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This project attempts to represent the plant community or vertebrate species on lands not being managed for conservation in Kansas.
<DIV STYLE="text-align:Left;"><DIV><DIV><P STYLE="margin:0 0 7 0;"><SPAN STYLE="font-weight:bold;"><SPAN>Citation</SPAN></SPAN><SPAN><SPAN>: Kansas Applied Remote Sensing Program. 2001. Kansas GAP Land Cover Map. Available at </SPAN></SPAN><A href="http://kars.ku.edu/geonetwork/srv/en/main.home"><SPAN><SPAN>http://kars.ku.edu/geonetwork/srv/en/main.home</SPAN></SPAN></A><SPAN><SPAN>(accessed {DATE}; verified {DATE}. Kansas Applied Remote Sensing Program, Kansas Biological Survey, University of Kansas, Lawrence, Kansas. </SPAN></SPAN></P><P STYLE="margin:0 0 0 0;"><SPAN><SPAN>The Kansas GAP land cover map was developed to meet the requirements of Gap Analysis and to fill the need for a detailed map of land cover at the vegetation alliance level. Sponsorship for the Kansas GAP land cover map came from a number of state and federal agencies that provided support through both direct funding and in-kind aid (Table 1 of the final report). The alliance-level land cover map was developed using multi-seasonal Landsat Thematic Mapper imagery and a hybrid classification approach. Assessment of the map’s accuracy was conducted using independent ground verification samples and standard accuracy assessment analysis and reporting procedures. Details of the classification methodology, accuracy assessment, results, and discussion are presented in the final report.</SPAN></SPAN></P><P STYLE="margin:0 0 7 0;"><SPAN><SPAN>GAP data and final report can be downloaded at the Kansas Biological Survey Metadata Catalog; </SPAN></SPAN></P><P STYLE="margin:0 0 14 0;"><A href="http://kars.ku.edu/geonetwork/srv/en/main.home"><SPAN><SPAN>http://kars.ku.edu/geonetwork/srv/en/main.home</SPAN></SPAN></A></P></DIV></DIV></DIV>
Kansas Applied Remote Sensing Program / Kansas Biological Survey
floodplain
forest
GAP
grassland
land cover
playa lake
Prairie
shrubland
USGS GAP Analysis Program
wet prairie
woodland
<DIV STYLE="text-align:Left;"><DIV><P><SPAN><SPAN>The Kansas Biological Survey (KBS) and its representatives make no warranty or representation, either expressed or implied, with respect to the digital data and their documentation, their quality, performance, merchantability, or fitness for a particular purpose. The digital data are distributed on "as is" basis, and the user assumes all risk as to their quality, the results obtained from their use, and the performance of the data. In no event will the KBS or its representatives be liable for any direct, indirect, special, incidental or consequential damages resulting from and defect in the KBS's digital data or in their documentation. This disclaimer of warranty is exclusive and in lieu of all others, oral or written, expressed or implied. No agent or employee is authorized to make any modification, extension, or addition to this warranty.</SPAN></SPAN></P></DIV></DIV>
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