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Titolo:
An approach to linking remotely sensed data and areal census data
Autore:
Chen, K;
Indirizzi:
Macquarie Univ, Dept Phys Geog, Nat Hazards Res Ctr, N Ryde, NSW 2109, Australia Macquarie Univ N Ryde NSW Australia 2109 Ctr, N Ryde, NSW 2109, Australia
Titolo Testata:
INTERNATIONAL JOURNAL OF REMOTE SENSING
fascicolo: 1, volume: 23, anno: 2002,
pagine: 37 - 48
SICI:
0143-1161(200201)23:1<37:AATLRS>2.0.ZU;2-B
Fonte:
ISI
Lingua:
ENG
Soggetto:
SPOT HRV IMAGERY; LAND-COVER; DIGITAL CLASSIFICATION; URBAN FRINGE; TM DATA; POPULATION; FEATURES;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
Citazioni:
28
Recensione:
Indirizzi per estratti:
Indirizzo: Chen, K Macquarie Univ, Dept Phys Geog, Nat Hazards Res Ctr, N Ryde, NSW 2109, Australia Macquarie Univ N Ryde NSW Australia 2109 yde, NSW 2109, Australia
Citazione:
K. Chen, "An approach to linking remotely sensed data and areal census data", INT J REMOT, 23(1), 2002, pp. 37-48

Abstract

With the increasing availability of remotely sensed data and census data, discussing their relationship is one of the important issues in GIS data integration. This paper proposed an approach to linking three levels (macro, medium and micro) of land classifications with areal census data on hierarchical census boundaries. Specifically, a method of building the correlations between areal census dwelling data and residential densities classified by a remote sensing approach was demonstrated. First, a texture statistic (homogeneity) along with six Thematic Mapper (TM) bands (bands 1-5 and 7) wasput together to classify residential density levels. The homogeneity slightly enhanced classification accuracy. Then, to test the correlations between census dwelling data and residential densities, a multiple linear regression was conducted. It was found that areal census dwelling data had higher correlations with areas of different residential densities than with the aggregated area of a whole residential area at an individual census zone level. Finally, the paper discussed that dis-aggregation of areal census data based on dwelling densities within the framework of remote sensing and GIS would be very useful for multidisciplinary studies, such as natural hazards risk assessment.

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Documento generato il 28/03/20 alle ore 12:03:06