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Description: Data_IDA code which corresponds to the source database of the features, listed on the Google Document "methods_notes_data_etc" (Owner: Marc Lambruschi, Field Museum of Natural History).PropNameThe most common name for this property.SubunitAny subunits or alternate names for this property.OwnerOwner of this property.ManagerParty in charge of management decisions, conservation-related and otherwise.SourceOriginal source of data.DedicatedIndicates a state-dedicated nature preserve.YearYear dataset was downloaded.NaturalSite_IDAccessStatus of public accessibility.ORIG_FIDIdentifier from data's original source.PropClassClass or type of property.For questions, contact Erika Hasle at the Field Museum of Natural History, Chicago, IL, at ehasle@fieldmuseum.org or (312)-665-7487.

Copyright Text: Data from Save the Dunes Properties, Shirly Hinze Land Trust Properties, Forest Preserve District of Cook County, Chicago Park District, Wisconsin DNR, Indiana DNR, Field Museum of Natural History, Conservation and Recreation Lands, National Conservation Easement, Protected Areas Dataset United States, CMAP, Green Infrastructure Vision, Will County Forest Preserve District, Dave Holman, and Lake County Forest Preserve District. Credit to Mark Bouman, Mark Johnston, Erika Hasle, and Marc Lambruschi, Field Museum of Natural History, Chicago, IL.

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Description: High resolution land cover dataset for Cook County, IL. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at Sixty-Four square feet. The primary sources used to derive this land cover layer were 2008 LiDAR data and 2010 NAIP imagery. Ancillary data sources included GIS data provided by Cook County or created by the UVM Spatial Analysis Laboratory. This land cover dataset is considered current as of Summer, 2010. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3,000 and all observable errors were corrected.

Copyright Text: University of Vermont Spatial Analysis Laboratory in colaboration with the Chicago Metropolitan Agency for Planning, the Field Museum, and Cook County, IL.

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