README updates

Author: hynesconnor

README.md

@@ -1,9 +1,18 @@
 # Querying and Processing Sentinel-2 Satellite Imagery Using SentinelAPI
 Using the [Copernicus Open Access Hub](https://scihub.copernicus.eu/) with the [Sentinel Python API](https://sentinelsat.readthedocs.io/en/master/api_overview.html) to source and process satellite rasters for zonal statistics.
 
+Below is an example of an RGB GeoTIFF layer produced by the program. This layer can be loaded into any GIS applications such as ArcGIS and QGIS. 
+
+<div style="text-align: center;">
+
+![](/mkdwn/rgb.png "Satellite Imagery - Greater Seattle")
+
+</div>
+
+
 ## Installation and Use
 
-Download and unzip the latest release [here](). Unzip into location of your choice.
+Download and unzip the latest release [here](https://github.com/hynesconnor/sentinel_processing/releases). Unzip into location of your choice.
 
 **IMPORTANT**: Specifications on the Python versions used and the packages installed can be found in `requirements.txt`.
 
@@ -32,4 +41,12 @@ Download and unzip the latest release [here](). Unzip into location of your choi
     - NDVI .tif file will be output to `../data/processed/ndvi`.
     - NDWI .tif file will be output to `../data/processed/ndwi`.
 
-4. The mean vegitation density of the raster is also printed to the console.
+4. The mean vegitation and water density of the rasters are also printed to the console.
+
+## In Practice. . . 
+
+Below are two examples of NDVI and NDWI layers produced by the image processesing steps. The layers are produced using Sentinel-2 data of the greater Seattle area. These layers can be loaded into GIS applications such as ArcGIS or QGIS.   
+
+NDVI | NDWI
+- | -
+![](/mkdwn/ndvi_color.png) | ![](/mkdwn/ndwi_color2.png)

image_processing.py

@@ -11,7 +11,7 @@
 
 # parameters to be adjusted
 BAND_COUNT = 3 # used to control the number of bands being written. (rgb default = 3)
-FOLDER = 'sentinelLV' # satellite folder, change to desired satellite data within the image folder
+FOLDER = '' # satellite folder, change to desired satellite data within the image folder
 
 # creates array of band names, returns band names and path to bands
 def get_file_names():
@@ -57,7 +57,7 @@ def rgb():
     band_names, IMG_DIR = get_file_names()
     bands = band_process(band_names, IMG_DIR)
     band_write(bands)
-    print("Completed.")
+    print("RGB Completed.")
 
 rgb()
 
@@ -116,14 +116,14 @@ def ndvi():
     ndvi = ndvi_calc()
     export_normalized_diff(ndvi, 'ndvi/', FOLDER + '_ndvi.tif')
     print('Mean vegitation density of raster: ' + np.nanmean(ndvi[0]).astype(str))
-    print('Completed.')
+    print('NDVI Completed.')
 
 # reports and writes ndwi statistics 
 def ndwi():
     ndwi = ndwi_calc()
     export_normalized_diff(ndwi, 'ndwi/', FOLDER + '_ndwi.tif')
-    #print('Mean water density of raster: ' + np.nanmean(ndwi[0]).astype(str))
-    print('Completed.')
+    print('Mean water density of raster: ' + np.nanmean(ndwi[0]).astype(str))
+    print('NDWI Completed.')
 
 ndvi()
 ndwi()

mkdwn/ndvi_color.png

@@ -13,8 +13,8 @@
 URL = 'https://developers.google.com/public-data/docs/canonical/countries_csv'
 COU_DIR = '/data/country/'
 IMG_DIR = '/data/image/'
-USER = ''
-PASS = ''
+USER = '' # enter username
+PASS = '' # enter password
 DATE_END = date.today()
 
 # adjustable search parameters