xiaolei Yu,et al.  Land Surface Temperature Retrieval from Landsat 8 TIRS - Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method , 2014, Remote. Sens..

[18]  Rita Adrian,et al.  Homogenised daily lake surface water temperature data generated from multiple satellite sensors: A long-term case study of a large sub-Alpine lake , 2016, Scientific Reports.

[19]  D. Quattrochi,et al.  The impact of distinct anthropogenic and vegetation features on urban warming , 2013, Landscape Ecology.

[20]  Alan T. Murray,et al.  Monitoring the composition of urban environments based on the vegetation-impervious surface-soil (VIS) model by subpixel analysis techniques , 2002 .

[21]  Patricio A. Vela,et al.  A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm , 2012, Expert Syst. Appl..

[22]  M. Batistella,et al.  Linear mixture model applied to Amazonian vegetation classification , 2003 .

[23]  D. Roberts,et al.  Multiple Endmember Spectral Mixture Analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries , 2013 .

[24]  Jean Dubranna,et al.  Assessing phytoplankton community composition from hyperspectral measurements of phytoplankton absorption coefficient and remote-sensing reflectance in open-ocean environments , 2015 .

[25]  B. Xu,et al.  Monitoring two decades of urbanization in the Poyang Lake area, China through spectral unmixing , 2012 .

[26]  Zhihua Wang,et al.  Spatio-Temporal Modeling of the Urban Heat Island in the Phoenix Metropolitan Area: Land Use Change Implications , 2016, Remote. Sens..

[27]  S. Myint,et al.  Spatial configuration of anthropogenic land cover impacts on urban warming , 2014 .

[28]  Thomas Blaschke,et al.  Examining Urban Heat Island Relations to Land Use and Air Pollution: Multiple Endmember Spectral Mixture Analysis for Thermal Remote Sensing , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[29]  Sergio M. Vicente-Serrano,et al.  Drought impacts on vegetation activity in the Mediterranean region: An assessment using remote sensing data and multi-scale drought indicators , 2017 .

[30]  Susan L. Ustin,et al.  Evaluation of the potential of Hyperion for fire danger assessment by comparison to the Airborne Visible/Infrared Imaging Spectrometer , 2003, IEEE Trans. Geosci. Remote. Sens..

[31]  Qihao Weng.  A remote sensing?GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China , 2001 .

[32]  Zhengqiang Li,et al.  Remote sensing of atmospheric fine particulate matter (PM2.5) mass concentration near the ground from satellite observation , 2015 .

[33]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[34]  C. Woodcock,et al.  Continuous change detection and classification of land cover using all available Landsat data , 2014 .

[35]  Yong-liang Ma,et al.  Characteristics of aerosol pollution during heavy haze events in Suzhou, China , 2015 .

[36]  Feng Gao,et al.  Representative lake water extent mapping at continental scales using multi-temporal Landsat-8 imagery , 2016 .

[37]  L. Pei,et al.  The Quantitative Relationship between Land Surface Temperature and Land Cover Types Based on Remotely Sensed Data , 2010 .

[38]  Wang Jie.  A STUDY ON URBAN THERMAL FIELD IN SUMMER BASED ON SATELLITE REMOTE SENSING , 2002 .

[39]  Menghua Wang,et al.  Remote Sensing of Inherent Optical Properties : Fundamentals , 2009 .

[40]  J. M. Santos,et al.  Principal components and generalized linear modeling in the correlation between hospital admissions and air pollution , 2014, Revista de saude publica.

[41]  Huopo Chen,et al.  Understanding the Recent Trend of Haze Pollution in Eastern China: Roles of Climate Change , 2016 .

[42]  D. Sakellariou,et al.  Application of the MESMA Framework. Case Study: Inner Ionian Archipelago & adjacent gulfs , 2015 .

[43]  Fenglei Fan,et al.  Enhancing endmember selection in multiple endmember spectral mixture analysis (MESMA) for urban impervious surface area mapping using spectral angle and spectral distance parameters , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[44]  Hong Jiang,et al.  Leaf Area Index Estimation Using MESMA Based on EO-1 Hyperion Satellite Imagery , 2014 .

[45]  Daniel Schläpfer,et al.  Combined Haze and Cirrus Removal for Multispectral Imagery , 2016, IEEE Geoscience and Remote Sensing Letters.

[46]  Burak Berk Ustundag,et al.  Land Surface Temperature Retrieval for Climate Analysis and Association with Climate Data , 2014 .

[47]  Margaret E. Gardner,et al.  Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models , 1998 .

[48]  A. Álvarez,et al.  Multivariate statistical assessment of functional relationships between soil physical descriptors and structural features of soil organic matter in Mediterranean ecosystems , 2014 .

[49]  Xiaoxiao Li,et al.  Object-based land-cover classification for metropolitan Phoenix, Arizona, using aerial photography , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[50]  Shaun Quegan,et al.  Haze Modelling and Simulation in Remote Sensing Satellite Data , 2014 .

[51]  D. Gong,et al.  Possible influence of atmospheric circulations on winter haze pollution in the Beijing–Tianjin–Hebei region, northern China , 2015 .