140
Chapter 15
change detection accuracy and might lead to the detection of spurious
changes (Lambin, 1996).
3.3
Landscape heterogeneity
A major attribute of a landscape is its spatial pattern, i.e., the
arrangement in space of its different elements. The concept of landscape
spatial pattern covers, for example, the patch size distribution of residual
forests, the location of agricultural plots in relation to natural vegetation, the
shapes of fields or the number, types and configuration of landscape
elements (i.e., their spatial heterogeneity). Landscape spatial pattern is
seldom static due both to natural changes in vegetation and human
intervention. The spatial dynamics of landscapes interact with ecological
processes which have important spatial components (Turner, 1989), such as
flows of energy and matter between landscape components, biological
productivity, bio-diversity, or the spread of disturbances. Remote sensing
offers the possibility to analyze changes in spatial structure at the scale of
landscapes. Indicators of the degradation of the vegetation cover can be
derived from such measures (Jupp, Walker and Pendridge, 1986; De Pietri,
1995).
4.
MODEL INVERSION
Physically based approaches, relying on model inversion, have been
developed to derive biophysical surface attributes (e.g., Goel and Strebel,
1983; Li and Strahler, 1986; Pinty and Verstraete, 1992; Gobron et al.,
1997). In these methods, a scene model is constructed to describe the form
and nature of the energy and matter within the scene and their spatial and
temporal order (Strahler et al., 1986). The scene model is coupled with an
atmospheric and a sensor model. This coupled model is then inverted against
the remote sensing data to infer, from these measurements, some of the
properties or parameters of the scene which were unknown (Strahler et al.,
1986). In the case of complex landscapes, these methods are not yet
sufficiently robust to be applied routinely to the large-scale monitoring of
land-cover changes, due to the difficulty for representing adequately in a
model the natural variability in landscape structure and composition.
In the future, modeling approaches should allow the scientific
community to derive surface variables such as hemispherical albedo, land
surface temperature and emissivity, soil moisture, snow cover, leaf area
index, net primary production, total biomass, evapotranspiration, incident
short-wave radiation, outgoing long-wave radiation, photosynthetically
Chapter 15
change detection accuracy and might lead to the detection of spurious
changes (Lambin, 1996).
3.3
Landscape heterogeneity
A major attribute of a landscape is its spatial pattern, i.e., the
arrangement in space of its different elements. The concept of landscape
spatial pattern covers, for example, the patch size distribution of residual
forests, the location of agricultural plots in relation to natural vegetation, the
shapes of fields or the number, types and configuration of landscape
elements (i.e., their spatial heterogeneity). Landscape spatial pattern is
seldom static due both to natural changes in vegetation and human
intervention. The spatial dynamics of landscapes interact with ecological
processes which have important spatial components (Turner, 1989), such as
flows of energy and matter between landscape components, biological
productivity, bio-diversity, or the spread of disturbances. Remote sensing
offers the possibility to analyze changes in spatial structure at the scale of
landscapes. Indicators of the degradation of the vegetation cover can be
derived from such measures (Jupp, Walker and Pendridge, 1986; De Pietri,
1995).
4.
MODEL INVERSION
Physically based approaches, relying on model inversion, have been
developed to derive biophysical surface attributes (e.g., Goel and Strebel,
1983; Li and Strahler, 1986; Pinty and Verstraete, 1992; Gobron et al.,
1997). In these methods, a scene model is constructed to describe the form
and nature of the energy and matter within the scene and their spatial and
temporal order (Strahler et al., 1986). The scene model is coupled with an
atmospheric and a sensor model. This coupled model is then inverted against
the remote sensing data to infer, from these measurements, some of the
properties or parameters of the scene which were unknown (Strahler et al.,
1986). In the case of complex landscapes, these methods are not yet
sufficiently robust to be applied routinely to the large-scale monitoring of
land-cover changes, due to the difficulty for representing adequately in a
model the natural variability in landscape structure and composition.
In the future, modeling approaches should allow the scientific
community to derive surface variables such as hemispherical albedo, land
surface temperature and emissivity, soil moisture, snow cover, leaf area
index, net primary production, total biomass, evapotranspiration, incident
short-wave radiation, outgoing long-wave radiation, photosynthetically