296
given progress towards high resolution digital and multi-spectral temporal images
(the equivalent of videos) from space. In the next few years, people will be able to
look back in time to see how whole landscapes have changed over their lifetimes.
Linked with equivalent advances in ground-based observations, this will provide an
unprecedented view of our planet and the opportunity to tell stories of what we have
done and how things might change over our lifetimes.
The ability to observe the events and processes that have shaped our landscape
over the past 32 years (at least since 1985) has, to some extent, already been achieved
through time-lapses of Landsat sensor imagery provided by the Google Earth
Engine. Within this system, we can observe erupting volcanoes, surging glaciers,
large floods, shifting coastlines, clearing of forests and expanding cities. We have a
record of man’s impact on the planet in the recent past and some of it makes for
uneasy viewing.
The public release of the Landsat archive allowed us to have this unique perspective and many scientists have subsequently provided detailed temporal classifications of land cover. These have included forest losses and gains (Hansen et al. 2013),
tree canopy density (Hansen et al. 2013; Sexton et al. 2013), hydro-periods (Pekel
et al. 2016), open water (Feng et al. 2016), bare ground (Hansen et al. 2013), impervious surfaces (Langanke et al. 2013) and ecosystem extent and dynamics (e.g.,
mangroves; Giri et al. 2011). Using coarser spatial resolution (typically 0.25–1 km)
sensors, such as the NOAA AVHRR and MODIS, an extensive historical archive of
other features of the Earth’s surface, including snow cover (Hall and Riggs 2016)
and land and sea surface temperature (Merchant et al. 2008), has been generated
over past decades, giving us a unique insight into recent global change.
The amounts of data that have currently been acquired and will be provided in
the future are vast. However, our ability to handle large amounts of, what is often
termed big data, is being addressed through cloud and other high performance computing, with these providing substantive storage and processing capability.
Furthermore, image data can be downloaded and distributed to users rapidly and, in
some cases, in real or near real time (as in the case of the Planet Lab’s CubeSat
data). This new capability provides opportunities to understand the changes that
have happened, both over past decades and more recently, and to monitor and plan
into the future. If used effectively, these systems can be used to prevent or reverse
some of the damage that is being or has been inflicted on the planet and to conserve
what is remaining.
In this chapter, we describe the Earth Observation Data for Ecosystem Monitoring
(EODESM) system, which uses retrieved environmental variables and specified
classifications from earth observation data to characterise and map land covers.
Changes are identified by considering evidence obtained from earth observation
data and from other sources. The system provides insight into the causes and consequence of change and redistribution of physical elements (e.g., water, sediments and
carbon). The system can also be used to recommend where and how to restore or
protect ecosystems. The approach we describe is easy to understand, simple to operate and revise, and provides a wealth of information that can be used for a wide
range of purposes, including for the conservation of nature.
R. Lucas and A. Mitchell
given progress towards high resolution digital and multi-spectral temporal images
(the equivalent of videos) from space. In the next few years, people will be able to
look back in time to see how whole landscapes have changed over their lifetimes.
Linked with equivalent advances in ground-based observations, this will provide an
unprecedented view of our planet and the opportunity to tell stories of what we have
done and how things might change over our lifetimes.
The ability to observe the events and processes that have shaped our landscape
over the past 32 years (at least since 1985) has, to some extent, already been achieved
through time-lapses of Landsat sensor imagery provided by the Google Earth
Engine. Within this system, we can observe erupting volcanoes, surging glaciers,
large floods, shifting coastlines, clearing of forests and expanding cities. We have a
record of man’s impact on the planet in the recent past and some of it makes for
uneasy viewing.
The public release of the Landsat archive allowed us to have this unique perspective and many scientists have subsequently provided detailed temporal classifications of land cover. These have included forest losses and gains (Hansen et al. 2013),
tree canopy density (Hansen et al. 2013; Sexton et al. 2013), hydro-periods (Pekel
et al. 2016), open water (Feng et al. 2016), bare ground (Hansen et al. 2013), impervious surfaces (Langanke et al. 2013) and ecosystem extent and dynamics (e.g.,
mangroves; Giri et al. 2011). Using coarser spatial resolution (typically 0.25–1 km)
sensors, such as the NOAA AVHRR and MODIS, an extensive historical archive of
other features of the Earth’s surface, including snow cover (Hall and Riggs 2016)
and land and sea surface temperature (Merchant et al. 2008), has been generated
over past decades, giving us a unique insight into recent global change.
The amounts of data that have currently been acquired and will be provided in
the future are vast. However, our ability to handle large amounts of, what is often
termed big data, is being addressed through cloud and other high performance computing, with these providing substantive storage and processing capability.
Furthermore, image data can be downloaded and distributed to users rapidly and, in
some cases, in real or near real time (as in the case of the Planet Lab’s CubeSat
data). This new capability provides opportunities to understand the changes that
have happened, both over past decades and more recently, and to monitor and plan
into the future. If used effectively, these systems can be used to prevent or reverse
some of the damage that is being or has been inflicted on the planet and to conserve
what is remaining.
In this chapter, we describe the Earth Observation Data for Ecosystem Monitoring
(EODESM) system, which uses retrieved environmental variables and specified
classifications from earth observation data to characterise and map land covers.
Changes are identified by considering evidence obtained from earth observation
data and from other sources. The system provides insight into the causes and consequence of change and redistribution of physical elements (e.g., water, sediments and
carbon). The system can also be used to recommend where and how to restore or
protect ecosystems. The approach we describe is easy to understand, simple to operate and revise, and provides a wealth of information that can be used for a wide
range of purposes, including for the conservation of nature.
R. Lucas and A. Mitchell