GO Back to Favourite Websites GO Back to Favourite Websites 
go
to Marks site(but then he does have a PHd)
THIS MATERIAL IS ALL ©COPYRIGHT Dr M. J. Chopping PHd
BRDF Applications in Semiarid Grassland Monitoring with the AVHRR
1. Objectives : what do we want to do?
The aim of all Earth Observation is to gather information on
the target surface and for land surfaces this generally means
information on the status of vegetation and soils. There are two
main approaches to this task : the "image-centred" approach
and the "data-centred" approach (Schowengerdt, 1997).
Broadly, the former uses the spatial relationships between features
in the remotely-sensed image and requires high spatial resolution,
while the latter uses the dimension of the data itself to infer
surface properties. For sensors with a large IFOV (instantaneous
field-of-view) and which scan at large angles relative to the
surface, such as the AVHRR (NOAA) the ATSR-2 (ESA), POLDER (CNES),
VEGETATION (CNES) MODIS (NASA) and MISR (NASA), the focus is largely
on data-centred information extraction over large areas.
For monitoring semiarid grasslands (~20% of terrestrial surface)
the objective is to provide reliable and consistent quantitative
indicators of vegetation status over large areas and over long
periods of time. We might also be interested in vegetation dynamics,
that is, short-term changes in grassland condition, which can
be related to various forms of disturbance (grazing, drought,
fire, rodent infestation, flooding). Spectral vegetation indices
calculated as ratios of visible and near-infrared reflectances
are often used to provide an indication of vegetation cover and
vigour, although none are sensitive to the entire range of values
(i.e. both sparse and dense vegetation). It is therefore important
to know what kind of grassland biome is being sensed. This can
be seen as a hard classification task (classification of each
sample into discrete cateogories) or as a soft classification
task (estimating proportions of elements or end-members making
up the reflectance value sample). Some success has been obtained
with the soft classification approach via linear spectral unmixing,
although this is less widely adopted with AVHRR since there are
only two channels in the reflective domain. Hard land cover classifications
have been achieved using data from the AVHRR with some success
at various scales; for example, broad land cover classes such
as cropland, forest, desert, urban areas and water bodies are
easily discriminated and it has even been possible to distinguish
subclasses within certain of these broad categories, such as different
crop varieties, forest types and grassland biomes. See these examples.
However, problems arise when attempts are made to differentiate
more subtle features, such as grasslands with different species
composition, an application which may be termed community type
differentiation (Kremer and Running, 1993; Trodd et al,
1997). This is particularly important for the monitoring application
because one of the most consistent indicators of incipient degradation
in semiarid grasslands is invasion of vulnerable areas by shrubs
and annuals (see also), reducing the space available for more
useful perennial species (which also help to maintain a stable
topsoil). If a temporal dimension is added, changes in the locations
of the community types as a result of secondary succession will
indicate where degradation or restoration are occuring so that
appropriate action can be taken. This is important in monitoring
trends in desertification and its rehabilitation, especially for
susceptible regions of the world such as northern China. See the
CIESIN server for more information on the status and extent of
land degradation and desertification worldwide.
Major objectives therefore include:
* discrimination of grassland community types and
* provision of reliable and consistent vegetation indicators
to allow monitoring changes in type location and condition through time.
------------------------------------------------------------------------