143
Remote Sensing Drought Assessment in a Coastal Urban Region
method to monitor drought of the Loess Plateau region, China. Based on the spatial
VITT, Sandholt et al. (2002) proposed the temperature vegetation drought index
(TVDI) for monitoring the regional drought. The TVDI method, based on the spatial
VITT, is also a kind of moisture index method. Mika et al. (2005) modified the PDSI
for monitoring regional soil moisture. Helen et al. (2006) detected drought effects
on vegetation water content and fluxes in Chaparral, CA, with a 970-nm water band
index. Bhuiyan et al. (2006) studied drought dynamics in the Aravalli region, India,
and compared different drought indices with one another. These indices include the
standardized precipitation index (SPI), standardized water-level index, VCI, TCI,
and vegetation health index. Livada and Assimakopoulos (2007) detected drought
events across spatial and temporal domains with SPI.
With the advancement of the TVDI design principle, this chapter presents several composite indicators of TVDIs (TVDI_NDVI, TVDI_ANDVI, TVDI_SAVI, and
TVDI_MSAVI) that were designed to combine temperature with different VIs (NDVI,
ANDVI, SAVI, and MSAVI) for comparisons. It leads to the test of TVDI adaptation
and application potential for monitoring regional drought holistically. In addition,
the regional water stress index (RWSI) was designed based on the CWSI mechanism
as a reference basis to aid in the classification of regional drought events in concert
with the identified spatial patterns between LST and VIs (NDVI, ANDVI, SAVI, and
MSAVI). The interrelationship between TVDIs and RWSI can then be analyzed to
provide scientific guidance for monitoring regional drought events using a suite of
integrated remote sensing technologies.
7.2 MATERIALS AND METHODS
7.2.1 Study aRea
The study area is located at Laizhou Bay in Shandong, China (Figure 7.1), latitude
36°49ʹ30ʺ–37°21ʹ40ʺ and longitude 119°0′50″–119°43′44″. The length along the east–
west and north–south directions is approximately 62 km × 58 km, respectively. The
total study area is 223,316 ha, but the length of the meandering coastal line within
the study area, where the active floodplain was formed by sediment laden water
being released from the neighboring river channel through the regional morphological and sedimentary dynamics, is about 100 km long. Five cities, including part of
Shouguang City, Laizhou City and Pingdu City, the Hangting area of Weifang City,
and most of Changyi City, are situated along this coastal line. The sediment distribution in the alluvial plain ranges from fine sand (close to the low water line) to the
typical mud carried by flood currents. Close to the open ocean, this area has a moist,
warm, temperate continental monsoon climate (Cao 2002; Wang et al. 2002; Guan
et al. 2001).
7.2.2 iMage PRoceSSing of MultiSenSoR data
A flowchart of image processing can be used to explore the relationships between
LST and VIs for drought monitoring (Figure 7.2). First, Landsat TM/ETM+ images,
digital elevation model (DEM) data, and climate data were collected. The DEM data
Remote Sensing Drought Assessment in a Coastal Urban Region
method to monitor drought of the Loess Plateau region, China. Based on the spatial
VITT, Sandholt et al. (2002) proposed the temperature vegetation drought index
(TVDI) for monitoring the regional drought. The TVDI method, based on the spatial
VITT, is also a kind of moisture index method. Mika et al. (2005) modified the PDSI
for monitoring regional soil moisture. Helen et al. (2006) detected drought effects
on vegetation water content and fluxes in Chaparral, CA, with a 970-nm water band
index. Bhuiyan et al. (2006) studied drought dynamics in the Aravalli region, India,
and compared different drought indices with one another. These indices include the
standardized precipitation index (SPI), standardized water-level index, VCI, TCI,
and vegetation health index. Livada and Assimakopoulos (2007) detected drought
events across spatial and temporal domains with SPI.
With the advancement of the TVDI design principle, this chapter presents several composite indicators of TVDIs (TVDI_NDVI, TVDI_ANDVI, TVDI_SAVI, and
TVDI_MSAVI) that were designed to combine temperature with different VIs (NDVI,
ANDVI, SAVI, and MSAVI) for comparisons. It leads to the test of TVDI adaptation
and application potential for monitoring regional drought holistically. In addition,
the regional water stress index (RWSI) was designed based on the CWSI mechanism
as a reference basis to aid in the classification of regional drought events in concert
with the identified spatial patterns between LST and VIs (NDVI, ANDVI, SAVI, and
MSAVI). The interrelationship between TVDIs and RWSI can then be analyzed to
provide scientific guidance for monitoring regional drought events using a suite of
integrated remote sensing technologies.
7.2 MATERIALS AND METHODS
7.2.1 Study aRea
The study area is located at Laizhou Bay in Shandong, China (Figure 7.1), latitude
36°49ʹ30ʺ–37°21ʹ40ʺ and longitude 119°0′50″–119°43′44″. The length along the east–
west and north–south directions is approximately 62 km × 58 km, respectively. The
total study area is 223,316 ha, but the length of the meandering coastal line within
the study area, where the active floodplain was formed by sediment laden water
being released from the neighboring river channel through the regional morphological and sedimentary dynamics, is about 100 km long. Five cities, including part of
Shouguang City, Laizhou City and Pingdu City, the Hangting area of Weifang City,
and most of Changyi City, are situated along this coastal line. The sediment distribution in the alluvial plain ranges from fine sand (close to the low water line) to the
typical mud carried by flood currents. Close to the open ocean, this area has a moist,
warm, temperate continental monsoon climate (Cao 2002; Wang et al. 2002; Guan
et al. 2001).
7.2.2 iMage PRoceSSing of MultiSenSoR data
A flowchart of image processing can be used to explore the relationships between
LST and VIs for drought monitoring (Figure 7.2). First, Landsat TM/ETM+ images,
digital elevation model (DEM) data, and climate data were collected. The DEM data