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Multiscale Hydrologic Remote Sensing: Perspectives and Applications
21.1 INTRODUCTION
Drought is the most complex and least understood of all natural hazards, affecting
more people than any other hazard (Wilhite 2000). Bryant (1991) ranked natural
hazards on the basis of various criteria, such as severity, duration, spatial extent, loss
of life, economic loss, social effect, and long-term impact, and found that drought
ranks first among all natural hazards (Narasimhan 2004). In spite of the economic
and the social impact caused by drought, it is the least understood of all natural
hazards owing to the complex nature and varying effects of droughts on different
economic and social sectors (Wilhite 2000). The wide variety of sectors affected by
drought, its diverse geographical and temporal distribution, and the demand placed
on water supply by human-use systems make it difficult to develop a single definition of drought (Richard and Heim 2002). After analyzing more than 150 definitions
of drought, Wilhite and Glantz (1985) broadly grouped those definitions into four
categories: meteorological, agricultural, hydrological, and socioeconomic drought.
Traditional drought monitoring is based on weather station observations or on
the development of drought indices to investigate the severity of a drought incident.
The direct station observations are currently restricted to discrete measurements at
specific locations and lack continuous spatial coverage. A number of different indices based on hydrological and meteorological data have been developed to quantify
drought. The most commonly used drought index is the Palmer Drought Severity
Index (PDSI), which was developed by Palmer in 1965 (Palmer 1965). PDSI, however, was solely based on lump parameters, which do not consider the spatial variability, and therefore cannot reveal detailed spatial patterns of drought conditions.
Recent technological advances in satellite remote sensing have offered a means
for more effective drought monitoring across a much wider spatial and temporal scale
and, at the same time, at a much higher spatial and temporal resolution (Engman
1990). Research by using satellite data to monitor a variety of dynamic land surface
processes began in the mid-1970s shortly after the surge in satellite development (for
example, see the work of Anderson et al. 1976, Reed et al. 1994, Yang et al. 1998,
and Peters et al. 2002). Numerous satellite-derived indices were developed to detect
and monitor drought.
While the first generation of remote sensing–based drought indices relied on the
few optical bands as provided by traditional sensors such as the National Oceanic
and Atmospheric Administration Advanced Very High Resolution Radiometer
(NOAA AVHRR) or Landsat thematic mapper (TM) sensors, a new generation
attempts to make use of the multiband capabilities of the National Aeronautics and
Space Administration Moderate Resolution Imaging Spectroradiometer (MODIS)
sensor onboard the Terra and Aqua satellites. Most recently, Wang and Qu (2007)
have designed a new drought index, Normalized Multiband Drought Index (NMDI),
based on one near-infrared and two shortwave infrared channels, exploiting the slope
of the two water-sensitive absorption bands 6 and 7 of MODIS. It is essentially an
improvement of the traditional normalized indices that usually use one sensitive
band and one insensitive band. In this chapter, the theoretical basis of NMDI and
its applications in drought monitoring and fire detection are presented sequentially.
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
21.1 INTRODUCTION
Drought is the most complex and least understood of all natural hazards, affecting
more people than any other hazard (Wilhite 2000). Bryant (1991) ranked natural
hazards on the basis of various criteria, such as severity, duration, spatial extent, loss
of life, economic loss, social effect, and long-term impact, and found that drought
ranks first among all natural hazards (Narasimhan 2004). In spite of the economic
and the social impact caused by drought, it is the least understood of all natural
hazards owing to the complex nature and varying effects of droughts on different
economic and social sectors (Wilhite 2000). The wide variety of sectors affected by
drought, its diverse geographical and temporal distribution, and the demand placed
on water supply by human-use systems make it difficult to develop a single definition of drought (Richard and Heim 2002). After analyzing more than 150 definitions
of drought, Wilhite and Glantz (1985) broadly grouped those definitions into four
categories: meteorological, agricultural, hydrological, and socioeconomic drought.
Traditional drought monitoring is based on weather station observations or on
the development of drought indices to investigate the severity of a drought incident.
The direct station observations are currently restricted to discrete measurements at
specific locations and lack continuous spatial coverage. A number of different indices based on hydrological and meteorological data have been developed to quantify
drought. The most commonly used drought index is the Palmer Drought Severity
Index (PDSI), which was developed by Palmer in 1965 (Palmer 1965). PDSI, however, was solely based on lump parameters, which do not consider the spatial variability, and therefore cannot reveal detailed spatial patterns of drought conditions.
Recent technological advances in satellite remote sensing have offered a means
for more effective drought monitoring across a much wider spatial and temporal scale
and, at the same time, at a much higher spatial and temporal resolution (Engman
1990). Research by using satellite data to monitor a variety of dynamic land surface
processes began in the mid-1970s shortly after the surge in satellite development (for
example, see the work of Anderson et al. 1976, Reed et al. 1994, Yang et al. 1998,
and Peters et al. 2002). Numerous satellite-derived indices were developed to detect
and monitor drought.
While the first generation of remote sensing–based drought indices relied on the
few optical bands as provided by traditional sensors such as the National Oceanic
and Atmospheric Administration Advanced Very High Resolution Radiometer
(NOAA AVHRR) or Landsat thematic mapper (TM) sensors, a new generation
attempts to make use of the multiband capabilities of the National Aeronautics and
Space Administration Moderate Resolution Imaging Spectroradiometer (MODIS)
sensor onboard the Terra and Aqua satellites. Most recently, Wang and Qu (2007)
have designed a new drought index, Normalized Multiband Drought Index (NMDI),
based on one near-infrared and two shortwave infrared channels, exploiting the slope
of the two water-sensitive absorption bands 6 and 7 of MODIS. It is essentially an
improvement of the traditional normalized indices that usually use one sensitive
band and one insensitive band. In this chapter, the theoretical basis of NMDI and
its applications in drought monitoring and fire detection are presented sequentially.