Lab #13 VECTOR GIS - SPATIAL ANALYSIS USING POINT DATA PART I

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1 FOR 324 Natural Resources Information Systems Lab #13 VECTOR GIS - SPATIAL ANALYSIS USING POINT DATA PART I Name: Section: As with other aspects of spatial analysis, there are numerous methods that can be employed when producing maps from point data. In this lab you will compare the visual interpretation of information derived from different point data summary methods and evaluate the integrity of the output maps. Learning Objectives for this lab: To learn how to create point layer based on attribute table with XY data fields To learn how to complete more advanced Table manipulations in ArcGIS, including Summary Tables, Calculations, and Query features To learn how to join tables based on an index field To learn how to apply Spatial Analyst tools to query, extract, and interpolate point data Task You have been hired by a not-for-profit NGO (e.g., The Nature Conservancy) that is interested in the spatial distribution of forest cover within each of the hydrologic units (i.e., watersheds) across New York State. As the new GIS consultant working for this company, you have asked to complete the following task: o to summarize the amount of forest cover across New York State using USDA Forest Service FIA sample plot data. o to summarize and map the spatial distribution of the forest cover across New York State using USDA Forest Service FIA sample plot data using three different approaches, including within each hydrologic unit (i.e., watershed) Materials required: Computer with access to the internet, and ArcGIS TM What to submit for this lab: Answers to all questions Three (3) maps showing distribution of the forest cover within each hydrologic unit across New York State: 1) point data from FIA sample plots symbolized based on forest or non-forest status; 2) voronoi polygon map derived from FIA sample plot locations and symbolized based on forest or non-forest status; and 3) hydrologic unit-level polygon map showing summary statistics of percent forest cover by area, based on overlapping voronoi polygons. Examples of the three maps you are to produce are provided on the course web page. FOR324_LAB13_2008.doc Page 1 of 10

2 Background information on datasets 1) USDA FIA sample point data The U.S. Department of Agriculture (USDA) Forest Service, Forest Inventory and Analysis (FIA) program provides the information needed to assess America's forests. FIA reports on status and trends in forest area and location; in the species, size, and health of trees; in total tree growth, mortality, and removals by harvest; in wood production and utilization rates by various products; and in forest land ownership. The data files you will be using are from the 1993 New York State Inventory, the most recent complete inventory of the state. The files been condensed (by removing certain data fields which we do not need for analysis) so as to keep the file sizes as small as possible, and were downloaded from the following website: 2) Hydrologic unit maps The United States is divided and sub-divided into successively smaller hydrologic units which are classified into four levels: regions, subregions, accounting units, and cataloging units. The hydrologic units are arranged within each other, from the smallest (cataloging units) to the largest (regions). Each hydrologic unit is identified by a unique hydrologic unit code (HUC) consisting of two to eight digits based on the four levels of classification in the hydrologic unit system. The fourth level of classification is the cataloging unit, the smallest element in the hierarchy of hydrologic units. A cataloging unit is a geographic area representing part of a surface drainage basin, a combination of drainage basins, or a distinct hydrologic feature. You will be using the 8- digit cataloging units in this lab and were obtained from the following website: General Procedures 1. Read through the complete lab assignment (this document) before you sit down in front of a computer. 2. Create a new folder named LAB13 on the C:\Workspace folder of the hard disk for computer you are using (i.e., C:\Workspace\LAB13). 3. Download the ZIP file containing the datasets for this lab to the LAB13 folder created in step 2 and extract the data files to the same folder. 4. Start up ArcMap. Remember to save your work often. FOR324_LAB13_2008.doc Page 2 of 10

3 5. Extracting (i.e., clipping) only those parts of the hydrologic units (HUC) that fall within the political boundary of NY State. a) Add ( ) the NY_HUC and NY_STATE shapefiles to the Data frame. b) The hydrologic units (watersheds) generally do not follow political boundaries, so you will need to clip only those portions for the watersheds that fall within NY State boundaries. c) Open up ArcToolbox (insert icon here) and select Analysis Tools Extract Clip. The Input Features refers to the watershed layer and the Clips Features refers to the NY State layer. Give the Output Feature Class a file name that describes what is contained in the output layer and is meaningful to you (e.g., NY_HUC_Clip). The output polygon layer should contain polygons that contain portions of the watersheds that fall within NY State boundaries. 6. Create a point shape file from the FIA point data. a) The FIA sample plot data are not current saved as a vector point file. But the database does include fields that contain the location of each plot. Add the file FIAPLOTNY1993.DBF to the table of contents. Open the table and scan through the fields. (Appendix A gives detailed descriptions for the fields in this database.) (1) How many FIA plots were measured during the 1993 inventory? (2) Which field(s) in the database provide(s) the location data for the plots? (3) What datum is used for the positional information? (4) Looking at the actual values recorded in the table for these fields, what projection (State Plane, UTM, or none) is used? b) Mapping the FIA plot locations using data from the FIAPLOTNY1993 database. (1) Select the Tools Add XY Data from the menus at the top of the ArcMap window (also available by right-clicking on the FIAPLOTNY1993.dbf file and selecting Display XY Data). (2) Choose FIAPLOTNY1993 as the table containing the XY data. (3) Select the fields in the table that give the X and Y coordinates (from question (2) above). (NOTE, sometimes ArcMap makes a guess based on the field names as to the X and Y coordinate fields within the table. The guess is not always correct! Be careful.) (4) Press the Edit button to change the positional reference from Unknown Coordinate System to the appropriate reference system based on your answers to questions 3 and 4 above. (5) Click OK when done. Note, this tool creates a new layer that will be added to the table of contents (FIAPLOTNY1993_EVENTS) and a map of this layer showing the location of the FIA plots will appear in the data view. FOR324_LAB13_2008.doc Page 3 of 10

4 7. Symbolize the FIA plots based on whether they are forested or non-forested. a) Assessing forested and non-forested plots. (1) Using the information in Appendix A and the attribute table for the FIAPLOTNY1993_EVENTS layer, identify one of the fields in the attribute table that will allow you to differentiates between forested and nonforested plots (there are two which you can use)? (2) What is/are the value(s) recorded in this field that represent a non-forested plot, and what value(s) represent a forested plot? Non-Forested = forested = b) Change the symbology of the FIAPLOTNY1993_EVENTS feature so that forested plots are GREEN and non-forested plots are YELLOW. (1) Do you see any obvious, discernable spatial pattern across the state as to where forested and non-forested plots are found? Explain. c) Print this map to a PDF file. Make sure you include any and all cartographic options appropriate so that the reader can fully interrupt what you are trying to convey using the map data (e.g., title, legend, north arrow, scale, etc.). 8. State-wide spatial distribution of forest cover by watershed units a) Since the location of the FIA plots are not evenly distributed, each plot contains a different area expansion factor for scaling up to the state level. To compute the area represented by each plot, we can create a voronoi polygon around each plot. Call up the Geostatisical Analyst toolbar (View Toolbars Geostatistical Analyst). Create a Voronoi Map of the FIA plot data by selecting Geostatistical Analyst Explore Data Voronoi Map. FOR324_LAB13_2008.doc Page 4 of 10

5 b) If you get a prompt on how to handle coincidental sample points, select the USE MAXIMUM radio button. c) The following options should be selected Layer: FIAPLOTNY1993_Events Attribute : LUC Clip Layer : NY_STATE d) After you have made the necessary changes, select the Export button in the dialog box, and give the voronoi polygon map the filename FIA_VORONOI, and then add it as another layer in the project. e) Symbolize the FIA_VORONOI polygons according to the LUC field, so that forested polygons are GREEN and non-forested polygons are YELLOW. (1) Does this map give a better or worst discernable spatial pattern of forest land across the state compared to the individual plot data? Explain. f) Print this map to a PDF file. Make sure you include any and all cartographic options appropriate so that the reader can fully interrupt what you are trying to convey using the map data (e.g., title, legend, north arrow, scale, etc.). g) To compute the area of each of the voronoi polygons, use the same procedures you used in a previous labs [i.e., add a new field named AREA to attribute table (use Long integer as the Field Type) and use Calculate Geometry tool to compute area (use acres for units)]. h) To compute the total area of forested and non-forested land across NY State, you need to sum up the AREA field by LUC code using the ArcToolbox Analysis Tools Summary Statistics tool. i) Use the data table produced from the above step to compete the following table. Distribution of forest land cover within NY State Land cover Area (acres) Percent (%) Forested Non-forested Total 100% FOR324_LAB13_2008.doc Page 5 of 10

6 9. Map showing spatial distribution of percent forest cover by watershed basin. The flowchart below provides a general overview of steps that need to be completed to produce the requested map (more details for each step are given on next page). (i) Determine in which watersheds (HUC) each of the FIA plots falls, and add HUC_NAME field to plot data table by INTERSECTing the vector files (new file is automatically created) (ii) Summarize the total AREA of both forested and non-forested voronoi polygons by HUC_NAMES and save these in separate tables. FIA_VORONOI (polygon data) SELECT by ATTRIBUTES (LUC = 1) STATISTICAL SUMMARY SUM(AREA) by HUC_NAME INTERSECT FIA_HUC (polygon data) NY_HUC_Clip (polygon data) SELECT by ATTRIBUTES (LUC = 0) STATISTICAL SUMMARY SUM(AREA) by HUC_NAME HUC_FOR (dbase table) HUC_NONFOR (dbase table) (iii) Join both summary tables to each other JOIN by HUC_NAME (iv) Join the combined summary table to the NY_HUC_Clip shapefile (v) Compute percent forest cover by area for each HUC polygon (vi) Produce symbolized map showing percent forest by area for each HUC polygon JOIN by HUC_NAME ADD FIELD and CALCULATE VALUES PER_FOREST SYMBOLIZE MAP PER_FOREST by HUC_NAME FOR324_LAB13_2008.doc Page 6 of 10

7 a) To determine in which watershed polygon (i.e., NY_HUC_Clip) each voronoi polygon (i.e., FIA_voronoi) overlaps, you need to use the ArcToolbox Analysis Tools Overlay Intersect tool. The output from this analysis will add the NY_HUC_Clip attribute data as new fields to each record in the FIA_voronoi polygon attribute table. Give the output feature class the file name FIA_HUC. b) Next step is to summarize the total area of forested and non-forested polygons by HUC polygon in separate tables. c) Select all forested voronoi polygons from within the FIA_HUC polygon file using Selection Select by Attributes (your answers to previous questions will be needed here). d) Use ArcToolbox Analysis Tools Summary Statistics to compute the total acreage (field: AREA ; Statistic type: SUM) of forested land within each HUC polygon (Case field: HUC_NAME). Remember, ArcMap will only calculate summary statistics of selected records if there is at least one recorded selected. Give the output dbase table the name HUC_FOR. (1) There are 52 HUC watersheds in New York State. How many records are in the HUC_FOR table? (2) Why does the HUC_FOR table not contain forest acreages for some HUC watersheds? (Hint, figure out which watersheds are missing.) FOR324_LAB13_2008.doc Page 7 of 10

8 e) Repeat the same process, this time selecting only non-forested voronoi polygons. Again, use ArcToolbox Analysis Tools Summary Statistics to compute the total acreage of nonforested land within each watershed. Give the output dbase table the name HUC_NONFOR. f) Join the two summary tables (HUC_FOR and HUC_NONFOR) to each other and then to the NY_HUC_Clip shapefile using the HUC_NAME field as the index field. g) You now need to symbolize the watersheds in the NY_HUC_Clip shapefile with respect to percentage forest cover by area based on data derived from the FIA plots. To accomplish this, you will need to create a new field containing the percent forest cover by area based on values from the two separate summary tables. h) To calculate the percent forest cover for each HUC region, open the joined table and add a short integer field called PER_FOREST. i) Calculate values for the PER_FOREST field in each polygon using the equation: forest _area % forest _area= 100 forest _area+ nonforest _area j) Symbolize the map using an appropriate color ramp (e.g., increasing shades of green from white to dark green) according to the following five classes of percent forest cover: <20%; 20-40%; 40-60%; 60-80%; >80% (1) Do you feel this map of the spatial distribution of percent forest cover by watershed area gives you more or less information compared to the map produced using the voronoi polygons above? Explain your reasoning. k) Print this map. Make sure you include any and all cartographic options appropriate so that the reader can fully interrupt what you are trying to convey using the map data (e.g., title, legend, north arrow, scale, etc.). 10. BONUS (3% of total course grade): The FIA plot dataset has data for on local forest type (main species) and volume per acre measured on each plot. Produce a map showing the distribution of black cherry forest type across the state with respect to volume, using either 1) summarized data for each watershed basin, or 2) voronoi polygons for each plot. FOR324_LAB13_2008.doc Page 8 of 10

9 APPENDIX A: Descriptions of database fields within the FIAPLOTNY1993.DBF file Field Name RECNUM YEAR STATEABB LAT LON AEF FORCODE FORTYPE LOCALTYPE SPCLASS TPA_1 STDIAM_1 BALIVE_1 CUBICSW CUBICHW CUBIC LUC Field Description Record number. Unique condition identifier. RPA Year. This is set to inventory year. State abbreviation. Two character identifier for each state Latitude NAD 83 datum. The approximate latitude of the plot in decimal degrees. The precision of this item along the meridian is ± 1542 m at latitude 45 degrees north. However, in some cases the county centroid may be entered when the actual location is not available. Actual plot locations cannot be released. The LAT is based on fuzzed and swapped plot coordinates. Longitude NAD 83 datum. The approximate longitude of the plot in decimal degrees. The precision of this item along the parallel is ± 1094 m at latitude 45 degrees. However, in some cases the county centroid may be entered when the actual location is not available. Actual plot locations cannot be released. The LON is based on fuzzed and swapped plot coordinates. Area expansion factor. The number of acres the sample plot represents for making current estimates of area, where the sample excludes outside-of-the-population, denied-access, and hazardous plots. The sum of AEF over all plot-level records for a particular State is the total land and water area of the State as calculated by the Bureau of Census. Forestland code. Used to differentiate between productive/unproductive and reserved/nonreserved forestland. Code Description Nonforest Productive nonreserved forestland reserved forestland reserved forestland reserved Productive Unproductive non- Unproductive forestland Forest Type Group. The forest cover type of the inventoried stand, based on the tree species forming a plurality of the stocking (See APPENDIX B). Local Forest Type. (See APPENDIX B). Site productivity class. The class identifies the average potential growth in cubic feet/acre/year. Class ft 3 /ac/yr < 20 Unproductive Number of live trees per acre one inch in diameter and larger. Quadratic mean stand diameter (in) using all live trees over 1 inch in diameter. Basal area (sq.ft./ac.) of all live trees one inch and larger in diameter. Softwood cubic foot volume. Net volume/acre (cubic feet) for live trees of softwood growing stock five inches in diameter and larger. Hardwood cubic foot volume. Net volume/acre (cubic feet) for live trees of hardwood growing stock five inches in diameter and larger. Cubic foot volume. Net volume/acre (cubic feet) of growing stock for all live trees five inches in diameter and larger. Land Use Class. Indicates the basic land cover. Code 0 1 Description Non-forest land Forest land FOR324_LAB13_2008.doc Page 9 of 10

10 Appendix B Forest Type and Local Forest Type Group Codes EASTERN FOREST TYPES CODE FOREST TYPE CODE FOREST TYPE 100 WHITE-RED-JACK PINE TYPE 160 OAK-GUM-CYPRESS TYPE 101 Jack pine 161 Swamp chestnut oak - cherrybark oak 102 Red pine 162 Sweetgum - Nuttall oak - willow oak 103 White pine 163 Sugarberry - American elm - green ash 104 White pine - hemlock 165 Overcup oak - water hickory 105 Hemlock 166 Atlantic white cedar 211 Ponderosa pine 167 Baldcypress - water tupelo 110 SPRUCE-FIR TYPE 168 Sweetbay - swamp tupelo - red maple 111 Balsam fir 169 Palm-mangrove-other tropical 112 Black spruce 170 ELM-ASH-COTTONWOOD TYPE 113 Red spruce - Balsam fir 171 Black ash - American elm - Red maple 114 Northern white-cedar 172 River birch - sycamore 115 Tamarack 173 Cottonwood 116 White spruce 174 Willow 120 LONGLEAF-SLASH PINE TYPE 175 Sycamore - pecan - American elm 121 Longleaf pine 176 Red maple-lowland 122 Slash pine 179 Mixed lowland hardwoods 130 LOBLOLLY-SHORTLEAF PINE TYPE 180 MAPLE-BEECH-BIRCH TYPE 131 Loblolly pine 181 Sugar maple - beech - yellow birch 132 Shortleaf pine 182 Black cherry 133 Virginia pine 183 Black walnut 134 Sand pine 184 Red maple-northern hardwoods 135 Eastern redcedar 187 Red maple-upland 136 Pond pine 188 Northern hardwood-reverting field 137 Spruce pine 189 Mixed northern hardwoods 138 Pitch pine 190 ASPEN-BIRCH TYPE 139 Table-mountain pine 191 Aspen 140 OAK-PINE TYPE 192 Paper birch 141 White pine - northern red oak - white ash 194 Balsam poplar 142 Eastern redcedar - hardwood 198 OTHER FOREST TYPES 143 Longleaf pine - scrub oak 199 NONSTOCKED 144 Shortleaf pine - oak 145 Virginia pine - southern red oak 146 Loblolly pine - hardwood 147 Slash pine - hardwood 149 Other oak - pine 150 OAK-HICKORY TYPE 151 Post oak, black oak or bear oak 152 Chestnut oak 153 White oak - red oak - hickory 154 White oak 155 Northern red oak 156 Yellow poplar - white oak - northern red oak 157 Southern scrub oak 158 Sweetgum - yellow poplar 159 Mixed hardwoods FOR324_LAB13_2008.doc Page 10 of 10