Mapping residential density patterns using multi-temporal Landsat data and a decision-tree classifier

We examined the utility of Landsat Thematic Mapper (TM) imagery for mapping residential land use in Montgomery County, Maryland, USA. The study area was chosen partly because of the availability of a unique parcel-level database of land use attributes and an associated digital map of parcel boundaries. These data were used to develop a series of land use classifications from a combination of leaf-on and leaf-off TM image derivatives and an algorithm based on ‘decision tree’ theory. Results suggest potential utility of the approach, particularly to state and local governments for land use mapping and planning applications, but greater accuracies are needed for broad practical application. In general, it was possible to discriminate different densities of residential development, and to separate these from commercial/industrial and agricultural areas. Difficulties arose in the discrimination of low-density residential areas due to the range of land cover types within this specific land use, and their associated spatial variability. The greater classification errors associated with these low-density developed areas were not unexpected. We found that these errors could be mitigated somewhat with techniques that consider the mode of training data selection and by incorporation of methods that account for the presence and amount of impervious surfaces (e.g. pavement and rooftops).

[1]  C. Brodley,et al.  Decision tree classification of land cover from remotely sensed data , 1997 .

[2]  S. Goetz,et al.  IKONOS imagery for resource management: Tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region , 2003 .

[3]  Marvin E. Bauer,et al.  Integrating Contextual Information with per-Pixel Classification for Improved Land Cover Classification , 2000 .

[4]  Philip J. Howarth,et al.  Change-detection accuracy assessment using SPOT multispectral imagery of the rural-urban fringe☆ , 1989 .

[5]  R. Leemans,et al.  Comparing global vegetation maps with the Kappa statistic , 1992 .

[6]  S. Goetz,et al.  Using the Sleuth Urban Growth Model to Simulate the Impacts of Future Policy Scenarios on Urban Land Use in the Baltimore-Washington Metropolitan Area , 2004 .

[7]  金田 重郎,et al.  C4.5: Programs for Machine Learning (書評) , 1995 .

[8]  James L. Regens,et al.  Predicting the urbanization of pine and mixed forests in Saint Tammany Parish, Louisiana , 2000 .

[9]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[10]  M. Ramsey,et al.  Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers , 2001 .

[11]  C. Kontoes,et al.  The potential of kernel classification techniques for land use mapping in urban areas using 5m-spatial resolution IRS-1C imagery , 2000 .

[12]  R. DeFries,et al.  Classification trees: an alternative to traditional land cover classifiers , 1996 .

[13]  Thomas J. Stohlgren,et al.  Assessing the accuracy of Landsat Thematic Mapper classification using double sampling , 1998 .

[14]  Alan H. Strahler,et al.  A note on procedures used for accuracy assessment in land cover maps derived from AVHRR data , 2000 .

[15]  N. Bockstael.  Modeling Economics and Ecology: The Importance of a Spatial Perspective , 1996 .

[16]  N. Bockstael,et al.  EVIDENCE OF THE EFFECTS OF WATER QUALITY ON RESIDENTIAL LAND PRICES , 2000 .

[17]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[18]  Anthony Downs,et al.  The Costs of Sprawl-Revisited , 1998 .

[19]  Jonathan Cheung-Wai Chan,et al.  Multiple Criteria for Evaluating Machine Learning Algorithms for Land Cover Classification from Satellite Data , 2000 .

[20]  F. Lindsay,et al.  Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations , 2000 .

[21]  J. Cihlar,et al.  From Land Cover to Land Use: A Methodology for Efficient Land Use Mapping over Large Areas , 2001 .

[22]  Stuart Barr,et al.  Monitoring Urban Land Use by Earth Observation , 2000 .

[23]  N. Bockstael,et al.  Spatial landscape indices in a hedonic framework: an ecological economics analysis using GIS , 1997 .

[24]  Alan T. Murray,et al.  Monitoring Growth in Rapidly Urbanizing Areas Using Remotely Sensed Data , 2000 .

[25]  William R. Dolphin,et al.  Costs of Sprawl -- 2000 , 2002 .