306
movement of materials within a landscape. For example, a deforestation event
within a catchment can result in the loss of carbon to the atmosphere, reduced
uptake of carbon dioxide (CO 2 ), the increased movement of water through the
catchment and the transfer of sediment down slope, into water courses and ultimately to coastal regions. This movement of material can be modelled but can also
often be observed within earth observation data or quantified within derived products (e.g., temporal vegetation biomass maps reflecting the accumulation of
carbon).
The EODESM system has been designed to associate a change (described
through the accumulation of evidence) with a number of causes and consequences
(including movements of materials), which the most likely determined through consideration of evidence. As such, the system provides a range of users (e.g., scientists, nature conservation managers, politicians) with knowledge that can be used to
make informed decisions on many aspects of the landscape relating to, for example,
emergency response to adverse events, land management over varying time frames,
the impacts of past and current policies and planning future landscapes.
Concluding Remarks
Using the vast archives of historical earth observation data and new concepts, such
as those developed through the EODESM system, we can already place ourselves
within a landscape, both currently and at specific points over the past 30 or so years,
and describe the key elements relating to vegetation, water, bare areas and artificial
and cultivated environments. This capacity has been enhanced considerably through
the recent provision of near daily data from multiple sensors on board satellites
including the RapidEye, Planetscope and Sentinel-1/2 and viewing platforms such
as Google Earth, Google Earth Engine and Planet Lab's Explorer. Through knowledge of past landscapes, we can now better understand the reasons for their composition today and plan for future landscapes that balance human use of the land with
the requirements of its flora and fauna, with this ultimately leading to societal and
economical benefits. This capacity is set to increase significantly with advances in
computing technology and engineering. This will also raise our understanding of
the environment and how it functions and adapts in response to change. The ability
to observe changes now and back in time and into the future is therefore becoming
possible and the path is open for this to occur routinely and on demand.
These new advances create significant opportunities for nature conservation as
events and processes within the landscape can now be observed in near real time and
historically. Much of the perceived complexity in obtaining and pre-processing
imagery, and extracting information that is of practical use, has been overcome by
the provision of analysis ready datasets and classification and change detection systems such as EODESM. Many of the algorithms used for the retrieval of environmental variables and classifications of landscape, as well as local to global products,
are becoming openly and freely available and transparent, with these generated by
R. Lucas and A. Mitchell
movement of materials within a landscape. For example, a deforestation event
within a catchment can result in the loss of carbon to the atmosphere, reduced
uptake of carbon dioxide (CO 2 ), the increased movement of water through the
catchment and the transfer of sediment down slope, into water courses and ultimately to coastal regions. This movement of material can be modelled but can also
often be observed within earth observation data or quantified within derived products (e.g., temporal vegetation biomass maps reflecting the accumulation of
carbon).
The EODESM system has been designed to associate a change (described
through the accumulation of evidence) with a number of causes and consequences
(including movements of materials), which the most likely determined through consideration of evidence. As such, the system provides a range of users (e.g., scientists, nature conservation managers, politicians) with knowledge that can be used to
make informed decisions on many aspects of the landscape relating to, for example,
emergency response to adverse events, land management over varying time frames,
the impacts of past and current policies and planning future landscapes.
Concluding Remarks
Using the vast archives of historical earth observation data and new concepts, such
as those developed through the EODESM system, we can already place ourselves
within a landscape, both currently and at specific points over the past 30 or so years,
and describe the key elements relating to vegetation, water, bare areas and artificial
and cultivated environments. This capacity has been enhanced considerably through
the recent provision of near daily data from multiple sensors on board satellites
including the RapidEye, Planetscope and Sentinel-1/2 and viewing platforms such
as Google Earth, Google Earth Engine and Planet Lab's Explorer. Through knowledge of past landscapes, we can now better understand the reasons for their composition today and plan for future landscapes that balance human use of the land with
the requirements of its flora and fauna, with this ultimately leading to societal and
economical benefits. This capacity is set to increase significantly with advances in
computing technology and engineering. This will also raise our understanding of
the environment and how it functions and adapts in response to change. The ability
to observe changes now and back in time and into the future is therefore becoming
possible and the path is open for this to occur routinely and on demand.
These new advances create significant opportunities for nature conservation as
events and processes within the landscape can now be observed in near real time and
historically. Much of the perceived complexity in obtaining and pre-processing
imagery, and extracting information that is of practical use, has been overcome by
the provision of analysis ready datasets and classification and change detection systems such as EODESM. Many of the algorithms used for the retrieval of environmental variables and classifications of landscape, as well as local to global products,
are becoming openly and freely available and transparent, with these generated by
R. Lucas and A. Mitchell