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First published in Photogrammetric Engineering & Remote Sensing, n11 published by American Society for Photogrammetry and Remote Sensing. DOI: https://doi.org/10.14358/PERS.72.11.1275

Document Type

Article

Publication Version

Published Version

Journal/Book/Conference Title

Photogrammetric Engineering & Remote Sensing

Volume

72

Issue

11

First Page

1275

Last Page

1286

Abstract

In remote sensing studies of land surface temperatures (LST), thematic land-use and land-cover (LULC) data are frequently-employed for simple correlation analyses between LULC types and their thermal signatures. Development of quantitative surface descriptors could improve our capabilities for modeling urban thermal landscapes and advance urban climate research. This study developed an analytical procedure based upon a spectral unmixing model for characterizing and quantifying the urban landscape in Indianapolis, Indiana. A Landsat Enhanced Thematic Mapper Plus image of the study area, acquired on 22 June 2002, was spectrally unmixed into four fraction endmembers, namely, green vegetation, soil, high and low albedo. Impervious surface was then computed from the high and low albedo images. A hybrid classification procedure was developed to classify the fraction images into seven land-use and land-cover classes. Next, pixel-based LST measurements were related to urban surface biophysical descriptors derived from spectral mixture analysis (SMA). Correlation analyses were conducted to investigate land-cover based relationships between LST and impervious surface and green vegetation fractions for an analysis of the causes of LST variations. Results indicate that fraction images derived from SMA were effective for quantifying the urban morphology and for providing reliable measurements of biophysical variables such as vegetation abundance, soil, and impervious surface. An examination of LST variations within census block groups and their relationships with the compositions of LULC types, biophysical descriptors, and other relevant spatial data shows that LST possessed a weaker relation with the LULC compositions than with other variables (including urban biophysical descriptors, remote sensing biophysical variables, CIS-based impervious surface variables, and population density). Further research should be directed to refine spectral mixture modeling. The use of multi-temporal remote sensing data for urban time-space modeling and comparison of urban morphology in different geographical settings are also feasible.

Department

Department of Geography

Original Publication Date

11-1-2006

Object Description

1 PDF File

DOI of published version

10.14358/PERS.72.11.1275

Repository

UNI ScholarWorks, Rod Library, University of Northern Iowa

Copyright

©2006 American Society for Photogrammetry and Remote Sensing

Language

en

File Format

application/pdf

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