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Recognizing User Needs
Individuals, groups or organisations charged with managing, conserving, protecting
and/or restoring environments desire both historical, recent and, often, real time
spatial information on landscapes. Whilst the satellite and aircraft images themselves provide a pictorial (and also digital) overview, the information extracted or
derived from these is often far more useful, particularly if this is: consistent over
time and within and between areas of interest; includes environmental variables
(e.g., biomass, soil moisture, salinity and water flows) or thematic classifications of
land cover, and changes in these; considers historical contexts, present situations
and future prospects; is provided at scales that are appropriate to the questions being
asked; and accurately reflects the state and dynamics of landscapes over varying
time frames. Accessibility of information is also critical, whether provided as products (e.g., tree cover density) or as software or processes that allow the users to
extract the required information by themselves based on their own requirements or
those of others. These requirements have been considered during the design and
development phases of the EODESM system.
The EODESM System
The EODESM System was developed through the EU Horizon 2020 Project,
ECOPOTENTIAL and was designed to provide consistent classifications of land
covers and change at multiple scales. The EODESM System was a later iteration of
the Earth Observation for Dynamic Habitat Monitoring (EODHaM; Lucas et al.
2014), which was conceptualised through the FP7 Biodiversity Multi-SOurce
Monitoring System (BIOSOS) project.
Both the EODHaM and EODESM system use the Food and Agricultural
Organisation’s (FAO’s) Land Cover Classification System (LCCS; Version 2; Di
Gregorio 2005) taxonomy to classify land covers within protected areas and their
immediate surrounds. However, for classification, the earlier EODHaM system
applied a rule-based classification to very high resolution (VHR) Worldview-2
(acquired in the pre- and peak-vegetation flush periods) and (if available) airborne
LIDAR to extract the components of the LCCS classes. These included life form
(i.e. shrubs, trees, grasses, forbs, lichens or mosses), leaf type (broadleaved, needleleaved or aphyllous), phenology (e.g., evergreen or deciduous), water movement
(standing or flowing) and sediment loads in water (turbid or clear). These extracted
components were then combined to generate a string of codes (e.g., A3.A10.B2.C1.
D1.E1), which were translated subsequently and automatically to descriptive text
(in this case, trees of closed canopy (>70–60%) that are tall (14–30 m), continuous,
broadleaved and evergreen). The classifications of each of the layers within the
EODHaM system were conducted by defining and adjusting thresholds of spectral
bands or indices, including the Normalised Difference Vegetation Index (NDVI),
Integrated Land Cover and Change Classifications
Recognizing User Needs
Individuals, groups or organisations charged with managing, conserving, protecting
and/or restoring environments desire both historical, recent and, often, real time
spatial information on landscapes. Whilst the satellite and aircraft images themselves provide a pictorial (and also digital) overview, the information extracted or
derived from these is often far more useful, particularly if this is: consistent over
time and within and between areas of interest; includes environmental variables
(e.g., biomass, soil moisture, salinity and water flows) or thematic classifications of
land cover, and changes in these; considers historical contexts, present situations
and future prospects; is provided at scales that are appropriate to the questions being
asked; and accurately reflects the state and dynamics of landscapes over varying
time frames. Accessibility of information is also critical, whether provided as products (e.g., tree cover density) or as software or processes that allow the users to
extract the required information by themselves based on their own requirements or
those of others. These requirements have been considered during the design and
development phases of the EODESM system.
The EODESM System
The EODESM System was developed through the EU Horizon 2020 Project,
ECOPOTENTIAL and was designed to provide consistent classifications of land
covers and change at multiple scales. The EODESM System was a later iteration of
the Earth Observation for Dynamic Habitat Monitoring (EODHaM; Lucas et al.
2014), which was conceptualised through the FP7 Biodiversity Multi-SOurce
Monitoring System (BIOSOS) project.
Both the EODHaM and EODESM system use the Food and Agricultural
Organisation’s (FAO’s) Land Cover Classification System (LCCS; Version 2; Di
Gregorio 2005) taxonomy to classify land covers within protected areas and their
immediate surrounds. However, for classification, the earlier EODHaM system
applied a rule-based classification to very high resolution (VHR) Worldview-2
(acquired in the pre- and peak-vegetation flush periods) and (if available) airborne
LIDAR to extract the components of the LCCS classes. These included life form
(i.e. shrubs, trees, grasses, forbs, lichens or mosses), leaf type (broadleaved, needleleaved or aphyllous), phenology (e.g., evergreen or deciduous), water movement
(standing or flowing) and sediment loads in water (turbid or clear). These extracted
components were then combined to generate a string of codes (e.g., A3.A10.B2.C1.
D1.E1), which were translated subsequently and automatically to descriptive text
(in this case, trees of closed canopy (>70–60%) that are tall (14–30 m), continuous,
broadleaved and evergreen). The classifications of each of the layers within the
EODHaM system were conducted by defining and adjusting thresholds of spectral
bands or indices, including the Normalised Difference Vegetation Index (NDVI),
Integrated Land Cover and Change Classifications