Issue Lesson
[Challenges] The major challenge is to establish a NSDI organization that is viewed by authors as a robust clearinghouse of their spatial datasets. Authors should be glad to submit their data and should be delighted that others will have easy access, instead of having to handle ‘data requests’ every time someone wants it. The current cultural views the NSDI as an unfunded mandate, with a lot of hassles to submit data, with very little benefits in return. Trying to establish a new organization - or revamping the existing one - is always extremely difficult, and the establishment of this one is even more difficult because of the lack of the basic understanding of the true value.

[What has not worked?] US Topo is a solution looking for a problem. The focus should be more on content, rather than packaging. GeoPDF may satisfy a certain niche, but without excellent content, it serves little purpose.

[has worked] Several national seamless datasets have been very successful, including NED, NHD, NLCD, and NHDPlus. These are providing very useful data that is nationally consistent, well organized, and easy to access.

[not worked] The WRD NSDI node is just a tabular list of 646 datasets - some datasets are listed by theme, such as, ag, aquifers, etc; a lot are listed with obscure names, such as, darea, diffus, etc.; some are listed by OFR #, by SIR # and by WRIR#. How in the world can anyone find what their [sic] after? We need a better way of assigning searchable ‘key words’ to the datasets and tools that can search and retrieve datasets that meet a specified query.

[not worked] Main sticking points are access to updated, high-quality satellite-derived imagery, and access to sufficient field-based observations of vegetation (i.e., we need 500,000 current georeferenced samples – with sufficient vegetation composition and structure documented - maintained and accessible) to support map development and accuracy assessment.

[Challenges] technical challenges come mainly from a lack of certain critical data sets required to develop robust spatial models. We work across local/regional/national/ continental scales, so access to data that are standardized across these scales presents the greatest challenge.

[Domain] Our work is centered on biological data, including the characterization and assessment of ecological systems and habitats for species of concern. But in order to address this domain successfully, we rely on a wide range of non-biological data inputs, such as imagery (of varying types and resolutions), digital elevation, synthesized climate data (past, current, future), surficial geology, soils, surface drainages, wetland location, hydrography, land use, land ownership, and land use policy.

[worked well] The understanding that data needs to be consistent and of known quality so that decisions can be more easily made on how the data can/should be used.

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