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<idPurp>The purpose of this project was to conduct a top down canopy assessment approach. Utilizing the 2017 National Agricultural Imagery Program (NAIP) 60cm imagery and advance remote sensing technology, land cover features were identified by using an object-based image analysis (OBIA) methodology to process and analyze high resolution imagery. This technique allows a more accurate and cost-effective automated feature extraction of land cover classes. The final GIS land cover layer allows Reno, Nevada to conduct additional spatial analyses necessary to identify and map the existing land cover layer for future.</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 &lt;/span&gt;&lt;span&gt;217.9 square miles, a cost-effective and accurate strategy for assessing the urban forest is the use of remotely sensed and semi-automated classification methods to inventory the current canopy cover and to analyze data for future planting goals.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;</idAbs>
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<stepRat>To analyze land cover metrics and deliver all data in an organized fashion.</stepRat>
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