20220420 14575300 1.0 TRUE TRUE natl_wetlands_inv This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the United States and its Territories. These data delineate the areal extent of wetlands and surface waters as defined by Cowardin et al. (1979). Certain wetland habitats are excluded from the National mapping program because of the limitations of aerial imagery as the primary data source used to detect wetlands. These habitats include seagrasses or submerged aquatic vegetation that are found in the intertidal and subtidal zones of estuaries and near shore coastal waters. Some deepwater reef communities (coral or tuberficid worm reefs) have also been excluded from the inventory. These habitats, because of their depth, go undetected by aerial imagery. By policy, the Service also excludes certain types of "farmed wetlands" as may be defined by the Food Security Act or that do not coincide with the Cowardin et al. definition. Contact the Service's Regional Wetland Coordinator for additional information on what types of farmed wetlands are included on wetland maps. Surface water USFWS Wetlands Swamps marshes bogs fens U.S. Fish and Wildlife Service National Wetlands Inventory Deepwater habitats NWI Coastal waters Hydrography oceans geoscientificInformation inlandWaters environment The present goal of the Service is to provide the citizens of the United States and its Trust Territories with current geospatially referenced information on the status, extent, characteristics and functions of wetlands, riparian, deepwater and relat iVBORw0KGgoAAAANSUhEUgAAASwAAADICAYAAABS39xVAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAO xAAADsQBlSsOGwAACYBJREFUeJzt3VtvG8cZgOHhQeeDz07cJm6bBg7ai6KX/QP91e0PyEWvC/Sq RdGT7ViyZcvWidziW3JlkiJpUVYcfebzAIItJWICiXgxMzs7262qqioACXR/6v8BgMsSLCANwQLS ECwgDcEC0hAsIA3BAtIQLCANwQLSECwgDcEC0hAsIA3BAtIQLCANwQLSECwgDcEC0hAsIA3BAtIQ LCANwQLSECwgDcEC0hAsIA3BAtIQLCANwQLSECwgDcEC0hAsIA3BAtIQLCANwQLSECwgDcEC0hAs IA3BAtIQLCANwQLSECwgDcEC0hAsIA3BAtIQLCANwQLSECwgDcEC0hAsIA3BAtIQLCANwQLSECwg DcEC0hAsIA3BAtIQLCANwQLSECwgDcEC0hAsIA3BAtIQLCANwQLSECwgDcEC0hAsIA3BAtIQLCAN wQLSECwgDcFiIf1q8Ge75QfHpydYLOS/z3tlZaVVHt5p+8nxyQkWi2mV8vKgXx7cbpfWxCjr6KQq T1/0ys8edMpK1xCM6ydYnDt8V5XN9VapZkz5ev1SVrutsnW3dSFWYX21VR7d75Tjk1KqqirdbsvU kWslWJxb6ZZyclqV12+rcu/WxRHU8/1+2VgrpTNnAWt1Jf5ZVY5PqrJ3UJW7u636a71eKWe9qqyt GnlxdYJFOeuVOk4RltOzqp7aHRxW5db2eFwiPhGzCNK741K6nVJOz8pgVFaV8uJVv9y/3a5fJ6aE rfZwhb6UcnDYL91uKe12hFG0uBrBop4KdtqlbG8OQvPVw055e/Q+No0IUT8uE3ZK+ds/zsrvv1up R2WNeI1GBHB7YxCmZ/u98vBOp/57v+8HztUJ1hKLkVSsO22uR2AGcYmRUnzEqGlSxGZ9tZTNjXbp dHr1NK/TeR+orWGgJjWxCq/f9sutbVcYuRrBYrAmNWzN8WlVj7hiDWtSTOd2tgZf/+Wjbv3vbnZa E+tX8x0dl3Jr2w+dqxGsJRajqyZEjRhdTZu2vTseTBE31gbfs7M1WLeaFIv29frVjHY9sH+LjyBY 1CI+EZm4ujctKtPiFGGL72k+wg8v3y+8R+RiK0SzltUsvt/eMSXkagSLWoQqthzMism0Na3914Mr f1vrg8X6uMIY61PNVcD4/OmLfvn1193z/VjN69e3+FTjozv4EMGiFiOiWdO4WRtM42Ow8D74xtgw GnutmgX83a12WVtplVdvqvLmsF8ePejUWyFCXG2MUVvbTYksQLCoLTLSiXWqGCHtbI4XLrZFRJwi Yq12qf++uVbKn78/Kr/9plse3muX7jBuzZ+wCMFirlinmozZnd32+SL8yelga0OzB6vZshCjp057 MNr63ZOV8t0vZr/VDo+qeloJHyJYzBVbF5orgydncS/h4OvN1yJW02Z1MSuMKeGsBfuxN2FnMGq7 zLYIlptgMdfovX91VEZuq4nRV3x0uuPrW+trrbFd708ej7/NYnTWBK9+E3Za5eg4guWXwXyCxVyj o6fYnhD3DsboKYJUTRk99WJxqxofKU1OKeM1nu316u0T9dXH2PtVv5ARFvMJFgtp9lzFSCoW2UdH UqGZBs6zuxXf1z7/3phW7mwOPjk7K/VWCZjGW4OFNNsSPvaYmFn3Hcb9jdtOc2AGwWJh//xfrz7R YZ447WHaZtO9g365u3txFBbTxJh+zgoZBMHiw3uu4pSGkUXyx18OYnV2VpX//DA4LnljIk6xJFWf szWc8jUiVvGaMZKKTaVxPvzIvdeX3rzKchIs5mpOC401q8nRTxyB3MRr1u0+sU7V7IQ///ppVa91 NUEbvJZfBB/mbcIHxe02zdrVZU1ubZg8ziZOJ512hM3klgcYJVjMPLmhcZUF9iZWcYP07e3x8+Hj SmCMvqaJ0RzMIliMibWlfz3tlYd32+dbFGKEFaOiq6wv3Zly+sPudqvsbk8fssVWidjKFetmi47q +PwJFhcO9fv262698zzGQD/sD07zu3e79cm2dcZi/vHpxZurQbCYqrkqGCOeEFfy9l71y93hutOs bQvXIc7TMrpiGsFirtEF8DilofGn74/LH/+w9qNEa/QE02aPVjyY1c3RCBZzTyAdFaOq2NoQf377 OA7jmx2r2LJwXaOkuD+xipmpm6OXnmAxVTPCiTPZ46phxKcJWIx4njwefybhtOB1r2nXevOwjGd7 /fpiAMtLsJiqmX41zyms3yyd91f5WjMiFYELP8ZUcXI3PctHsJhp1ubOWdloRmBxhTG2Jrw7qsae mHNZTSAnt1HEVcPm4a8sJ8FipmmxWugK4xWnhBG72EA67UC/Fy/75ecfuPGaz5dgcePEFor2jHfm F/fEapkJFnPFFCx2nU9bk3obj/u6hoX1eH7h3kFVvhguqDcPdI2bq0fvR4yZYtyE3SpV2R0+7ILl IljMNW+9aPQs9496E3biQaxV/dToe7fb9b2G+6+rcnd3cKN04+BNf/iQVmtYy0qwuLI4z+o6xIgq bpB+dRh7KIaf77QubBRtHiEW61vTHj/G50+w+Mk93++VjbX2+Y3SsX1i1qbUmD7+/d+98uX9Tn02 PMtFsBhT77uaeFrOPHHk8eZaa+xE0kU9uHO5hfTY4/WXv56Wna2WB68uKcFiTEy1TnuX3+sUR9Bc x9SsPn10+PiwWd6+65ff/KpbtjfaY8cuszwEizH1Y+fn3CN44Q10TeGY/C++Pox7Gd/vuA9x/vuO TaNLTbC4ESZHTKurcfb7+82jB4f9+myub77yll1mfvvc2CuQL1/3zw/xi6nn1obLgstOsLixmo2k sa5WDe9R9NzC5SZY3Hhxb2FcvfyYK5F8HgSLGy9OG40R1uiud5aTYHHjtYWKIcHixpu3N4vlIlhA GoIFpCFYQBqCBaQhWEAaggWkIVhAGoIFpCFYQBqCBaQhWEAaggWkIVhAGoIFpCFYQBqCBaQhWEAa ggWkIVhAGoIFpCFYQBqCBaQhWEAaggWkIVhAGoIFpCFYQBqCBaQhWEAaggWkIVhAGoIFpCFYQBqC BaQhWEAaggWkIVhAGoIFpCFYQBqCBaQhWEAaggWkIVhAGoIFpCFYQBqCBaQhWEAaggWkIVhAGoIF pCFYQBqCBaQhWEAaggWkIVhAGoIFpCFYQBqCBaQhWEAaggWkIVhAGoIFpCFYQBqCBaQhWEAaggWk IVhAGoIFpCFYQBqCBaQhWEAaggWkIVhAGoIFpCFYQBqCBaQhWEAaggWkIVhAGoIFlCz+D6tZJ64f FhALAAAAAElFTkSuQmCC