387
quickly. The examples included in this chapter are intended to illustrate what worked
in particular situations and to point out pitfalls to avoid; many other approaches are
as valuable. Flexibility (knowledge about different techniques and tools, a plan B,
etc.) is important for adjusting to particular circumstances and challenges. The “best
practice” is probably to learn about several “good practices”; read protocols; talk to
field ecologists, data administrators, geographic information system (GIS) professionals, programmers, and communication experts; and get some hands-on experience. A selection of excellent protocols is available from Australia’s Terrestrial
Ecosystem Research Network (TERN, http://www.auscover.org.au/wp-content/
uploads/AusCover-Good-Practice-Guidelines_web.pdf), the Field Spectroscopy
Facility at UK’s Natural Environment Research Council (NERC, http://fsf.nerc.
ac.uk/resources/guides/), the US National Ecological Observatory Network (NEON,
http://data.neonscience.org/documents), the Global Airborne Observatory (GAO,
https://gao.asu.edu/spectranomics), and the Canadian Airborne Biodiversity
Observatory (CABO, http://www.caboscience.org), among others. For more indepth coverage of particular topics, see texts on the general principles of RS (e.g.,
Warner et al. 2009) and RS of vegetation (e.g., Jones and Vaughan 2010; Thenkabail
et al. 2012), field methods in RS (e.g., McCoy 2005), spatial statistics (e.g., Stein
et al. 2002), vegetation sampling (e.g., Bonham 2013), and plant trait measurements
(e.g., Perez-Harguindeguy et al. 2013).
15.1.1 Why Plan? The Data Life Cycle
Central to every field campaign are the research questions and proposed explanations outlined in the form of testable hypotheses. It seems natural that planning the
science (What data do we need to tackle our questions? What methods are available?) and planning the logistics (Where do we collect data and when? What
resources do we need?) often rank above planning data organization and communication. However, starting a project with a data management plan (DMP) has a series
of advantages. A DMP integrates several planning aspects in a structured way; it
ensures the long-term sustainability of a project and its data, which is important not
only because sustainability furthers scientific advancement (e.g., through data sharing and the reuse of data in meta-analysis) but also because it provides accountability for spending resources on research. DMPs are usually required in research
proposals and make, through self-defined standards on data acquisition, data formats, documentation, and archiving, scientific work, including collaborations, more
effective.
Funding sources often have their own guidelines about the structure and content
of a DMP. Although only some of them might be required or relevant for a particular
project, common components include:
• Data collection and documentation: description of the types, formats, and volumes of data and samples and other materials collected, observed, or generated
15 Spectral Field Campaigns: Planning and Data Collection
quickly. The examples included in this chapter are intended to illustrate what worked
in particular situations and to point out pitfalls to avoid; many other approaches are
as valuable. Flexibility (knowledge about different techniques and tools, a plan B,
etc.) is important for adjusting to particular circumstances and challenges. The “best
practice” is probably to learn about several “good practices”; read protocols; talk to
field ecologists, data administrators, geographic information system (GIS) professionals, programmers, and communication experts; and get some hands-on experience. A selection of excellent protocols is available from Australia’s Terrestrial
Ecosystem Research Network (TERN, http://www.auscover.org.au/wp-content/
uploads/AusCover-Good-Practice-Guidelines_web.pdf), the Field Spectroscopy
Facility at UK’s Natural Environment Research Council (NERC, http://fsf.nerc.
ac.uk/resources/guides/), the US National Ecological Observatory Network (NEON,
http://data.neonscience.org/documents), the Global Airborne Observatory (GAO,
https://gao.asu.edu/spectranomics), and the Canadian Airborne Biodiversity
Observatory (CABO, http://www.caboscience.org), among others. For more indepth coverage of particular topics, see texts on the general principles of RS (e.g.,
Warner et al. 2009) and RS of vegetation (e.g., Jones and Vaughan 2010; Thenkabail
et al. 2012), field methods in RS (e.g., McCoy 2005), spatial statistics (e.g., Stein
et al. 2002), vegetation sampling (e.g., Bonham 2013), and plant trait measurements
(e.g., Perez-Harguindeguy et al. 2013).
15.1.1 Why Plan? The Data Life Cycle
Central to every field campaign are the research questions and proposed explanations outlined in the form of testable hypotheses. It seems natural that planning the
science (What data do we need to tackle our questions? What methods are available?) and planning the logistics (Where do we collect data and when? What
resources do we need?) often rank above planning data organization and communication. However, starting a project with a data management plan (DMP) has a series
of advantages. A DMP integrates several planning aspects in a structured way; it
ensures the long-term sustainability of a project and its data, which is important not
only because sustainability furthers scientific advancement (e.g., through data sharing and the reuse of data in meta-analysis) but also because it provides accountability for spending resources on research. DMPs are usually required in research
proposals and make, through self-defined standards on data acquisition, data formats, documentation, and archiving, scientific work, including collaborations, more
effective.
Funding sources often have their own guidelines about the structure and content
of a DMP. Although only some of them might be required or relevant for a particular
project, common components include:
• Data collection and documentation: description of the types, formats, and volumes of data and samples and other materials collected, observed, or generated
15 Spectral Field Campaigns: Planning and Data Collection