NLCD Land Cover - Revision history

Neteler: minor fixes

2024-11-04T08:52:46Z

minor fixes

← Older revision		Revision as of 08:52, 4 November 2024
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	=== North Carolina ===		=== North Carolina ===

	You can easily import the NC_NLCD2019only dataset into the NC sample dataset mapset by the following steps		You can easily import ({{cmd\|r.import}}) the NC_NLCD2019only dataset into the NC sample dataset mapset by the following steps

	<~~source~~ lang="bash">		<syntaxhighlight lang="bash">
	mkdir NC_NLCD2019only		mkdir NC_NLCD2019only
	cd NC_NLCD2019only		cd NC_NLCD2019only
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	r.import input=nc_nlcd2019 output=nc_nlcd2019		r.import input=nc_nlcd2019 output=nc_nlcd2019
	</~~source~~>		</syntaxhighlight>

	=== Spearfish ===		=== Spearfish ===

	Rather than import the entire 16GB raster image, just make a virtual link to it:		Rather than import the entire 16GB raster image, just make a virtual link to it:
	<~~source~~ lang="bash">		<syntaxhighlight lang="bash">
	r.external in=NLCD_2016_Land_Cover_L48_20190424.img out=NLCD_2016_Land_Cover_L48_20190424 title="Land Cover 2016"		r.external in=NLCD_2016_Land_Cover_L48_20190424.img out=NLCD_2016_Land_Cover_L48_20190424 title="Land Cover 2016"
	</~~source~~>		</syntaxhighlight>

	Then use {{cmd\|g.region}} to zoom to your area of interest and {{cmd\|r.mapcalc}} to extract a subset		Then use {{cmd\|g.region}} to zoom to your area of interest and {{cmd\|r.mapcalc}} to extract a subset
	<~~source~~ lang="bash">		<syntaxhighlight lang="bash">
	g.region -p n= s= e= w= res=30 align=NLCD_2016_Land_Cover_L48_20190424		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"		r.mapcalc "NLCD.extract = NLCD_2016_Land_Cover_L48_20190424"
	</~~source~~>		</syntaxhighlight>

	== Categories ==		== Categories ==
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	[[image:NLCD_Colour_Classification_Update.jpg\|right\|thumb\|NLCD2016 land cover classification legend from mrlc.gov]]		[[image:NLCD_Colour_Classification_Update.jpg\|right\|thumb\|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 {{cmd\|r.colors}} module's rules= option:		These should load in automatically, but if they don't here are the official colors which you can load in with the {{cmd\|r.colors}} module's ''rules='' option:

	<pre>		<pre>
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	== Legend ==		== Legend ==

	Use the -n and -c flags of {{cmd\|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



	[[Image:North Carolina landuse 2019 Raleigh area.jpeg\|center\|thumb\|500px\|North Carolina landuse 2019 - Raleigh area]]		[[Image:North Carolina landuse 2019 Raleigh area.jpeg\|center\|thumb\|500px\|North Carolina landuse 2019 - Raleigh area]]
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	References:		References:
	* [~~http~~://www.fsl.orst.edu/geowater/FX3/help/8_Hydraulic_Reference/Mannings_n_Tables.htm Ven Te Chow, 1959] (Corvallis)		* [https://www.fsl.orst.edu/geowater/FX3/help/8_Hydraulic_Reference/Mannings_n_Tables.htm Ven Te Chow, 1959] (Corvallis)
	* [https://pubs.usgs.gov/wsp/2339/report.pdf USGS's Guide for Selecting Manning's Roughness Coefficients for Natural Channels and Flood Plains]		* [https://pubs.usgs.gov/wsp/2339/report.pdf USGS's Guide for Selecting Manning's Roughness Coefficients for Natural Channels and Flood Plains]

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	* 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.		* 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.		* 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 ==		== See also ==

Neteler: /* Legend */ Screenshot added

2022-08-14T11:42:55Z

Legend: Screenshot added

← Older revision		Revision as of 11:42, 14 August 2022
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	* 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



			[[Image:North Carolina landuse 2019 Raleigh area.jpeg\|center\|thumb\|500px\|North Carolina landuse 2019 - Raleigh area]]

	== Manning's n ==		== Manning's n ==

Neteler: /* Importing */ NC import

2022-08-14T11:35:45Z

Importing: NC import

← Older revision		Revision as of 11:35, 14 August 2022
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	== Importing ==		== Importing ==

			=== North Carolina ===

			You can easily import the NC_NLCD2019only dataset into the NC sample dataset mapset by the following steps

			<source lang="bash">
			mkdir NC_NLCD2019only
			cd NC_NLCD2019only
			unzip /path/to/NC_NLCD2019only.zip

			r.import input=nc_nlcd2019 output=nc_nlcd2019
			</source>

			=== Spearfish ===

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

Neteler: /* Obtaining data from the USGS */ NC added

2022-08-14T11:34:11Z

Obtaining data from the USGS: NC added

← Older revision		Revision as of 11:34, 14 August 2022
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	: ''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.''		: ''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~~ ==		== Obtaining NLCD data ==

			=== North Carolina ===

			* Download site: National Land Cover Database (NLCD) - 30m
			** https://www.lib.ncsu.edu/gis/nlcd
			*** download: NC_NLCD2019only.zip

			=== Spearfish ===

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

⚠️HamishBowman: /* Importing */

2020-11-12T23:23:21Z

Importing

← Older revision		Revision as of 23:23, 12 November 2020
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	</source>		</source>

	Then use {{cmd\|g.region}} to zoom to your area of interest and r.mapcalc to extract a subset		Then use {{cmd\|g.region}} to zoom to your area of interest and {{cmd\|r.mapcalc}} to extract a subset
	<source lang="bash">		<source lang="bash">
	g.region -p n= s= e= w= res=30 align=NLCD_2016_Land_Cover_L48_20190424		g.region -p n= s= e= w= res=30 align=NLCD_2016_Land_Cover_L48_20190424

⚠️HamishBowman: /* Importing */

2020-11-12T23:22:54Z

Importing

← Older revision		Revision as of 23:22, 12 November 2020
Line 46:		Line 46:
	</source>		</source>

	Then use g.region to zoom to your area of interest and r.mapcalc to extract a subset		Then use {{cmd\|g.region}} to zoom to your area of interest and r.mapcalc to extract a subset
	<source lang="bash">		<source lang="bash">
	g.region -p n= s= e= w= res=30 align=NLCD_2016_Land_Cover_L48_20190424		g.region -p n= s= e= w= res=30 align=NLCD_2016_Land_Cover_L48_20190424

⚠️HamishBowman: /* Manning's n */

2020-11-12T23:20:25Z

Manning's n

← Older revision		Revision as of 23:20, 12 November 2020
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	== Manning's n ==		== Manning's n ==

	For using as a basis for Mannings n friction coefficients, you can make a virtual reclass map with {{cmd\|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 {{cmd\|r.mapcalc}} and divide by 10000.0 at the same time to get the Manning's n number.		For using as a basis for Mannings n friction coefficients, you can make a virtual reclass map with {{cmd\|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 {{cmd\|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.		Actual values for your location will vary widely, the following is simply a starting point.

⚠️HamishBowman: /* Manning's n */

2020-11-12T23:19:11Z

Manning's n

← Older revision		Revision as of 23:19, 12 November 2020
Line 109:		Line 109:
	== Manning's n ==		== 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.		For using as a basis for Mannings n friction coefficients, you can make a virtual reclass map with {{cmd\|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 {{cmd\|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.		Actual values for your location will vary widely, the following is simply a starting point.

⚠️HamishBowman: /* Legend */

2020-11-12T23:18:23Z

Legend

← Older revision		Revision as of 23:18, 12 November 2020
Line 103:		Line 103:
	== 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

⚠️HamishBowman: /* Colors */

2020-11-12T23:18:00Z

Colors

← Older revision		Revision as of 23:18, 12 November 2020
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	[[image:NLCD_Colour_Classification_Update.jpg\|right\|thumb\|NLCD2016 land cover classification legend from mrlc.gov]]		[[image:NLCD_Colour_Classification_Update.jpg\|right\|thumb\|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:		These should load in automatically, but if they don't here are the official colors which you can load in with the {{cmd\|r.colors}} module's rules= option:

	<pre>		<pre>