20221101 17000000 1.0 TRUE file://\\DESKTOP-MF54D1G\D$\POA_2022\PROD_03_MAPA_DinamicaCoberturaForestal2020\3.0_MapsDinamica\MapasOficiales\MapDincf_2016_2020_oficial20221101.img Local Area Network CopyMultiple "'C:\TEMPORAL\Nueva carpeta (2)\mapdincf_2016_2020_oficial20221101.img' raster_file_general;'C:\TEMPORAL\Nueva carpeta (2)\mapdincf_2016_2020_oficial20221101_rec.img' raster_file_general" "C:\TEMPORAL\Nueva carpeta (2)\mapas" mapdincf_2016_2020_oficial20221101;mapdincf_2016_2020_oficial20221101_rec "mapdincf_2016_2020_oficial20221101.img RasterDataset mapdincf_2016_2020_oficial20221101.img #;mapdincf_2016_2020_oficial20221101_rec.img RasterDataset mapdincf_2016_2020_oficial20221101_rec.img #" Copy "C:\TEMPORAL\Nueva carpeta (2)\mapas\mapdincf_2016_2020_oficial20221101_rec.img" "C:\TEMPORAL\Nueva carpeta (2)\MapasCober\mapdincf_2016_2020_oficial20221101_rec.img" "Dataset ráster" # Rename "C:\TEMPORAL\Nueva carpeta (2)\MapasCober\mapdincf_2016_2020_oficial20221101_rec.img" "C:\TEMPORAL\Nueva carpeta (2)\MapasCober\MapDincf_2016_2020_oficial20221101.img" RasterDataset Copy "C:\TEMPORAL\Nueva carpeta (2)\MapasCober\MapDincf_2016_2020_oficial20221101.img" "C:\TEMPORAL\Nueva carpeta (2)\Nueva carpeta (2)\MapDincf_2016_2020_oficial20221101.img" "Dataset ráster" # CopyMultiple "'C:\TEMPORAL\Nueva carpeta (2)\Nueva carpeta (2)\MapCF2016_InabConap_oficial20221101.img' raster_file_general;'C:\TEMPORAL\Nueva carpeta (2)\Nueva carpeta (2)\MapCF2020_InabConap_oficial20221101.img' raster_file_general;'C:\TEMPORAL\Nueva carpeta (2)\Nueva carpeta (2)\MapDincf_2016_2020_oficial20221101.img' raster_file_general" D:\POA_2022\PROD_03_MAPA_DinamicaCoberturaForestal2020\3.0_MapsDinamica\MapasOficiales MapCF2016_InabConap_oficial20221101;MapCF2020_InabConap_oficial20221101;MapDincf_2016_2020_oficial20221101 "MapCF2016_InabConap_oficial20221101.img RasterDataset MapCF2016_InabConap_oficial20221101.img #;MapCF2020_InabConap_oficial20221101.img RasterDataset MapCF2020_InabConap_oficial20221101.img #;MapDincf_2016_2020_oficial20221101.img RasterDataset MapDincf_2016_2020_oficial20221101.img #" cobertura forestal dinamica Estudio elaborado por: Instituto Nacional de Bosques (INAB), Consejo Nacional de Áreas Protegidas (CONAP). Apoyo en análisis de precisión: Banco Interamericano de Desarrollo (BID), Ministerio de Ambiente y Recursos Naturales (MARN), Universidad del Valle de Guatemala (UVG), En el Marco del Programa de Reducciones de Emisiones (PRE), Reporte de monitoreo de reducción de emisiones para el periodo de enero a diciembre de 2020. Edición: Departamento de Sistemas de Información Geográfica (SIG) -Dirección de Planificación, Evaluación y Seguimiento Institucional INAB, Dirección de Análisis Geoespacial CONAP. Fuente: Elaboración propia con base a datos de Imágenes LANDSAT 8 del año 2,020 descargadas de la Plataforma SEPAL (System for earth observation, data access, processing, analysis for land monitoring) de la FAO. El contenido del documento es responsabilidad única del: Instituto Nacional de Bosques (INAB), Consejo Nacional de Áreas Protegidas (CONAP). Se agradece también el apoyo otorgado por las siguientes instituciones: Ministerio de Agricultura, Ganadería y Alimentación (MAGA), Ministerio de Ambiente y Recursos Naturales (MARN), Universidad del Valle de Guatemala (UVG), Wildlife Conservation Society (WCS). Dinámica de la cobertura forestal RASTER /9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCADIASwDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3NlDj kA/Wo8iPcCGKnHU5xUwYHOD04NMcjIBOCelRsAqBPvLznvnNPqrvCkOMA5596nWRJB8rUXuBHcbi qhQDzzmpugxURAlbg4Ve/rUpIxTYETKFYBScsfWnnjGB0qN3GVfPyjrzVa5v54nIhsJLmPH34nX+ RIpAXjxyKQOD6/lUcc2+JPNRo3cfcbGR+XFLuQMRnJ9utMQrMuRyODzSfOqssQDEdzwKi8wGZQwI GT171MjJjaB0/wBmgBmCkm0LnP3mPenTcQMMcYxwKdI6xI0jcIqkkgdqz/8AhIdL3MrXO1l+8rIw K/XigEhjzatubyIrVk3cb3IOPwB9qcZtaAXbbWrEcMPMP88VbguLe6Rbi2dXQtgsvGe386sEgcYo Ar2T3bxt9siiR88eWxIx+NWGOB0rMbVWheVPsE8iox+eIqR+pGKadXleIuumXoIGcbV59utJgUfF utXGi2Ec9t5e8nGXPAH06n8Ky4fF9zJa28jpGWcZkxxsH97nqK1PEVg2oR2k+WTysuUIOTkdCR0r n76AeRDZRTZOAzCPBCEngfezj3xWEm76FW0H6trviCLTIrvTnhnyTvHkHK9x3rOXx/eLpFrcSzQ+ fLu3rs27APxOf88Vf0QX+mxNFcRlN7sPlRyFH+0Bxj6c/WsTxHY6K9nPKIpZXtJAZ/KkAJzj1Jxk eg+tTd7mU1K3MjIk+KXiIOdv2Tbkgfuj/jTW+KHiB4wP9FLZ6+Wen51xkhQyNsBCZ4B6ik56A9Mn rTuzzXWn3OvPxM8Qn+K1H/bL/wCvQPiZ4h/vW3/fr/69ccBk4HWikL2s+52P/CzPEPra/wDfo/40 n/Cy/EHra/8Afo/41yGM5xkgUlFw9rPudoPih4iXo9r/AN+v/r16v4Wv5tR8N2l7cbfOmTe20YGa +dK948Dw/aPCFg3myp+724VuODVRdzow1SUpNSZ1i7iRlqkJJ4DcCqS2nDA3E5Df7eMfSpYLfySc Syvn++2au52krZBTnFO6jioiyhDI3UZ5qBNQtiNwnG3v8p4qrXAsTHgL+NRU26uY7ZTLPIqIpxk9 BVYarYOhZLuJ8DJ2tnj147V200oxSOWd22ydulA6UkMkc8YkidXjPRlOQaeRW1zGxb8wDPPH0qFX wSWyfQt2oc87ckH6dah2lFxk4z0avKcrnoEkuPL7j6HmordEgLkSu5bjDSFsfnTi+fuikhiIkJxn d1qldbhctJMFUqcbR0pjyksdhBUjjnvTWXDfc9jURYx7tqbh/CM9DQ5DQG4RYkVuhbB3HrUcmx42 MTA57buRVeRTPOiMf4gx9vao7lXilQlQRz8ynpU8wF1pQ5WXeR2Cn1qD7ZNbzh5OY36461C0ReVX zuGMMo9+4qRVjRvKlwwX5UyetDYF15oJFUecoOQ2DVmRRPGQp59jg1n7Ea1ChVd1z+AzTY7gRuoU lh/EM9KL9wLUaTjEbEkDpu71aL4ADhcntVaSVXQbmKN2cUguOdkm3kYPoad7AWSTuBHO3naeM01p zu5R1X1AzSoflGACMVHIxEbmMMXVSQnqarmAj/szTpZfOaBDK2SX6E560z+xNMjkDragOCCDk/41 xUnxNt7KQ293pd3bvznzBt/IEVGPizpillayuQQOPmBBqHUWxn7WHc6Xxm96ulr9gleOQsFIVclg eoxism3gt2WEXUAivBgIXyx6dsADNSjxnb3sSSJAyoy4EhIIB7HHXFaqWVwzW8s0aXCxp8pCgsWJ znJxiolq9DZLqUpJIVs3kltbmFSTuMZzvA7cetc54sgQeHGlspwzsu6QSTHJHH8JB5+vArtZrW5V WRW2q75JCg5+vp9awde0MX0XlTRhsDhgdrgegJ6/nStbYJpyi0eJs+SMgDb6HrVzT7KO9fBdFAYc SPjPsDV23022tNRmj1O1mYBgiQI2JNx6H3H49xTrrSZ7O/YSW80UcE20wsctGOO/frUSPKjSe7Rm 6npsun3RibBGcAjr9D7jvVRlCqMHnHIr0bX7i31PwtBFa20kt3M+xHRQTkcsCeucYz9frXnBU7mD fKR1Bpp3FXpqnKyFMZWJZMjDZHHamUUdaDFhXvngBlXwXYZPJU/zrwVCFcFl3D+6e9e/eAVaLwlY q8ZRsHIYHjn3q47nVhPiZ0W/kAKx/ClDP2Uj3NLcXEVtC88hxGgyTjpWXetHqBURahJbmNjzHxk4 6c1pyneaMaFZTkk5HTtUpdVODj8654W7MUhi1q6LOcbjyT3rYtrY21oEkmkuGX+OQ81S3SB6IW4R JnG4BlHQHpUf2eDbgQxj1wo5qUnJNNPArujFJJHHKTbYiRqgCIoVfQcCmscGpRx8341CxySauO5E thSweEAkhuuCabIrMMKTg8ZzTIQpzlRk859aerB3YJ16D2rykd5LGEVcfgPrTWJD7OjEcZ706ONw 2Gb58enFJLuON6854xWntJ3J5EPSUhXBO1veq7TDzo0DgZ5GPX3qyFc9PTuOaiNqsg2uAMjoKTd2 UlYpmOQu5RScPkkVIsZe1aOQ4OD+dRxOyFonDMQxBOe1XxgxlCvynipsPczYXkgVIycnnd/hUoZJ A7NEDtPapPsaouC7HbkjNVBqNkiSxK6iQZ3AZzgdTRZoZPHbDdJKrYOeCOoqwYUkmRh8jpyfcd6y YdSsIJcm7YPnkY45q2L61uGRIrhZJZFLKu3G7HX+RpWETyS7RsLKAOPmWngL5ZEmMcdO3vSm2iuo o3DkDAOO4pqK0B2PF5kf8LjqB70WESCeNVVRyBxkVMpz8rnP91xwR9arboRMWUkRsvzDHepQAx3q c59O9MZW1DS7bVI5opoYxcvE0aylQSAe4rwXxB4dm0HUZLOVmZgco5XAdfUHNfQyEOADncp49qpa zo9rqlnJBdQrJG6kE4+ZfcGk9UY1aSqI8N8N3klvdeW8ww3yiJ2+U17Hpmuwx2sMFwV37fwUe/tX mM3gnUNOuJPPt3kQt+4ljG4Y9/Sux8KaXI48+8V1TkpBMRlccce2awtJM0oN8qizt4b2OV0RQfm5 46Cq97PE9ykTKCVOSc42Y5qtC5s5OECJnauewJzj6VNczB044bkKxXODg9apa7m+zM+80mzt4Fub sLcTKSyTCMB0U9ACAOmab5FtPYrcmyZlcbBHKu9gM4IyCT+FbVuitbx7D8mQ3KgZHHaqk1xEk5YK FRcYK9+e350O1rCsYlvMttc3Ea2x+zxpuC7Qq7uN3uSc5/DFcEvguG5urua4vre1iEpZdrArsIyA PfGK7rxDZRX2lSx299LCssX/ACzAOVH3semOenrXBab4RsJY7iyux5UqoqxzbW5PB5/l7ciuilDD uF6lRp9kr/fdr8LmNWHO1dXGXHgZo5wkU8kwcgKyrwM9Sf0rKvPCmqW822K3eZc4yuM9cdOtei2a faNDMEaywvF8qTMxUZOefrz/AC9K2TZ7ooTJdCNwRkZGW54B/wA9q5lKz7kPCU3rscL4Y8M/ZLyO 5dXluI4/MIGNseTx14ORmvW9PcrYRtIVLHrgVkPEsWnSkyBIyctjg4HbP5Vc0R9+j27s2S/zfnVw erNFCMI2iaLy7kZQAR3BNV3mAXd5Y45NLPFAx3GME+tNLJwCPlUdKtsBVcNKowB6ZHWpwZNwXKgE +lVVZH+bepPbnpU8T4kUGRAo65Ip9RjnQo3JznvTcZFSSyBzxggdCKjr0YX5Vc4ppczsNzSUU7Ar Qz3IIkZYUMi/MeCPQ1PbIm7e3HYCq6+bMRHnByW3Cr8cKBVwvOe/rXlxR6Iy9Be0dUXMjDArnBZ3 xYEQXqkelxgV1Lk52L94Hk0u8IQrDmr0GU7Ox8orO0s+/YAUkkyB/wDXqxIDlSTgk8GiRhHwWHJz 9BUchF18qk7R1IpXAz7jSjPOGkkdC+VyjY4obQ4kiWJpbjaucMJD3rTiYrtWYc5+VvWpwSWYY/Ch AYMelJCgMcs7spyA0hwfagaZMfNc3fLDKqEHy+3vW2IASSpKmqrlI58FkDdDzihpgZ9rp8sUge4u RLHjG0xgVYIjjVSgAcDGNtWt6FgA6k+gNEzAAmoYiK3kUwiNvlZOgPXFHnEqSjYZTyGqUwglZVIJ HDe9ULyRIbncZBG54B6/mPSlsIk8wI28AbckOn1706JW277Z1KH+E1XndMpcq6sCQr7egzTra5SF jEUxk5UjvRcCzHMTlu44I9KnWTcoJqpbskk0qtlZPQ9xVgHO4Y+YfrSAci7d0bDK9qrXGnhiZbeR opCMDB+X8qtBsoCKeOlME7GGb+KK4SG7dYW3YZXxhwfetcpFMjmMozHnCmsTxGkcUsUzW8chkUoH YfcI561XS7ug0UQRFlkTczN93HPT3HH51nJWdjXpc6UxybSd2BjAVewxisq4XEZtyoUPGTkjt6D3 zzWVaS3e6S0h1BZWC+Y0gbOBycD9Kvzaz5EDreQqJ4138cjB6H+dK1xOVjBs7SWHTohI2zcgjllb n5QCdxPUdun9K2reWRtQijeKIWT71DsBuJPfjoOv5irFtcCRWESrJFGjB4ynORgDH6j8aVUsLi8V 0TEkcnz7l4yRgfyx+FSl3DyQ67dI7VyEWPbgADvjnP0pLKS3lginljB2jaHBzz049+aXVLiS1055 d8Xnso2Fug9R+tZejQvfRE7Sq4DbSeATzn/PrUvRlXuaV9NvRoXKCA4IK53AjknNOstQt4baKKC2 mZEXAIXIxXO/2adLurp59YVo9jKDIclST0x06VuW1kILZSLubGMnnGc8/wBa0g02RJ+7Zmitx9pi d1R48HGHXFNDL91jio47s+UsASRyoA3NyT9amigdnAyMdTWltbkWFS2tSQVjVj34qwlnC4ZpIV57 EU5I1iYE9D1p8rjbhW5Nawi2wbsrlcKFG1QAB0AobpS0HpXoJWOF6jBxSM3PFBIpuauxDZJLEYcM hO08H2p0k5Vo0yVycGp5CJkaNByaZNa+db+Tkqcbd46ge1ea4tHpJp6oc06iPepBAGfrULy+aQU5 Dc/Ss2509raeNzfXDxjrGcYqSO4XcYkjKnqKiTA01hRwQcSYPehyI0bZ+IFRQGRY8MVwTyQasRos cQwT7Z61S1GRr+8CecMZ+6CeRVhiq8k4P86plJGbewPHAp5dmYJJ09qqCuTKViyhI+93qCWzglmD NGCepPrT8NGTgbk9PSpFfccryPSm0CfQrCxt4SHSFRjofT/OTTSf3Lqw6A9auls9OvoagdfNLKRj A5rOSuUyFAbeEuwOACxUfyrLmvrdbkytBcMrNtU+X0/zmugwGwcVHNHuHOCBzzQ1ZCObTUomEiLb zbXOBmI4/GryQr5IiIwpHDdx7VcYlZRn7uDz7VEYscsQO+R0qAFwGiywxKvy5A5NLtJaOQE7cU8I 3G1s7e9M3nYU4+UZGKGSTrg8jkVKq5PFQxAIhwc85qY8AYNCGNnihmCrJEG2nIz2rNvtOjELSo2z ywWx2JrTPPWql8DIscfGGbBzTvfcqLdzIsdNsLmNbiK2CTAHzAh2gZzkj16VdsbcJbJ9oYSzKc7j 1wTwD9Aab9gmtY4vs87qqA4XgLnJ4P51bNs8QVwfn3HPPAH+RWCb2LaViC8sTNDCUPlvyWKnG4YI wf8APUVVuta0vS1kaSZcISjRgfMep/HHAq5KZvJkQvheUMp4AzkL+uB+NZWpWWjyWrTaj9njtSo8 vzcAq5JB579vrQwVjA1HVIdYtUt1ingmkkHlM3KKw5/lz+FTRi8l0jEDxWoA8t2TIPAOPz61TtNa ttQhh0/T3gWZXPlqy7sgZXOfU5z+VdFY6DKbczXOomW4cYkCDCAj+WKgj4tTiNK028edLnU7gTq2 4IrfxHpn365zXqqxIIEXaD8ozWCuhWETqbx0iKnbEFfrz1+p4rp1CLhEUsR3rWktxctkkRKV2/Lt HrU0Q2jg9aje0t5Gy0Y3exNCqkC7Y1wCema3UbuyJbSV2SmYK/qPWoncu2e3am5OMUV10qXJqznn U5tEFNc8YpScCoycmuhIwbGmkpcUYqiC15nU4w3qKfHO2cHkd6horFwTOhTaZPdWkN0o85QyjnB9 fWsSfTobGU3EC7WPB5J4Na4kyMPlh9aFhM0uSymLHTvmuSpTaR1RmpbGdFNvyCQqJg4xya0BKS24 nO0Z2iq91YLGd6Aluv1qok0kWW8s73BzWGsSjQW6MxVkHJHr0qxH/rAAVI/nWLb3DQny2GGLc/Sr 8t2EWIKAc5H0pqXcCaafbK4XKlRk+9NWRSgYPtJ6knGabNGkwEpJDqOmfve1VY353SA5f5lBHHFW qjRMopkqyFZ1U3D7R33A1pgrKuVYEHoRVW3+zyEpsiJ9QBVhYxG/yDA64HArSUoy1FGLiN2TJkKV 56e1AWRULMQ2RzUxOVyO1V5XYzJEEJXqxHQVm7JXNDC1ubXLON7qzS1lhGSY3baQuP1rkrTx7exy xW91bxojsxbaQTt6jAJ616BfGGdJrcSR+eIztD/w5GM15Pr3h23t9QS4gu/ux4RVUAMVBDYJPOMf WseZmFXmTvE6t/FQLJ9nvbchpDGCw+8QMnofep7DXbq7Mg8lY5IidzOMqR1zmvM7Wx1f7ZFPFYyz RSvztTco9eB0r0Z/s8VlLfoktreIBG1uwxu6Hp3+opc1tRU5OT10Hv4rmimKvLagHaEZQSCTVqz8 VSvOIJURpMldy8Kcemea5A3McF6kk/2eUswIjLHPPc56GteBLdJjIwKzYLhWBx7H8KUZuSKu07XO lPiBROYWIT5SRIR8p74/KpBqSyNGZJQBkEOFwOnXPSvJ4bq5h8SzILx71mJ2Qpkq2em3P8qnvNT1 e2MVrKgcBDmJgGB44z6Y9qttWsTGvbVo9Kv/ABCdM097m8RGgMm1WtzvIHqf0rUgu/ttjHdWjB0k VWB+prz/AMNz6tp/h2Y3FuWgGT5CoJN4PVu/T0NS6Xr1jDqLfZo5Y7Irl4RIpVjkYbqCD7VkkzVV luzq7oXkTPNbRSSQShS0IxvB5zgHp1zj2qhc29pq1ysN3pc8ieZ5IfYQMcfMfTHNaR1dRavNbx/a CzA/KQMDHU/989PehL9SJ7mVJIVV/maXK8ADkD05p2NHJM5TUPCGnQzzkGKGbaFhPIJwQd2Bz0zV zUPGtlocKWzJJNtGdyYALDORjtz61YutQXxBavb2gZZAD5jp1UZ6Z6jOKzr/AMNW0enI11JEiouX 8sHf36A9ahxd9DNyX2Chb3d1rusWuoLkI8ijZkAIARn+lenYxyTXn/guxuULbIrS2tpGLx723zMM 9eOg4rd1K81KGeKMXUUakFnPklto7Zwa0glFEqTcbs6LeuM96YSScmuO/wCEn1DTjPNqNrC9nDx5 1u+4yH2Hb8a2dL8RWmp7EP7id13LDIw3Efga9DDKNtNzGpJtmtkE4yM0tcL40vrrw1qVjqWnxTSm 6l8qeNWyCMdl9a6ew1u2vFVWzFKUDFXGMZ9fQ+1dUo2S8zI0GNNxTzQBmhMlrUZg0YNTLGGHUDJw KQxsxyhG3tms5VorcuNGT1CikBpasQUAkHIODRRSavuCdizG6vwxOajvLZ5YmET7HIwGwDt/Coqm Sc9HP41zVKVtVsdNOrfRnPNpurZZmu4S3YGPk/rU5LgFW5AxlSMc/Wt+SJZkxkgnuOtUhZNEjBcc cjmuWUexsZss0rZVWCN1yf4SaqefIrRwzMzHcCsvZqtOY2BViQwOcgd6gco4ZCgIZSpU+lRcC9Ff LHMAIF28ZYLWhDqME0qqucsxGD7VgSTTWc5lI821bC7FHMeBx9f/AK5qR9iWov4mGYnyFzwQRnH1 5q02gOgN0rbgqk444FI7FUBGeTgn61xPiDxSdJuPJg8wXIKMhdflcE4IBxj8enX6VqaPrbz2kr3U +24luCI4LpdgHQqoOOmMYPPaplK+hPtI35RNbgS3SWW6R5kkOdsIO9QAODgnI4rhNSnt2vbe1+zm e2kZWCOTGBkYLAj0HWu48USTSaY8kkbwz27hojES2cnHUDuCcj8aU6TZapp6QRRG11CGIBZGTJT6 kcHOPrUGU48zsjzWw1o+HPEE22cNbopAEYyc9cDPXPPNdzLMnijS7W4gmhN+nziPcu8DH6GsO+8O GwuIZJZ7aeZ5CpZ4zsHvgYwetPi8Ox6PE0dnPM13MQzeWGG70A6Db+JoITmtHsK8kFveC2ntjLqM S8wyLudh6hhlfwroPKhu7bdJB5e4qRuUAgehODXByeGb46r9ujvrgvndKyITInr8qkn8K0rjxXda WzWk+nNdzMwKzhSp2+hGOGpR00KU7L31YvCytp9RNxHAI5Vk2BlYsD/wIH5avx2jGeP7RYoodvKL ysMOPQYJJPH/AOqpNPuX1jSZZ1szCjKQUnh6Y7jOdw96zbPxRrHmf2ZbWjXCxuBkr823qQMHqKY1 ypX7mpa2V5PDOZ/Mjg3EKryudo4BA7+v59qqJpFrcXj2saxkOAPLBDHAAP1xxj15ro7r+xb6JI9T tTw24LKhGw9cZ78+maa2n6bbzwiyLWUhJVWijVS7HjnI59f+A0Giic2dKNlcXItbsJluYTlW39cj 5uT2/wD1VnTNqctxOZp5JmIEawNyOTk5Gc9zjH6V14VbWSea7dkKnbJL5QZSzAYHHsevHJH0qnfe I2tZYYYLZktmUZnCKAM9gxwO/YEe9JO4OKQWWqQaRaF1tXigQEPHDGDkjjpng8ZqqdSsryK9ZxHK XbelyYg+wN0AHt6nApk+pLpiQw7jf3TtuX7TiNnzxxtHHHY+1R6zqD2WmtqF7DPYWuBGAyhmZsdF XPI68miMJykox6jcopWZTuVj0kpeXF1PHqEb/u8yH94M5wQCRg81peH7/UNRm3app0VvFJlUnxy7 dcAHt+lecan8TLQ2MLWNrPNqkbACe7RQiKP7qqfp1rndR8e+I9ag+wm5WKGTCmK2QJu7cnr+tfUY XhPH1VzVLQXnv93/AATF1le56D44LXV+kVrIbTTThiqs2+XA5KqOAPc0WOtxAP5OgrHBsUM/nhZW 9PvHJrptL0jVJPC1kY4ImuhAivCHWNY2xg46g/TirsvhO/u7YIL54V43K7Z2jHOMCvCxFRp+yjbl i3a3Xzv1/qwOEm7ooafd/wBtyWAkd5JUf5omVX8sH3I5PuOa6rUNJt9TtTYzB2hGMsDywB6Z9PWs 20l0Lw5JHbiSN73bgBIyXbP06mtu2edw3ykd9rHLD6n+lZOTm7tmsIpKzKunWl9azywyBEsI1Hk7 judic578AcdaumZFYD+I9Aaqzzusqh+QW5HpUEole+faGBCYTPPJ71pKtOS5QVOKd0bEYZo/PdSS oyqCpI8qgGzcOxU8VlxJrCW5zd2ylSMHyyc8dD9T6VQOqazbSPCUSbaxxIkLFWzzkHNHQ0NoDNKB ikHWnV6DPOQUUUUhhRRRQBPFPj5W6etT8MKo1JHKU4zxWFSl1idFOr0kElhG0hdsnP6VR+wGAkJN uyd2SOg9K2FfcKjniLp8nUHIrlcUzoMYQZ+VQdzHLMW4rE1iW10eA3EgbBYuYlGfNPXA5HYE/hXR G0KwbH/1hJbIPeszULQT3cLzQB2tvnRWPBNRsS720MS78UpDHa3SafCsjsVLsvcDcFyMnPXj270+ 28YS3G5b2yeOUSnZ5P8ADnjn1AyMniqGmT2koHn2zI4kAnjhUEJ8vfJ453DNbs9jYXVn8kAS3kYf vx8zNnjJx09KgwTk9Uy7cQ2motvJILIIzuQFjxwx+g/nRYGe1tyBclxED+9nyAVBPO04H5Vl3OpQ +H7XaZnManCBU2sx6EnPQc5pLLxJBNEjxs25f9aCMoCe2fpQXzK+u5tqXv5XSa1aIvyHADKR9c1S n8N3SMqw3MLKo+TeHDJ7gg4/DFSrPcf2e7xLuXIJyckAemKp61fyCyVIJ5hbthpGDHcRnse1BVk9 yqvhrVomeKW5geKUdYw4OfcEkflUMujrpapHdQSXSZDSP5XDfT5uverttrw05oPOmi+zAbgrKzy4 P8qNR12HUJY4YZ7d7J1bfvBByPQg8e3FIlqKQ6KGy1NVksri6QAlVDpv+vynP61De2dnZK1w0Zbc /IdRG272GAD0p/h26igNxp+i2ggiA8wSTFmBJ7nnit+Kzna5V7mJSxwHZG4Ppxjp+fWnZWKjqrnH RzSFspJHFGXyf3iNhepyAcFuCP6VZgjvp7giI3MVxC5ltna3AQoTk5J79Rg47cVr+JtNYaTcXVnD m5iQsm0c+uCOhGQPSuCtfFF/plhMEUkbw/mpj5UPbbnnnPFKxnJ8j949D0m2vd8u+YqeAzSkyyMT z1JIUdPlx2zWHJoEo1n7XdPb+TBudXlkHK/eJI6Ag5IxWRdfEaxsLwyxJJeyGPepVtqFmH8Qxnjp 3471zlx4m1gW88smmSJZ3LEh2RwMFiwAbp7D2FApVqe3Y9UtItLPmXBnt3nmBffHLuKgA+h+6Bji sbxJrkD2oiC2cttghLqSdBj/ALZnk8+npXm0Wravq8jWun28jTPziLOR6/QZNbUHw48Q3hge/kCI V5LOGZck8H1/+vVcz6CdVyVoI59dA0VLu+nvGGsXsz4hgQNEm485Gw7m6+w+tdt4R8FRWV9b31to 1qqMoZ52mk3IR1CqSf1ro7HwdpumxNIttbx3O35XG44OPU9s1p6DeLqWnmYQmNUkZNpPy/LxleOK 7quaY2tHkqVXy2SteysvJaFQpWfvGsXDAplUT+LI5/8ArVjeItMvtQtFs7DUPsqt94bAd3sW6j8K sXSMs8YjmVcsWYkZ3DsBz+tWVy9sGBJOen/1640r6G71VmZuieH7bTrhXeNHuNvMuzHPt6VtpAGk d2U/NwO1RxyBZBgZwOfao7i9VSwklVSFyB61SQJJKyJjboXXHAXpnoKgniJmEkcmGyBgAUyGZ7iH Eb/d4yfWpI0cHezDCdAKEBLFC43xSTGVRyWZQNtQeckZZAvCngKowKtgMLZvlyG5P5VRSxt2GZHy x6nOKJajepPSg4pganV6h5iY7vRmkBxQSCKRVx1FNBxTqATCiiikMkifBAPTtVuqFTwzbflbp2NY VKXWJ0UqnRkrxh+DWdeT28O9JGQykAKrHGcnjn8/yrT256nn2rNv7BHg5VZJC+VeQZ2ntj/PeuaS 6m9jMbSYry2MUm1JJWIeMgEAHHUdzheM+ufSsuB76wuE0q8t/tMjSN5bB9uI1ztbjhR0GKv3Wof2 dfW8XmjDfOWbGT8o4HfnFWdU0xr7VbO/S4VRbkEpyDjkn6/w/lXOnciUeqOYmih81pJrcNAykMxd nCD6HvkjGKuQeHbRNLuJbOeRo8ExsAEPHbpk9MVBe3d9Z6pcC7ijiiWfzEcg4ZRyAO3Pv6Vq6ZqO kppguBCEhf8AeuPMLKv/ANf2oM4pX1MvQPENlFK1q8/kyD+CZSMj15q9qF9HJayLNidJPmUK3Qeg Izisa9tbfWIv9KjSSRW3IN+GAJOe3b8qfDbJZ6TFKcsI/wB2pictjjjg8UCUpbFwWFjqNrAPMZ0Y BV3OVb6Zqa70CAt5MjllEeFiTgj0wRzWZpetTLcNB5DSRRDNwCn3B2wfX6VbW9W2kN5bJGwxjqeF 6n8aCo2mZ91cXOhM00Nz++K7AjkYx+XWr9prOv3EaQJH5yn5lnDgsADnBx1PatCG8sdTaSCZI533 Ajeu4qSOMjFTQ3EcDSxRqVZFB2J09unTpTsPlae+hDd3+vzW6KbO0a3mDNK8oYhEwc5H4jiud1Dw te31/C1vcE7UVRJDGNp479sDGO9egQqzpHgYhZRlSST9KvJDHHGERFVRjAA9KVi3BS3PM7b4bWj6 TCs1vJLeNJl5Fl24Ge4PTI5+tdDovhMaXvhklL2OQyWzv5gRhggjPoc/hiuge+QyNBBHvlVsbd20 Z681YZ1jWR9uSvOB60wjSgtkUrDR7HTS8ttbhGc/MxOSRml1HUDavFHColmf+EckL649OKksLl5V dZXSRgzEMnQLngH3xVTUtHsNTkiklybi2IMbqxDLjkZ9eaC2rLQoS25S7nvrtsuyBGVUy208AAdq uaGkEWmwtGhQMSQhXbjn0HApY7SaBlnmnCszZMa8cdPxHSrqCN9pRgRwRt6YqorqSl1EureOWROC APSpywSFscbRgVT+zxTzSANIuDg4Y1KYRAChJKkfxc1oiiF3fyiIgM55y3WoFt5HJ3SyKWPCselX TbqtqQevr61BdylogVH8W0DuaT0AmhiW3V5C7scY59ahiZZ7hgj5PU4PSrK7XgK5z681SitXtHLt yztlm9BRu0I11kDRZI2/Ws8ht7EIXyeoNJczSJ5fBKk9B3qeMs0YIIHtTuriuQBqcDUVO3V61jzL kw5oqMNinBqmxVx1KDTQcmnLSY0OooozUlBRRRQBPDNj5WPHrRduAsS4yXkGPw5P6A1BVHUX1GGI z6esc0qjiKUkD8D2NYVKV9Ym9OrbSRU1bS55IoZoVE16JA2W4AGACR7ZxxWfJqUmmFEvmkQwp80k hz53bAHoa0dB8TDVx9nli8m9Rv30DDBQA4/Hp+taGp6dFqNk9vcJuVlwXx8wHqK8+UOVvudDfMro qJq9hrFgjeSJoZhgxEDdwecis6/8KW2sXdtNBeNbxRMDJAo6kYwPyqb+zLbS1eOO3llLHzAc4PoB +X8qps8to008ULRxxShWYklnYcDHsQQc1N2JpNWkQ+KEtNKjjZYJJIl3IJV5KSE9WPtXJaHpWtRa t/aL+IEvooyGubU9BGRx7ZrVvJJZNc2RtP5Fw4d4mXcH9R+lWo7TTtCubi6iEbQzKwaB3wWGfX69 q6cNj6lCM4JK0u6T0/Qycbu6Lem6xpF7eXEVpIw25LAjnp7dcVENPt9Uke4S/SaAsEeNVKge/wBa vW8llBFNq1nZKdQ8gkwouSFHas/TNQtbXTr3Ur2Lcke1hGqbNznkgD61zi8pEAwdYtLPzIijkhmU 7TgDvjkdBWxJZW9hGZJ8h7rEce3kEdsk9un0pNGtZpLmbVZbQS+ZGJIEcbWQN95at31mNRYXMCq0 ZiwocnCOP5HjFVyu1xxWlzVs5vNlji8yNdiAFEPQ8E/z/WtBSOVEnK9R6DP/ANaudstFvYC1xHP5 UspTcF5A6Zx+oqzqFi097Fa/bkCTJ88RbEjAdwaSTexqm7bFi70tZyghuprUtlQYTgnvz+R/On3u mm9he2+1TwsUT94nsSf6VaRfIEaEMwX5QxOSox3qXcRJ8wBPTI/z9KCrIo22nLpsRb7QzqoAG8DC 9M9PpUkjSxXQ2ohDcnPB9M1OrQXUb7GEiSDaSDkEYrB8TXV7YGG6s0VkhQtIDySARwPfGaBSdlc1 riOW7VVb92FPzHufpSJGGDKhBCErle2Khg1D7dGssaZDgMitxjjvSySS26gpbqqg4bDdc1pFdR+Z PY5Z3Rhim3J8uXEjYQ85zTo2uWAdbcJ0BO/tUFz+9LRg/vSMZPSq2QE4aG6hTDjA9GpJIGjC7DkK Pumq1rbQW1qudhYHnB71cuHdtrR8AjGD3pb6iKVoxQsMHk8jsKshmdsNzhafCFYMRwSuKh09i7ur Z4JGfWkuwExjJTdn5QPlXNZP9ry5I+w3SlTgqFBxWnOz28meSh/zmoJHikfeXALcmm7DGhqcDTCK TJHWvZseQS0oNRhqcDmlYZKDTw1Qg4pwNS0NMm3U3PNMzS0rFXJAadUeRTgcUmhpjqKKKkopz2CN c/bIAsd4F2iXHUZzg1NZ37SMYZo3jmUYIbndjuD+tTVHPCs8ZRiR6FTgis6tKNReZrTqOPoSSIZJ vOEnyJuAHvxVeayjb5ivmBSMpjgk45P4VQiS80skSOZ7TBLPj5x+FbNvOlzboYmBDdfb1rhqU5Rd nsdcZRlqjMbRFWCMQMVEQYALwGB7Vl32gnULq1HyxQhMMjx9DnPX1rrx7AbaaMSFspwDwT3rK1wc UzAudBMOnGKxOCDuOTgyd8Z9KqyRXepwRiayhjiUAoJBjDjtXS4YFnwcgZx2H0qOSKOa2XzULJ1A HUHNFrIOVWOMtmv7fxOyTSm5sH4kUNtWNgM8fSuqaRbyPbAoRAwLt6+uPWquraOkkam2j2lTnao+ 9zk1nxz6hY3yQW9uJbaY5lUtgwkDnHr9KDP4HbodTJMsewswwcD86Y9ssk6zBIw68K5UFgPQGqkF 6FuDCYyURRhm6gdKtPL8rPHmRVJPycn6U9ehqtR7wLJKGZjvQ5XHGMiiWFXnim2gyJuVST0B6/yF LuYnBAU5wue/enhspuPOPSkBVup4dOsnmlTbCmN3lryBnrgVKEguoMcMr9Peod2Y3d1IQfKQec9q mACxIsQCoBwB2raPKo26iKUdoonOBtVRwRxiobuPy3yGLhuufStKKIuN55PI46U7yVYFTgdh7Uah crRTGRlSM7oyO9VJ4iZwgOf6VLPA1ltaEnOeR2IoMu8rhGDse44xSYEy2NusWCgJxkmnwlEiXcPk wCo9KdKHCAIeo/Kq0BP2UgjJyVAPanfUCV7SB/nA4J55NRfZ0gmQQbue+euP8mnM7kqnvk+1WkQJ IGYZ5zuoAGjDJsflj3PaspoWjdlXBUHg1bnuoWRypfLcZA+7/nNRxtvjV9p+YZFFrsEIRkU0rTqK 9c8kjK0Zx1qSmladxWANTgfSo9pBoyR1osFyYNTs1CGpwNJodyUGnA5qLdSg1Nh3JgcUbqjDUoap sVclHSikBpaRYVAbUKS0DtA55OzofqKnopNX0Y02tisLq+gVluIhcLnh4eDj3U/0p8+t2VnEHn82 MHoDEc/lU1FZPD030NVXktytaa5p93GWSUxjd0cFSfzp7atZQoUecZHBA5yT2FTUVH1WF92V9YfY zx4ghE0UPlXISQE+YYyAox0+tSnUNJlvxaSOgn27lV+Nwz1HrVuqV7pGn6gpW6tIpM/xbcMPxHNN 4Wm1oJV31RfZUmIeNVZc4Y4x+OfaqttZvYyxw2i5tGLmQs+SGJyCM/jVZYL7T7YR2bi6jXOI7h8H GOgbHP41Z0++N3DGlzC1rckYeFj6ehHUe9c0sPOK12NozjLYvbxISmdoHf1FBaKJcM23Hv1qKW3J YPG3TgKKRrdy4ZtpA46VC00sVdjbmeJ4SVkRNxzkmqKaNablKXNySCHCibjj+lSzaa0l2H4MQXGw 9PrirtvbRRjcoUPjBIpq7AgttNjtJRNHNOSBgqz5X8qtTFCcbh5g5qOS7WOfy26k8GqP9vadIHIL +aoyw2HIqk2tUDV1ZmhIfOAVkGfU0yaBZduG+ZehHaqA8RaXtJMrE9B8h61csbuC8jMlvkrnBypG DSfcFoU7jU1s5jDNMuQMjfx+VMXV9OjIzcpuPJyeBWmbdXk3Oq4/XmmjTbbaAYgxXu3OKe4ynHqM N7c+Vayo5CljU/mTKxWR/lKnbxjn0p84FqwZEUBuMKMVIypMih1G0/Ng0r9AuQyPsiRgVJx82TSQ OqoVySoPykeh5/rToIVJGxFznIbHQGqs4S1k2G48vI3BducU0r7DsPooor1jyAooooAKaVoooAbj FAPrRRVEjg1OB9KKKTQxcmlDUUUhjw1ODUUVLRSY8NmlooqGWgooopDCkaiimJjafRRQwQUUUUhj kkaM8dPSmXNxfnLWdtFKmOjvtO78unSiisKsUldHRRk27DYHv3kkN5DFEoI2bH3Z69f0obcJSV6H qKKK45bm4PAske6RMkj8aArfKFGfX2ooppuL0E1dWJdwihLyJnHYCmQ3HmwiUKyg9jRRSuMmAkdi GQLGR1J5qTI24UgY70UVfkAgiVhuc5PWnMsSruKggDiiiiwxkWxWOO9RwxwztLJJGHy5Clh2GB/M GiinHYEf/9k=