521
This approach was compatible with the computing environment available at the
time; since then, however, huge advances in computing technology, including
greatly decreased cost and the availability and flexibility of the cloud (which, in
turn, enables new levels of co-development), have enabled alternative approaches.
In fact, these approaches are necessitated by the increase in the amount and diversity of RS data available (e.g., downloading huge volumes of data for local processing is impractical).
20.2 Remote Sensing for Global Biodiversity Monitoring:
Building on GEO BON
Taking advantage of these new opportunities requires not only a new level of agility
in algorithm and product development but also new approaches for data management and processing. For example, collaboration both within and across disciplines
is crucial because advances in science often require multidisciplinary collaboration.
Effective use of data from different sources or collected at different times and scales
requires a greater degree of data sharing and collaboration than is currently common. Not only have advances in understanding ecosystems and the interaction of
their many components made the science increasingly multidisciplinary, but technology, particularly its move toward cloud computing, makes collaboration both
easier and more natural. The workspaces the cloud provides also facilitate experimentation with and exploration of both algorithms and data, and the cloud addresses
the data volume issue by “bringing the user to the data.”
Recognition of the benefits of multidisciplinary collaboration, shared workspaces, and a shift toward cloud computing is not completely new. The NASA Earth
Exchange, for example, first became available in 2012, and Google Earth Engine
started around the same time. The ESA Thematic Exploitation Platforms, which
focus on specific themes, began in 2014, and the European Commission-funded
Data and Information Access Services started to go online in 2016. Most recently,
development began on the Multi-mission Algorithm and Analysis Platform (MAAP),
a cloud-based, joint NASA-ESA activity that will support several new missions.
Key features of MAAP include support for collaborative algorithm development as
well as flexibility in terms of which algorithms are developed or products generated.
However, despite the critical importance to society, no system has yet emerged that
provides these and the suite of other capabilities and data needed to monitor biodiversity or related ecosystem functions.
A concept for such a system is summarized as a high-level architecture in Box
20.1. It makes extensive use of RS data, particularly satellite RS, because that is the
only practical way to obtain the periodic coverage needed for regular global monitoring. As such, it augments GEO BON’s global monitoring work with a focus on
RS and cloud-based processing that can take advantage of a variety of new
technology- enabled opportunities. It also anticipates several new sensors and sensor
types, particularly imaging spectroscopy, lidar, and radar; these technologies and
their application to biodiversity monitoring are discussed in many of the book’s
20 Epilogue: Toward a Global Biodiversity Monitoring System
This approach was compatible with the computing environment available at the
time; since then, however, huge advances in computing technology, including
greatly decreased cost and the availability and flexibility of the cloud (which, in
turn, enables new levels of co-development), have enabled alternative approaches.
In fact, these approaches are necessitated by the increase in the amount and diversity of RS data available (e.g., downloading huge volumes of data for local processing is impractical).
20.2 Remote Sensing for Global Biodiversity Monitoring:
Building on GEO BON
Taking advantage of these new opportunities requires not only a new level of agility
in algorithm and product development but also new approaches for data management and processing. For example, collaboration both within and across disciplines
is crucial because advances in science often require multidisciplinary collaboration.
Effective use of data from different sources or collected at different times and scales
requires a greater degree of data sharing and collaboration than is currently common. Not only have advances in understanding ecosystems and the interaction of
their many components made the science increasingly multidisciplinary, but technology, particularly its move toward cloud computing, makes collaboration both
easier and more natural. The workspaces the cloud provides also facilitate experimentation with and exploration of both algorithms and data, and the cloud addresses
the data volume issue by “bringing the user to the data.”
Recognition of the benefits of multidisciplinary collaboration, shared workspaces, and a shift toward cloud computing is not completely new. The NASA Earth
Exchange, for example, first became available in 2012, and Google Earth Engine
started around the same time. The ESA Thematic Exploitation Platforms, which
focus on specific themes, began in 2014, and the European Commission-funded
Data and Information Access Services started to go online in 2016. Most recently,
development began on the Multi-mission Algorithm and Analysis Platform (MAAP),
a cloud-based, joint NASA-ESA activity that will support several new missions.
Key features of MAAP include support for collaborative algorithm development as
well as flexibility in terms of which algorithms are developed or products generated.
However, despite the critical importance to society, no system has yet emerged that
provides these and the suite of other capabilities and data needed to monitor biodiversity or related ecosystem functions.
A concept for such a system is summarized as a high-level architecture in Box
20.1. It makes extensive use of RS data, particularly satellite RS, because that is the
only practical way to obtain the periodic coverage needed for regular global monitoring. As such, it augments GEO BON’s global monitoring work with a focus on
RS and cloud-based processing that can take advantage of a variety of new
technology- enabled opportunities. It also anticipates several new sensors and sensor
types, particularly imaging spectroscopy, lidar, and radar; these technologies and
their application to biodiversity monitoring are discussed in many of the book’s
20 Epilogue: Toward a Global Biodiversity Monitoring System