Land cover classification is a valuable tool for professionals in a diverse range of fields, ranging from environmental and ecosystem management, to land use planning and fire management, these applications play an important role in both the public and private sectors. The most thorough and recent of the datasets used for land cover classification have been the 1992 and 2001 30-meter-posted National Land Cover Database (NLCD) datasets created by the United States Geological Survey (USGS). While these datasets provide a good medium-scale land cover dataset, there are limitations to the NLCD's accuracy and use in finer-scale applications. Under the NEXTMap ® USA program, Intermap Technologies™ is assembling a nationwide dataset of high-resolution 1.25-m orthorectified radar imagery (ORI) and 5 m elevation datasets for the entire conterminous United States. NLCD data and NEXTMap ® Land Cover Data were compared in five different study areas across the United States (California, Colorado, Montana, and two locations in Minnesota), and verified with field measurements. Nine land cover classes (water, barren, grassland, urban, shrub, mixed forest, deciduous forest, evergreen forest, and wetlands) constituted the majority of the study areas. Overall, the result of using NEXTMap radar and elevation data for classification of land cover yielded very favorable results. The majority of the land cover classes were delineated with an overall accuracy ranging between 86.30% -86.91% on the order of 90%, versus 59.16% -63.93% for the NLCD. The NLCD map often confusing deciduous and wetland, underestimating evergreen, and overestimating shrub vegetation classes.

We conducted a 2-year study (2014)(2015) in North Carolina, USA, to compare precision and efficiency between 2 methods used to estimate Canada goose (Branta canadensis) abundance. The first method (i.e., band-return estimation) used hunter band-returns and harvest estimates. The second (i.e., plot survey) used surveys of 1-km 2 plots randomly located across potential goose habitat in the state. To quantify efficiency, we recorded all expenses and time dedicated to goose banding and plot surveys. In June 2014, we banded 2,102 adult geese at 44 sites. During the 2014-2015 hunting season, we received 173 direct band recoveries from birds banded as adults. We used the Lincoln-Peterson formula to calculate an abundance estimate of 148,839 (coeff. of variation ¼ 7.9) and determined the band-return method required US $72,858 and 2,317 person-hours to complete. We surveyed 300 1-km 2 plots across North Carolina in April 2015, and calculated an abundance estimate of 155,655 Canada geese (coeff. of variation ¼ 308.9). We determined the plot-survey method required US $80,767 and 2,857 person-hours to complete. Although population estimates were similar, we recommend the band-return technique to estimate Canada goose abundance because it provided a more precise estimate with similar overall costs and, if continued for multiple years, will allow calculation of additional population metrics including survival, recovery rates, and harvest distributions. Ó 2017 The Wildlife Society.

Feral swine are among the world's most destructive invasive species wherever they are found, with translocations figuring prominently in their range expansions. In contrast, sea turtles are beloved species that are listed as threatened or endangered throughout the world and are the focus of intense conservation efforts. Nest predation by feral swine severely harms sea turtle reproduction in many locations around the world. Here we quantify and economically assess feral swine nest predation at North Island, South Carolina, an important loggerhead sea turtle nesting beach. Feral swine depredation of North Island sea turtle nests was first detected in 2005, with annual nest monitoring initiated in 2010 documenting nearly total losses to feral swine in 2010 and 2011. The cumulative valuation of annual losses for North Island from 2010 to 2016 ranged as high as $1,166,500. To improve nesting success, an integrated approach for eliminating feral swine was implemented in 2010 and greatly intensified in 2013 by adding federal experts. Removal efforts were challenging due to the island's remoteness and impenetrable habitats, weather, hazards in accessing the island, and wariness of the animals, especially as their population diminished. Removal of the final 11 swine required efforts from 2014 to 2016. Nest predation was highly variable and provided another example of the significance of conditioning by feral swine to sea turtle nests on the consequent severity of nest predation. Even the final individual inflicted heavy losses before his removal. Genetic analyses of feral swine removed from North Island and the adjacent mainland revealed that the island's population did not originate from the nearby mainland, meaning they were (illegally) introduced to the island.

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