Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers

William L. Stefanov, Michael S. Ramsey, Philip Christensen

Research output: Contribution to journalArticle

341 Citations (Scopus)

Abstract

The spatial and temporal distribution of land cover is a fundamental dataset for urban ecological research. An expert (or hypothesis testing) system has been used with Landsat Thematic Mapper (TM) data to derive a land cover classification for the semiarid Phoenix metropolitan portion of the Central Arizona-Phoenix Long Term Ecological Research (CAP LTER) site. Expert systems allow for the integration of remotely sensed data with other sources of georeferenced information such as land use data, spatial texture, and digital elevation models (DEMs) to obtain greater classification accuracy. Logical decision roles are used with the various datasets to assign class values to each pixel. TM reflectance data acquired in 1998 [visible to shortwave infrared (VSWIR) bands plus a vegetation index] were initially classified for land cover using a maximum likelihood decision role. In addition, spatial texture of the TM data was calculated. An expert system was constructed to perform postclassification sorting of the initial land cover classification using additional spatial datasets such as texture, land use, water rights, city boundaries, and Native American reservation boundaries. Pixels were reclassified using logical decision roles into 12 classes. The overall accuracy of this technique was 85%. Individual class user's accuracy ranged from 73% to 99%, with the exception of the commercial/industrial materials class. This class performed poorly (user's accuracy of 49%) due to the similarity of subpixel components with other classes. The results presented here indicate that the expert system approach will be useful both for ongoing CAP LTER research, as well as the planned global Urban Environmental Monitoring (UEM) program of the Advanced Spacebome Thermal Emission and Reflection Radiometer (ASTER) instrument.

Original languageEnglish (US)
Pages (from-to)173-185
Number of pages13
JournalRemote Sensing of Environment
Volume77
Issue number2
DOIs
StatePublished - 2001

Fingerprint

expert systems
expert system
land cover
Expert systems
Monitoring
Textures
monitoring
texture
spatial data
Land use
pixel
heat emissions
land use
Pixels
water rights
radiometers
digital elevation models
American Indians
environmental monitoring
hypothesis testing

Keywords

  • Arid environment
  • Knowledge-based systems
  • Surface properties
  • Thematic mapper
  • Urban environment

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Earth-Surface Processes
  • Environmental Science(all)
  • Management, Monitoring, Policy and Law

Cite this

Monitoring urban land cover change : An expert system approach to land cover classification of semiarid to arid urban centers. / Stefanov, William L.; Ramsey, Michael S.; Christensen, Philip.

In: Remote Sensing of Environment, Vol. 77, No. 2, 2001, p. 173-185.

Research output: Contribution to journalArticle

@article{1a2262d2c1d4444ca1bd4aaf08c7ce67,
title = "Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers",
abstract = "The spatial and temporal distribution of land cover is a fundamental dataset for urban ecological research. An expert (or hypothesis testing) system has been used with Landsat Thematic Mapper (TM) data to derive a land cover classification for the semiarid Phoenix metropolitan portion of the Central Arizona-Phoenix Long Term Ecological Research (CAP LTER) site. Expert systems allow for the integration of remotely sensed data with other sources of georeferenced information such as land use data, spatial texture, and digital elevation models (DEMs) to obtain greater classification accuracy. Logical decision roles are used with the various datasets to assign class values to each pixel. TM reflectance data acquired in 1998 [visible to shortwave infrared (VSWIR) bands plus a vegetation index] were initially classified for land cover using a maximum likelihood decision role. In addition, spatial texture of the TM data was calculated. An expert system was constructed to perform postclassification sorting of the initial land cover classification using additional spatial datasets such as texture, land use, water rights, city boundaries, and Native American reservation boundaries. Pixels were reclassified using logical decision roles into 12 classes. The overall accuracy of this technique was 85{\%}. Individual class user's accuracy ranged from 73{\%} to 99{\%}, with the exception of the commercial/industrial materials class. This class performed poorly (user's accuracy of 49{\%}) due to the similarity of subpixel components with other classes. The results presented here indicate that the expert system approach will be useful both for ongoing CAP LTER research, as well as the planned global Urban Environmental Monitoring (UEM) program of the Advanced Spacebome Thermal Emission and Reflection Radiometer (ASTER) instrument.",
keywords = "Arid environment, Knowledge-based systems, Surface properties, Thematic mapper, Urban environment",
author = "Stefanov, {William L.} and Ramsey, {Michael S.} and Philip Christensen",
year = "2001",
doi = "10.1016/S0034-4257(01)00204-8",
language = "English (US)",
volume = "77",
pages = "173--185",
journal = "Remote Sensing of Environment",
issn = "0034-4257",
publisher = "Elsevier Inc.",
number = "2",

}

TY - JOUR

T1 - Monitoring urban land cover change

T2 - An expert system approach to land cover classification of semiarid to arid urban centers

AU - Stefanov, William L.

AU - Ramsey, Michael S.

AU - Christensen, Philip

PY - 2001

Y1 - 2001

N2 - The spatial and temporal distribution of land cover is a fundamental dataset for urban ecological research. An expert (or hypothesis testing) system has been used with Landsat Thematic Mapper (TM) data to derive a land cover classification for the semiarid Phoenix metropolitan portion of the Central Arizona-Phoenix Long Term Ecological Research (CAP LTER) site. Expert systems allow for the integration of remotely sensed data with other sources of georeferenced information such as land use data, spatial texture, and digital elevation models (DEMs) to obtain greater classification accuracy. Logical decision roles are used with the various datasets to assign class values to each pixel. TM reflectance data acquired in 1998 [visible to shortwave infrared (VSWIR) bands plus a vegetation index] were initially classified for land cover using a maximum likelihood decision role. In addition, spatial texture of the TM data was calculated. An expert system was constructed to perform postclassification sorting of the initial land cover classification using additional spatial datasets such as texture, land use, water rights, city boundaries, and Native American reservation boundaries. Pixels were reclassified using logical decision roles into 12 classes. The overall accuracy of this technique was 85%. Individual class user's accuracy ranged from 73% to 99%, with the exception of the commercial/industrial materials class. This class performed poorly (user's accuracy of 49%) due to the similarity of subpixel components with other classes. The results presented here indicate that the expert system approach will be useful both for ongoing CAP LTER research, as well as the planned global Urban Environmental Monitoring (UEM) program of the Advanced Spacebome Thermal Emission and Reflection Radiometer (ASTER) instrument.

AB - The spatial and temporal distribution of land cover is a fundamental dataset for urban ecological research. An expert (or hypothesis testing) system has been used with Landsat Thematic Mapper (TM) data to derive a land cover classification for the semiarid Phoenix metropolitan portion of the Central Arizona-Phoenix Long Term Ecological Research (CAP LTER) site. Expert systems allow for the integration of remotely sensed data with other sources of georeferenced information such as land use data, spatial texture, and digital elevation models (DEMs) to obtain greater classification accuracy. Logical decision roles are used with the various datasets to assign class values to each pixel. TM reflectance data acquired in 1998 [visible to shortwave infrared (VSWIR) bands plus a vegetation index] were initially classified for land cover using a maximum likelihood decision role. In addition, spatial texture of the TM data was calculated. An expert system was constructed to perform postclassification sorting of the initial land cover classification using additional spatial datasets such as texture, land use, water rights, city boundaries, and Native American reservation boundaries. Pixels were reclassified using logical decision roles into 12 classes. The overall accuracy of this technique was 85%. Individual class user's accuracy ranged from 73% to 99%, with the exception of the commercial/industrial materials class. This class performed poorly (user's accuracy of 49%) due to the similarity of subpixel components with other classes. The results presented here indicate that the expert system approach will be useful both for ongoing CAP LTER research, as well as the planned global Urban Environmental Monitoring (UEM) program of the Advanced Spacebome Thermal Emission and Reflection Radiometer (ASTER) instrument.

KW - Arid environment

KW - Knowledge-based systems

KW - Surface properties

KW - Thematic mapper

KW - Urban environment

UR - http://www.scopus.com/inward/record.url?scp=0034881595&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0034881595&partnerID=8YFLogxK

U2 - 10.1016/S0034-4257(01)00204-8

DO - 10.1016/S0034-4257(01)00204-8

M3 - Article

AN - SCOPUS:0034881595

VL - 77

SP - 173

EP - 185

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

IS - 2

ER -