523
Box 20.2 Some Key Characteristics of a Global Biodiversity Monitoring
System
• Easy collaboration and facilitation of cross-discipline interactions. This
enhances algorithm development, scientific experimentation, and
applications.
• Agility and flexibility in algorithm development and data processing
approaches. This facilitates a broad range of algorithms and products,
makes the system responsive to the needs of users of all types, and can
increase product quality.
• Integration – including fusion among different sensors, such as optical and
radar, as well as among RS and in-situ data. Integration also involves
addressing the challenge of data interpretation across spatial and temporal
scales. The data already available provides opportunities that have not yet
been fully utilized, in part because different types of data are handled by
different communities.
• Simplified processing. Development of analysis-ready data will save both
algorithm development time and computer resources. Analysis-ready data
are standardized data for which some key processing steps, such as atmospheric correction, have already been executed.
• Utilization of advancing technologies. These include those related to sensors, such as imaging spectroscopy, thermal, radar, and lidar, but also of
genomic technology, and those related to processing huge volumes of time
series data in the cloud.
• Derived products and tools to increase usability and understanding. To
extract the full value from data acquired and the products derived from it,
many users, particularly decision-makers such as land managers, will need
more highly derived products as well as tools to help them understand what
the data mean and the problems they can address. A cloud-based system
can facilitate development, generation, analysis, and sharing of derived
products.
• Increased access to in-situ, flux tower, and airborne or UAV RS data. These
data are absolutely essential because they tie the spaceborne data to what
is happening on the ground. Although a tremendous amount of this type of
data has been collected, only a fraction of it is accessible for integration
with satellite RS data. These data will be needed to take full advantage of
the opportunities discussed, but accessing them is a challenge.
of the key characteristics that such a system should have. Most of these explicitly
take advantage of advancing science and technology, but there are some that technology alone cannot enable. Addressing these is important because they limit the
value that a system can extract from RS data and thus the value of those data to
society. By addressing the challenges of integrating vastly different data types
20 Epilogue: Toward a Global Biodiversity Monitoring System
Box 20.2 Some Key Characteristics of a Global Biodiversity Monitoring
System
• Easy collaboration and facilitation of cross-discipline interactions. This
enhances algorithm development, scientific experimentation, and
applications.
• Agility and flexibility in algorithm development and data processing
approaches. This facilitates a broad range of algorithms and products,
makes the system responsive to the needs of users of all types, and can
increase product quality.
• Integration – including fusion among different sensors, such as optical and
radar, as well as among RS and in-situ data. Integration also involves
addressing the challenge of data interpretation across spatial and temporal
scales. The data already available provides opportunities that have not yet
been fully utilized, in part because different types of data are handled by
different communities.
• Simplified processing. Development of analysis-ready data will save both
algorithm development time and computer resources. Analysis-ready data
are standardized data for which some key processing steps, such as atmospheric correction, have already been executed.
• Utilization of advancing technologies. These include those related to sensors, such as imaging spectroscopy, thermal, radar, and lidar, but also of
genomic technology, and those related to processing huge volumes of time
series data in the cloud.
• Derived products and tools to increase usability and understanding. To
extract the full value from data acquired and the products derived from it,
many users, particularly decision-makers such as land managers, will need
more highly derived products as well as tools to help them understand what
the data mean and the problems they can address. A cloud-based system
can facilitate development, generation, analysis, and sharing of derived
products.
• Increased access to in-situ, flux tower, and airborne or UAV RS data. These
data are absolutely essential because they tie the spaceborne data to what
is happening on the ground. Although a tremendous amount of this type of
data has been collected, only a fraction of it is accessible for integration
with satellite RS data. These data will be needed to take full advantage of
the opportunities discussed, but accessing them is a challenge.
of the key characteristics that such a system should have. Most of these explicitly
take advantage of advancing science and technology, but there are some that technology alone cannot enable. Addressing these is important because they limit the
value that a system can extract from RS data and thus the value of those data to
society. By addressing the challenges of integrating vastly different data types
20 Epilogue: Toward a Global Biodiversity Monitoring System