Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Impervious polygons derived from circa 2016 high-resolution remotely sensed data. Object Based Image Analysis (OBIA) techniques were employed to automatically extract building, roads, other paved,and railroads polygons from a combination of 2016 LiDAR and 2016 Orthoimagery. The resultant Impervious polygons were then subjected to a manual review at a scale of 1:3000.</SPAN></P></DIV></DIV></DIV>
Service Item Id: 0e8b8b17bc2c4a3e961465a3ffefdd7b
Copyright Text: University of Vermont Spatial Analysis Laboratory in collaboration with Vermont Center for Geographic Information.
Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Shrubland polygons derived from circa 2016 high-resolution remotely sensed data. Object Based Image Analysis (OBIA) techniques were employed to automatically extract shrubland polygons from a combination of 2016 LiDAR and 2016 Orthoimagery. The resultant shrubland polygons were then subjected to a manual review at a scale of 1:3000.</SPAN></P></DIV></DIV></DIV>
Service Item Id: 0e8b8b17bc2c4a3e961465a3ffefdd7b
Copyright Text: University of Vermont Spatial Analysis Laboratory in collaboration with Vermont Center for Geographic Information.
Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This dataset depicts wetlands in Vermont. It was created with an automated feature-extraction process that relied on high-resolution LiDAR and multispectral imagery. In particular, a Compound Topographic Index (CTI) layer derived from LiDAR was used to identify landscape features that have suitable topography and flow potential for wetlands. Moderate-scale (10 m) statistical models developed by Patrick Raney of Ducks Unlimited were also used during classification in a data-fusion approach that maximized the value of individual inputs. After initial identification, mapped features were assigned to one of three primary wetlands classes: Emergent, Scrub\Shrub, and Forested. The class assignments were based primarily on vegetation height (as estimated from LiDAR) and spectral characteristics (e.g., features with short vegetation that appeared very bright in leaf-off multispectral imagery were assigned to the Emergent category). The initial map was then generalized to eliminate unnecessary detail using a minimum mapping unit of 0.1 acres. In a final step, the automated output was manually reviewed against multispectral imagery and obvious errors of commission and omission were corrected. More than 57,000 manual corrections were incorporated into the final layer. Overall, the combination of automated feature extraction and manual corrections was biased toward over-prediction, focusing on capture of borderline features whose functional status cannot be definitvely established with remote-sensing data alone (i.e., it is generally easier to discount false wetland features than it is to locate omitted ones). Known areas of overestimation include managed forestlands with extensively-modified drainage patterns and wide river and stream channels. This map is considered current as of 2016, the year of the most-recent multispectral imagery used during manual review.</SPAN></P></DIV></DIV></DIV>
Service Item Id: 0e8b8b17bc2c4a3e961465a3ffefdd7b
Copyright Text: Developed by the University of Vermont Spatial Analysis Laboratory with funding from: State of Vermont Clean Water Fund, Vermont Agency of Natural Resources, Vermont AGency of Transportation, Lake Champlain Basin Program, and the Vermont Center for Geographic Information.
Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Agriculture polygons derived from circa 2016 high-resolution remotely sensed data. Object Based Image Analysis (OBIA) techniques were employed to automatically extract building polygons from a combination of 2016 LiDAR and 2016 Orthoimagery. The resultant agriculture polygons were then subjected to a manual review at a scale of 1:3000. The reviewed polygons were subjected to a second round of OBIA to differentiate crops, hay and pasture.</SPAN></P></DIV></DIV></DIV>
Service Item Id: 0e8b8b17bc2c4a3e961465a3ffefdd7b
Copyright Text: University of Vermont Spatial Analysis Laboratory in collaboration with Vermont Center for Geographic Information.