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Multiscale Hydrologic Remote Sensing: Perspectives and Applications
18.1 INTRODUCTION
Environmental changes and human activities can alter the earth’s ecosystems and
biogeochemical cycles, which are critical to sustaining the earth’s living environment. Ecosystems respond continuously to environmental change and variability as
well as to numerous disturbances caused by human activities and natural events.
Responses include changes in ecosystem distribution and extent, impacts on natural
resources (e.g., food, fiber, fuel, and pharmaceutical products), ecosystem services
(e.g., treatment of water and air, climate and weather regulation, carbon and nutrient storage and cycling, habitat, maintenance of water resources), and variations in
fundamental processes, including exchanges of energy, momentum, trace gases, and
aerosols with the atmosphere, which in turn influence climate. Vegetation properties
are key elements in the study of the global carbon cycle and ecosystems. Monitoring
global vegetation properties from space can contribute significantly in improving our
understanding of land surface processes and their interactions with the atmosphere,
biogeochemical cycle, and primary productivity.
One commonly used tool in vegetation monitoring is a remote sensing–based
index such as the normalized difference vegetation index (NDVI) derived from optical satellite sensors, which are mainly dependent on the green leaf material of the
vegetation cover (Tucker 1979; Huete 1988; Myneni et al. 1995a,b; Gitelson et al.
1996). NDVI is directly related to the photosynthetic capacity (i.e., the live green
material of the vegetation) and, hence, energy absorption of plant canopies (Myneni
and Ganapol 1992; Sellers et al. 1992; Inoue et al. 2007). NDVI observations have
significantly improved our understanding of the characteristics and variability of
vegetation cover at the pixel, local, regional, and global scales. Numerous vegetation properties have been derived from NDVI. Examples include leaf area index
(Chen and Cihlar 1996; Fassnacht et al. 1997; Turner et al. 1999; Haboudane et al.
2004), photosynthetically active radiation (Asrar et al. 1984; Hatfield et al. 1984;
Choudhury 1987; Baret and Guyot 1991; Asrar et al. 1992; Myneni and Williams
1994; Friedl and Davis 1995; Myneni et al. 1997; Cohen et al. 2003), chlorophyll
concentration in leaves (Yoder and Waring 1994; Gitelson and Merzlyak 1997; Broge
and Leblanc 2000; Daughtry et al. 2000; Dawson et al. 2003; Sims and Gamon
2003), above-ground biomass (Todd et al. 1998; Labus et al. 2002; Foody et al.
2003), net primary productivity (Ruimy et al. 1994; Hunt 1994), fractional vegetation cover (Purevdorj et al. 1998; Gitelson et al. 2002), and vegetation water content
(Tucker 1980; Ceccato et al. 2001).
Calculation of the NDVI is sensitive to a number of perturbing factors that include
(1) atmospheric effects (the actual composition of the atmosphere with respect to
water vapor and aerosols); (2) clouds (deep, thin, shadow); (3) soil effects (moisture
state, color); (4) snow cover and anisotropic effects (geometry of the target); and (5)
spectral effects (different instruments). These factors introduce uncertainty in quantitative assessments. A major limitation of the NDVI and similar indices is that the
optical sensors can only monitor a very thin layer of the canopy. They cannot provide
information on woody biomass and total above-ground live carbon, which are of great
interest to carbon cycle modeling and ecological applications. However, frequent coverage and high spatial resolution are of great benefit in the application of the data.
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
18.1 INTRODUCTION
Environmental changes and human activities can alter the earth’s ecosystems and
biogeochemical cycles, which are critical to sustaining the earth’s living environment. Ecosystems respond continuously to environmental change and variability as
well as to numerous disturbances caused by human activities and natural events.
Responses include changes in ecosystem distribution and extent, impacts on natural
resources (e.g., food, fiber, fuel, and pharmaceutical products), ecosystem services
(e.g., treatment of water and air, climate and weather regulation, carbon and nutrient storage and cycling, habitat, maintenance of water resources), and variations in
fundamental processes, including exchanges of energy, momentum, trace gases, and
aerosols with the atmosphere, which in turn influence climate. Vegetation properties
are key elements in the study of the global carbon cycle and ecosystems. Monitoring
global vegetation properties from space can contribute significantly in improving our
understanding of land surface processes and their interactions with the atmosphere,
biogeochemical cycle, and primary productivity.
One commonly used tool in vegetation monitoring is a remote sensing–based
index such as the normalized difference vegetation index (NDVI) derived from optical satellite sensors, which are mainly dependent on the green leaf material of the
vegetation cover (Tucker 1979; Huete 1988; Myneni et al. 1995a,b; Gitelson et al.
1996). NDVI is directly related to the photosynthetic capacity (i.e., the live green
material of the vegetation) and, hence, energy absorption of plant canopies (Myneni
and Ganapol 1992; Sellers et al. 1992; Inoue et al. 2007). NDVI observations have
significantly improved our understanding of the characteristics and variability of
vegetation cover at the pixel, local, regional, and global scales. Numerous vegetation properties have been derived from NDVI. Examples include leaf area index
(Chen and Cihlar 1996; Fassnacht et al. 1997; Turner et al. 1999; Haboudane et al.
2004), photosynthetically active radiation (Asrar et al. 1984; Hatfield et al. 1984;
Choudhury 1987; Baret and Guyot 1991; Asrar et al. 1992; Myneni and Williams
1994; Friedl and Davis 1995; Myneni et al. 1997; Cohen et al. 2003), chlorophyll
concentration in leaves (Yoder and Waring 1994; Gitelson and Merzlyak 1997; Broge
and Leblanc 2000; Daughtry et al. 2000; Dawson et al. 2003; Sims and Gamon
2003), above-ground biomass (Todd et al. 1998; Labus et al. 2002; Foody et al.
2003), net primary productivity (Ruimy et al. 1994; Hunt 1994), fractional vegetation cover (Purevdorj et al. 1998; Gitelson et al. 2002), and vegetation water content
(Tucker 1980; Ceccato et al. 2001).
Calculation of the NDVI is sensitive to a number of perturbing factors that include
(1) atmospheric effects (the actual composition of the atmosphere with respect to
water vapor and aerosols); (2) clouds (deep, thin, shadow); (3) soil effects (moisture
state, color); (4) snow cover and anisotropic effects (geometry of the target); and (5)
spectral effects (different instruments). These factors introduce uncertainty in quantitative assessments. A major limitation of the NDVI and similar indices is that the
optical sensors can only monitor a very thin layer of the canopy. They cannot provide
information on woody biomass and total above-ground live carbon, which are of great
interest to carbon cycle modeling and ecological applications. However, frequent coverage and high spatial resolution are of great benefit in the application of the data.