Differences

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--- canopy:start [2020-03-05 05:42 am] – hcho
+++ canopy:start [2020-05-22 07:11 pm] (current) – hcho
@@ Line 10: / Line 10: @@
 ===== Introduction =====
-We developed this module to conduct the Georgia statewide canopy analysis study using the 2009 National Agriculture Imagery Program (NAIP) imagery data. To assess tree canopy, we needed four bands including red (R), green (G), blue (B), and near-infrared (NIR). The three-band RGB NAIP imagery data is available freely online at https://nrcs.app.box.com/v/naip. However, the four-band NAIP imagery is not available online and needs to be purchased from [[https://www.usda.gov/|the United States Department of Agriculture (USDA)]]. Since our study is statewide, there are a large number of NAIP quarter quad (QQ) imagery tiles---specifically, 3,913 GeoTIFF files across the entire Georgia---and it would not be feasible to manually pre-process these tiles before we feed them to a canopy classification algorithm. It is not only the number of input tiles, but also the number of output files that makes it challenging to complete this analysis efficiently. This module provides utility functions for pre-processing input tiles and post-processing output files to finally create the final seamless canopy raster for specified physiographic regions.
+We developed this module to conduct the Georgia statewide canopy analysis study using the 2009 [[https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/|National Agriculture Imagery Program (NAIP) imagery data]]. To assess tree canopy, we needed four bands including red (R), green (G), blue (B), and near-infrared (NIR). The three-band RGB NAIP imagery data is available freely online at https://nrcs.app.box.com/v/naip. However, the four-band NAIP imagery is not available online and needs to be purchased from [[https://www.usda.gov/|the United States Department of Agriculture (USDA)]]. Since our study is statewide, there are a large number of NAIP quarter quad (QQ) imagery tiles---specifically, 3,913 GeoTIFF files across the entire Georgia---and it would not be feasible to manually pre-process these tiles before we feed them to a canopy classification algorithm. It is not only the number of input tiles, but also the number of output files that makes it challenging to complete this analysis efficiently. This module provides utility functions for pre-processing input tiles and post-processing output files to finally create the final seamless canopy raster for specified physiographic regions.
+===== Documents =====
+  * [[User manual]]
+  * [[Tutorial]]
+  * [[Technical manual]]
 ===== Project team =====
@@ Line 17: / Line 23: @@
     * Principal investigator
     * Python development
+  * [[:Owen Smith]], Undergraduate Student, IESA, UNG
+    * Python development
+    * Technical documentation
   * [[:Jennifer McCollum]], Undergraduate Student, IESA, UNG
     * Research about Feature Analyst
     * Literature review
     * Documentation
-  * [[:Owen Smith]], Undergraduate Student, IESA, UNG
-    * Python development
-    * Technical documentation
+/*
 ===== TODOs =====
 ~~TODOLIST ns:canopy~~
+*/
 ===== Acknowledgement =====