298
Plant Senescence Reflectance Index (PSRI), Water Band Index (WBI) and (where
available) Canopy Height Models (CHMs) derived from LIDAR. Whilst providing
highly detailed classifications, the main limitation was the consistency in the use of
spectrally-based rules as these had to be adjusted regularly to allow for differences
in atmospheric, illumination and environmental (e.g. phenological) conditions prevalent at the time of the Worldview-2 overpasses. For this reason, a new concept was
developed for the EODESM system.
Our Unchanging World
Whilst there are significant changes in land cover arising from both natural and
human-induced events and processes, the basic building blocks of landscapes (e.g.,
foliage, wood, rocks, water in various states) generally do not change and neither do
the quantitative measures that are used to describe these (e.g. biomass, canopy
cover, amounts of dead or senescent material, species type, temperature, water
flows). The measures that satellite sensors record are largely consistent, with these
including spectral reflectance (%), radar backscatter (e.g., γ
°
), surface heights and
dimensions (m) and temperature (°C). Therefore, regardless of what happens in the
future, descriptors of the building blocks of our environment will largely be the
same, as will the data and measures obtained from satellite, airborne and groundbased systems. The challenge is to define the best algorithms and combinations of
data to describe these building blocks in a way that is consistent, reliable over time,
accurate and understandable. In effect, what is needed is a system with longevity
that will allow classifications of landscapes in, for example, 2100 to be compared to
those of the 1970s, when the Landsat sensors first acquired spectral data, and even
before then.
The FAO LCCS-2 is a taxonomy that is fundamentally well suited for providing
consistent classifications of land covers in the long term as many of its inputs are
derived from well-defined and established environmental descriptors and variables.
For example, for natural and semi-natural vegetation, key descriptors are life form,
canopy cover, the vertical and horizontal distribution of plant material, leaf type and
phenology, all of which can be derived from earth observation data acquired in different or similar modes. For this reason, rather than focusing on providing the best classification algorithm, the EODESM system places emphasis on retrieving continuous
environmental variables as well as generating thematic classifications (e.g., of life
form or leaf type), which are combined subsequently to form the LCCS-2 classes. For
purposes of nature conservation, an additional and essential descriptor is plant species or genus type (that is not considered in the LCCS classification but is derived
independently), which can be mapped remotely although is often restricted to those
that are spectrally distinct. An overview of the main layers that are required as direct
input to the LCCS-2 scheme are outlined in Table 1, with these relating to essential
variables associated with the broad categories of agriculture, biodiversity and ecosystems, human settlements, bare surfaces and water/renewable energy/climate.
R. Lucas and A. Mitchell
Plant Senescence Reflectance Index (PSRI), Water Band Index (WBI) and (where
available) Canopy Height Models (CHMs) derived from LIDAR. Whilst providing
highly detailed classifications, the main limitation was the consistency in the use of
spectrally-based rules as these had to be adjusted regularly to allow for differences
in atmospheric, illumination and environmental (e.g. phenological) conditions prevalent at the time of the Worldview-2 overpasses. For this reason, a new concept was
developed for the EODESM system.
Our Unchanging World
Whilst there are significant changes in land cover arising from both natural and
human-induced events and processes, the basic building blocks of landscapes (e.g.,
foliage, wood, rocks, water in various states) generally do not change and neither do
the quantitative measures that are used to describe these (e.g. biomass, canopy
cover, amounts of dead or senescent material, species type, temperature, water
flows). The measures that satellite sensors record are largely consistent, with these
including spectral reflectance (%), radar backscatter (e.g., γ
°
), surface heights and
dimensions (m) and temperature (°C). Therefore, regardless of what happens in the
future, descriptors of the building blocks of our environment will largely be the
same, as will the data and measures obtained from satellite, airborne and groundbased systems. The challenge is to define the best algorithms and combinations of
data to describe these building blocks in a way that is consistent, reliable over time,
accurate and understandable. In effect, what is needed is a system with longevity
that will allow classifications of landscapes in, for example, 2100 to be compared to
those of the 1970s, when the Landsat sensors first acquired spectral data, and even
before then.
The FAO LCCS-2 is a taxonomy that is fundamentally well suited for providing
consistent classifications of land covers in the long term as many of its inputs are
derived from well-defined and established environmental descriptors and variables.
For example, for natural and semi-natural vegetation, key descriptors are life form,
canopy cover, the vertical and horizontal distribution of plant material, leaf type and
phenology, all of which can be derived from earth observation data acquired in different or similar modes. For this reason, rather than focusing on providing the best classification algorithm, the EODESM system places emphasis on retrieving continuous
environmental variables as well as generating thematic classifications (e.g., of life
form or leaf type), which are combined subsequently to form the LCCS-2 classes. For
purposes of nature conservation, an additional and essential descriptor is plant species or genus type (that is not considered in the LCCS classification but is derived
independently), which can be mapped remotely although is often restricted to those
that are spectrally distinct. An overview of the main layers that are required as direct
input to the LCCS-2 scheme are outlined in Table 1, with these relating to essential
variables associated with the broad categories of agriculture, biodiversity and ecosystems, human settlements, bare surfaces and water/renewable energy/climate.
R. Lucas and A. Mitchell