280
J.C. Ritchie
detennine inputs for soil erosion models with good success (i.e., Wilson et a!. 1989;
Morgan and Nalepa 1982).
Pelletier (1985) provided a framework for combining soil erosion models with
remotely sensed data in a Geographic Information System to expand the applications
of soil erosion models and to improve our understanding of soil erosion processes
across the landscape. Pelletier showed that remotely sensed data could be used more
objectively and efficiently with a GIS to evaluate the landscape for problem areas of
soil erosion.
Jiirgens and Fander (1993) use the USLE, remote sensing data, and a GIS to develop soil erosion risk maps (see Color Plates 12.C and 12.D). Using their system
they simulated soil protection measures on the landscape and detennine their effectiveness based on their soil risk maps. They concluded that remote sensing data in
combination with GIS was the most efficient way to simulate soil protection measures
and select sites for agricultural production with the least soil erosion risk. Similarly,
Hession and Shanholtz (1988) used the USLE, remote sensing data, and a GIS to
estimate sediment loading from the Chesapeake Bay catchment and developed a
method for targeting efforts on the catchment where soil conservation practices should
be placed to reduce soil erosion.
12.4.3 Spectral Properties
Soil erosion removes the surface soil layer exposing different layers with different
spectral properties. Based on this concept, Robinove et a!. (1981) proposed using
albedo differences between Landsat overpasses to monitor arid land soil erosion and
degradation. They calculated albedo from Landsat MSS digital data and found that
decreases in albedo was related to improved land use patterns (more soil moisture,
organic matter, and increased vegetation productivity) and increases in albedo related
to soil degradation (erosion, low soil moisture, organic matter, and productivity).
Using albedo difference between different Landsat images they were able to identify
areas of soil degradation and erosion in cold desert areas of the southwestern United
States. Frank (1984) also used albedo differences from Landsat to assess change in
the surface characteristics of a semiarid rangeland in Utah and reached the same
conclusion as Robinove. Landsat radiance parameters have also been used to distinguish soil erosion, stability, and degradation in arid central Australia (Pickup and
Chewing 1988). These techniques have been used extensively by international
organizations (F AO) to map soil degradation in developing countries.
Seubert et a!. (1979) used laboratory measurements of spectral properties of the
surface soil to distinguish between slightly, moderately, and severely eroded areas in
agricultural lands. Latz et al (1984) expanded on this study and showed that these
spectral differences were due to the amounts of iron oxide and organic matter in the
soil surface. They found that the differences in spectral intensity were related to the
severity of soil erosion and suggested that Landsat data would be useful in detecting
soil erosion. Pelletier and Griffm (1985) used Landsat data to map soil erosion by
using spectral data to identifying areas with higher iron oxide concentration (reddish
color) of the subsoil soil. They used the presence of iron oxide to map eroded areas
J.C. Ritchie
detennine inputs for soil erosion models with good success (i.e., Wilson et a!. 1989;
Morgan and Nalepa 1982).
Pelletier (1985) provided a framework for combining soil erosion models with
remotely sensed data in a Geographic Information System to expand the applications
of soil erosion models and to improve our understanding of soil erosion processes
across the landscape. Pelletier showed that remotely sensed data could be used more
objectively and efficiently with a GIS to evaluate the landscape for problem areas of
soil erosion.
Jiirgens and Fander (1993) use the USLE, remote sensing data, and a GIS to develop soil erosion risk maps (see Color Plates 12.C and 12.D). Using their system
they simulated soil protection measures on the landscape and detennine their effectiveness based on their soil risk maps. They concluded that remote sensing data in
combination with GIS was the most efficient way to simulate soil protection measures
and select sites for agricultural production with the least soil erosion risk. Similarly,
Hession and Shanholtz (1988) used the USLE, remote sensing data, and a GIS to
estimate sediment loading from the Chesapeake Bay catchment and developed a
method for targeting efforts on the catchment where soil conservation practices should
be placed to reduce soil erosion.
12.4.3 Spectral Properties
Soil erosion removes the surface soil layer exposing different layers with different
spectral properties. Based on this concept, Robinove et a!. (1981) proposed using
albedo differences between Landsat overpasses to monitor arid land soil erosion and
degradation. They calculated albedo from Landsat MSS digital data and found that
decreases in albedo was related to improved land use patterns (more soil moisture,
organic matter, and increased vegetation productivity) and increases in albedo related
to soil degradation (erosion, low soil moisture, organic matter, and productivity).
Using albedo difference between different Landsat images they were able to identify
areas of soil degradation and erosion in cold desert areas of the southwestern United
States. Frank (1984) also used albedo differences from Landsat to assess change in
the surface characteristics of a semiarid rangeland in Utah and reached the same
conclusion as Robinove. Landsat radiance parameters have also been used to distinguish soil erosion, stability, and degradation in arid central Australia (Pickup and
Chewing 1988). These techniques have been used extensively by international
organizations (F AO) to map soil degradation in developing countries.
Seubert et a!. (1979) used laboratory measurements of spectral properties of the
surface soil to distinguish between slightly, moderately, and severely eroded areas in
agricultural lands. Latz et al (1984) expanded on this study and showed that these
spectral differences were due to the amounts of iron oxide and organic matter in the
soil surface. They found that the differences in spectral intensity were related to the
severity of soil erosion and suggested that Landsat data would be useful in detecting
soil erosion. Pelletier and Griffm (1985) used Landsat data to map soil erosion by
using spectral data to identifying areas with higher iron oxide concentration (reddish
color) of the subsoil soil. They used the presence of iron oxide to map eroded areas