141
Remote Sensing Drought Assessment in a Coastal Urban Region
based on multiple drought indices might be more suitable to justify the extent and
severity of a drought event and to trigger appropriate actions within a drought mitigation plan. Achieving such goals must rely on remote sensing technologies to collect intensive spatial information with different features.
7.1.2 BackgRound of the Single dRought index
Various satellite-derived VIs have been developed to quantitatively assess vegetation
covers using spectral measurements (Bannari et al. 1995). The uses of red and NIR
spectral bands of the sensors on board satellites are well suited for assessing vegetation
covers (Weier and Herring 2006). Photosynthetic pigments in green vegetation strongly
absorb red light (such as Landsat band 3) through chlorophyll a. In contrast, NIR wavelengths are half reflected by and half passed through the leaf tissues, regardless of their
color (USGS-ARS 2006). Bannari et al. (1995) reviewed 35 VIs and found that most
used red and NIR bands, whereas others incorporated additional parameters to compensate for effects of confounding factors, such as background effects (soil brightness
and soil color), atmospheric effects (absorption and scattering), and the effects of sensor
response and calibration. In 2001, Peddle and Brunke conducted a review on the 10
most commonly used VIs in forestry applications. Each VI has strengths and weaknesses. For instance, the ratio vegetation index does not perform well when the vegetation cover is less than 50% but is the best index for dense vegetation cover (Jackson
1983). The NDVI first proposed by Rouse et al. (1974) is able to reduce the effect of sensor degradation by normalizing the spectral bands; this index is sensitive to low-density
vegetation such as semiarid areas (Tucker and Miller 1977; Kerr et al. 1989; Nicholson
et al. 1990). Besides, LST retrieval was carried out using the thermal bands of thematic
mapper/enhanced thematic mapper plus (TM/ETM+) data to support the application
of the radiance transfer equation (Qin et al. 2001). The equations for NDVI (Rouse et
al. 1974; Tucker 1979), SAVI (Huete 1988), MSAVI (Qi et al. 1994), and adjusted NDVI
(ANDVI; Liu et al. 2008) were collectively employed to produce a suite of VIs in support
of advanced drought impact assessment. Examples of VIs that are insensitive to atmospheric effects include the global environment vegetation index (Pinty and Verstraete
1992) and the EVI (Huete et al. 1999). The EVI was developed to improve sensitivity in
high-biomass regions while reducing atmospheric effects and can be regularly produced
from the moderate resolution imaging spectroradiometer (MODIS) on the National
Aeronautics and Space Administration (NASA)’s Terra satellite (Huete et al. 1999).
Using NDVI and SAVI without regard to LST conditions cannot accurately
address actual urban drought episodes, yet LST can be directly linked to LULC, soil
moisture, VIs, and ET. Monitoring temperature conditions using a remote sensing
method can obtain spatial distribution and temporal changes of soil heat flux with
high precision to capture the spatiotemporal variations of drought impact associated
with varying weather conditions. With the aid of remote sensing technologies, the
vegetation index/temperature trapezoid (VITT) eigenspace may explain such land
surface processes (Han et al. 2006).
Waston et al. (1971) first proposed a simple model to calculate thermal inertia with
daily difference in LST. Many scientists have carried out a variety of experimental
studies with respect to thermal inertia principles (Price 1977, 1985; England 1990;
Remote Sensing Drought Assessment in a Coastal Urban Region
based on multiple drought indices might be more suitable to justify the extent and
severity of a drought event and to trigger appropriate actions within a drought mitigation plan. Achieving such goals must rely on remote sensing technologies to collect intensive spatial information with different features.
7.1.2 BackgRound of the Single dRought index
Various satellite-derived VIs have been developed to quantitatively assess vegetation
covers using spectral measurements (Bannari et al. 1995). The uses of red and NIR
spectral bands of the sensors on board satellites are well suited for assessing vegetation
covers (Weier and Herring 2006). Photosynthetic pigments in green vegetation strongly
absorb red light (such as Landsat band 3) through chlorophyll a. In contrast, NIR wavelengths are half reflected by and half passed through the leaf tissues, regardless of their
color (USGS-ARS 2006). Bannari et al. (1995) reviewed 35 VIs and found that most
used red and NIR bands, whereas others incorporated additional parameters to compensate for effects of confounding factors, such as background effects (soil brightness
and soil color), atmospheric effects (absorption and scattering), and the effects of sensor
response and calibration. In 2001, Peddle and Brunke conducted a review on the 10
most commonly used VIs in forestry applications. Each VI has strengths and weaknesses. For instance, the ratio vegetation index does not perform well when the vegetation cover is less than 50% but is the best index for dense vegetation cover (Jackson
1983). The NDVI first proposed by Rouse et al. (1974) is able to reduce the effect of sensor degradation by normalizing the spectral bands; this index is sensitive to low-density
vegetation such as semiarid areas (Tucker and Miller 1977; Kerr et al. 1989; Nicholson
et al. 1990). Besides, LST retrieval was carried out using the thermal bands of thematic
mapper/enhanced thematic mapper plus (TM/ETM+) data to support the application
of the radiance transfer equation (Qin et al. 2001). The equations for NDVI (Rouse et
al. 1974; Tucker 1979), SAVI (Huete 1988), MSAVI (Qi et al. 1994), and adjusted NDVI
(ANDVI; Liu et al. 2008) were collectively employed to produce a suite of VIs in support
of advanced drought impact assessment. Examples of VIs that are insensitive to atmospheric effects include the global environment vegetation index (Pinty and Verstraete
1992) and the EVI (Huete et al. 1999). The EVI was developed to improve sensitivity in
high-biomass regions while reducing atmospheric effects and can be regularly produced
from the moderate resolution imaging spectroradiometer (MODIS) on the National
Aeronautics and Space Administration (NASA)’s Terra satellite (Huete et al. 1999).
Using NDVI and SAVI without regard to LST conditions cannot accurately
address actual urban drought episodes, yet LST can be directly linked to LULC, soil
moisture, VIs, and ET. Monitoring temperature conditions using a remote sensing
method can obtain spatial distribution and temporal changes of soil heat flux with
high precision to capture the spatiotemporal variations of drought impact associated
with varying weather conditions. With the aid of remote sensing technologies, the
vegetation index/temperature trapezoid (VITT) eigenspace may explain such land
surface processes (Han et al. 2006).
Waston et al. (1971) first proposed a simple model to calculate thermal inertia with
daily difference in LST. Many scientists have carried out a variety of experimental
studies with respect to thermal inertia principles (Price 1977, 1985; England 1990;