headerpos: 9460
 
 
  Estonian Journal of Ecology

ISSN 1736-7549 (electronic)   ISSN 1736-602X (print)
An international scientific journal

Formerly: Proceedings of the Estonian Academy of Sciences: Biology, Ecology
(ISSN 1406-0914)
Published since 1952
 

Estonian Journal of Ecology

ISSN 1736-7549 (electronic)   ISSN 1736-602X (print)
An international scientific journal

Formerly: Proceedings of the Estonian Academy of Sciences: Biology, Ecology
(ISSN 1406-0914)
Published since 1952
 

Publisher
Journal Information
» Abstractring/Indexing
List of Issues
» 2014
» 2013
» 2012
» 2011
» 2010
» 2009
» 2008
» 2007
Vol. 56, Issue 4
Vol. 56, Issue 3
Vol. 56, Issue 2
Vol. 56, Issue 1
» Back Issues
» Back issues (full texts)
  in Google
Publisher
» Other Journals
» Staff

Remote sensing of urban areas: linear spectral unmixing of Landsat Thematic Mapper images acquired over Tartu (Estonia); 19–32

(Full article in PDF format)


Authors

Tõnis Kärdi

Abstract

Urban areas are characterized by a pattern of very heterogeneous patches resulting from the co-occurrence of different materials within the ground instantaneous field of view of a moderate resolution scanner, e.g. Landsat Thematic Mapper (TM). The main objective of this study was to map vegetation, impervious surface, and soil from Landsat TM images acquired over the town of Tartu (Estonia) on three different dates (in 1988, 1995, and 2001). The linear spectral unmixing method was utilized for endmember fraction estimation. Accuracy assessment was conducted on the 1995 fraction images using the Estonian basic map at 1 : 10 000 scale. The overall fraction estimation error was 9% (by classes: vegetation and soil 6%, impervious surface 15%).

Keywords

Landsat, linear spectral unmixing, urban remote sensing, Estonia, Tartu.

References

Adams , J. B. , Sabol , D. E. , Kapos , V. , Filho , R. A. , Roberts , D. A. , Smith , M. O. & Gillespie , A. R. 1995. Classification of multispectral images based on fractions of endmembers: application to land-cover change in the Brazilian Amazon. Remote Sens. Environ. , 52 , 137–154.

Carlson , T. N. & Traci Arthur , S. 2000. The impact of land use – land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective. Global Planet. Change , 25 , 49–65.

Clapham , W. B. , Jr. 2003. Continuum-based classification of remotely sensed imagery to describe urban sprawl on a watershed scale. Remote Sens. Environ. , 86 , 322–340.

Eastman , R. J. 2001a. IDRISI 32 Release 2. Guide to GIS and Image Processing. Vol. 1. Clark Labs.

Eastman , R. J. 2001b. IDRISI 32 Release 2. Guide to GIS and Image Processing. Vol. 2. Clark Labs.

Gillies , R. R. , Brim Box , J. , Symanzik , J. & Rodemaker , E. J. 2003. Effects of urbanization on the aquatic fauna of the Creek watershed , Atlanta – a satellite perspective. Remote Sens. Environ. , 86 , 411–422.

Herold , M. , Roberts , D. A. , Gardner , M. E. & Dennison , P. E. 2004. Spectrometry for urban areas for remote sensing – development and analysis of a spectral library from 350 to 2400 nm. Remote Sens. Environ. , 91 , 304–319.

Herold , M. , Couclelis , H. & Clarke , K. C. 2005. The role of spatial metrics in the analysis and modelling of urban land use change. Comput. Environ. Urban Systems , 29 , 369–399.

Lewis , H. G. & Brown , M. 2001. A generalised confusion matrix for assessing area estimates from remotely sensed data. Int. J. Remote Sens. , 22 , 3223–3235.

Lu , D. , Morana , E. & Batistella , M. 2003. Linear mixture model applied to Amazonian vegetation classification. Remote Sens. Environ. , 87 ,456–469.

Lunetta , R. S. 1998. Applications , project formulation , and analytical approach. In Remote Sensing Change Detection: Environmental Monitoring Methods and Applications (Lunetta , R. S. & Elvidge , C. D. , eds) , pp. 1–19. Taylor & Francis , London.

Maa-ameti kartograafiabüroo. 2002. Eesti põhikaardi 1 : 10000 digitaalkaardistuse juhend. Kinni­tatud Maa-ameti peadirektori käskkirjaga nr 13 , 27. veebruar 2002. a. Tartu.

O’Meara Sheehan , M. 2002. What will it take to halt sprawl? World Watch Magazine , January/February 2002 , 12–23.

Phinn , S. , Stanford , M. , Scarth , P. , Murray , A. T. & Shyy , P. T. 2002. Monitoring the composition of urban environments based on the vegetation–imperious surface–soil (VIS) model by subpixel analysis techniques. Int. J. Remote Sens. , 23 , 4131–4153.

Ridd , M. K. 1995. Exploring a V-I-S (vegetation–impervious surface–soil) model for urban eco­system analysis through remote sensing: comparative anatomy for cities. Int. J. Remote Sens. , 16 , 2165–2185.

Roberts , D. A. , Batista , G. T. , Pereira , J. L. G. , Waller , E. K. & Nelson , B. 1998. Change identification using multitemporal spectral mixture analysis: applications in eastern Amazonia. In Remote Sensing Change Detection: Environmental Monitoring Methods and Applications (Lunetta , R. S. & Elvidge , C. D. , eds) , pp. 137–161. Taylor & Francis , London.

Small , C. 2001. Estimation of urban vegetation abundance by spectral mixture analysis. Int. J. Remote Sens. , 22 , 1305–1334.

Small , C. 2002. Multitemporal analysis of urban reflectance. Remote Sens. Environ. , 81 , 427–442.

Small , C. 2003. High spatial resolution spectral mixture analysis of urban reflectance. Remote Sens. Environ. , 88 , 170–186.

Small , C. 2004. The Landsat ETM+ spectral mixing space. Remote Sens. Environ. , 93 , 1–17.

Song , C. 2005. Spectral mixture analysis for subpixel vegetation fractions in the urban environment: how to incorporate endmember variability? Remote Sens. Environ. , 95 , 248–263.

Song , C. , Woodcock , C. E. , Seto , K. C. , Lenney , M. P. & Macomber , S. A. 2001. Classification and change detection using Landsat TM data: when and how to correct atmospheric effects? Remote Sens. Environ. , 75 , 230–244.

Souza , C. , Jr. , Firestone , L. , Silva , L. M. & Roberts , D. 2003. Mapping forest degradation in the Eastern Amazon from SPOT 4 through spectral mixture models. Remote Sens. Environ. , 87 , 494–506.

Tammaru , T. 2000. Suburbanisatsioon Eesti linnastumises. In Inimesed , ühiskonnad ja ruumid. Inimgeograafia Eestis (Jauhiainen , J. & Kulu , H. , eds) , pp. 77–88. Tartu Ülikooli Kirjastus , Tartu.

Tompkins , S. , Mustard , J. F. , Pieters , C. M. & Forsyth , D. W. 1997. Optimization of endmembers for spectral mixture analysis. Remote Sens. Environ. , 59 , 472–489.

Weng , Q. , Lu , D. & Schubring , J. 2004. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens. Environ. , 89 , 467–483.

Wu , C. 2004. Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sens. Environ. , 93 , 480–492.

Wu , C. & Murray , A. T. 2003. Estimating impervious surface distribution by spectral mixture analysis. Remote Sens. Environ. , 84 , 493–505.

Yang , X. & Liu , Z. 2005. Use of satellite-derived landscape imperviousness index to characterize urban spatial growth. Comput. Environ. Urban Systems , 29 , 524–540.

Zhang , J. , Rivard , B. & Sánchez-Azofeifa , A. 2005. Spectral unmixing of normalized reflectance data for the deconvolution of lichen and rock mixtures. Remote Sens. Environ. , 95 , 57–66.

 
Back

Current Issue: Vol. 63, Issue 4, 2014




Publishing schedule:
No. 1: 20 March
No. 2: 20 June
No. 3: 20 September
No. 4: 20 December