Abstract
Studies of urban ecological systems can be greatly enhanced by combining ecosystem modelling and remote sensing which often requires establishing statistical relationships between field and remote sensing data. At the Central Arizona-Phoenix Long-Term Ecological Research (CAPLTER) site in the south-western USA, we estimated vegetation abundance from Landsat ETM+ acquired at three dates by computing vegetation indices (NDVI and SAVI) and conducting linear spectral mixture analysis (SMA). Our analyses were stratified by three major land use/land covers - urban, agricultural, and desert. SMA, which provides direct measures of vegetation end member fraction for each pixel, was directly compared with field data and with the independent accuracy assessment dataset constructed from air photos. Vegetation index images with highest correlation with field data were used to construct regression models whose predictions were validated with the accuracy assessment dataset. We also investigated alternative regression methods, recognizing the inadequacy of traditional Ordinary Least Squares (OLS) in biophysical remote sensing. Symmetrical regressions - reduced major axis (RMA) and bisector ordinary least squares (OLSbisector) - were evaluated and compared with OLS. Our results indicated that SMA was a more accurate approach to vegetation quantification in urban and agricultural land uses, but had a poor accuracy when applied to desert vegetation. Potential sources of errors and some improvement recommendations are discussed.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 269-291 |
| Number of pages | 23 |
| Journal | International Journal of Remote Sensing |
| Volume | 28 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jan 2007 |
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ASJC Scopus subject areas
- Computers in Earth Sciences
Cite this
Estimating vegetation cover in an urban environment based on Landsat ETM+ imagery : A case study in Phoenix, USA. / Buyantuyev, A.; Wu, Jianguo; Gries, C.
In: International Journal of Remote Sensing, Vol. 28, No. 2, 01.2007, p. 269-291.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Estimating vegetation cover in an urban environment based on Landsat ETM+ imagery
T2 - A case study in Phoenix, USA
AU - Buyantuyev, A.
AU - Wu, Jianguo
AU - Gries, C.
PY - 2007/1
Y1 - 2007/1
N2 - Studies of urban ecological systems can be greatly enhanced by combining ecosystem modelling and remote sensing which often requires establishing statistical relationships between field and remote sensing data. At the Central Arizona-Phoenix Long-Term Ecological Research (CAPLTER) site in the south-western USA, we estimated vegetation abundance from Landsat ETM+ acquired at three dates by computing vegetation indices (NDVI and SAVI) and conducting linear spectral mixture analysis (SMA). Our analyses were stratified by three major land use/land covers - urban, agricultural, and desert. SMA, which provides direct measures of vegetation end member fraction for each pixel, was directly compared with field data and with the independent accuracy assessment dataset constructed from air photos. Vegetation index images with highest correlation with field data were used to construct regression models whose predictions were validated with the accuracy assessment dataset. We also investigated alternative regression methods, recognizing the inadequacy of traditional Ordinary Least Squares (OLS) in biophysical remote sensing. Symmetrical regressions - reduced major axis (RMA) and bisector ordinary least squares (OLSbisector) - were evaluated and compared with OLS. Our results indicated that SMA was a more accurate approach to vegetation quantification in urban and agricultural land uses, but had a poor accuracy when applied to desert vegetation. Potential sources of errors and some improvement recommendations are discussed.
AB - Studies of urban ecological systems can be greatly enhanced by combining ecosystem modelling and remote sensing which often requires establishing statistical relationships between field and remote sensing data. At the Central Arizona-Phoenix Long-Term Ecological Research (CAPLTER) site in the south-western USA, we estimated vegetation abundance from Landsat ETM+ acquired at three dates by computing vegetation indices (NDVI and SAVI) and conducting linear spectral mixture analysis (SMA). Our analyses were stratified by three major land use/land covers - urban, agricultural, and desert. SMA, which provides direct measures of vegetation end member fraction for each pixel, was directly compared with field data and with the independent accuracy assessment dataset constructed from air photos. Vegetation index images with highest correlation with field data were used to construct regression models whose predictions were validated with the accuracy assessment dataset. We also investigated alternative regression methods, recognizing the inadequacy of traditional Ordinary Least Squares (OLS) in biophysical remote sensing. Symmetrical regressions - reduced major axis (RMA) and bisector ordinary least squares (OLSbisector) - were evaluated and compared with OLS. Our results indicated that SMA was a more accurate approach to vegetation quantification in urban and agricultural land uses, but had a poor accuracy when applied to desert vegetation. Potential sources of errors and some improvement recommendations are discussed.
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UR - http://www.scopus.com/inward/citedby.url?scp=34250826694&partnerID=8YFLogxK
U2 - 10.1080/01431160600658149
DO - 10.1080/01431160600658149
M3 - Article
AN - SCOPUS:34250826694
VL - 28
SP - 269
EP - 291
JO - International Joural of Remote Sensing
JF - International Joural of Remote Sensing
SN - 0143-1161
IS - 2
ER -