Southern Ontario Interim Landcover (SIL) Data Compilation Documentation

Transcription

1 Southern Ontario Interim Landcover (SIL) Data Compilation Documentation Introductory Datasets: Goal: To utilize all relevant corporate MNR datasets as well as external datasets that hold ecological value. The following datasets have been utilized to create a seamless product (Vector and Raster) that represents a full ecodistrict (6E6, 6E9, etc.) The following datasets are the most up to date, and cover southern Ontario from the Canadian Shield south. SOLRIS (Southern Ontario Land and Resource Information System) SOLRIS Woodlands SOLRIS Wetlands SOLRIS Built Up Areas OBM (Ontario Base Mapping) OBM Water OBM Unevaluated Wetlands ORN (Ontario Road Network) Pits and Quarries Sensitive Layers NHIC Sand Barrens NHIC Alvars NHIC Prairie/ Savannah Non-MNR Datasets Coastal Wetlands (Environment Canada) Spectral Information NDVI (Normalized Difference Vegetation Index) Process for vector data preparation Goal: To prepare datasets for the purpose of automation including data manipulation (attributes) and linear feature checks. In order to meet the needs for Source Water Protection, data must be modified to meet specific goals. SIL is using SOLRIS Phase 1, ORN, OBM, and Non-MNR datasets, checks and attribute manipulation must be completed to ensure accuracy. The SIL product is being derived from an district standpoint thus, datasets must be extracted on an ecodistrict basis. Following are the procedures to compile the datasets.

2 SOLRIS Datasets: Woodlands: Woodlands are checked for coding errors such as miscoding of plantations, hedgerows, and woodlots Any features below a quarter of a hectare are deleted Linear features (woodlots that should be hedgerows) are checked to ensure data accuracy, if errors are found they are corrected and the District GIS tech is notified All code 4 (no longer represented features) are deleted from the dataset Two integer fields are added to the attribute table called WOOD_CLAS and WOODED Attributes are calculated based on all Woodlands = 1, Hedgerows (Code 12) = 2 and Plantation (Code 26) 3 into the WOOD_CLAS field Attributes are calculated based on all wooded features = 1 into the WOODED field All other attribute fields are deleted except WOOD_CLAS and WOODED, see below: Wetlands: Two integer fields are added to the attribute table called WET_CLAS and WETLAND All wetlands are calculated to 1 in the WET_CLAS field including the WETLAND field is calculated to 1 All other fields in the attribute table are deleted except WET_CLAS and WETLAND, see below:

3 Built Up Area: Polygons are checked for the consistency of pervious and impervious feature attribution If there are any misclassifications (miscoding) features attributes are changed to represent the area Two integer fields are added to the attribute table called URBAN_CLAS and URBAN Pervious features (Code 23) are calculated in the URBAN_CLAS field to 1 and impervious features are calculated to 2 in the URBAN_CLAS field All Built Up Area features are calculated to 1 in the URBAN Field All other fields are deleted except URBAN_CLAS and URBAN, see below:

4 Ontario Base Mapping: Preparing OBM Water: When working with the OBM Water dataset two queries must be made to isolate all water features and unevaluated wetlands The first query is to isolate water features Query based on attributes (GUT_NUMBER = 1281) Export the selected features Second query is to isolate all unevaluated wetlands Query based on attributes (GUT_NUMBER = 1800) Export the selected features OBM Water: Two integer fields are added to the attribute table called WATER_CLAS and WATER Long linear features (Rivers) are selected from the dataset and calculated to 2 in the WATER_CLAS field All other features are calculated to 1 in the WATER_CLAS field All features in the shapefile are calculated to 1 in the attribute field called WATER All other fields are deleted except for WATER_CLAS and WATER, see below:

5 OBM Unevaluated Wetlands: Two integer fields are added to the attribute table called WET_CLAS and WETLAND All wetlands are calculated to 2 in the WET_CLAS field and the WETLAND field is calculated to 1 All other fields in the attribute table are deleted except WET_CLAS and WETLAND, see below: Next Merge the SOLRIS Wetlands with the OBM Wetlands Aggregates: Pits and Quarries: Add an integer field to the aggregate attribute field called AGGERGATE Calculate the AGGERGATE field to 1 Delete all fields except the AGGREGATE field, see below:

6 Sensitive Layers: Alvars: Add an integer field to the Alvar shapefile called ALVAR Give all Alvar shapes a code of 1 in the ALVAR field Delete all fields except the ALVAR field Prairie/ Savanna: Add two integer fields to the attribute table called PRAIRIE and SAVANNA All savannah are calculated and given 1 in the SAVANNA field and all prairie are to be calculated and given 2 in the PRAIRIE field All fields are to be deleted except PARAIRIE and SAVANNA Sand Barrens: Add an integer field to the attribute table called SAND All Sand barrens are calculated and given 1 in the SAND field All fields are to be deleted except SAND Non-MNR Datasets: Coastal Wetlands: Add an integer field called COASTAL_WET Give a value of 1 to all the polygons Delete all fields except COASTAL_WET

7 Process for automation Goal: To eliminate the processes of manual data procedures. To use system resources for the purpose of efficiency as well as productivity. ArcGIS 9.1 has greatly enhanced the functionality of processing multiple routines at one time. By utilizing this technology, many tasks can be completed at one time thus enhancing productivity. With the addition of VBA (Visual Basic Applications) coding, many tasks can be completed programmatically reducing the amount of time it takes to do a manual process. ArcGIS 9.1 Model Builder: For the purposes of SIL ArcGIS 9.1 Model builder is used to create automation models for the purpose of automating many tasks to produce an outcome. Below is an example of a simple model that changes null raster values into a zero value for raster manipulation. Con (IsNull([missinglakes2]), 0, [missinglakes2]) Union Large Output Model: The Union Large Output model has been created to automate massive unions at once to compile a final dataset based on the data preparations mentioned above. This model allows for the changing of inputs and outputs of shapefiles and the compiling of a finalized version of the vector product. See below:

8 In order to use this model, inputs and outputs must be changed according to the ecodistrict datasets that are being computed. This tool will union all layers together creating the dataset that will be used for raster analysis. The output of this tool will generate fields that will need to be deleted. An FID field will be generated for each union that has occurred (Delete these fields). Now that the dataset is ready, it should look like the example below:

9 Query Executor: The Query Executor has been created utilizing VBA code to run 221 independent SQL statements that isolate areas in ecodistricts that hold certain values, see Appendix A. The Query Executor runs off a.dbf that is added to the table of continence in ArcMAP. This table holds all of the SQL queries that will provide results based on the attributes of a certain shapefile (aka, EcoDistrict 6E6). Below is the interface of the tool. The tool allows a user to pick from a drop down menu to pick the.dbf to run the queries from, the shape file that the user wants updated and the field that holds the SQL s from the.dbf. It also allows for fields from the.dbf to be added to the shapefile for the tool to work. Below is an example of the results, Process of raster data preparation Goal: To create a seamless raster dataset that will be used for the process of delineating areas of potential restoration. In order to meet the needs for SIL a raster dataset (15m) must be made that encompasses the prepared vector dataset including the NDVI to incorporate agriculture into the dataset. This raster dataset will be a surrogate for analysis of Source Water.

10 Preparing Raster Dataset: Converting Vector to Raster: The vector dataset must be converted to a 10 metre raster dataset Use the spatial analysis tool bar to create the raster Make sure the extent is set to the vector product The input field that is going to be used for attribution is NEW_CLASS Output cell size must be 10 metres See example below: Calculating NULL values: In order to manipulate the new raster dataset null values must be calculated Run the ISNULL model created for SIL or use the raster calculator to run the same procedure To run the is null command input the following statement Con (IsNull([Dataset]), 0, [Dataset]) See model below: Con (IsNull([missinglakes2]), 0, [missinglakes2])

11 Your dataset should look as below: Green Values = 0 as null values Converting ORN to GRID: For the purposes of SIL ORN is used to represent fragmentation of the landscape including the representation of imperviousness on the landscape In the vector file for ORN select out all the roads that are 2 lane in the NBRLANES attribute field Export to new shapefile and delete all fields except for NBRLANES Select out all the roads that are 3-7 lanes in the NBRLANES attribute field Export to new shapefile and delete all fields except for NBRLANES Convert both shapefiles to 10m rasters

12 11 Reclassify 1 21 original IsNull raster. All Class Data = 100 and 0 data value = NDVI 100 X Multiply grid by landcover X000 X000 This diagram shows the steps on how to create the combined raster landcover that will be used for analysis. Reclassify raster. Keep NDVI classes, e.g. 1 Monoculture, 3 Unimproved Hay, Pasture/Idle Land, 2 Mixed Agriculture. Reclass all other values X000 to 0 *MAKE SURE YOU SET THE EXTENT AND CELL SIZE IN SPATIAL OPTIONS TO YOUR SIL GRID* 1,2,3 GRID A 0 Creating Grid B = 1,2,3 11 1,2,3 Multiply original IsNull grid by Grid A GRID B

13 Creating Grid C No Data 2 21 No Data Run the IsNull command on the road grid Reclassify the grid to change 0 values to 1 And 2 or 3-7 to Multiply the reclassified grid by grid B X ,2, = 21 GRID C Reclassify the grid to change road values so they are represented as 100. Where roads fall over impervious surfaces (Code 42) the impervious shall hold, all other values will be given a value of 100. GRID B