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--- canopy:technical_manual [2020-01-30 10:50 pm] – hcho
+++ canopy:technical_manual [2020-04-19 08:47 pm] (current) – hcho
@@ Line 1: / Line 1: @@
 ====== CanoPy technical manual ======
-CanoPy is the Python module for the Georgia Canopy Analysis 2009 project sponsored by the Georgia Forestry Commission (GFC). For further information about this project, please refer to the [[CanoPy:]] page.
+[[CanoPy:]] is the Python module for [[:GFC canopy assessment|the Georgia Canopy Analysis 2009 project]] sponsored by [[http://www.gfc.state.ga.us/|the Georgia Forestry Commission (GFC)]]. For further information about this project, please refer to the [[CanoPy:]] page.
-This document outlines the uses and methodology of the functions contained within the [[CanoPy:]] module. To learn how to use this module, please read the [[user manual]].
+This document outlines the uses and methodology of the functions contained within the [[CanoPy:]] module. To learn how to use this module, please read the [[user manual]]. Also refer to the [[tutorial]].
 ===== Authors =====
-  * Owen Smith
+  * [[:Owen Smith]]
-  * Huidae Cho, Ph.D.
+  * [[:Huidae Cho]], Ph.D.
 ===== Requirements =====
@@ Line 15: / Line 15: @@
   * ArcPy
   * Python 2 standard module: os
-  * Feature Analyst (TM) by the Textron Systems
+  * Feature Analyst(tm) by the Textron Systems
   * Automated Feature Extraction (AFE) models trained using Feature Analyst
@@ Line 131: / Line 131: @@
   * Type: ''str''
   * Snap raster to which all output tiles will be snapped
-  * This input/output raster is used to snap NAIP tiles to a consistent grid system. If this file does not already exist, the filename part of ''snaprast_path'' must be ''r'' + the filename of an existing original NAIP tile so that ''canopy.reproject_input_tiles()'' can automatically create it based on the folder structure of the NAIP imagery data (''naip_path'').
+  * This input/output raster is used to snap NAIP tiles to a consistent grid system. If this file does not already exist, the filename part of ''snaprast_path'' must be ''r'' + the filename of an existing original NAIP tile so that ''canopy.reproject_naip_tiles()'' can automatically create it based on the folder structure of the NAIP imagery data (''naip_path'').
   * Example: ''snaprast_path = '%s/Data/rm_3408504_nw_16_1_20090824.tif' % analysis_path''
@@ Line 162: / Line 162: @@
   - All selections are cleared and each NAIP QQ polygon will contain the ''naip_phyregs_field'' filled with the IDs of physiographic regions that the QQ tile intersects.
-==== reproject_input_tiles(phyreg_ids) ====
+==== reproject_naip_tiles(phyreg_ids) ====
 This function reprojects and snaps the NAIP tiles that intersect selected physiographic regions.
@@ Line 270: / Line 270: @@
   * ''results_path = canopy_config.results_path''
-==== generate_ground_truthing_points(phyreg_ids, analysis_years, point_count) ====
+==== generate_gtpoints(phyreg_ids, point_density, max_points=400, min_points=200) ====
-**NOTE:** This function is still a work in progress.
+This function generates randomized points for ground truthing with fields for corresponding years populated with the corresponding canopy value at each point.
-This function generates randomized points for ground truthing with fields for
-corresponding analysis years.
 Arguments:
   * ''phyreg_ids'' (list of ''int''): IDs of physiographic regions to process
-  * ''analysis_years'' (list of ''int''): Years to add as fields
+  * ''point_density'' (''int''): Number of randomly generated points per square kilometer
-  * ''point_count'' (''int''): Number of randomly generate points in each region.
+  * ''max_points'' (''int, //default=400//''): Maximum number of points generated per region
+  * ''min_points'' (''int, //default=200//''): Minimum number of points generated per region
 Config variables assigned with ''canopy_config'':
-  * ''phyregs_layer = canopy_config.phyregs_layer''
+  *''phyregs_layer = canopy_config.phyregs_layer''
-  * ''spatref_wkid = canopy_config.spatref_wkid''
+  *''naipqq_layer = canopy_config.naipqq_layer''
-  * ''project_path = canopy_config.project_path''
+  *''spatref_wkid = canopy_config.spatref_wkid''
-  * ''analysis_path_format = canopy_config.analysis_path_format''
+  *''analysis_year = canopy_config.analysis_year''
+  *''results_path = canopy_config.results_path''
 Process:
   - The physiographic regions are selected using the input physiographic IDs.
   - Random points in each region are created using ''arcpy.CreateRandomPoints''.
-  - Fields are created in each point shapefile with the field name of ''GT_//analysis_year//'' (e.g., ''GT_2009'').
+  - Fields are created in each point shapefile with the field name of ''GT'' (e.g., ''GT'').
-  - The values of each classified cell that contains the randomized points will be read. This task will be implemented using the NumPy module.
+  - The attributes of the ''naip_layer'' are spatially joined with the created random points shapefile and saved as a new point layer.
+  - A new spatially joined point shapefile allows for the file names of each point's corresponding classified NAIP QQ to be read and converted to a NumPy array. The conversion of each NAIP QQ to a NumPy array allows the function to handle the memory requirements of the regions.
+  - Each point is converted to corresponding rows and columns within the corresponding NAIP QQ using ''calculate_row_column()''.
+  - The value of the NumPy cell at each point's row and column is then added to the ''GT'' attribute field of the spatially joined shapefile.
+  - After all point values are read, all fields except ''FID'', ''Shape'', and ''GT'' are deleted in the spatially joined shapefile.
+  - The originally generated point shapefile is deleted.
 {{tag>software}}