Abstract
Little research has been conducted on how differing spatial resolutions or classification techniques affect image-driven identification and categorization of slum neighborhoods in developing nations. This study assesses the correlation between satellite-derived land cover and census-derived socioeconomic variables in Accra, Ghana to determine whether the relationship between these variables is altered with a change in spatial resolution or scale. ASTER and Landsat TM satellite images are each used to classify land cover using spectral mixture analysis (SMA), and land cover proportions are summarized across Enumeration Areas in Accra and compared to socioeconomic data for the same areas. Correlation and regression analyses compare the SMA results with a Slum Index created from various socioeconomic data taken from the Census of Ghana, as well as to data derived from a "hard" per-pixel classification of a 2.4 m Quickbird image. Results show that the vegetation fraction is significantly correlated with the Slum Index (Pearson's r ranges from-0.33 to-0.51, depending on which image-derived product is compared), and the use of a spatial error model improves results (multivariate model pseudo-R 2 ranges from 0.37 to 0.40 by image product). We also find that SMA products derived from ASTER are a sufficient substitute for classification products derived from higher spatial resolution QB data when using land cover fractions as a proxy for slum presence, suggesting that SMA might be more cost-effective for deriving land cover fractions than the use of high-resolution imagery for this type of demographic analysis.
| Original language | English |
|---|---|
| Pages (from-to) | 31-52 |
| Number of pages | 22 |
| Journal | GIScience and Remote Sensing |
| Volume | 49 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 1 2012 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
Cite this
Assessing the utility of satellite imagery with differing spatial resolutions for deriving proxy measures of slum presence in Accra, Ghana. / Stoler, Justin B; Daniels, Dean; Weeks, John; Stow, Douglas; Coulter, Lloyd; Finch, Brian.
In: GIScience and Remote Sensing, Vol. 49, No. 1, 01.01.2012, p. 31-52.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Assessing the utility of satellite imagery with differing spatial resolutions for deriving proxy measures of slum presence in Accra, Ghana
AU - Stoler, Justin B
AU - Daniels, Dean
AU - Weeks, John
AU - Stow, Douglas
AU - Coulter, Lloyd
AU - Finch, Brian
PY - 2012/1/1
Y1 - 2012/1/1
N2 - Little research has been conducted on how differing spatial resolutions or classification techniques affect image-driven identification and categorization of slum neighborhoods in developing nations. This study assesses the correlation between satellite-derived land cover and census-derived socioeconomic variables in Accra, Ghana to determine whether the relationship between these variables is altered with a change in spatial resolution or scale. ASTER and Landsat TM satellite images are each used to classify land cover using spectral mixture analysis (SMA), and land cover proportions are summarized across Enumeration Areas in Accra and compared to socioeconomic data for the same areas. Correlation and regression analyses compare the SMA results with a Slum Index created from various socioeconomic data taken from the Census of Ghana, as well as to data derived from a "hard" per-pixel classification of a 2.4 m Quickbird image. Results show that the vegetation fraction is significantly correlated with the Slum Index (Pearson's r ranges from-0.33 to-0.51, depending on which image-derived product is compared), and the use of a spatial error model improves results (multivariate model pseudo-R 2 ranges from 0.37 to 0.40 by image product). We also find that SMA products derived from ASTER are a sufficient substitute for classification products derived from higher spatial resolution QB data when using land cover fractions as a proxy for slum presence, suggesting that SMA might be more cost-effective for deriving land cover fractions than the use of high-resolution imagery for this type of demographic analysis.
AB - Little research has been conducted on how differing spatial resolutions or classification techniques affect image-driven identification and categorization of slum neighborhoods in developing nations. This study assesses the correlation between satellite-derived land cover and census-derived socioeconomic variables in Accra, Ghana to determine whether the relationship between these variables is altered with a change in spatial resolution or scale. ASTER and Landsat TM satellite images are each used to classify land cover using spectral mixture analysis (SMA), and land cover proportions are summarized across Enumeration Areas in Accra and compared to socioeconomic data for the same areas. Correlation and regression analyses compare the SMA results with a Slum Index created from various socioeconomic data taken from the Census of Ghana, as well as to data derived from a "hard" per-pixel classification of a 2.4 m Quickbird image. Results show that the vegetation fraction is significantly correlated with the Slum Index (Pearson's r ranges from-0.33 to-0.51, depending on which image-derived product is compared), and the use of a spatial error model improves results (multivariate model pseudo-R 2 ranges from 0.37 to 0.40 by image product). We also find that SMA products derived from ASTER are a sufficient substitute for classification products derived from higher spatial resolution QB data when using land cover fractions as a proxy for slum presence, suggesting that SMA might be more cost-effective for deriving land cover fractions than the use of high-resolution imagery for this type of demographic analysis.
UR - http://www.scopus.com/inward/record.url?scp=84855646397&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84855646397&partnerID=8YFLogxK
U2 - 10.2747/1548-1603.49.1.31
DO - 10.2747/1548-1603.49.1.31
M3 - Article
AN - SCOPUS:84855646397
VL - 49
SP - 31
EP - 52
JO - GIScience and Remote Sensing
JF - GIScience and Remote Sensing
SN - 1548-1603
IS - 1
ER -