Difference between revisions of "NLCD Land Cover"

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== Legend ==
== Legend ==

Use the -n and -c flags of d.legend to limit the legend to named categories.
Use the -n and -c flags of {{cmd|d.legend}} to limit the legend to named categories.

* See also https://www.mrlc.gov/data/legends/national-land-cover-database-2016-nlcd2016-legend
* See also https://www.mrlc.gov/data/legends/national-land-cover-database-2016-nlcd2016-legend

Revision as of 23:18, 12 November 2020

NLCD2016 land cover example, south shore of Long Island, NY


  • Raster grid covering the Continental US at 30 meter resolution

From the Spearfish dataset's landcover.30m history file:

National Land Cover Data Set NLCD, U.S. Geological Survey (USGS)
The NLCD was compiled from Landsat TM imagery (circa 1992) with a spatial resolution of 30 meters supplemented by various ancillary data (where available). The satellite images were analyzed and interpreted by using very large, sometimes multi-State, image mosaics (that is, up to 18 Landsat scenes). Because a relatively small number of aerial photographs were necessarily conducted from a broad spatial perspective. Furthermore, the accuracy assessments correspond to Federal Regions, which are groupings of contiguous States. Thus, the reliability of the data is greatest at the State or multi-State level. The statistical accuracy of the data is known only for the region.

Obtaining data from the USGS

The classic Spearfish GRASS sample dataset contains a landcover.30m raster map which is an excerpt from an older version of the NLCD dataset.

  • Data is provided in ERDAS Imagine .img format.
As of April 2019 the zipped dataset is a 1.4 GB.
  • Create an Albers location
this can also be done automatically with the Location Wizard using the downloaded .img file.

Here are the values for the CONUS dataset:

name: Albers Equal Area
proj: aea
datum: wgs84
ellps: wgs84
lat_1: 29.5
lat_2: 45.5
lat_0: 23
lon_0: -96
x_0: 0
y_0: 0
towgs84: 0,0,0,0,0,0,0
no_defs: defined


Rather than import the entire 16GB raster image, just make a virtual link to it:

 r.external in=NLCD_2016_Land_Cover_L48_20190424.img out=NLCD_2016_Land_Cover_L48_20190424 title="Land Cover 2016"

Then use g.region to zoom to your area of interest and r.mapcalc to extract a subset

 g.region -p n= s= e= w= res=30 align=NLCD_2016_Land_Cover_L48_20190424
 r.mapcalc "NLCD.extract = NLCD_2016_Land_Cover_L48_20190424"


These can be applied with the r.category module.

11:Open Water
12:Perennial Snow/Ice
21:Developed, Open Space
22:Developed, Low Intensity
23:Developed, Medium Intensity
24:Developed, High Intensity
31:Barren Land
41:Deciduous Forest
42:Evergreen Forest
43:Mixed Forest
82:Cultivated Crops
90:Woody Wetlands
95:Emergent Herbaceous Wetlands


NLCD2016 land cover classification legend from mrlc.gov

These should load in automatically, but if they don't here are the official colors which you can load in with the r.colors module's rules= option:

11 70:107:159
12 209:222:248
21 222:197:197
22 217:146:130
23 235:0:0
24 171:0:0
31 179:172:159
41 104:171:95
42 28:95:44
43 181:197:143
52 204:184:121
71 223:223:194
81 220:217:57
82 171:108:40
90 184:217:235
95 108:159:184


Use the -n and -c flags of d.legend to limit the legend to named categories.

Manning's n

For using as a basis for Mannings n friction coefficients, you can make a virtual reclass map with r.reclass. The module will only reclass as integers so the table below is reclassed into parts per ten-thousand. Crop your area of interest with r.mapcalc and divide by 10000.0 at the same time to get the Manning's n number.

Actual values for your location will vary widely, the following is simply a starting point.

0 = 0  Unclassified
11 = 0250 Open Water
12 = 0220 Perennial Snow/Ice
21 = 0400 Developed, Open Space
22 = 1000 Developed, Low Intensity
23 = 0800 Developed, Medium Intensity
24 = 1500 Developed, High Intensity
31 = 0275 Barren Land
41 = 1600 Deciduous Forest
42 = 1800 Evergreen Forest
43 = 1700 Mixed Forest
52 = 1000 Shrub/Scrub
71 = 0350 Herbaceous
81 = 0325 Hay/Pasture
82 = 0375 Cultivated Crops
90 = 1200 Woody Wetlands
95 = 0700 Emergent Herbaceous Wetlands


  • Kalyanapu, Alfred & Burian, Steve & Mcpherson, Timothy. (2009). Effect of land use-based surface roughness on hydrologic model output. Journal of Spatial Hydrology. 9. 51-71.
  • Liu, Zhu & Merwade, Venkatesh & Jafarzadegan, Keighobad. (2018). Investigating the role of model structure and surface roughness in generating flood inundation extents using 1D and 2D hydraulic models. Journal of Flood Risk Management. e12347. 10.1111/jfr3.12347.
  • Bunya, et al.. (2010). A High-Resolution Coupled Riverine Flow, Tide, Wind, Wind Wave, and Storm Surge Model for Southern Louisiana and Mississippi. Part I: Model Development and Validation. Monthly weather review 138.2: 345-377.

See also