20201201
13403000
1.0
FALSE
Append Truckee;Sierraville;Smartsville;Downieville;Colfax;Bidwell "C:\Users\SG\Documents\ArcGIS\Projects\Yolo CWPP\01-Spatial Data\Leiberg\Leiberg.gdb\Cover\All_Cover" NO_TEST "Type "Type" true true false 50 Text 0 0 ,First,#,Truckee,Type,-1,-1,Sierraville,Type,-1,-1,Smartsville,Type,-1,-1,Downieville,Type,-1,-1,Colfax,Type,-1,-1,Bidwell,Type,-1,-1;Type_code "Type_code" true true false 4 Long 0 0 ,First,#,Truckee,Type_code,-1,-1,Sierraville,Type_code,-1,-1,Smartsville,Type_code,-1,-1,Downieville,Type_code,-1,-1,Colfax,Type_code,-1,-1,Bidwell,Type_code,-1,-1;SHAPE_Length "SHAPE_Length" false true true 8 Double 0 0 ,First,#,Truckee,Shape_Length,-1,-1,Sierraville,Shape_Length,-1,-1,Smartsville,Shape_Length,-1,-1,Downieville,Shape_Length,-1,-1,Colfax,Shape_Length,-1,-1,Bidwell,Shape_Length,-1,-1;SHAPE_Area "SHAPE_Area" false true true 8 Double 0 0 ,First,#,Truckee,Shape_Area,-1,-1,Sierraville,Shape_Area,-1,-1,Smartsville,Shape_Area,-1,-1,Downieville,Shape_Area,-1,-1,Colfax,Shape_Area,-1,-1,Bidwell,Shape_Area,-1,-1" #
Dissolve All_Cover C:\Users\SG\Documents\ArcGIS\Default.gdb\All_Cover_Dissolve Type;Type_code # SINGLE_PART DISSOLVE_LINES
All_Cover_Dissolve
002
file://\\LAPTOP-9OCJPADC\C$\Users\SG\Documents\ArcGIS\Projects\Yolo CWPP\01-Spatial Data\Leiberg\Leiberg.gdb
Local Area Network
Geographic
GCS_Clarke_1866
Angular Unit: Degree (0.017453)
<GeographicCoordinateSystem xsi:type='typens:GeographicCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/10.8'><WKT>GEOGCS["GCS_Clarke_1866",DATUM["D_Clarke_1866",SPHEROID["Clarke_1866",6378206.4,294.9786982]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433],AUTHORITY["EPSG",4008]]</WKT><XOrigin>-399.99999999157239</XOrigin><YOrigin>-399.99999999381384</YOrigin><XYScale>511999999.5231626</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>3.1250000029103846e-08</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><LeftLongitude>-180</LeftLongitude><WKID>4008</WKID><LatestWKID>4008</LatestWKID></GeographicCoordinateSystem>
20201201
13311800
20201201
14324500
150000000
5000
Version 6.2 (Build 9200) ; Esri ArcGIS 10.8.0.12790
Timber Cover
In 1902, the U.S. Geological Survey published Forest Conditions in the Northern Sierra Nevada, California, by John B. Leiberg. This book contains generalized...
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN STYLE="font-weight:bold;"><SPAN>Title:</SPAN></SPAN><SPAN><SPAN> </SPAN></SPAN></P><P><SPAN><SPAN>Forest Conditions in the Northern Sierra Nevada, California – Digital Version</SPAN></SPAN></P><P><SPAN STYLE="font-weight:bold;">Date started:</SPAN><SPAN><SPAN> </SPAN></SPAN></P><P><SPAN><SPAN>September 12, 2005</SPAN></SPAN></P><P><SPAN STYLE="font-weight:bold;">Date completed:</SPAN><SPAN><SPAN> </SPAN></SPAN></P><P><SPAN><SPAN>January 10, 2005</SPAN></SPAN></P><P STYLE="font-weight:bold;"><SPAN>Summary:</SPAN></P><P><SPAN><SPAN>In 1902, the U.S. Geological Survey published </SPAN></SPAN><SPAN STYLE="font-style:italic;"><SPAN>Forest Conditions in the Northern Sierra Nevada, California</SPAN></SPAN><SPAN><SPAN>, by John B. Leiberg. This book contains generalized vegetation information on about 3.5 million acres between 39º and 40º N latitude and between 120º and 121.5º west longitude. This information is in the form of both a written description and maps of the area.</SPAN></SPAN></P><P><SPAN>In an effort to utilize the maps in this book in a GIS, we digitized the maps using ArcMap 8.3. First, all maps were scanned to a digital format (tif format). The digital maps were then georeferenced using ArcMap 8.3. Finally, we used on-screen digitizing techniques to delineate the polygons for each map unit of interest.</SPAN></P><P><SPAN>There are four main themes in the Leiberg maps and in the final digital product.</SPAN></P><OL STYLE="margin:0 0 0 0;padding:0 0 0 0;"><LI><P><SPAN STYLE="text-decoration:underline;"><SPAN>Cover</SPAN></SPAN><SPAN><SPAN> – The “cover” theme is divided into six distinct 30-minute quad maps (Bidwell, Colfax, Downieville, Sierraville, Smartsville and Truckee). In the “cover” theme, each polygon is labeled as either:</SPAN></SPAN></P><OL STYLE="margin:0 0 0 0;padding:0 0 0 0;"><LI><P><SPAN><SPAN>less than 2000 merchantable board feet/acre</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>2000-5000 merchantable board feet/acre</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>5000-10,000 merchantable board feet/acre</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>10,000-25,000 merchantable board feet/acre</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>greater than 25,000 merchantable board feet/acre</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>chaparral</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>cultivable</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>cultivated</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>pasture</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>rock</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>water</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>woodland</SPAN></SPAN></P><P><SPAN><SPAN>**** It is not clear whether the board/feet estimates in the maps are calculated using the “Michigan practice” or the “local practice”. The Michigan practice includes trees greater than 8” dbh and having at least 10 feet clear lumber in the trunk. The local practice includes pine trees over 12” dbh and fir trees over 16” dbh. Neither practice, we believe, includes incense cedar.</SPAN></SPAN></P><P /></LI></OL></LI><LI><P><SPAN STYLE="text-decoration:underline;"><SPAN>Culled Timber</SPAN></SPAN><SPAN><SPAN> – Polygons in this theme represent areas where cutting of trees has occurred, whether for timber, mining or fuel. No information exists as to the intensity the cutting. However, the intensity of cutting appears to vary from very light to complete clearing. The “culled timber” theme is divided into six distinct 30-minute quad maps (Bidwell, Colfax, Downieville, Sierraville, Smartsville and Truckee).</SPAN></SPAN></P><P /></LI><LI><P><SPAN STYLE="text-decoration:underline;"><SPAN>Species Distributions</SPAN></SPAN><SPAN><SPAN> – This theme shows the geographic distributions of:</SPAN></SPAN></P><OL STYLE="margin:0 0 0 0;padding:0 0 0 0;"><LI><P><SPAN><SPAN>Douglas fir</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>Grey pine</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>Mountain hemlock</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>Red fir</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>Sugar pine</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>Western juniper</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>Western white pine</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>Yellow pine (ponderosa and Jeffrey)</SPAN></SPAN></P><P /></LI></OL></LI><LI><P><SPAN STYLE="text-decoration:underline;"><SPAN>Fire</SPAN></SPAN><SPAN><SPAN> – These polygons represent fire perimeters and the intensity of those fires. The categories are as follow:</SPAN></SPAN></P><OL STYLE="margin:0 0 0 0;padding:0 0 0 0;"><LI><P><SPAN><SPAN>5-25% of timber burned</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>25-50% of timber burned</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>50-75% of timber burned</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>75-100% of timber burned</SPAN></SPAN></P></LI></OL></LI></OL><P><SPAN STYLE="font-weight:bold;">Data format and delivery:</SPAN></P><P><SPAN><SPAN>All data is delivered in a zip file named “leiberg.zip”. This zip file contains the following files and documents:</SPAN></SPAN></P><OL STYLE="margin:0 0 0 0;padding:0 0 0 0;"><LI><P><SPAN><SPAN>This brief final report (Leiberg_final_report.doc)</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>A geodatabase containing vector information for the four themes described above (Leiberg.mdb).</SPAN></SPAN></P></LI><LI><P><SPAN><SPAN>A folder labeled “images_rectified”, which contains the digitally-scanned, georeferenced images from which the geodatabase was digitized. </SPAN></SPAN></P></LI><LI><P><SPAN>FGDC metadata (leiberg_metadata.html). </SPAN></P></LI></OL></DIV></DIV></DIV>
Authors: Sean A. Parks and Sarah L. Thrasher
USDA Forest Service, Pacific Southwest Research Station, Sierra Nevada Research Center
2121 Second St., Suite A-101, Davis, California 95616
530-759-1717 (ph), 530-747-0241 (fx); sean_parks@fs.fed.us
Source: Leiberg, John B. 1902. Forest Conditions in the Northern Sierra Nevada, California. United States Geological Survey, Department of the Interior. Government Printing Office, Washington D.C.
Sierra Nevada
TNF
File Geodatabase Feature Class
dataset
EPSG
6.5(3.0.1)
0
Simple
FALSE
0
TRUE
FALSE
All_Cover_Dissolve
Feature Class
0
OBJECTID
OBJECTID
OID
4
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
SHAPE
SHAPE
Geometry
0
0
0
Feature geometry.
Esri
Coordinates defining the features.
Type
Type
String
50
0
0
Type_code
Type_code
Integer
4
0
0
SHAPE_Length
SHAPE_Length
Double
8
0
0
Length of feature in internal units.
Esri
Positive real numbers that are automatically generated.
SHAPE_Area
SHAPE_Area
Double
8
0
0
Area of feature in internal units squared.
Esri
Positive real numbers that are automatically generated.
20201201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==