139
pattern. These information requirements correspond to the three major
information sources provided by remote sensing.
3.
SURFACE ATTRIBUTES REQUIRED FOR
CHANGE DETECTION
3.1
Biophysical variables
Rather than detecting changes on the basis of land-cover categories,
change detection is better performed on the basis of the continuous variables
defining these categories, whether these are reflectance values measured by a
satellite sensor or biophysical attributes derived by model inversion. Coppin
and Bauer (1996) review techniques used for this comparison, such as image
differencing, image ratioing, multi-spectral or multi-temporal change vector
analysis, image regression or multi-temporal linear data transformation.
Empirical studies demonstrated that there is not a single optimal change
detection technique but that different techniques are best suited for different
change patterns.
3.2
Seasonal variations
Land-cover changes take place at a variety of temporal scales, e.g.,
short events with detectable effects only for a few months, modifications in
seasonal trajectories of ecosystem attributes, processes that affect the land
cover through several seasonal cycles and long-term, permanent changes.
Land-cover changes may affect, and therefore be indicated by, the
phenology of the vegetation cover. The analysis of the temporal trajectories
of vegetation indices based on high temporal frequency remote sensing data
allows to monitor vegetation phenology and biome seasonality (Justice et al.,
1985).
Processes such as a shortening of the growing season, a de-phasing of
the phenology of different vegetation layers or modifications of the cover
due to disturbances such as fires can only be detected if inter-annual changes
in the seasonal trajectories of vegetation covers are analyzed. For any
landscape with a strong seasonal signal, the detection of inter-annual
changes needs to explicitly take into account the fine scale temporal
variations. If data from only one or a few dates a year are used to measure
inter-annual changes, the under-sampling of the temporal series hinders the
15. LAND-COVER CATEGORIES VERSUS BIOPHYSICAL
ATTRIBUTES TO MONITOR LAND-COVER CHANGE BY
REMOTE SENSING
pattern. These information requirements correspond to the three major
information sources provided by remote sensing.
3.
SURFACE ATTRIBUTES REQUIRED FOR
CHANGE DETECTION
3.1
Biophysical variables
Rather than detecting changes on the basis of land-cover categories,
change detection is better performed on the basis of the continuous variables
defining these categories, whether these are reflectance values measured by a
satellite sensor or biophysical attributes derived by model inversion. Coppin
and Bauer (1996) review techniques used for this comparison, such as image
differencing, image ratioing, multi-spectral or multi-temporal change vector
analysis, image regression or multi-temporal linear data transformation.
Empirical studies demonstrated that there is not a single optimal change
detection technique but that different techniques are best suited for different
change patterns.
3.2
Seasonal variations
Land-cover changes take place at a variety of temporal scales, e.g.,
short events with detectable effects only for a few months, modifications in
seasonal trajectories of ecosystem attributes, processes that affect the land
cover through several seasonal cycles and long-term, permanent changes.
Land-cover changes may affect, and therefore be indicated by, the
phenology of the vegetation cover. The analysis of the temporal trajectories
of vegetation indices based on high temporal frequency remote sensing data
allows to monitor vegetation phenology and biome seasonality (Justice et al.,
1985).
Processes such as a shortening of the growing season, a de-phasing of
the phenology of different vegetation layers or modifications of the cover
due to disturbances such as fires can only be detected if inter-annual changes
in the seasonal trajectories of vegetation covers are analyzed. For any
landscape with a strong seasonal signal, the detection of inter-annual
changes needs to explicitly take into account the fine scale temporal
variations. If data from only one or a few dates a year are used to measure
inter-annual changes, the under-sampling of the temporal series hinders the
15. LAND-COVER CATEGORIES VERSUS BIOPHYSICAL
ATTRIBUTES TO MONITOR LAND-COVER CHANGE BY
REMOTE SENSING