by their sum [70, 71]. These two spectral bands represent the most detectable
spectral characteristics of green plants. This is because the red radiation is absorbed
by the chlorophyll in the surface layers of the plant (palisade parenchyma) and the
NIR is reflected from the inner leaf cell structure (spongy mesophyll) as it penetrates
several leaf layers in a canopy. Since the NIR reflectance depends on the abundance
of plant tissue and the red reflectance indicates the leaf surface health condition of
the plant, the NDVI can be related to plant biomass or stress. It has been shown by
researchers that time series of remote sensing data can be used effectively to
identify long-term trends and subtle changes of NDVI by means of principal
component analysis [68, 72, 73]. However, the NDVI must be used with caution
in areas with less than 20 % vegetation cover because the vegetation spectra will be
distorted by the soil reflectance. Care must also be used in areas where the
vegetation fraction is higher than 50 % and the signal is pushed into the saturated
region of the nonlinear transfer curve.
Visible, infrared, and microwave sensors have varying sensitivities to aboveground vegetation biomass. Optical and radar remote sensing methods and empirical and statistical regression models, some using NDVI, have been applied to
estimate the amount and variability of aboveground biomass [74–78]. Remote
sensing has been used to collect large amounts of biomass data on a global scale
for forested areas, such as upland forests, forested wetlands, and mangroves
[79]. Extensive work is under way with L-band SAR to develop operational
biomass programs across a range of countries, using an approach that is effective
over a biomass range of 0–200 tons/ha [80].
5.3 Biomass as a Drought Indicator
Drought is a gradual phenomenon, slowly taking hold of an area and stressing its
vegetation. In severe cases, drought can last for many years with devastating effects
on agriculture and water supplies. The underlying cause of most droughts, deficiency in precipitation, can be related to variations in large-scale atmospheric
circulation patterns and the locations of anticyclones, or high pressure systems.
Sometimes, whirling masses of air separate from the main westerly airflow and
effectively prevent the usual west to east progression of weather systems. When
these “blocking systems” persist for extended periods of time, weather extremes
such as drought, floods, heat waves, and cold snaps can occur [81].
One of the worst droughts of the twentieth century occurred in the Horn of Africa
in 1984 and 1985. Figure 5 shows the NDVI anomaly for August 1984. Within this
figure, dark red/brown indicates the most severe drought, light yellow areas are
normal, and green areas have denser than normal vegetation. This figure demonstrates the widespread areas of severe drought that can be mapped using remote
sensing techniques.
The NDVI anomaly indicates the vigor of vegetation relative to the long-term
average. Using NOAA’s AVHRR, scientists have been collecting images of the
Using Remote Sensing to Map and Monitor Water Resources in Arid and Semiarid. . .
47
spectral characteristics of green plants. This is because the red radiation is absorbed
by the chlorophyll in the surface layers of the plant (palisade parenchyma) and the
NIR is reflected from the inner leaf cell structure (spongy mesophyll) as it penetrates
several leaf layers in a canopy. Since the NIR reflectance depends on the abundance
of plant tissue and the red reflectance indicates the leaf surface health condition of
the plant, the NDVI can be related to plant biomass or stress. It has been shown by
researchers that time series of remote sensing data can be used effectively to
identify long-term trends and subtle changes of NDVI by means of principal
component analysis [68, 72, 73]. However, the NDVI must be used with caution
in areas with less than 20 % vegetation cover because the vegetation spectra will be
distorted by the soil reflectance. Care must also be used in areas where the
vegetation fraction is higher than 50 % and the signal is pushed into the saturated
region of the nonlinear transfer curve.
Visible, infrared, and microwave sensors have varying sensitivities to aboveground vegetation biomass. Optical and radar remote sensing methods and empirical and statistical regression models, some using NDVI, have been applied to
estimate the amount and variability of aboveground biomass [74–78]. Remote
sensing has been used to collect large amounts of biomass data on a global scale
for forested areas, such as upland forests, forested wetlands, and mangroves
[79]. Extensive work is under way with L-band SAR to develop operational
biomass programs across a range of countries, using an approach that is effective
over a biomass range of 0–200 tons/ha [80].
5.3 Biomass as a Drought Indicator
Drought is a gradual phenomenon, slowly taking hold of an area and stressing its
vegetation. In severe cases, drought can last for many years with devastating effects
on agriculture and water supplies. The underlying cause of most droughts, deficiency in precipitation, can be related to variations in large-scale atmospheric
circulation patterns and the locations of anticyclones, or high pressure systems.
Sometimes, whirling masses of air separate from the main westerly airflow and
effectively prevent the usual west to east progression of weather systems. When
these “blocking systems” persist for extended periods of time, weather extremes
such as drought, floods, heat waves, and cold snaps can occur [81].
One of the worst droughts of the twentieth century occurred in the Horn of Africa
in 1984 and 1985. Figure 5 shows the NDVI anomaly for August 1984. Within this
figure, dark red/brown indicates the most severe drought, light yellow areas are
normal, and green areas have denser than normal vegetation. This figure demonstrates the widespread areas of severe drought that can be mapped using remote
sensing techniques.
The NDVI anomaly indicates the vigor of vegetation relative to the long-term
average. Using NOAA’s AVHRR, scientists have been collecting images of the
Using Remote Sensing to Map and Monitor Water Resources in Arid and Semiarid. . .
47