20200909 17203500 1.0 TRUE FGDC 002 150000000 5000 iVBORw0KGgoAAAANSUhEUgAAASwAAADICAYAAABS39xVAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAO xAAADsQBlSsOGwAAIABJREFUeJztnQuPHFfVRQcwjxC/4wQiQCAkJPj//wYkkECACLYT27F5k0+r pTXaOdwaO/4m01XJ3lKpu+txq3ri3jln333OvfXZZ599dlGcDf/6178uvvnNb1584xvf+Nz+//73 v6ftT3/60+mc99577+LBgwcXr169uvj73/9+8Z///Of0nus4zr5333334h//+Mfps+dy3rNnzy5+ /etfXzx69OjiO9/5zsVPf/rTi+9973un/dyDMf79739fPHny5OL3v//9xaeffnrxk5/85OKXv/zl xS9+8YvT8/DPhI1zc+PZi+KmcOvG7lQs8e1vf/tEKv/85z9PrxAAG+8hnu9+97sX3/rWt07nQRB8 lmA457e//e3pfM6BsDjOud///vdP47/zzjsX9+7dO72HsCCohw8fns7n+o8//vg0FoQH2XH+/fv3 L37+85+fiI2xJSpfQYmqOAdKWDuAP34IAxIhCoJ02Ih2IBSIhuOQBu8hM45BMEZokBDH+UwE5biQ FxHaD37wg0tSSnLkOu4JsRGZQVg//OEPL0lvBuEzGiyKm0IJ68yAjG7dunXaICLTOD4TLb3//vsn cjLikjD4zPmkbuzP9BACgmzYPvnkk8sIjigKkvvb3/52Ii6iK8jLyAmSg7Qgt9u3b18+24yuiuJc KGHtBJADJGJq6Kvp3MuXL0+kJJFxPttf/vKX0yvHiMgYw2iJV4jn+fPnp+OQFxvEA3nxnrSPayAx rgdcx2Y0J1GVtIpzo4R17v8AEVlBGpAU20cffXQiI9NDCAuSIWUjAvK6p0+fntI63hNZEWFBdgjo 7P/ggw9O4zI+RMcYnMcGmQEjsjt37pz0LaIuwHNIVhldZYrYqKu4SZSwdgJ1KKMaSAeS4TNEY9pI 2oa4znvgcUnnxYsXJ1LiPI6R9hFBMbbXAEgJguK+XM9n0k8jOiM4oy6Q5JWzhkVxUyhhnRmSi2QD uQA+Ex2xIYJzjGiKqAji4TwIhegJ4iASU9cCEJhalzOFXAuBQYamfc5IaqNgTCMonsvxJjGVqIpz oIR1ZhDBkMahM0FCEAQkBJER+UBWRjKQEFEXr5CJURf7IDSIh6hJYmIciIp73L1793QPSIpr2RiH e3IN95EME1fZF0paxU2jhHVmQBqAqIcZPF41ghrtoGU9fvz4RDzMCkIuRFuYPPFLaU+AhBTLAeQD iSGuQ2jcC/0LguI+GEfRxIjAuM5n8foSUrE3lLB2AiIr7QxaEoh+/vCHP5wiI8iLKIn9EJfRF2TE NRCO4jrjSDpcB7iO80wVJSmiKl4Zz2gq9akaRIs9oYR1ZkAokIJGUcgDImIfkdZvfvObE9lAKJCL dgeI50c/+tEpSoKIOJ+xLPVxdhFCIy1kDK7T0a5BVK/VjK6KYo8oYe0gsoIwiHiMgkzPeP+rX/3q lCpqDM3oCXJyVk8PFwSkRgXJcT4g+mI8y3ecRbTkZ8JawaLYE0pYZ4ak4EwenyERDZukgVlf6Oye znctBs4Y8grZOevHWOyXGInSIC1rBK/yVpWwir2hhHVmqBHxSoRlagfJQEiI7RAMxCUgHgiG40ZJ 7LOI2QgLYoOo9G3xau1h6lVbqM+q2BtKWGeGxOGMoK53iEoBXTLTxMl5HEOMt0uDEZGmU4iL90RU khpkpVZlqujniRpDiz2ihHVmGOFkSxlIBFHcmTvEcdNFiAiPlWRmeQ4aFu1juE4h3vIbOzjkjN/r Zv+qYRV7RAnrzFBb0qpgGxmIibQQwjFqgkRMFRXfIS4IjSgLUrMWkRTSdjPZcK8ojowS1plh4TKE hLmTImcIyVYygPccI23kNV3xpHnO/Fn8jHZlmqgInx0XiuKoKGGdGc7mQSRaEbJlMm52jum/sobQ dJA0UD+V0Ze2CKI0u5ZmsTRotFUcESWsc/8HuHXrcrbPPu1oUojuRFOU3mhP0FiKAM817Cf1Y5/k pKcLMA7EpZEUdOavODJKWDuALV4gIgiIzU6jEEyaSdW4AFEXKaT9stTD2E+0xQIWRF/OEKb3avqw iuIIKGHtADrYSeu0JNjfytId00TtDxZIQ1CuogMgKvvAS2boWdk5tOlgcVSUsHZUSwgU3G24l6U4 ziaidaU/i81+7i4DJlGpeUFY08pQ4iqOhhLWmZEzeJCKxc8CcrLpnk34TA1tk2wDP/xXvGfFG8hJ PQviclkv2zAzrlpXURwFJawdIFu5EBnx2ZVtrBO0I4NtaIymbGOsj8t6QQX6bI0M6bHpqC+Ko6GE dWZYTqMbXYOnDf0gJiIk0j5bGktGEBotlO3K4AIUtJSxkR9RWS5SYX2hutlWaU5R7BElrJ0gox4F dAuhXdrLzgwQlgtIqGe5RL2LsRppQWC5gIXnG6mVsIojoYS1A2QdobOFkJL1g67inAup2n1B4vE8 Ccg2NMwUClNDvVlNC4ujoYS1Exg9SUpEVC4YYTuZFOchHfUuXp0BJKJKn5b1iaaLrknoVhRHQglr J4Bw7DIKCUkozOSZ5nlccuMaLAyuqONxfVuSmNYItbK2jimOihLWjmDUpChuZIT4znt0K71ZFjVD aGpYLryquK5lgs0ZQyB5qY8VxVHQf61nhjpSRkQSlqI6UFSXgHgP4UBiWCCoG5TQiLqcJdSvlSZR 3yvOF8VR0H+tZ0a2KnZ5egjIguUsq7GO0NpCoivO5ZWuDezzetsmmz763m029CuKI6CEtRNkgz1n AW0146wekZN1hhJaHtcOAYGZKlrOM9NBU0JLgoriCChhnRk5+5czf5AV0VUWPyexSTwYSnlPOxrO dbkwrqfRXwryEhVQgC+KI6GEdWaktjSjnyyENnqCiNgU573Glso2+oPg6KdlsbTRGHA2sbaG4mgo Ye0IWUtoP3ZJxW4L6ldqXhQ8YzBlg8hsT+MqO2l3yEiuKI6IEtbOip/tzZ6kZUpoMXT6qYCud0jK zqPaGBhjtkj2fu0+WhwNJaydIEkkm/npqcrzgLOAdhKFzGz6x3W5PP1MOyXH9sMqjoYS1pmRUY71 hMCoiJQPDcqFJyAkvVauYQj5KNBDUlogIDWvy2JqXfOdISyOhhLWzmCUBXilv7sERKTlohUuQQ9o 8McmIfFKpwauhcDS9a7wXg9WcUSUsHbsx9Lwaf92SIfUz1IdXfBGZTjduUatS3uEWhjI/vBFcTSU sHaEuUJzriGoy931BjkGcZEi2tHBXleOYeqnSF+He3F0lLB2gDRwSipGSERPto+h7MZmfJARva5I 92wh8+TJk8/1fs8FLHLJ+qI4KkpYO8KMrtgwf0JIvCLA814ysiCaVNAmf5b0ZD+sObaoraE4GkpY O8O0IBBFQVRET/a+Qki3cR9pIYCo1LBIAyEyrtWLterK0GirOBpKWDufJeQzpMRmIbOudyItl6ln 4QlmDVMHk9yyM2lJqjgySlhnRqaASSbWE0JU7MeiAIiWLMOR2DSPznQynfI5U7h1z6LYO0pYOwZk 8uGHH15qVTb0y1KbrDW0YDrLeTSIuujEXE6sKI6EEtbOkFGQS9FnfyzLdNSmsm+7q+7MQuksdrYZ YMmqOCJKWDvUrvyseM5sH0TjjJ9eK+0NuRhrEt4cc7rbmxIWR0MJ68yYpJGOdAExubSXqV1qVjkT aK0gr9mZwdIcoy27PrTVTHEklLB2hhkhuWxXLiahsTRXvUknuwtUcA0C/RYxteNocTSUsHaIFM2B toTZkRQ7A7OH7rP2EEBQFkLnAqyODapjFUdDCWunkExsIaMeZVRkbaFL1ed6hEZgEpmWhiS2FkAX R0QJ68yY6wWa/qlBZTRku2NnBF1k1dnD2QCQ45hLgYR21TqEaVgVbUNT7AklrDNjNVOXOlXaFjxP P5YdGlxYVTHegmi27N4AXDVHlJCKI6GEdWZM9/mMuEz5TPvUtmyRzKvH7SwqCRqpGZkB08Js5pfP 4X0ryBd7RAnrzLhK+M4ULdcRzCZ8SXi5/qCr52S30TnufP+mz1UU50IJ68xYtXxJE6gFy3MFnawH zLKbiS1xvW734ogoYe0MGVVNT9a0KLjQhOeaEqptoWGREs4OplsRWqIRVrFHlLB2htSy1KFyifnc n6RmBDY1MLUrawznfUReW7Iq9ooS1k5F97m6TZbZ6IB39WeiLEV4iYz9RFcUT6doD1qOUxwVJayd a1iYQ+28kDN9Rkx2b8hreK9PS++Vq+94zyTCvLdjiZJbsSeUsHaIWdQ8hfPUs/LcVYqYJT6K8yJ9 XrmvKWGxV5SwdpoSSkA607e0qdXMoKSUJT15baaXb/pcRbEHlLB2CElleqdet1TXJKdZAO0YK5LL YzlTWSd8sSeUsA6ASSKpQa3ITSc7hIX4DjKFTPJbkddqzKLYA0pYB/FiZYqW+539k8QgKIT6WTto 8bP7ZhPAHLso9ooS1g5nCbeIKl9zRk/y0TzqghXOLM4+7unrmgTVqKrYM0pYO0SS1jSS5jkAW0MS UrZSdg1DHfBZwrMypK7uUwIr9oQS1o6wEsRnR4UZeWUERrEzS9rzntIcjtkzi4VW7Y/14MGDS32L 8h1JyT5awELqq7Ss6lzFTaOEtXOsym0SzgK6/iCrP0tyEBWkxOrPOt/tm8W5EtKM6Kaov4VGX8VN o4S1M6xI4Co7Qzb2g4CImHh9+fLlxdOnTy9evXp1qW3N3u5ipo2rDg+rYuyiuGmUsM6MqVOtTJ4S xBTcgdEVcIkv9vGeyIpUMFeLtjupvbKsP0wdbOW9mulqCas4B0pYO8Mqslkh+2VJWC66qgfrnXfe OREThGVnUlNEl6+3iDrHLIq9ooS1Q7yuZCZn+FwN2mNu7EdcN0WEzBDkLaa27Gemm6aP05i6eo5G WcVNo4S1A6x+/CuiyG4M2d9K/cnODBIQKZ+RFETlSjyzoNr3uQyY+7d6aPksLd0pbhIlrAMK8Elc vNpVNFM89rkytL2yjKocl+MuDyZBOqbjrRr7TUtFUdwUSlhnxuvSqlxAYst2oJtda4NL1aNdAdLC x48fX/z1r3/9XIqoxpWiPoQ2o69Zx9hZwuJcKGHtHElW6ZXKDgzOFM4oif25BJiEpP8KsvK6HDMx 1zEsinOihHVAZJSjQD7rBI2K1LBI/WiXTFRlRMZmijjJaquLQ74WxU2jhLVz6I/aqifM4yLrCiUk iApy0wJh6pfjrLBqLFgU50IJa+fIVZpXs3R2JJVwOM/lvTSSKrwTZTFbKJFxPJcJ2zKprsiqHUmL c6CEdUCkwdMoyb5YalLOCpr6sR/CspaQ8zGQKsxPolq56ovi3Chh7RwK5KlLJbkYgU0rQ1oVICmt Da6yg+BOcTTF0vq3coUcfV5XpYI1jxY3jRLWzpFiehYoa/4kxVNEp+wmU0DrBtln3aDFzRAWVocP PvjgUpjPhS1sNSPBFcUeUMLaOYxiMmrSgU5KZ6Ezr0RLaSSFgNhnF9K5bD1EZXG0vi1rE7NGcWtt whJacdMoYR0Ic5ZQgT1TRf1WppE62UGK6nfv3j3to6EfrWiIuNiH9cHzcyVpYIooeSr4F8VNof/i DoJV7/WtyGfVCia7h0o4kBkF0fTM4vO9e/dOUVYK9aSZKexPR3xR3CRKWDvHVbN02aM908VM5dS4 pmhus79nz56d0j/SuydPnpwIDCLDtyV5Oe5cbboobholrAMgF4pIs2gSx3wv0aFdAaOq2fEBjYu0 kO6kv/vd704ERmr44YcfXty5c+fUooZrM/2zvnC1VFiis4jFdaOEtXPM9C6X9Zr1g7wnUiKNs5Zw q8OC4xCBQVqQ1P379y+NpVgeXLRC02kWQkt4K9KcxdJFcV0oYR0swuL9XH4+y2p0t0tqGkPTV5Xe KvahYXEuUZUivCZUU8rUrJKEMmWcz9x+WcV1o4S1c2xFRu5LE+lcQCIjME2lrlfo+ZDUH//4x5O9 QS0LuwTXEnURrbGf19TGkjCvSgmL4jpRwto5iHA0fdqZAeuB5OEMX6ZrGkk5nwiL49YXQjz2fZd0 Hj58eLoOHYtU0FpDWyr/7Gc/uyQmxHjG4b5EY7MffEZvW7OYRfG2KGHtHLMb6JuscDMb/BlhpY/K cZgZ1EEPuVljiC8LUnr06NGJ7HTVG6FBmvbbyvuu3hfFdaGEtXNYNgOSuKbFYPqu8rgN/Kam5L50 wrNBTOhYvEJiRF7MGALujTgv6Xm/vLdoSlhcN0pYByCsVbSV7vbpRAcru0HqXW4Q0fPnz0/kRERl 3aH3Zh+ExXicm2ke5HbVcvaNsorrRgnrIEit6Kq2xZLHJDGJRTjzpxDPe9I8uzYQcUFiwDSS4wrz Kbp7TlF82Shh7RxqREk26lHZwC/Jw/OnID4d8b4n7SPN814uyMor3RxMFZlJ1I+FroV/S0I0TfVZ iuLLQAlr58gIxlIc1xfc8jllwTKYi1DMBSxcjJX0D+0K0Z3oytlFCI1WNH/+859PhMVnyIrZRWYK 57MmSl7FdaKEtXNISL6axkk2W4tH5GyiS9TbNiaLmT2P41ga0LMAehWbNYVEV2pZkBTkRgQGcc17 gxJV8WWghLVzzFWeXQHHCMnIKcVwozAjq4yWuJ4Iys+MYWTFGKaGWhYgMVspYzLFBoHlwXbLjM04 PlN2eihpFdeNEtbOkdaB1J6SGIDENVspzyXtPSfPzQ6jwvtoUnXmkM82/7PJXzrecyazhFVcN0pY B4EEoqN9bolcbDUd8LNvu2PajcHrJCg2x4eciL5s/Gf0Zoo5F3mVBOt2L64TJaydQzvCKoIB+X61 SAXXO0a2mBHT9mA/eMmNVBHiyojK0h9SQoluPqOpbAmruE6UsHaOXKVZGBWtoqyVOTRnF+fq0NYX ZuopybAf7cpaRsdzphHtaqahRfFlooR1QMz2MDNqyuO+pu6VDfz0dHmeEZYpXbrbITDPz0UqVppV lg0VxXWhhLVzTM/VysIwySFTxNnoT5JJktLm4KIVWBkykjL9Y7ZQ3cqxFOOnH6zdGoovAyWsncM0 bkYr2WU0taMtd3tGYOntksyym0PWL7quIVYGS3VMU71PPl+9WMWXiRLWzmHJizNvEIhR0arweM7W GR1l8770SE0im2U26fUi8lKA99nmvVeRW2KK80XxRVDC2jk0ZE47QxJOeqkkDGfzchWdec1cpEKz qV1G0xoxySeLp9MfNmcFVwtmFMXbooS1c+RqNUY/Qo3KyGbWHUoyEldelySYLY91xmtxUEPzOq0K W2P6XvKTMFN/y/NKZMUXQQlr59iyC0ztKL1UmY7N9GtqTHM1HM9RO8tl7zOS8rxJWmxEaBZQYzad NosZJRbFm6KEdTDCmumV/dlBkkl2GF1FNwIvlRYHZwW9VsE9zaSZmkJmb7tcfcmqeBuUsHaOmT7N H/o0iGZRtOL5aikujxNhWWajKM8rZCUprVJRPVmmhynmQ4K8R6TvatHFdaKEtXNMq0ISmAbOVT2h 12pBSBtEzhy6mR5mSY+dSNN0mppZmk6n1mYpjzOMtT0U14ES1kGQvqd0qhPFZAua1UzgjJAkG31V CuwuLJFF0HZnSPE808K0XTh2RnRbS9k3JSzeBiWsAwBy0X9lUXJqVe5PTHHd41mek+731K5mv/b8 7Oyf+ha1hplqTkJbOeCL4m1RwjoA0t+U4rn61MSW29xr9FjxSjcGkNFQRmy2T05XfKaL07KQz9CC 6OK6UcI6AGaTvNyfTfcSOZMn0laQmpQmUIlsLiwxxf70cF2V8s2SIPcXxduihHUApMdqFjxPy0Jq SzPS8rir4rBl2xjvI4F5veSWK/g4vpHfyhfmtfm5/qvi/4MS1gGxIqJJWp6XAnpaHnxFgzKactYv 0zzHmm1okjDns23Napa0iv8vSlgHwNSDJJitUpy8jmgqV9zJKIoUkFlGz83rMi2cROX1M1XM6G6u m5jPXhRvixLWAZAdE7LkZhYez3QrzzeC8lz1qinmz/uu0s4kRtLK7Dc/n3HVTaKkVbwtSlgHwPQ2 ASOZJJxJKpxDyucYHjPlg7Toc5Uzfqk9sWU5ziRQ7Q1en2PkQhQlqeK6UMI6AHJVG6DY7SyhqR+Q LLKbQrZCzkiIDWOo0Ze1gaZxmktNN9M4KuFhe9CTla72Sag+Wwr5YNUzqyi2UML6CmAK77NTqRFO 6klqTHRVyNVwgOdnfWBuSZxZ1uO4K2/YanKgKL4oSlgHhcSREU9GNvmaERLnGA3Z+2pGVWpd2fxv Jax7PmSl7QJNK/W2jLqu6hpRFG+CEtaBYdS0EtqTMGZNn58hHZfxWnm4LK6emKlptqJJs6nP0+iq uC6UsA6CaWuYhc1pfUiLw0ojSl/W1LpmLaEp35abHULjHMR7oy10LcbIWcmtMYrii6CEtXNs1ePN 2bzs1mAaN7uDZjeHVe8sj2dLmVz0YqVBSXYWaDtT6LGtlZ+nM74o3gQlrINj6kMzrZslPdmlgY3I aFWrmDpVXreCM4aZSs4WzYmVE74o3gQlrIMjneeSUBLWTB9TmGdDJCeFM6oyDUziuSqds+uorZbd pw1jZR4F7eRQvA1KWDtHRjzz81xCKwlJ42b2fM8awXmdRMVxdCjJzOgtV4F2JtD9pqTZISLLd/JZ U1Ob6Wx2ODVVfdue8cVXE/3XcABcFY3M7p9CQtLpnkBr+vTTTy9evXp1mc5JFLlKjt6qNJ7OWUR8 XIxjX60HDx5c3Lt373KG8nUpaW0OxRdBCesrglmknOsMZjQlyTx58uS0sQwXxMSmPYFXZ/okHqOm 9HM5Hu+5DuLyeogrdS2QKeKc9czXub8oRAnrIFgVESeyfi/TRkT16YWyBpBjkFe2XzbVy/TOtsh2 eDASg4zsC28K6MrR7l9ZMFa+MZ971kMWRaKEdfDUcKsmz3Ozl7uzhqSJt2/fPkVEWayclgTJCuJJ L1bqUuD58+eXKSHHSAeJrhjvxYsXp/usBPdVuU8jrOJ1KGHtHDN9msfSe7UiBlI7yceOoURJFD1D Jgr0s+eVXUmNpFIMT6Hc5bw4RsT27NmzU3p5586dy3uDVbSU+5r+FW+CEtbBYQoHkrS2Zg89D1Kx eZ+lOel09zrOcZ8pn7YHjj98+PDi3XffPR17/PjxScy3P/zdu3f/J9VbWSz8nMdzX1GIEtbBNaw5 Ozg7j+a6gWk1IPqBjCAWIi0XXFVcNz2cfqqM1CyaJhpjHNJBx+Men3zyyWnfm3yvKcx39rBYoYR1 AGT5zJwFzBbHzhTOEhxftSmoZaE15TmrKM2UMI2otp3hOjQuyO/HP/7x6RkR8bVKcO3HH398Oq45 VT8XSKPpLAnKSK8oRAlr55hlN+7LlGplF5ip1YzO3CAcRPgU57VCSGxZZgNWK/XYrsY+XM5C6vFS J9MFn2sgVnAv3hQlrAPgqtIYj+f7JKt0w6d3KlO8jNymg16imW2SBZGTBAeykZ/RmOmlBdKK9Zm+ XvUdikKUsHaOrTo8SWa1iET+2OdCq1Mrsvle3it7bK2IY0Z7WZYjSaVVQe3M6E2CS61sjr/13Yuv N0pYO8dqFi23VfuWJJRVBJNjEyGlViVhgdTN5nXeg4iJtHJqbGmDcJ+zjGmLUKuaM5olqmKFEtbB MPWrrQZ7EtrsiQVSnAdGPfqtPHeufTh1K2ChdI6RM5JaIDICS9Hd9jY+p1FXfo/X/T2Krw9KWDvH VrSRelQK8yDF95kqzg4JREdzNRyJKusQ5+Z4IJcS4xrbKmctoXaItFlMvSyf3VSzM4VFooS1c0xy mK9JVquZQ+HnSVguxZW605a7PknRe5tKKqLnrF92a0j/Vs48WhIEMv302UpYRaKEdQDkrF7O3q1I JFslb3UhVWNSw5plPVl3mPcz6vK+ko3poKlgLsLKNUmGsxsp7+fMYRKpJLZl02hK+PVCCesAmFHN LHqeqzqnEJ9EZ2RzVcqYYjhI0vJ+K9d9PkNaJRT1QQrzueTYLN/JZ5H0SlIFKGEdAFsm0dlQ7yrB eitKypRzivEzesnnyHvM9HSmfMDODxlx5XJkk7DmDOPqeYqvH0pYB8LKa5VL1LvKzUwTZ0Rkkz2J YxJRksisU8wxUw/LqGm1wnSK7klAzDJmGpiR3NbMZ+p4baH89UIJ62CYfiV/tCluZwEzLWKMviAH y2e8fi60mpGRtYKpI2VKumU5mKZS75WzkRJUOuO1QPjsK8LydWtioPhqo4S1c8wfZYrgEkIWFKcm pKDNPh3tRkRqXXYbzbFXM5JzEYnplE8Ta6avW6mf0WCOr/ieZLVlKC1hfT1Rwto58sef++ZsoZiR 0kz7MpJZ6U/ZmcFZvJmGJXFkwfN8HqOqJMF8PiPCuc/nzFnF7CU/i7GLrw9KWAfD1KPALIvJKART pymghGDU5TJeU/BOUqN9zZbjHdDzyrSSGUE1qSQrn2UWYidBQawSZNoxSEm1TxjFXVVuVHy1UcI6 4CxhRkimT5NwfJ2aFf2q+GyXUUtrUry3nxUwAjLtA0ZijE2DPnq68xy0XdaTlbYF7+2YQPKZS4hl dOXmQq0ek2iTvBxzFTX6t5h/p8SMYIt9ooR1QEheOYuX+tDUmTIi0dVuE77UlhJGRNMQmpGSM3TT jrCKqmZU5KuklWSYaej0lXnOTDW3NK6cNdXSkWntqtuF16/2F+dFCetAyB9j/gAlLTt65nlJNEZU wD5VOa5Ir5a1gZJO1gNm5Jf3SDK5SshPwjLCszMpyNnJTEszqpKQVq2h85ly9nO67a+KukpY+0IJ 64CY6d/sR5WplkvJG+mYRs4f7tb4W0XV3jMLnC2ncYzZ/WGaU9XYIB1bK+eK016XZtZJqCt9TQ2O jfTRqDK7QpiqmvJOwitR7RMlrINh/pAkiGxrnIbOnPmbY+Siqbk/9Ryjq9kby6jICG5rNnOF1Jp8 XnW2jIrA/JypKq9EZCt4ztOnTy/TYhfecJVr7R4SJShp7RslrIPCH7Bm0KkVObNm6jcb5hltJAnN 8VMe8NiFAAALMUlEQVTfyigtiWh6rXJfamw57ix+Xs1Ubp2fXSamPuf1GTEl2VpcPVPhSaCNrvaL EtYBkfoQUJhObSvTH3/02hkcYysqmkTl59ntQeLTae8zZMS29X6mnSmsm6r6PbzW8h6jsexgmumu Y7KxZqKzqtousiBb4+wU7Eta+0QJ66BIssmZtyQfS2vUsVx23ijDNGmaQh2LzbRP0tIkmoXWsy97 PtfUyDINnOTm8ZUmlmlouvvRqCSsnDHNa1J342+gxSLtG957vi9x7QslrJ1jis3zx+4PLn/Y6Z0y 0vKHboqkKVRikogyqgGSgvvVfDzXmUd1KInMz7PMxsgniYL3Glx5NtomZ4THMd97Ld+HNQ/v379/ IiGekf3qckZeL1++vIz8WJWaV6Iu1mS8c+fO/0RTUy8rYe0LJawDIUlEIjPFm72rUpzO4mh+1ERV s4tCLlM/BfpV9OP7WbpjVJRFznlsjp2esGlDMELU8uA1jgFRQkKQlTOCfPY7eV+/s6ksBldIMCOq /PvO12I/KGEdAKsfjkQxScFjkpDgx0y0QVTFD5YfrhGRSLF83iefZfq/RJLZ7GI6bQ2OJcHNSQCf A6JRg9JCofeK74IdwgJszuMzznujTOD4Roh8f8C5psSpYzWy2i9KWDvH6/5vv5rtyx+e6Zk/ZI2g /NhTg5raFZgi+bQazPNTEJdgVjaBfLUsJ9vceA+f1feWE6UHzLROEiVy4jyf49mzZ59bioxXjnuO aaQivn/Llf5WnB8lrJ3jKi+TyCZ6GXnNmTBIiv0QF+fcvXv38np/9BlRzbKffKa0DWxFgD5nprJz PIV8n1/ydJVol7yfplWvh3CMwLRx8D1NIz2O3iV53b59++Lhw4eXqbEaHWSXbaSL/aGEdSDMtCpN l5mKJSkopjulDyAARfiZUqY1AqQWtIq8MgrJ2cJJlpl6ZsTm/X1OoxuJytRTCwP3kFiMvnLlHn1n TgTwSnTJWOhbjEtqTFrIuURc7Pe+RnHFPlHCOjhhrZbOmkK316pZpXt9YkZMq3QQTKNonu+9Um/a umamiV4nySiYG3mpQ6l/Tcd8amCSJOT03nvvnT4TTfFMtMVhHAiL904gZKpc7A/9L3MQJAHlbJ1R yuwomsd55cf/4sWL04+flEhbwaxDBElO+p22dK0ki9SyJIZVq+OJXNZeEnbjeV2KzEgyv5Pk6/Pa rx6Y2nEOhAUZ8TdQz+P6JCyIDZtELi82O0UU50UJ6yDIFCojGslJvSeFazUpfuiQFAK10C9lauhr WgD0MVmDZ9QyDZbulzhsvWw6KJmqPdmMD1KbGpYRFOfglwIaXWf/d6PEubhFWjoU2fkO+rkYj+/F MWoN7a+lCVXibKS1P5SwDo4kpxn5AH6Ynme0kNFURkDZZ8rz1IsytZQIJzlKGhIgG6STxKJWleNL IpKZ42bvqvmd/S72gXd/+sLUtQBkBNTt3M/zQcaA15zZ3OpmUZwPJayDI2cFVzNbCvGaMyWNJLis Q8yaQFPNTDO9JlM4kObT9ENJQL46jlFP6luTVKcvbGpVK80qI0Bd7ZpKcybS7qjqYbahydnWlcZX nBclrK8AZgSSQrTpmGmYdgG1nUlcs+3wSoSfNofZ4yqJEzsBY2pY9fk0hToDOK0YjuFzp7Ylgfod chEMt1wj0fv5jEZY3F9rB5YHSVYirX61P5SwDo5ZLpP7kwTUuSx1SWKYjvQ5fkZfK43Me+S9HAeN aEZPEscsuZnCf3Z/mGU9PoPkYtRoWprLmOXfQcJTE1Ov47ysp1z9bYrzo4R1cGQ0Na0C+T5nDTNF 9BwJIU2Z85qVU91n8LNEke719Ecl8YFc0UekIXX2ewergmoJL8lbIuJ8ZxoV0hXhJTgiwNTsHKtR 1r5Qwjo4pgcqZxJzpivToky9Vh6rLGBOHckfMZjFzSnKZ8QFAdjlU7JK7WrqVI6XpLXyeyVyljCj KFNgyYnN2sHUygCfLevJ5yr2hRLWwTF/1PlDT89S/vic7lfjSgE9Sc9z3ZeOdGfZUuyXaICRFlYK iEBXej6jorfXphbmfbMNTXYblZCsBUwtzFlK9uG18pnR0JIo8+8yx/U7FPtCCesgSC0nU0A1mEzZ 0lbAjzS1I8VmiYYftKmSXivvlz9gSUahPkXwWbcI2Kf3C7Mm4rv7kly1GyTJWoKTkwKe4/HsPupq QZnCcsxZSs7JoufU97bKihpd7RMlrINh6lPT2Z36Cz9aXNxEOBY9UzfHdRAJ5JHu8ZVWNdvPzJTQ 81Jsn5sakmQDeTiutgkJJK0J076QYyaBmubaBJDvybh8xyyCnvWM+b3mpMU8p9gHSlgHQZLI/GGt FgdVv+EV86hRhuUuswf7FKtXwvrsfwWmHiaSADRjGhXpeZIc02ahX8tlunKZMu+Xfd4tr/FaCYvP RJezhMlny7/lFmHN71ScHyWsA2FFDCB1Fz9LUJAD1oLnz5+fog5q6oisIAn2W4s3zZmrezv7N4X2 N71WJEnif0r9KY+lniZJrYhW0rS8yNIedbDpG8vnWO2fumCxH5SwDo7Uc1Kw1leUgjQpE1FH1gqu LAFzxi9bL6t9JUnmGMJzjZ4ylZsaWKaiuU/SmbOGfkc2ZyDZZ6pJv3ZgHy1IORdnneU2q/8RlKz2 iRLWwbHlvVJcJ8K6d+/exaNHj04/bs2UHEPj8UedqdP8sc4IafUM80c/jZpp6NQOwaYIn6lgRlcS 36xZzJlJwHVEkUmqiP1GkCsH/+q5i32jhHUwzJRLE+W0DGhX+Oijjz7XA2u63SfRrTajoYyUUhOa PqpEnpPnSiC5IMbK85VkNT1XfCeb/KHTQVB8L0V3IzMjzjSDZrF1Pt/8O5fI9oUS1kHJKn9QqenM lZFJj3R7E2HNRR2yDi9LaDJCynElFW0AW9qQSONq7s8yHguTZyRlaphWBdsZO+tHmksNoOTGORAX NYJ8d8hZTc80WRtElhX5N8hnZasXa18oYR2QpFav+cPLZa7ef//9074ZtXiu5SmeMzUtkPuybxX7 nPGbwrbvV2J7Cub2olIoz+/l83gPZwEzcjJa1KRKdwYjMI45i+j3gLT1m9n/yig1fWVbQn1xXpSw do6VjSGPpZ6TEYKfTQXZxw/UiCPbu3h8/khXs37uswZw6/yrnjlfIZjsMJrPkK1nJD4JNtvqZMqo IO91GTFaFsQYIN33fieXB7PLRbEvlLAOgEyL8jWPpQYEtmb2dJbzg8wVZ0DO4mVXgySQXPBUgvFa MZ/F5/R1Ri6SlRFW+spM5WyV7H1M82Z0OIuoSQ1zf7abyS1nV43mSlj7QwnrYJizW/4Qp0Eyy3FS NJeAJIcZoU3LgedNW4Hnb5GV7yehzcjLtCz9VblPL9nsPS+xzlnFSVh2PM1ne50Bdis6LM6PEtZB sIqqElPQFjPdcm1C4CIO09KQ0dBc5ivtAUZA03+Vz5PPOjUskPV7diqlfCgLpyVYyTKfg+/gd1ZI d8zZEjoLtVPvE0ZWs5NEsR+UsA5MWmA271vNxqXWY6kLkYslLeltmrNms2neVmo3CS+fY4u0AGme wrgr2DDLR2TEMVaxSYKas5WmiRyzBGiSobOmc0Ihv4PPlJpXCWt/KGEdDFtpyyolSmvAqhtB2iFy 9i07i05SymdY7Z8+qvmMM0XUF+ayYPZf5zyiLUX2JJ90x+vjmmmuUAOzU4UaVkZqvOcc9b3U/4p9 oYR1UOTM3hZhGUn5QxX6mPIHrPs8OybM3lGZWl31XPk8M12cs4SuxqzeBJyB5HkwgU7CnT6pjKoy WmRzeXpAWZJu/5xMUGyXKO3BVcK62B3+D69TOtHIjT7BAAAAAElFTkSuQmCC dataset Paige Brochu University of Vermont Spatial Analysis Laboratory Director 81 Carrigan Dr. Burlington Vermont 05405 Paige.Brochu@uvm.edu US 802-656-3324 Paige Brochu FirstMap State of Delaware 801 Silver Lake Blvd. Dover DE 19904 FirstMap@delaware.gov 302-739-9500 20251223 Enterprise Geodatabase Feature Class DE_Impervious_Poly 2024-06-28T00:00:00 Paige Brochu University of Vermont Spatial Analysis Laboratory Director 81 Carrigan Dr. Burlington Vermont 05405 Paige.Brochu@uvm.edu US 802-656-3324 This map shows impervious surfaces in Delaware, as of 2022. This is a polygon layer created from the original raster created by University of Vermont. The University of Vermont Spatial Analysis Laboratory developed this map in cooperation with the Chesapeake Conservancy and the U.S. Geological Survey. <div style='text-align:Left;'><div><div><p><span>This map was derived from a 12-class land-cover product developed for the Chesapeake Bay Watershed. It was extracted from a map showing land-cover conversion in the Chesapeake Bay Watershed during the period 2017/2018-2021/2022. The change-detection map was modeled with an existing 2017/2018 (Time Period 2, or T2) land-cover map containing the following classes: Water, Emergent Wetlands, Tree Canopy, Scrub\Shrub, Low Vegetation, Barren, Impervious Structures, Other Impervious, Impervious Roads, Tree Canopy Over Impervious Structures, Tree Canopy Over Other Impervious, and Tree Canopy Over Impervious Roads. Using object-based image analysis mapping techniques, the T2 map was first refined, where necessary, using all remote-sensing imagery and GIS datasets available for 2021\2022, including LiDAR, multispectral imagery, and thematic layers (e.g., roads, building footprints). The refined T2 map was then compared to imagery and GIS data corresponding to the period 2021/2022 (Time Period 3, or T3), identifying features that had changed during the analysis interval. Each change combination was assigned to a unique category that indicated both the "from" and "to" classes, enabling monitoring of specific types of change. Features that had not changed during the analysis interval were also incorporated into the change map, making it possible to reconstitute full land cover for either T2 or T3. Draft output was manually reviewed and edited to eliminate obvious errors of omission and commission, focusing on the most important types of landscape change: 1) tree-canopy loss; and 2) impervious-surfaces gain. The final map contained 79 change classes plus the original 12 classes for features that had not changed. The specific analysis periods for the watershed's 7 states\districts were: Delaware, 2018-2021; District of Columbia, 2017-2021; Maryland, 2018-2021; New York, 2017-2022; Pennsylvania, 2017-2022; Virginia, 2018-2021; and West Virginia, 2018-2022. The 12-class extract of the change-detection map was created by reassigning all change classes to their T3 status.</span></p><p><span>For specific use in land-use\land-cover (LULC) analyses for Delaware, the T3 map produced for the Chesapeake Bay Watershed was reclassifed into two classes: Impervious (1); and Pervious (2). It was reviewed and edited against 2022 leaf-off imagery to update it to 2022 conditions. </span></p><p><span>This map was based on a draft version of Chesapeake Bay Watershed land cover. A publicly-available version of the final Chesapeake Bay map will be available in autumn 2024, through the Chesapeake Conservancy (https://www.chesapeakeconservancy.org/).</span></p></div></div></div> impervious surfaces 2022 Delaware impervious surfaces Delaware 2022 Microsoft Windows 10 Version 10.0 (Build 26100) ; Esri ArcGIS 13.3.4.52636 1 -75.789019 -74.999624 39.839521 38.451140 Derived from 2022 aerial imagery. 2022-03-04T00:00:00 2022-04-01T00:00:00 FirstMap State of Delaware 801 Silver Lake Blvd. Dover DE 19904 FirstMap@delaware.gov 302-739-9500 FirstMap <div style='text-align:Left;'><div><div><p><span>The State of Delaware makes no warranty or representation, expressed or implied, with respect to the quality, content, accuracy, completeness, currency, or non-infringement of proprietary rights, of any of the GIS or other data or information, or any other materials and items, that are displayed or made available for download from this site. All such data, information, items and materials (collectively, the “FirstMap Data”) are provided "as is" and users are fully and solely responsible for any consequences of use. FirstMap Data may have been created from a variety of sources, including sources beyond the control of the State of Delaware, and are subject to change without notice. To the extent you use, apply, add to, modify or implement this information in your own information system or other setting, or otherwise for your own purposes, you do so at your own risk. In no event shall the State of Delaware or its agencies, officers, employees, agents, or representatives be liable for any damages of any kind or nature whatsoever including, but not limited to, direct, indirect, special, punitive, incidental, exemplary or consequential damages arising from your downloading, modifying, sharing, distributing, or using of FirstMap Data even if notified of the possibility of such damages. Further, the State of Delaware does not accept liability for any damages or misrepresentation caused by inaccuracies in the FirstMap Data or as a result of changes to the FirstMap Data, nor is there responsibility assumed to maintain the FirstMap Data in any manner or form.</span></p></div></div></div> This map was based on a land cover change-detection map for the Chesapeake Bay Watershed, previously developed for the period 2013\2014-2017\2018 (Time Period 1 through Time Period 2, or T1-T2). To update this map for the next analysis period, 2017\2018-2021\2022 (Time Period 2 through Timer Period 3, or T2-T3), the T2 map was first refined as necessary using LiDAR, multispectral imagery, and thematic GIS data, where available, that corresponded to the same 2017\2018 time period. Landscape changes that had occurred since T2 were then identified using LiDAR, multispectral imagery, and thematic GIS data, where available, that corresponded to 2021\2022 (Time Period 3, or T3). Each combination of land-cover change was coded into the output map, making it possible to reconstitute land cover for either T2 or T3. All modeling was performed in eCognition, state-of-the-art mapping software that uses object-based image analysis to segment and classify source data into meaningful landscape features. To create the T3 extract, all T2-T3 change classes were converted to their T3 status using eCognition, reducing the map to the standard 12-category classification. For specific use in land-use\land-cover (LULC) analyses for Delaware, the original 12-class map was reclassified into two classes: Impervious (1); Pervious (2). It was so reviewed and edited against 2022 leaf-off orthimagery to update it to 2022 conditions. 12-class Land Cover Map for the Chesapeake Bay Watershed, 2017\2018 (Time Period 2, or T2), 1-meter Resolution. Developed by the the University of Vermont and the Chesapeake Conservancy. This map contains the following classes: 1) Water; 2) Emergent Wetlands; 3) Tree Canopy; 4) Scrub\Shrub; 5) Low Vegetation; 6) Barren; 7) Impervious Structures; 8) Other Impervious; 9) Impervious Roads; 10) Tree Canopy Over Impervious Structures; 11) Tree Canopy Over Other Impervious; and 12) Tree Canopy Over Impervious Roads. This 12-class map was derived from a T1-T2 change-detection map previously developed for the Chesapeake Bay for the period 2013\2014-2017\2018. LiDAR, 2017/2018 (Time Period 2, or T2), Resolution Dependent on Location. Where available, original LiDAR point clouds were converted into derivatives that facilitate landscape analysis, including a digital elevation model (DEM) and a digital surface model (DSM). The DEM was subtracted from the DSM to produce a normalized digital surface model (nDSM) that indicates the height of aboveground features such as buildings and trees. NOTE: T2 LiDAR was not available for every county in the Chesapeake Bay Watershed. In such cases, modeling relied more on other input datasets and subsequent manual review. LiDAR, 2018/2021 (Time Period 3, or T3), Resolution Dependent on Location. Where available, original LiDAR point clouds were converted into derivatives that facilitate landscape analysis, including a digital elevation model (DEM) and a digital surface model (DSM). The DEM was subtracted from the DSM to produce a normalized digital surface model (nDSM) that indicates the height of aboveground features such as buildings and trees. NOTE: T3 LiDAR was not available for every county in the Chesapeake Bay Watershed. In such cases, modeling relied more on other input datasets and subsequent manual review. National Agricultural Imagery Program (NAIP) 4-band Imagery, Leaf-on, 2017/2018 (Time Period 2, or T2), Resolution Dependent on Location. National Agricultural Imagery Program (NAIP) 4-band Imagery, Leaf-on, 2021/2022 (Time Period 3, or T3), Resolution Dependent on Location. Thematic GIS Datasets, Various Dates. Thematic datasets representing building footprints, roads, road centerlines, sidewalks, driveways, parking lots, alleys, county boundaries, and other datasets were used to augment change detection. NOTE: Because datasets varied widely in availability, quality, and chronology, they were not used in all Chesapeake Bay counties. Delaware Statewide 4-band Orthoimagery, Leaf-off, 2022 leaf-off, 0.152-m resolution. Land-cover change mapping was performed in eCognition, state-of-the-art software for performing object-based image analysis. This expert-system technique segments input datasets into meaningful landscape objects and classifies them according to both their individual characteristics (e.g., vegetation content from spectral imagery, vegetation height from LiDAR) and their relationships to adjacent features. First, the existing T2 land-cover map for the Chesapeake Bay Watershed was refined, where necessary, using available LiDAR, multispectral imagery, and thematic GIS datasets. Refinement of the T2 map maximized its value in subsequent change detection. The T2 map was then compared to T3 data inputs, focusing on the most important areas of landscape change: 1) tree-canopy loss; and 2) impervious-surfaces gain. The final map contained 79 change classes plus the original 12 classes for features that had not changed. Change combinations unlikely to occur in the study area during the analysis period were ignored (e.g., Impervious Structures to Scrub\Shrub). The resulting T2-T3 change-detection map was then exported from its original raster format into vector polygons for subsequent QA\QC. 2024-01-31T00:00:00 The draft T2-T3 land cover map was reviewed manually in ArcGIS Pro using the input NAIP imagery and LiDAR as reference datasets. At an approximate review scale of 1:6,000, errors of omission and commission were fixed using polygon editing and labeling tools. The edited map was then converted back into raster format and submitted to project cooperators for external review. 2024-02-29T00:00:00 After receiving comments from project cooperators, a second round of manual review and editing was conducted on the draft vector map, scanning for errors missed by the previous review and also addressing any issues identified by the external review. However, errors implying sub-meter accuracy were beyond the scope of this project and thus were not addressed at any point during processing or post-processing. The twice-reviewed and edited map was then converted once more in raster format for final processing. 2024-04-01T00:00:00 Using eCognition, a T3 extract from the T2-T3 change-detection map was created by converting all change classes to their T3 status, reducing the map to the standard 12-category classification. 2024-06-01T00:00:00 Using eCognition, a limited set of additional error-checking routines were applied to the final T2-T3 map, focusing on errors too small to be reliably identified during manual review. These routines included false new buildings and impervious features surrounded by tree canopy or water. 2024-06-01T00:00:00 Also for use in LULC analyses for Delaware, the original raster was re-projected from Albers to Delaware State Plane Meters. This conversion may result in the warping of some features. 2024-06-18T00:00:00 For specific use in land-use\land-cover analyses (LULC) for Delaware, the 2021 land-cover map developed for the Chesapeake Bay Watershed was reclassified in ArcPro GIS, simplifying the original 12 classes to 2 classes: Impervious (1); and Pervious (2). It was also reviewed and edited against 2022 leaf-off orthoimagery to update it to 2022 conditions. 2024-06-28T00:00:00 Converted raster to polygon using RasterToPolygon NO_SIMPLIFY VALUE MULTIPLE_OUTER_PART #. Created feature with over 1 million records. 2024-09-16T00:00:00 external bfd6b84d3251a8f2ff48b1cfd3302a9f1c66797a FirstMap State of Delaware 801 Silver Lake Blvd. Dover DE 19904 302-739-9500 True FirstMap Used PairwiseIntersect with the impervious polygon layer and the USGS quad index to reduce the number of features to help make more manageable. Settings - "All attributes" # "Same as input" 2025-12-11T00:00:00 external bfd6b84d3251a8f2ff48b1cfd3302a9f1c66797a FirstMap State of Delaware 801 Silver Lake Blvd. Dover DE 19904 302-739-9500 True FirstMap 69683 1.000000 154120 1.000000 2 1 0 168045.358417 50101.361522 168045.358417 204221.361522 237728.358417 204221.361522 237728.358417 50101.361522 202886.858417 127161.361522 111 EPSG 6.18.3(9.3.1.2) bit Layer_1 2.000000 1.000000 8 Land Use Land Cover Raster False False False False False FIRSTMAP_OWNER.PLC_Impervious_PairwiseInter Feature Class 111 Table esri OBJECTID OBJECTID OID 4 10 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. FID_PLC_IMPERVIOUS_2022_POLY2 FID_PLC_Impervious_2022_Poly2 Integer 4 10 0 ID Id Integer 4 10 0 GRIDCODE gridcode Integer 4 10 0 IMPERV_AREA Imperv_Area Double 8 38 8 FID_BND_USGS_QUAD_INDEX FID_BND_USGS_Quad_Index Integer 4 10 0 PERIMETER PERIMETER Double 8 38 8 USGSINDEX_ USGSINDEX_ Double 8 38 8 QUADID QUADID Integer 4 10 0 CENTLAT CENTLAT Double 8 38 8 CENTLONG CENTLONG Double 8 38 8 NAME NAME String 35 0 0 STATE1 STATE1 String 2 0 0 STATE2 STATE2 String 2 0 0 STATE3 STATE3 String 2 0 0 STATE4 STATE4 String 2 0 0 LATLONG LATLONG String 9 0 0 QUADNO QUADNO String 6 0 0 K100 K100 String 35 0 0 K250 K250 String 35 0 0 TIF24K TIF24K String 15 0 0 TIF100K TIF100K String 15 0 0 TIF250K TIF250K String 15 0 0 DISCNAME DISCNAME String 15 0 0 QUADABV QUADABV String 6 0 0 SHAPE Shape Geometry 8 0 0 Feature geometry. Esri Coordinates defining the features. SHAPE.AREA SHAPE.AREA Double 0 0 0 SHAPE.LEN SHAPE.LEN Double 0 0 0 Value Numeric identifier for each land-cover class University of Vermont Spatial Analysis Laboratory 1 Impervious UVM 2 Pervious UVM Type 1 Details about the fields used to derive data Impervious Data UVM