305
in retrieved environmental variables (including spectral indices). By considering
these transitions, the EODESM system allows for automated detection of these
changes based on evidence and can highlight those change events or processes that
are adverse or beneficial, although opinion varies depending upon the nature of the
environment being affected. For example, the establishment and growth of pine
plantations may be beneficial in terms of biomass accumulation and carbon sequestration and storage but may have adverse impacts on the abundance and diversity of
faunal species.
The automated detection and description of changes over varying periods of time
and based on the accumulation of evidence often results in a large number of events
and processes being identified over the period of a time-series. However, more targeted detection and description of change may be achieved by identifying breaks
(e.g., using the BFAST algorithm; Verbesselt et al. 2010) or longer-term trends in
the time series of, for example, Normalized Difference Vegetation Index (NDVI)
data obtained from Landsat or Sentinel-2A/B data. Where an event is identified,
imagery acquired just prior to and following the date of change can be accessed. A
LCCS-2 class is then assigned and changes in the components of this class are
reviewed. When used in combination with time-series of retrieved environmental
variables, a better assessment of the change event can be provided. In the case of
longer-term processes, the transitions in component classes over the change period
(e.g., from trees to grasslands, to shrubs and back to trees in the case of regeneration
following deforestation) can be used to track the nature of change in land cover.
Changes in environmental variables can similarly be tracked. A particular advantage
of this approach is that changes can be automatically highlighted depending upon
their severity or benefit.
Causes and Consequences of Change
Often when we detect a change, there are clear drivers and consequences of this. For
example, dieback of trees may occur because of a prolonged flooding event, with
this evidenced primarily by a decrease in canopy cover. There are only a few likely
causes of the flooding, with these including those that are natural (e.g., increased
rainfall over an extended period or an intense rainfall event) or human-induced (the
creation of a dam and reservoir). The immediate consequence of the flooding is the
loss of foliage cover followed by full or partial mortality of all or some of the trees.
Follow-on consequences that would be considered negative include the loss of terrestrial elements of biodiversity and carbon in vegetation, with these occurring over
variable periods, whilst positive benefits might include an increase in aquatic biodiversity and long-term storage of carbon. The consequences may be relevant to the
specific area of ground that is affected or experienced in areas that are proximal or
even far removed. For all areas (or objects within a scene), the causes and consequences of change can often be pre-determined and hence mapped alongside the
change. The causes and, more often, the consequences of change also relate to the
Integrated Land Cover and Change Classifications
in retrieved environmental variables (including spectral indices). By considering
these transitions, the EODESM system allows for automated detection of these
changes based on evidence and can highlight those change events or processes that
are adverse or beneficial, although opinion varies depending upon the nature of the
environment being affected. For example, the establishment and growth of pine
plantations may be beneficial in terms of biomass accumulation and carbon sequestration and storage but may have adverse impacts on the abundance and diversity of
faunal species.
The automated detection and description of changes over varying periods of time
and based on the accumulation of evidence often results in a large number of events
and processes being identified over the period of a time-series. However, more targeted detection and description of change may be achieved by identifying breaks
(e.g., using the BFAST algorithm; Verbesselt et al. 2010) or longer-term trends in
the time series of, for example, Normalized Difference Vegetation Index (NDVI)
data obtained from Landsat or Sentinel-2A/B data. Where an event is identified,
imagery acquired just prior to and following the date of change can be accessed. A
LCCS-2 class is then assigned and changes in the components of this class are
reviewed. When used in combination with time-series of retrieved environmental
variables, a better assessment of the change event can be provided. In the case of
longer-term processes, the transitions in component classes over the change period
(e.g., from trees to grasslands, to shrubs and back to trees in the case of regeneration
following deforestation) can be used to track the nature of change in land cover.
Changes in environmental variables can similarly be tracked. A particular advantage
of this approach is that changes can be automatically highlighted depending upon
their severity or benefit.
Causes and Consequences of Change
Often when we detect a change, there are clear drivers and consequences of this. For
example, dieback of trees may occur because of a prolonged flooding event, with
this evidenced primarily by a decrease in canopy cover. There are only a few likely
causes of the flooding, with these including those that are natural (e.g., increased
rainfall over an extended period or an intense rainfall event) or human-induced (the
creation of a dam and reservoir). The immediate consequence of the flooding is the
loss of foliage cover followed by full or partial mortality of all or some of the trees.
Follow-on consequences that would be considered negative include the loss of terrestrial elements of biodiversity and carbon in vegetation, with these occurring over
variable periods, whilst positive benefits might include an increase in aquatic biodiversity and long-term storage of carbon. The consequences may be relevant to the
specific area of ground that is affected or experienced in areas that are proximal or
even far removed. For all areas (or objects within a scene), the causes and consequences of change can often be pre-determined and hence mapped alongside the
change. The causes and, more often, the consequences of change also relate to the
Integrated Land Cover and Change Classifications