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Copyright(c) 2020-
Author: Chaitanya Tejaswi (github.com/CRTejaswi)    License: GPL v3.0+

Satellite Imaging

Personal notes on satellite imaging & GIS.

Index

Software Setup

  • SciPy Stack
    Install the SciPy stack if not already installed. 
    python -m pip install --user numpy scipy matplotlib ipython jupyter pandas sympy nose
  • GDAL; rasterio/fiona
    Download binaries for gdal, rasterio, fiona.
    GDAL is the main software - rasterio/fiona are Py3 wrappers around GDAL to deal with raster/vector images. 
    python -m pip install --user gdal.whl rasterio.whl fiona.whl
  • PROJ4/5
  • Google Earth Engine 
    python -m pip install --user earthengine-api --upgrade
  • GeoPandas
    Pandas (data analysis), but for GIS. 
    python -m pip install --user geopandas
  • Folium
    To plot interactive maps. 
    python -m pip install folium

Data Types

See: Raster Images, Vector Images

Raster Images
Vector Images 

import numpy as np
import rasterio
from matplotlib import pyplot as plt
from rasterio import plot as rplt

'''
Plot Settings:
- Bands: B-G-R-nIR
- Open plots in maximized window (Tk)
'''
bands = [None, 'blue', 'green', 'red', 'nir']


def get_metadata(filepath='test.tif'):
    metadata = {}
    with rasterio.open(filepath) as data:
        metadata.update(data.profile)
    return metadata


def plot(filepath='test.tif'):

    data = rasterio.open(filepath)
    # rplt.show(data)

    blue, green, red, nir = data.read()
    plt.figure(1);
    plt.subplot(221); plt.imshow(blue); plt.set_cmap('gist_earth'); plt.colorbar();
    plt.subplot(222); plt.imshow(green); plt.set_cmap('gist_earth'); plt.colorbar();
    plt.subplot(223); plt.imshow(red); plt.set_cmap('inferno'); plt.colorbar();
    plt.subplot(224); plt.imshow(nir); plt.set_cmap('gist_earth'); plt.colorbar();
    plt.get_current_fig_manager().window.state('zoomed')
    # plt.savefig('test.png', bbox_inches='tight');

    rgb = np.dstack((red, green, blue))
    nrg = np.dstack((nir, red, green))
    plt.figure(2);
    plt.subplot(121); plt.imshow(rgb);
    plt.subplot(122); plt.imshow(nrg);
    plt.get_current_fig_manager().window.state('zoomed')

    plt.show()

    # profile = data.profile
    # profile.update(
    #     dtype=rasterio.uint8,
    #     count=1,
    #     compress='lzw')
    # with rasterio.open('output.tif', 'w', **profile) as f:
    #     f.write(data.astype(rasterio.unit8), 1)


'''
data.profile
Co-ordinate Reference System: EPSG:32610
Pixel Resolution:             3m x 3m
Physical Co-ordinates:        (623577.0,4214037.0) (637626.0,4185885.0)
'''

Graphical Indicators

Vegetation
Water
Road 

def get_vegetation():
    '''
    Gets/Plots vegetation (NDVI)
    '''
    ...
def get_water():
    '''
    Gets/Plots water bodies (NDWI)
    '''
    ...
def get_roads():
    '''
    Gets/Plots roads (?)
    '''
    ...
def get_():
    '''
    Gets/Plots ? regions (?)
    '''
    ...

Digital Elevation Models

Surface -> Terrain -> Height

GIS Datasets

Google Maps 

Roads:      https://mt1.google.com/vt/lyrs=r&x={x}&y={y}&z={z}
            https://mt1.google.com/vt/lyrs=m&x={x}&y={y}&z={z}
Satellite:  https://mt1.google.com/vt/lyrs=s@189&x={x}&y={y}&z={z}
Satellite+: https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}
Terrain:    https://mt1.google.com/vt/lyrs=t&x={x}&y={y}&z={z}
Roads:      https://mt1.google.com/vt/lyrs=h&x={x}&y={y}&z={z}

References