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<rpIndName>Jacob McMains</rpIndName>
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<useLimit>&lt;DIV STYLE="text-align:Left;"&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;P&gt;&lt;SPAN STYLE="font-size:10pt"&gt;As determined by the City of Columbia, Missouri&lt;/SPAN&gt;&lt;/P&gt;&lt;/DIV&gt;&lt;/DIV&gt;&lt;/DIV&gt;</useLimit>
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<idPurp>This Columbia, Missouri, natural resources inventory (NRI) dataset comprises a collection of feature classes representing various land use and land cover types within the Columbia area. Utilizing the most recent 2022 National Agricultural Imagery Program (NAIP) 60cm imagery, land cover features were identified by using an object-based image analysis (OBIA) methodology to process and analyze high-resolution imagery. LiDAR data and ancillary GIS data were also used to assist in classification. This approach allows a more accurate and cost-effective automated feature extraction of land cover classes. Feature classes within the dataset contain attribution on land use/land cover subclassification and elevation. The final GIS dataset serves to support the City of Columbia in informed decision-making in land use planning, natural resource management, and conservation efforts.</idPurp>
<idAbs>&lt;DIV STYLE="text-align:Left;"&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;P&gt;&lt;SPAN&gt;With the size of the study area measured at approximately 230 square miles, a cost-effective and accurate strategy for assessing current land use/land cover composition is the use of remotely sensed and semi-automated classification methods.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN /&gt;&lt;/P&gt;&lt;/DIV&gt;&lt;/DIV&gt;&lt;/DIV&gt;</idAbs>
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<rpIndName>Kenny Legleiter</rpIndName>
<rpOrgName>City of Columbia, Missouri</rpOrgName>
<rpPosName>GIS Data Analyst</rpPosName>
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<city>Columbia</city>
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<postCode>65205-6015</postCode>
<eMailAdd>kenny.legleiter@como.gov</eMailAdd>
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<rpIndName>Nicholas Antenucci</rpIndName>
<rpOrgName>Davey Resource Group, Inc.</rpOrgName>
<rpPosName>GIS Analyst</rpPosName>
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<delPoint>295 South Water Street</delPoint>
<city>Kent</city>
<adminArea>OH</adminArea>
<postCode>44340</postCode>
<eMailAdd>nicholas.antenucci@davey.com</eMailAdd>
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<cntHours>8:00 am - 4:30 pm EST</cntHours>
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<displayName>Nicholas Antenucci</displayName>
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<prcStep>
<stepDesc>Using advanced GIS and remote sensing software, Davey's GIS Analyst utlized an automated feature extraction approach to process and identify land cover. ArcGIS 10 and Overwatch's Feature Analyst were used to complete the initial automated feature extraction. Ancillary GIS data, provided by the City, was also utilized to aid in the initial extraction and the quality control process.</stepDesc>
<stepRat>Feature extraction provides a much more cost effective way of creating land cover layers than manually digitizing images</stepRat>
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<prcStep>
<stepDesc>The main QC process was completed at a scale of 1:2000 by GIS Technicians and Specialists in order to ensure that the land cover classification layer was 90% to 95% accurate. During this process, editing was completed using ArcGIS® to identify, clean, and correct any misclassification or topology errors in the final land cover dataset. Once the initial intensive QC process was completed, each of the layers were examined by the GIS Analyst to correct any missed misclassifications and also to add any additional features, if necessary.</stepDesc>
<stepRat>Multiple QC procedures ensure that Davey will provide the best possible land cover results.</stepRat>
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<stepDesc>Metadata created.</stepDesc>
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<prcStep>
<stepDesc>Random plot locations were generated and verified to ensure that the data met client standards and assured that automated mapping and data analysis performed by GIS specialists accurately reflected the true nature and extent of the land cover. A total of 1,000 points were generated for the city of Columbia, Missouri. Each point was examined and a confusion matrix was created to calculate overall accuracy, Kappa coefficient, and a user's and producer's accuracy.</stepDesc>
<stepRat>Davey statistically tests all land cover data to ensure the natural resources inventory yields statistically significant results.</stepRat>
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<evalMethDesc>Feature acreage is calculated using the ArcGIS field calculator.</evalMethDesc>
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