The National Land Cover Database (NLCD) for the USA is made available by the Multi-resolution Land Characteristics Consortium (MLRC). The rasters they distribute contain land cover classifications for the conterminous United States and Alaska. Data is also available for Hawaii and Puerto Rico. Data has been distributed in 2011, 2006 and 2001 so it is possible to monitor changes in land cover over that time period.
The data is free to download - find it here: https://www.mrlc.gov/
The rasters are delivered in ERDAS Imagine 9.3 format. These rasters can be directly visualised in MapInfo Pro. Note that you will need to set the appropriate coordinate system for each raster (choose "North American Coordinate Systems" and then "Albers Equal-Area Conic" EPSG:5069 for the conterminous states and EPSG: 2964 for Alaska). The rasters have a 30 metre cell size.
The rasters contain "ImagePalette" data - in other words they record an index into a color table in each cell. Unfortunately this is not very useful.
The best way to work with these rasters in MapInfo Pro Advanced is to convert them to "Classified" rasters in MRR format. This is not straight forward to do - I do it using the raster script processing tool (which we hope to ship in the next update) and information provided by the MLRC from which you can build a classification table. The script I used to classify the data is attached. This results in the following class table.
See Attachment
Here is the conterminous united states:
See Attachment
And Alaska:
See Attachment
The statistics window will provide you with a count of the number of cells that have each individual classification. From this you can determine the percentage of the area that has a particular land coverage class.
At this time, you cannot use the region statistics tool to compute the statistics for polygonal regions. To process polygonal sub-regions, use the clip tool to make a new raster of the area required and save it to MRR format. The statistics for this new raster will represent the region clipped out. For example, I used the Pitney Bowes world boundaries dataset to acquire a polygon for California.
Here is the raster:
See AttachmentAnd here are the statistics for land cover classifications in California:
See Attachment