20190910 20303900 1.0 TRUE USGS_NLCD_Tree_Canopy_2011 USGS NLCD Land Cover Trees Tree Canopy USGS NLCD Tree Canopy 2011. <div style='text-align:Left;font-size:12pt'><p><span>USGS NLCD Tree Canopy 2011. Percent tree canopy cover is a measure of local tree density that has a variety of applications in wildlife habitat analysis, fire behavior modeling, carbon accounting, and hydrologic analysis. Using medium spatial resolution imagery (30m) from Landsat, we have produced consistent, seamless per-pixel estimates of percent tree canopy cover ranging from 0 to 100 percent. This dataset is part of the larger National Land Cover Database (NLCD) that also includes information on land cover, percent imperviousness, and percent shrub/grass. The NLCD effort is coordinated by a partnership of federal agencies called the Multi-Resolution Land Characteristics (MRLC) Consortium (www.mrlc.gov).</span></p></div> USGS /9j/4AAQSkZJRgABAgAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCACFAMgDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iiigAooooAKKKKACiiigApM0veigBoHcnNLnmqlxdPa7i0eY+zDtTLe8aeEs67T2K85FK4rl3dhgMinVXVQSrckj1qxTGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBFNEk0TI4yp6j1rLtYXtstHKdoJHlk9Oa2aqGFY5ZGAUeZ6/SkxNFR9ShhYCV2UnvjIq7BcieMPGyup7g1makYLKxVFj3M5IUHkk0lldxWgjt44WyR1UfzpX1JvZm4GDDilqAyY9Ae9QXE7JFnuW49qdyrl6isH+2rg3/wBmSFXIXLUPrd0l0Ivsm4Bcvj+E0uZE86N6isn+24vsvmiGTfz8mOapza9exzKf7OfySOvU5ockDmjoqK5TSvFrXV0YL21eEZwsu07SfSunjljlUFHVgfQ5ojJS2HGaktCSiiiqKCiiigAooooAKKKKACiiigAooooAKKKKAGMV6E4zVO4khlja3J+bHHqPerbJubLY9KqXFjG+CnysP1oYmUriNpJLVFcMEb5jV15LWMDOAxOA2O9VkMEdyVRQzgc56iobx4yoIwoDd+9TsTcnluFgxucFD0bpXPyX89zfyJHKGRRxjjiptVnigRgrs+Odo528Vk6VvmviXJEQGeOmCKzlLWxnKWtjo7O282585H/d4w3POfWrOElnuJoh8xBGfXFR2aYJljIVPQfxCmrLJHbAJGAx+YgHjrVIpDLdlETPMu1i3PfFNvL2R7fKgx7WwCPT+lOu40eBZC6od3zIv8XPeuV1W++yRkRx43nsOCPpUylyoiUuVEFy8kDi5e5jdMF9jH5WrP0nxM76hkEpcPIo8pM7SNwH8qpXBnurJIFzJ824r7VUtLaODVrIuCim4Xnvw3Q1zOTvocjk7qx70hLRqT1IBp9MTBjXHTAxT67z0wooooAKKKKACmCRGcoGBYdR6U+qzqsE3mqvD8MaALNN3KGC55PalyCM03C7+gyB1oAfRQKKACiiigBCM1nX12bVGZDuftxWlWdfLDco8TdB6dc0mJmBFHNqM0kluWjm7M3GKmEcpRre4mRyo4YrjBq/Y2jQhpHchCeBTrgRkyMQAQOtRymajpcyhbRCTLHzDL8pbGOKrvpC2cG6JmTLHafx71b8uMlWUkR9Q+elWrgRCFA7O4PKsCOfrSsmKyM23meCTy2kDA4BCnOBWtnam8sGtwMe9UIbVJZ5GiZN235gfTtWnFEVtWVlAA+6CeKFcIplC4CLHJIEBjPADY49xXHXd0jFHlb5SCNoGee1dHqR8xWkc7dildobv1rjdWtRfxQmJimc7OCMEdayqMwqsSP7NHKcO+94ieOp5FQebb/2lZvHIrI8yAhl5HPfPTms2GVUyodpGQlffH86imlQPuibYWOPn49wRWHNoc7lofQcWPKTBBG0cipK5zwXqh1Pw5bmSQPcRDZLg9+36Yro69CL5lc9OMlJJoKKKKZQUUUUAFGKKKAG7QowOBSj1paTGOlADWdY8Z4BNPqCW3WZgzE8ds8GpQQuF6H0oAdSE96QE7iCAPSmZDg+1AD93HoaoT2wMvmBiWHJGaju7gxMeSM/dNVPNuDJgnGFHzZz+FS2S2iSS6VJSofooyvvVJ9QKyvhQVPX61HqCuMzIdvmdxWIbkh3UK3PJJPJqHIzlKxpTyXr27qsJEY7gcfhUWl3rPutpItswPAJ5K460g1S8a18tUcKqj5s9Khm+0CP7YjxrOV+8am/VEN9UadlARdrJGrKEJ81O1al80EsPlhs7/fofas/Q7yJNNknnkA5/wC+qqylb945ZNywA5UpkY9MYqr2RSdkZz2clwyxh8RFiJOeeD0FZ98bK1tms7Xa1yucruxjPviumuo5JFilQmJI87gw68V59fwPd31zLFcoUzkIuQc+5rGehhU91aGFO09ndMSNgTO0Acc0kc8csjJdW6sNhZXDYKHHFXtQuJ7vR40lJLwARxhBkke/5VkRI0keCQOgBI9+lcz3ON6Ox2vwp1C9GsS2KkfZSpdgeua9jrz34aabHFFeXzIvnOwQFTwFAHavQq7qCahqelhotU1cKKKK2OgKKKKACiiigAooooABSEDOe9LSE4GcZ9qADHOaMAdqQD5icnHpTqAIJreObhl+hrA1SKbT7WRkJkUnoR0FdNUUsaOCrqCrDBzUtEtXPPptbY2vkQxsXHzBuo96zp9TjuEA2MkhYcqOMGuzvfDFtM26M+WDwVx1rLl8KWNrbEySSHceBnjispRkYShMx5byOGOOLe/z4ywbio/Nm1KVbW2Uu/RvTrWvp/h62kmQlWeJSCN/rW1Ha28U8nkW5R+30pcjYlCT3Mu7tgsEccIUCMf6teQaXTIfs+mkZYRjpuPpVt4SDI2wlVABY8c1Rv5lvbKK2tgRtwNx6GnotSmrO5et52uoTc3EZEAYKAep/Co38MabPdSG3j2l1DMRnHP9a0LRPI01Su0BAAQRxnpnNV7bUY7q8i8lyqsSrKp4JqtNLjstLmLN4UCwr5VoBcBt27djjBH071e03wRpsEabrcSb1yzu2SCefpXVwxFSxIGG5qcADpTVKO9hqjG97FLTtMt9MiaO3QKGOSBV6iitErbGqSWwUUUUDCiiigAooooAKKKKACiiigAooooAKKKKAE61n3luJgBnGfbpWgaqzOocYGe2aTEynFClu4VFydvzelAmjBJlIUZ596uHzI227V2Y+93rE1Ahn2cGTPyjHApPQl6EF1ctPM0MCZZjkehqa3RJQtqd3mx9FI4b1q0mnKyxvGxLdTjjBq7a2BhnMpl3knnIqbO5Ki7ipbNLbujBQhG1UxwKitdEtLVyyIBk5wOma01UKMAUtXZGnKhAMDApaKKYwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACq00AZg3UZ6ZxVmkPIoAqXLvHBhBn171QVTJc+YsZLKACG9a1MgOfmIB7EVHIkjTAoBs6k570iWrksXyxg7CCeoFSgYqGFlV/LL5fGStT0xoKKKKBhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBC4DMEI4NR+VhyiswU84zmiikIdHbpvWY5L4xmrFFFCGFFFFMAooooAKKKKACiiigD/2Q==