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{'author': 'Sayre, R., J. Dangermond, C. Frye, R. Vaughan, P. Aniello, S. Breyer, D. Cribbs, D. Hopkins, R. Nauman, W. Derrenbacher, D.Wright, C. Brown, C. Convis, J. Smith, L. Benson, D. Paco VanSistine, H. Warner, J. Cress, J. Danielson, S. Hamann, T. Cecere, A. Reddy, D. Burton, A. Grosse, D. True, M. Metzger, J. Hartmann, N. Moosdorf, H. Dürr, M. Paganini, P. DeFourny, O. Arino, S. Maynard, M. Anderson, and P. Comer. ', 'author_email': '', 'creator_user_id': '0ed6839a-e7cd-4504-9192-fa73b3cc8756', 'id': '03a2ff61-3021-405b-bf0b-614f3fa2ce20', 'isopen': False, 'license_id': 'cc-nc', 'license_title': 'Creative Commons Non-Commercial (Any)', 'license_url': 'http://creativecommons.org/licenses/by-nc/2.0/', 'maintainer': 'Andrew Zammit Mangion', 'maintainer_email': 'azm [at] uow.edu.au', 'metadata_created': '2015-01-25T02:11:46.436349', 'metadata_modified': '2017-02-06T02:35:59.602201', 'name': 'global-elus', 'notes': 'Description:\r\n-------\r\n\r\nIn response to the need and an intergovernmental commission for a high resolution and data-derived global ecosystem map, land surface elements of global ecological pattern were characterised in an ecophysiographic stratification of the planet. The stratification produced 3,923 terrestrial ecological land units (ELUs) at a base resolution of 250 meters. The ELUs were derived from data on land surface features in a three step approach. The first step involved acquiring or developing four global raster datalayers representing the primary components of ecosystem structure: bioclimate, landform, lithology, and land cover. These datasets generally represent the most accurate, current, globally comprehensive, and finest spatial and thematic resolution data available for each of the four inputs. The second step involved a spatial combination of the four inputs into a single, new integrated raster dataset where every cell represents a combination of values from the bioclimate, landforms, lithology, and land cover datalayers. This foundational global raster datalayer, called ecological facets (EFs), contains 47,650 unique combinations of the four inputs. The third step involved an aggregation of the EFs into the 3,923 ELUs.\r\n\r\n\r\n\r\nUse constraint:\r\n-------\r\n\r\nAlthough these data have been processed successfully on a computer system at the U.S. Geological Survey, no warranty expressed or implied is made regarding the display or utility of the data on any other system, or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. The U.S. Geological Survey shall not be held liable for improper or incorrect use of the data described and/or contained herein. We make every effort to provide and maintain accurate, complete, usable, and timely information on our Web sites. These data and information are provided with the understanding that they are not guaranteed to be correct or complete. Users are cautioned to consider carefully the provisional nature of these data and information before using them for decisions that concern personal or public safety or the conduct of business that involves substantial monetary or operational consequences. Conclusions drawn from, or actions undertaken on the basis of, such data and information are the sole responsibility of the user. Any use of trade, product or firm names is for descriptive purposes only and does not imply endorsement by the U. S. Government.\r\n\r\nData quality:\r\n-----\r\n\r\nThe primary accuracy assessment approach was to compare EFs at randomly generated points to their corresponding locations on high resolution imagery. This match of EFs to imagery was generally very high, with the following level of confirmation observed: Africa, 91%; California, 98%; Australia, 98%; and elsewhere in North America, 97%. The secondary accuracy assessment approaches included comparing ELUs to their corresponding ecosystem labels on the three GEO continental-scale ecosystem maps for South America, the conterminous United States, and Africa. The results for those comparisons were 88%, 87%, and 94%, for South America, the conterminous United States, and Africa, respectively. The comparison of EFs to other sources of thematic information yielded the following probable matches: Africa (81%), California (88%), Australia (96%) and elsewhere in North America (93%). Finally, for the Degree Confluence project points, a 100% match between the EFs and the VGI (photos and descriptions) was observed for Australia, and a 98% match was observed for elsewhere in North America.\r\n\r\nThe quality of the data used in the global stratification will obviously influence the quality of the derived ecosystem products, and anomalous values were found in each of the input layers. While some of these data quality issues are discussed below, it is important to first note that both the input layers and the output products should be considered as collaborative best efforts and works in progress, rather than definitive, current, and complete representations of their themes. The production of any high resolution, globally comprehensive datalayer that characterises a particular feature of the environment is an ambitious and sometimes very difficult undertaking. These efforts to develop and disseminate best available datasets are appreciated by the scientific community, and making the information broadly available is the best way to ensure it can be improved over time. Identification of anomalous values and other data quality issues in underlying data is important for both the understanding of unexpected results, and for the improvement of the input datasets. The bioclimates layer, as mentioned, represents an interpolated data surface from point observations obtained at meteorological stations. Some areas of the planet are not well-covered by weather stations, and the modeled climate regions in those areas (e.g. western Sahara Desert region) were developed from very little data. Moreover, we felt the original bioclimate regions were underrepresentative of aridity, and we modified the data accordingly. The landforms layer was built from a 250 m global DEM, and 250 m was the base resolution of the effort, given the big data nature of the effort and the difficulty of working at finer spatial resolutions. Nevertheless, 90 m and 30 m global DEMs do exist, and a finer spatial resolution global landforms layer could be developed. The global lithology layer, built as a compendium of a variety of best available regional and national scale lithology datasets, lacks complete attribution at all levels of the hierarchy, and does not attempt to reconcile or harmonise classes across maps from adjacent geographies produced by different organizations. The above-mentioned limitations in the data really represent opportunities for collective improvements in the characterization of ecologically important Earth surface features, and we anticipate working with these data providers and others in future collaborations to advance the quality, currency, resolution, and accessibility of Earth science data.\r\n\r\nProcedure:\r\n-----\r\n\r\nThe fundamental approach undertaken was to stratify the Earth into physically distinct areas with their associated land cover. The stratification was executed as a geospatial combination of the four input layers (bioclimate, landform, lithology, and land cover) to produce a single raster datalayer where every cell represented a unique combination of the four inputs. Following the production of the foundational raster datalayer, a data reduction step was undertaken to reduce the large number of combinations produced from the union of the input datalayers. The approach was undertaken in three steps. Step One involved acquiring or developing the four input raster base layers (bioclimates, landforms, lithology, and land cover), and reconciling them to a standard, 250 meter global raster framework. The choice of 250 m as the base resolution for the project was based on the availability of a global 250 m digital elevation model (Danielson and Gesch, 2011) whose raster framework could be used as the geospatial reference standard, as well as the desire to improve over the typical square kilometer resolution associated with many global data products (e.g. Gesch et al., 1999; Hijmans et al., 2005). Step Two involved combining all four raster inputs into a single master 250 m global raster datalayer where each cell was the resulting combination of the values from the four input rasters. This foundational raster dataset was called the ecological facets (EFs) layer. Finally, Step Three involved reducing the many classes of EFs resulting from the spatial combination into a more manageable and cartographically approachable number of ecological land units (ELUs). The aggregation was achieved by generalising the input layer attribute classes. This approach to developing global ELUs can be considered as classification neutral in the sense that no a priori ecosystem classification was used to label the mapped entities.\r\n\r\nOther:\r\n----------\r\n\r\nFor more user-friendly information on this dataset visit http://blogs.esri.com/esri/esri-insider/2014/12/09/the-first-detailed-ecological-land-unitsmap-in-the-world/. An interactive application for viewing the data is available at http://ecoexplorer.arcgis.com/eco/\r\n\r\nReferences:\r\n---------\r\n\r\nSayre, R., J. Dangermond, C. Frye, R. Vaughan, P. Aniello, S. Breyer, D. Cribbs, D. Hopkins, R. Nauman, W. Derrenbacher, D.Wright, C. Brown, C. Convis, J. Smith, L. Benson, D. Paco VanSistine, H. Warner, J. Cress, J. Danielson, S. Hamann, T. Cecere, A. Reddy, D. Burton, A. Grosse, D. True, M. Metzger, J. Hartmann, N. Moosdorf, H. Dürr, M. Paganini, P. DeFourny, O. Arino, S. Maynard, M. Anderson, and P. Comer. 2014. A New Map of Global Ecological Land Units — An Ecophysiographic Stratification Approach. Washington, DC: Association of American Geographers. 46 pages.', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': '4207b363-dfff-475d-a24c-fd97ca7f9e9a', 'name': 'cei', 'title': 'Centre for Environmental Informatics (CEI)', 'type': 'organization', 'description': '', 'image_url': 'http://niasra.uow.edu.au/content/groups/webasset/@web/@inf/@math/documents/mm/uow170808.png', 'created': '2014-11-25T12:33:11.014268', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '4207b363-dfff-475d-a24c-fd97ca7f9e9a', 'private': False, 'state': 'active', 'title': 'Global Ecological Land Units (ELUs)', 'type': 'dataset', 'url': '', 'version': '', 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2015-01-25T13:12:29.526458', 'datastore_active': False, 'description': '', 'format': 'TIF', 'hash': '', 'id': 'a9361969-8413-4e3b-9423-32262592924c', 'last_modified': None, 'metadata_modified': '2015-01-25T13:12:29.526458', 'mimetype': None, 'mimetype_inner': None, 'name': 'USGS Geosciences and Environmental Change Science Center (GECSC) Outgoing Datasets', 'package_id': '03a2ff61-3021-405b-bf0b-614f3fa2ce20', 'position': 0, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'http://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/', 'url_type': None, 'has_views': False}], 'tags': [{'display_name': 'bioclimate', 'id': '5b7ad1ae-9d20-4659-b774-c7302c969f4f', 'name': 'bioclimate', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'ecosystems', 'id': '3d088806-ca29-4558-b0a0-6f9b9d685e8e', 'name': 'ecosystems', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'land cover', 'id': 'f8aeb8b0-4202-47ea-a7a6-471bea6a47c0', 'name': 'land cover', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'landforms', 'id': '52c5e391-69d2-48f0-9f3b-cf33c56b4a20', 'name': 'landforms', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'lithology', 'id': '84bd1f5a-8b14-42aa-bc45-73b25c1ff5f4', 'name': 'lithology', 'state': 'active', 'vocabulary_id': None}], 'extras': [], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []} |
| pkg |
<Package id=03a2ff61-3021-405b-bf0b-614f3fa2ce20 name=global-elus title=Global Ecological Land Units (ELUs) version= url= author=Sayre, R., J. Dangermond, C. Frye, R. Vaughan, P. Aniello, S. Breyer, D. Cribbs, D. Hopkins, R. Nauman, W. Derrenbacher, D.Wright, C. Brown, C. Convis, J. Smith, L. Benson, D. Paco VanSistine, H. Warner, J. Cress, J. Danielson, S. Hamann, T. Cecere, A. Reddy, D. Burton, A. Grosse, D. True, M. Metzger, J. Hartmann, N. Moosdorf, H. Dürr, M. Paganini, P. DeFourny, O. Arino, S. Maynard, M. Anderson, and P. Comer. author_email= maintainer=Andrew Zammit Mangion maintainer_email=azm [at] uow.edu.au notes=Description:
-------
In response to the need and an intergovernmental commission for a high resolution and data-derived global ecosystem map, land surface elements of global ecological pattern were characterised in an ecophysiographic stratification of the planet. The stratification produced 3,923 terrestrial ecological land units (ELUs) at a base resolution of 250 meters. The ELUs were derived from data on land surface features in a three step approach. The first step involved acquiring or developing four global raster datalayers representing the primary components of ecosystem structure: bioclimate, landform, lithology, and land cover. These datasets generally represent the most accurate, current, globally comprehensive, and finest spatial and thematic resolution data available for each of the four inputs. The second step involved a spatial combination of the four inputs into a single, new integrated raster dataset where every cell represents a combination of values from the bioclimate, landforms, lithology, and land cover datalayers. This foundational global raster datalayer, called ecological facets (EFs), contains 47,650 unique combinations of the four inputs. The third step involved an aggregation of the EFs into the 3,923 ELUs.
Use constraint:
-------
Although these data have been processed successfully on a computer system at the U.S. Geological Survey, no warranty expressed or implied is made regarding the display or utility of the data on any other system, or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. The U.S. Geological Survey shall not be held liable for improper or incorrect use of the data described and/or contained herein. We make every effort to provide and maintain accurate, complete, usable, and timely information on our Web sites. These data and information are provided with the understanding that they are not guaranteed to be correct or complete. Users are cautioned to consider carefully the provisional nature of these data and information before using them for decisions that concern personal or public safety or the conduct of business that involves substantial monetary or operational consequences. Conclusions drawn from, or actions undertaken on the basis of, such data and information are the sole responsibility of the user. Any use of trade, product or firm names is for descriptive purposes only and does not imply endorsement by the U. S. Government.
Data quality:
-----
The primary accuracy assessment approach was to compare EFs at randomly generated points to their corresponding locations on high resolution imagery. This match of EFs to imagery was generally very high, with the following level of confirmation observed: Africa, 91%; California, 98%; Australia, 98%; and elsewhere in North America, 97%. The secondary accuracy assessment approaches included comparing ELUs to their corresponding ecosystem labels on the three GEO continental-scale ecosystem maps for South America, the conterminous United States, and Africa. The results for those comparisons were 88%, 87%, and 94%, for South America, the conterminous United States, and Africa, respectively. The comparison of EFs to other sources of thematic information yielded the following probable matches: Africa (81%), California (88%), Australia (96%) and elsewhere in North America (93%). Finally, for the Degree Confluence project points, a 100% match between the EFs and the VGI (photos and descriptions) was observed for Australia, and a 98% match was observed for elsewhere in North America.
The quality of the data used in the global stratification will obviously influence the quality of the derived ecosystem products, and anomalous values were found in each of the input layers. While some of these data quality issues are discussed below, it is important to first note that both the input layers and the output products should be considered as collaborative best efforts and works in progress, rather than definitive, current, and complete representations of their themes. The production of any high resolution, globally comprehensive datalayer that characterises a particular feature of the environment is an ambitious and sometimes very difficult undertaking. These efforts to develop and disseminate best available datasets are appreciated by the scientific community, and making the information broadly available is the best way to ensure it can be improved over time. Identification of anomalous values and other data quality issues in underlying data is important for both the understanding of unexpected results, and for the improvement of the input datasets. The bioclimates layer, as mentioned, represents an interpolated data surface from point observations obtained at meteorological stations. Some areas of the planet are not well-covered by weather stations, and the modeled climate regions in those areas (e.g. western Sahara Desert region) were developed from very little data. Moreover, we felt the original bioclimate regions were underrepresentative of aridity, and we modified the data accordingly. The landforms layer was built from a 250 m global DEM, and 250 m was the base resolution of the effort, given the big data nature of the effort and the difficulty of working at finer spatial resolutions. Nevertheless, 90 m and 30 m global DEMs do exist, and a finer spatial resolution global landforms layer could be developed. The global lithology layer, built as a compendium of a variety of best available regional and national scale lithology datasets, lacks complete attribution at all levels of the hierarchy, and does not attempt to reconcile or harmonise classes across maps from adjacent geographies produced by different organizations. The above-mentioned limitations in the data really represent opportunities for collective improvements in the characterization of ecologically important Earth surface features, and we anticipate working with these data providers and others in future collaborations to advance the quality, currency, resolution, and accessibility of Earth science data.
Procedure:
-----
The fundamental approach undertaken was to stratify the Earth into physically distinct areas with their associated land cover. The stratification was executed as a geospatial combination of the four input layers (bioclimate, landform, lithology, and land cover) to produce a single raster datalayer where every cell represented a unique combination of the four inputs. Following the production of the foundational raster datalayer, a data reduction step was undertaken to reduce the large number of combinations produced from the union of the input datalayers. The approach was undertaken in three steps. Step One involved acquiring or developing the four input raster base layers (bioclimates, landforms, lithology, and land cover), and reconciling them to a standard, 250 meter global raster framework. The choice of 250 m as the base resolution for the project was based on the availability of a global 250 m digital elevation model (Danielson and Gesch, 2011) whose raster framework could be used as the geospatial reference standard, as well as the desire to improve over the typical square kilometer resolution associated with many global data products (e.g. Gesch et al., 1999; Hijmans et al., 2005). Step Two involved combining all four raster inputs into a single master 250 m global raster datalayer where each cell was the resulting combination of the values from the four input rasters. This foundational raster dataset was called the ecological facets (EFs) layer. Finally, Step Three involved reducing the many classes of EFs resulting from the spatial combination into a more manageable and cartographically approachable number of ecological land units (ELUs). The aggregation was achieved by generalising the input layer attribute classes. This approach to developing global ELUs can be considered as classification neutral in the sense that no a priori ecosystem classification was used to label the mapped entities.
Other:
----------
For more user-friendly information on this dataset visit http://blogs.esri.com/esri/esri-insider/2014/12/09/the-first-detailed-ecological-land-unitsmap-in-the-world/. An interactive application for viewing the data is available at http://ecoexplorer.arcgis.com/eco/
References:
---------
Sayre, R., J. Dangermond, C. Frye, R. Vaughan, P. Aniello, S. Breyer, D. Cribbs, D. Hopkins, R. Nauman, W. Derrenbacher, D.Wright, C. Brown, C. Convis, J. Smith, L. Benson, D. Paco VanSistine, H. Warner, J. Cress, J. Danielson, S. Hamann, T. Cecere, A. Reddy, D. Burton, A. Grosse, D. True, M. Metzger, J. Hartmann, N. Moosdorf, H. Dürr, M. Paganini, P. DeFourny, O. Arino, S. Maynard, M. Anderson, and P. Comer. 2014. A New Map of Global Ecological Land Units — An Ecophysiographic Stratification Approach. Washington, DC: Association of American Geographers. 46 pages. license_id=cc-nc type=dataset owner_org=4207b363-dfff-475d-a24c-fd97ca7f9e9a creator_user_id=0ed6839a-e7cd-4504-9192-fa73b3cc8756 metadata_created=2015-01-25 02:11:46.436349 metadata_modified=2017-02-06 02:35:59.602201 private=False state=active plugin_data=None> |
| pkg_dict |
{'author': 'Sayre, R., J. Dangermond, C. Frye, R. Vaughan, P. Aniello, S. Breyer, D. Cribbs, D. Hopkins, R. Nauman, W. Derrenbacher, D.Wright, C. Brown, C. Convis, J. Smith, L. Benson, D. Paco VanSistine, H. Warner, J. Cress, J. Danielson, S. Hamann, T. Cecere, A. Reddy, D. Burton, A. Grosse, D. True, M. Metzger, J. Hartmann, N. Moosdorf, H. Dürr, M. Paganini, P. DeFourny, O. Arino, S. Maynard, M. Anderson, and P. Comer. ', 'author_email': '', 'creator_user_id': '0ed6839a-e7cd-4504-9192-fa73b3cc8756', 'id': '03a2ff61-3021-405b-bf0b-614f3fa2ce20', 'isopen': False, 'license_id': 'cc-nc', 'license_title': 'Creative Commons Non-Commercial (Any)', 'license_url': 'http://creativecommons.org/licenses/by-nc/2.0/', 'maintainer': 'Andrew Zammit Mangion', 'maintainer_email': 'azm [at] uow.edu.au', 'metadata_created': '2015-01-25T02:11:46.436349', 'metadata_modified': '2017-02-06T02:35:59.602201', 'name': 'global-elus', 'notes': 'Description:\r\n-------\r\n\r\nIn response to the need and an intergovernmental commission for a high resolution and data-derived global ecosystem map, land surface elements of global ecological pattern were characterised in an ecophysiographic stratification of the planet. The stratification produced 3,923 terrestrial ecological land units (ELUs) at a base resolution of 250 meters. The ELUs were derived from data on land surface features in a three step approach. The first step involved acquiring or developing four global raster datalayers representing the primary components of ecosystem structure: bioclimate, landform, lithology, and land cover. These datasets generally represent the most accurate, current, globally comprehensive, and finest spatial and thematic resolution data available for each of the four inputs. The second step involved a spatial combination of the four inputs into a single, new integrated raster dataset where every cell represents a combination of values from the bioclimate, landforms, lithology, and land cover datalayers. This foundational global raster datalayer, called ecological facets (EFs), contains 47,650 unique combinations of the four inputs. The third step involved an aggregation of the EFs into the 3,923 ELUs.\r\n\r\n\r\n\r\nUse constraint:\r\n-------\r\n\r\nAlthough these data have been processed successfully on a computer system at the U.S. Geological Survey, no warranty expressed or implied is made regarding the display or utility of the data on any other system, or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. The U.S. Geological Survey shall not be held liable for improper or incorrect use of the data described and/or contained herein. We make every effort to provide and maintain accurate, complete, usable, and timely information on our Web sites. These data and information are provided with the understanding that they are not guaranteed to be correct or complete. Users are cautioned to consider carefully the provisional nature of these data and information before using them for decisions that concern personal or public safety or the conduct of business that involves substantial monetary or operational consequences. Conclusions drawn from, or actions undertaken on the basis of, such data and information are the sole responsibility of the user. Any use of trade, product or firm names is for descriptive purposes only and does not imply endorsement by the U. S. Government.\r\n\r\nData quality:\r\n-----\r\n\r\nThe primary accuracy assessment approach was to compare EFs at randomly generated points to their corresponding locations on high resolution imagery. This match of EFs to imagery was generally very high, with the following level of confirmation observed: Africa, 91%; California, 98%; Australia, 98%; and elsewhere in North America, 97%. The secondary accuracy assessment approaches included comparing ELUs to their corresponding ecosystem labels on the three GEO continental-scale ecosystem maps for South America, the conterminous United States, and Africa. The results for those comparisons were 88%, 87%, and 94%, for South America, the conterminous United States, and Africa, respectively. The comparison of EFs to other sources of thematic information yielded the following probable matches: Africa (81%), California (88%), Australia (96%) and elsewhere in North America (93%). Finally, for the Degree Confluence project points, a 100% match between the EFs and the VGI (photos and descriptions) was observed for Australia, and a 98% match was observed for elsewhere in North America.\r\n\r\nThe quality of the data used in the global stratification will obviously influence the quality of the derived ecosystem products, and anomalous values were found in each of the input layers. While some of these data quality issues are discussed below, it is important to first note that both the input layers and the output products should be considered as collaborative best efforts and works in progress, rather than definitive, current, and complete representations of their themes. The production of any high resolution, globally comprehensive datalayer that characterises a particular feature of the environment is an ambitious and sometimes very difficult undertaking. These efforts to develop and disseminate best available datasets are appreciated by the scientific community, and making the information broadly available is the best way to ensure it can be improved over time. Identification of anomalous values and other data quality issues in underlying data is important for both the understanding of unexpected results, and for the improvement of the input datasets. The bioclimates layer, as mentioned, represents an interpolated data surface from point observations obtained at meteorological stations. Some areas of the planet are not well-covered by weather stations, and the modeled climate regions in those areas (e.g. western Sahara Desert region) were developed from very little data. Moreover, we felt the original bioclimate regions were underrepresentative of aridity, and we modified the data accordingly. The landforms layer was built from a 250 m global DEM, and 250 m was the base resolution of the effort, given the big data nature of the effort and the difficulty of working at finer spatial resolutions. Nevertheless, 90 m and 30 m global DEMs do exist, and a finer spatial resolution global landforms layer could be developed. The global lithology layer, built as a compendium of a variety of best available regional and national scale lithology datasets, lacks complete attribution at all levels of the hierarchy, and does not attempt to reconcile or harmonise classes across maps from adjacent geographies produced by different organizations. The above-mentioned limitations in the data really represent opportunities for collective improvements in the characterization of ecologically important Earth surface features, and we anticipate working with these data providers and others in future collaborations to advance the quality, currency, resolution, and accessibility of Earth science data.\r\n\r\nProcedure:\r\n-----\r\n\r\nThe fundamental approach undertaken was to stratify the Earth into physically distinct areas with their associated land cover. The stratification was executed as a geospatial combination of the four input layers (bioclimate, landform, lithology, and land cover) to produce a single raster datalayer where every cell represented a unique combination of the four inputs. Following the production of the foundational raster datalayer, a data reduction step was undertaken to reduce the large number of combinations produced from the union of the input datalayers. The approach was undertaken in three steps. Step One involved acquiring or developing the four input raster base layers (bioclimates, landforms, lithology, and land cover), and reconciling them to a standard, 250 meter global raster framework. The choice of 250 m as the base resolution for the project was based on the availability of a global 250 m digital elevation model (Danielson and Gesch, 2011) whose raster framework could be used as the geospatial reference standard, as well as the desire to improve over the typical square kilometer resolution associated with many global data products (e.g. Gesch et al., 1999; Hijmans et al., 2005). Step Two involved combining all four raster inputs into a single master 250 m global raster datalayer where each cell was the resulting combination of the values from the four input rasters. This foundational raster dataset was called the ecological facets (EFs) layer. Finally, Step Three involved reducing the many classes of EFs resulting from the spatial combination into a more manageable and cartographically approachable number of ecological land units (ELUs). The aggregation was achieved by generalising the input layer attribute classes. This approach to developing global ELUs can be considered as classification neutral in the sense that no a priori ecosystem classification was used to label the mapped entities.\r\n\r\nOther:\r\n----------\r\n\r\nFor more user-friendly information on this dataset visit http://blogs.esri.com/esri/esri-insider/2014/12/09/the-first-detailed-ecological-land-unitsmap-in-the-world/. An interactive application for viewing the data is available at http://ecoexplorer.arcgis.com/eco/\r\n\r\nReferences:\r\n---------\r\n\r\nSayre, R., J. Dangermond, C. Frye, R. Vaughan, P. Aniello, S. Breyer, D. Cribbs, D. Hopkins, R. Nauman, W. Derrenbacher, D.Wright, C. Brown, C. Convis, J. Smith, L. Benson, D. Paco VanSistine, H. Warner, J. Cress, J. Danielson, S. Hamann, T. Cecere, A. Reddy, D. Burton, A. Grosse, D. True, M. Metzger, J. Hartmann, N. Moosdorf, H. Dürr, M. Paganini, P. DeFourny, O. Arino, S. Maynard, M. Anderson, and P. Comer. 2014. A New Map of Global Ecological Land Units — An Ecophysiographic Stratification Approach. Washington, DC: Association of American Geographers. 46 pages.', 'num_resources': 1, 'num_tags': 5, 'organization': {'id': '4207b363-dfff-475d-a24c-fd97ca7f9e9a', 'name': 'cei', 'title': 'Centre for Environmental Informatics (CEI)', 'type': 'organization', 'description': '', 'image_url': 'http://niasra.uow.edu.au/content/groups/webasset/@web/@inf/@math/documents/mm/uow170808.png', 'created': '2014-11-25T12:33:11.014268', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': '4207b363-dfff-475d-a24c-fd97ca7f9e9a', 'private': False, 'state': 'active', 'title': 'Global Ecological Land Units (ELUs)', 'type': 'dataset', 'url': '', 'version': '', 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2015-01-25T13:12:29.526458', 'datastore_active': False, 'description': '', 'format': 'TIF', 'hash': '', 'id': 'a9361969-8413-4e3b-9423-32262592924c', 'last_modified': None, 'metadata_modified': '2015-01-25T13:12:29.526458', 'mimetype': None, 'mimetype_inner': None, 'name': 'USGS Geosciences and Environmental Change Science Center (GECSC) Outgoing Datasets', 'package_id': '03a2ff61-3021-405b-bf0b-614f3fa2ce20', 'position': 0, 'resource_type': None, 'size': None, 'state': 'active', 'url': 'http://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/', 'url_type': None, 'has_views': False}], 'tags': [{'display_name': 'bioclimate', 'id': '5b7ad1ae-9d20-4659-b774-c7302c969f4f', 'name': 'bioclimate', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'ecosystems', 'id': '3d088806-ca29-4558-b0a0-6f9b9d685e8e', 'name': 'ecosystems', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'land cover', 'id': 'f8aeb8b0-4202-47ea-a7a6-471bea6a47c0', 'name': 'land cover', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'landforms', 'id': '52c5e391-69d2-48f0-9f3b-cf33c56b4a20', 'name': 'landforms', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'lithology', 'id': '84bd1f5a-8b14-42aa-bc45-73b25c1ff5f4', 'name': 'lithology', 'state': 'active', 'vocabulary_id': None}], 'extras': [], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []} |
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