161
Remote Sensing Drought Assessment in a Coastal Urban Region
are possible. Spatial information of RWSI calculated with the SEBAL model, LST
retrieved with an existing algorithm, and VIs computed with their respective four
algorithms can be collectively aggregated to conduct a holistic drought impact
assessment. Four refined TVDIs (i.e., TVDI_NDVI, TVDI_ANDVI, TVDI_MSAVI,
and TVDI_SAVI) were employed according to the principle of the spatial VITT
theory, which helps identify the spatiotemporal relational patterns between LST
and VIs directly and between TVDIs and RWSI indirectly.
Research findings indicate that, because the factor of soil background adjustment
was introduced into the algorithms for the derivation of the ANDVI, SAVI, and the
MSAVI, these three VIs are more adaptive to cope with the vegetation index saturation issues as compared to the use of traditional NDVI. When evaluating the subgroups
based on different densities of vegetation cover, the relational patterns between LST and
VIs were different in both 1987 and 2000. A lower density of vegetation cover resulted
in positive correlations between LST and VIs (correlation coefficient > 0.96), a medium
density resulted in negative correlations between LST and VIs (negative correlation
coefficient ≥ 0.99), and a higher density resulted in negative correlations between LST
and VIs in 1987 with less UHI, but became positive in 2000 with obvious UHI.
TVDIs and RWSI can be combined as a composite indicator to address soil moisture dynamics and drought impacts. When the values of RWSI were integrated into
TVDI_SAVI, TVDI_ANDVI, and TVDI_MSAVI for drought assessment, we found
that the shortage of soil water in 1987 was more severe than that in 2000; however,
the use of TVDI_NDVI did not produce the same conclusion because TVDIs are
suitable for monitoring situations of wet, normal, and light dry of drought when
RWSI < 0.752. In the situation of medium dry (RWSI ≤ 0.8), TVDIs can still accurately monitor drought. Yet, when dealing with medium dry and heavy dry (RWSI >
0.8), TVDIs cannot accurately portray the situation of water shortage and drought
assessment; therefore, TVDIs should not be used to monitor the medium and heavy
drought alone when RWSI > 0.8. We concluded that the composite indicator based
on combined TVDIs and RWSI would be more suitable than a single drought index
in drought impact assessment, especially in a fast-growing urban region. Overall,
the use of remote sensing technologies for the identification of LULC as well as the
calculations of vegetation cover and heat fluxes proved effective for developing a
composite indicator for drought impact assessment in urban regions.
ACKNOWLEDGMENT
The authors are grateful for the financial support of the United States Department of
Agriculture National Institute of Food and Agriculture (USDA NIFA) project (201034263-21075) in this study.
REFERENCES
Bannari, A., Morin, D., Bonn, F., and Huete, A. R. (1995). A review of vegetation indices.
Remote Sensing Reviews, 13, 95–120.
Bastiaanssen, W. G. M. (2000a). SEBAL-based sensible and latent heat fluxes in the irrigated
Gediz Basin, Turkey. Journal of Hydrology, 229(1–2), 87–100.
Remote Sensing Drought Assessment in a Coastal Urban Region
are possible. Spatial information of RWSI calculated with the SEBAL model, LST
retrieved with an existing algorithm, and VIs computed with their respective four
algorithms can be collectively aggregated to conduct a holistic drought impact
assessment. Four refined TVDIs (i.e., TVDI_NDVI, TVDI_ANDVI, TVDI_MSAVI,
and TVDI_SAVI) were employed according to the principle of the spatial VITT
theory, which helps identify the spatiotemporal relational patterns between LST
and VIs directly and between TVDIs and RWSI indirectly.
Research findings indicate that, because the factor of soil background adjustment
was introduced into the algorithms for the derivation of the ANDVI, SAVI, and the
MSAVI, these three VIs are more adaptive to cope with the vegetation index saturation issues as compared to the use of traditional NDVI. When evaluating the subgroups
based on different densities of vegetation cover, the relational patterns between LST and
VIs were different in both 1987 and 2000. A lower density of vegetation cover resulted
in positive correlations between LST and VIs (correlation coefficient > 0.96), a medium
density resulted in negative correlations between LST and VIs (negative correlation
coefficient ≥ 0.99), and a higher density resulted in negative correlations between LST
and VIs in 1987 with less UHI, but became positive in 2000 with obvious UHI.
TVDIs and RWSI can be combined as a composite indicator to address soil moisture dynamics and drought impacts. When the values of RWSI were integrated into
TVDI_SAVI, TVDI_ANDVI, and TVDI_MSAVI for drought assessment, we found
that the shortage of soil water in 1987 was more severe than that in 2000; however,
the use of TVDI_NDVI did not produce the same conclusion because TVDIs are
suitable for monitoring situations of wet, normal, and light dry of drought when
RWSI < 0.752. In the situation of medium dry (RWSI ≤ 0.8), TVDIs can still accurately monitor drought. Yet, when dealing with medium dry and heavy dry (RWSI >
0.8), TVDIs cannot accurately portray the situation of water shortage and drought
assessment; therefore, TVDIs should not be used to monitor the medium and heavy
drought alone when RWSI > 0.8. We concluded that the composite indicator based
on combined TVDIs and RWSI would be more suitable than a single drought index
in drought impact assessment, especially in a fast-growing urban region. Overall,
the use of remote sensing technologies for the identification of LULC as well as the
calculations of vegetation cover and heat fluxes proved effective for developing a
composite indicator for drought impact assessment in urban regions.
ACKNOWLEDGMENT
The authors are grateful for the financial support of the United States Department of
Agriculture National Institute of Food and Agriculture (USDA NIFA) project (201034263-21075) in this study.
REFERENCES
Bannari, A., Morin, D., Bonn, F., and Huete, A. R. (1995). A review of vegetation indices.
Remote Sensing Reviews, 13, 95–120.
Bastiaanssen, W. G. M. (2000a). SEBAL-based sensible and latent heat fluxes in the irrigated
Gediz Basin, Turkey. Journal of Hydrology, 229(1–2), 87–100.