The original description for this dataset is below. Here it has been clipped to the Missisquoi Watershed to be used to look at LULC types along river corridors. The raster also has simplified LULC type groupings, where (1) deciduous forest, (2) coniferous forest, (8) forested wetland are classified as (11) - Forest; (3) herbaceous, (4) shrub are classified as (22) - Shrub-Like; (6) emergent wetland, (7) scrub/shrub wetland are classfied as (33) - Wetland; (13) buildings, (15) other impervious are classified as (44) - Developed; (9) crops, (10) pasture, (11) hay are classified as (55) - Agriculture; (14) roads/railroads are classified as (66) - Roads-Rail; (12) barren is classified as (77) - Barren; and (5) water is set to Nodata. (16) orchards is not represented in Missisquoi and is left out. Water was set to nodata because the intention was to look at LULC along river reaches and water should not be counted into the percentages of surrouding LULC.

------------ Original Description Below ---------------

High resolution land cover dataset for the Lake Champlain Basin within the United States. Sixteen land cover classes were mapped: (1) deciduous forest, (2) coniferous forest, (3) herbaceous, (4) shrub, (5) water, (6) emergent wetland, (7) scrub/shrub wetland, (8) forested wetland, (9) crops, (10) pasture, (11) hay, (12) barren, (13) buildings, (14) roads/railroads, (15) other impervious, (16) orchards. This is the first 1-meter resolution land-cover dataset for Lake Champlain Basin. This dataset represents a 900-fold increase in the size of output land-cover dataset that had previously been mapped for this region through the National Land Cover Database.The primary sources used to derive this land cover layer were 2015/2016 LiDAR data and 2016 (VT) and 2013/2015 (NY) NAIP imagery. Ancillary data sources included GIS data provided by the states of New York and Vermont or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:5,000 and all observable errors were corrected.

The original description for this dataset is below. Here it has been clipped to the Missisquoi Watershed to be used to look at LULC types along river corridors. The raster also has simplified LULC type groupings, where (1) deciduous forest, (2) coniferous forest, (8) forested wetland are classified as (11) - Forest; (3) herbaceous, (4) shrub are classified as (22) - Shrub-Like; (6) emergent wetland, (7) scrub/shrub wetland are classfied as (33) - Wetland; (13) buildings, (15) other impervious are classified as (44) - Developed; (9) crops, (10) pasture, (11) hay are classified as (55) - Agriculture; (14) roads/railroads are classified as (66) - Roads-Rail; (12) barren is classified as (77) - Barren; and (5) water is set to Nodata. (16) orchards is not represented in Missisquoi and is left out. Water was set to nodata because the intention was to look at LULC along river reaches and water should not be counted into the percentages of surrouding LULC.

------------ Original Description Below ---------------

High resolution land cover dataset for the Lake Champlain Basin within the United States. Sixteen land cover classes were mapped: (1) deciduous forest, (2) coniferous forest, (3) herbaceous, (4) shrub, (5) water, (6) emergent wetland, (7) scrub/shrub wetland, (8) forested wetland, (9) crops, (10) pasture, (11) hay, (12) barren, (13) buildings, (14) roads/railroads, (15) other impervious, (16) orchards. This is the first 1-meter resolution land-cover dataset for Lake Champlain Basin. This dataset represents a 900-fold increase in the size of output land-cover dataset that had previously been mapped for this region through the National Land Cover Database.The primary sources used to derive this land cover layer were 2015/2016 LiDAR data and 2016 (VT) and 2013/2015 (NY) NAIP imagery. Ancillary data sources included GIS data provided by the states of New York and Vermont or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:5,000 and all observable errors were corrected.