Satellite imagery brought us the capacity to see the land surface on recent years but we haven’t been so successful to understand land cover dynamics and the interaction with economical, sociological and political factors. Some deficiencies for this analysis were found on the use of GIS commercial software, but there are other limitations in the way we apply logical and mathematical processes to a set of satellite imagery. Working with geospatial data on Python gives us the capability to filter, calculate, clip, loop, and export raster or vector datasets with an efficient use of the computational power providing a bigger scope on data analysis.

This tutorial covers the complete procedure to create a land cover change raster from a comparison of generated vegetation index (NDVI) rasters by the use of Python and the Numpy and Rasterio libraries. Results of the NDVI for given years and NDVI change are plotted on Jupyter Lab as color grid and contour grid.

Tutorial

Input data

You can download the input data from this link.

Python code

Import requires libraries

This tutorial uses just Python core libraries, Scipy (Numpy, Matplotlib and others) and Rasterio

#from osgeo import ogr, gdal, osr
import rasterio
from rasterio.transform import Affine
import numpy as np
import os
import matplotlib.pyplot as plt
from rasterio.plot import show

Setting up the input and output files

We declare the path for the raster bands and the output NDVI and land cover change rasters. The land cover change contour shape path is also defined.

#Input Raster and Vector Paths

#Image-2019
path_B5_2019="../Image20190203clip/LC08_L1TP_029047_20190203_20190206_01_T1_B5_clip.TIF"
path_B4_2019="../Image20190203clip/LC08_L1TP_029047_20190203_20190206_01_T1_B4_clip.TIF"

#Image-2014
path_B5_2014="../Image20140205clip/LC08_L1TP_029047_20140205_20170307_01_T1_B5_clip.TIF"
path_B4_2014="../Image20140205clip/LC08_L1TP_029047_20140205_20170307_01_T1_B4_clip.TIF"

#Output Files

#Output NDVI Rasters 
path_NDVI_2019 = '../Output/NDVI2019.tif'
path_NDVI_2014 = '../Output/NDVI2014.tif'
path_NDVIChange_19_14 = '../Output/NDVIChange_19_14.tif'

#NDVI Contours
contours_NDVIChange_19_14 = '../Output/NDVIChange_19_14.shp'

Open the Landsat image bands with GDAL

In this part we open the red (Band 4) and near infrared NIR (Band 5) with commands of the GDAL library and then we read the images as matrix arrays with float numbers of 32 bits.

#Check bands
B5_2019 = rasterio.open(path_B5_2019)
B5_2019.read(1)

array([[    0, 15510, 14779, ..., 18204, 18722, 18515],
       [    0, 11765, 13832, ..., 15842, 16966, 17672],
       [    0, 10027, 11470, ..., 14321, 14948, 15572],
       ...,
       [    0, 14584, 13352, ..., 15260, 14624, 13359],
       [    0, 14569, 13616, ..., 15065, 13242, 11065],
       [    0,     0,     0, ...,     0,     0,     0]], dtype=uint16)

#Open raster bands
B5_2019 = rasterio.open(path_B5_2019)
B4_2019 = rasterio.open(path_B4_2019)
B5_2014 = rasterio.open(path_B5_2014)
B4_2014 = rasterio.open(path_B4_2014)

#Read bands as matrix arrays
B52019_Data = B5_2019.read(1)
B42019_Data = B4_2019.read(1)
B52014_Data = B5_2014.read(1)
B42014_Data = B4_2014.read(1)

Compare matrix sizes and geotransformation parameters

The operation in between Landsat 8 bands depends that the resolution, size, src, and geotransformation parameters are the same for both images. In case these caracteristics do not coincide a warp, reproyection, scale or any other geospatial process would be necessary.

print(B5_2014.crs.to_proj4())
print(B5_2019.crs.to_proj4())
if B5_2014.crs.to_proj4()==B5_2019.crs.to_proj4(): print('SRC OK')

+init=epsg:32613
+init=epsg:32613
SRC OK

print(B5_2014.shape)
print(B5_2019.shape)
if B5_2014.shape==B5_2019.shape: print('Array Size OK')

(610, 597)
(610, 597)
Array Size OK

print(B5_2014.get_transform())
print(B5_2019.get_transform())
if B5_2014.get_transform()==B5_2019.get_transform(): print('Geotransformation OK')

[652500.0, 29.98324958123953, 0.0, 2166000.0, 0.0, -30.0]
[652500.0, 29.98324958123953, 0.0, 2166000.0, 0.0, -30.0]
Geotransformation OK

Get geotransformation parameters

Since we have compared that rasters bands have same array size and are in the same position we can get the some spatial parameters

#geotransform = B5_2014.get_transform()
#originX,pixelWidth,empty,finalY,empty2,pixelHeight=geotransform
cols =  B5_2014.width
rows =  B5_2014.height
print(cols,rows)

597 610

#transform = Affine.from_gdal(originX,pixelWidth,empty,finalY,empty2,pixelHeight)
transform = B5_2014.transform
transform

Affine(29.98324958123953, 0.0, 652500.0,
       0.0, -30.0, 2166000.0)

projection = B5_2014.crs
projection

CRS.from_epsg(32613)

Compute the NDVI and store them as a different file

We can compute the NDVI as a matrix algebra with some Numpy functions.

ndvi2014 = np.divide(B52014_Data - B42014_Data, B52014_Data + B42014_Data,where=(B52014_Data - B42014_Data)!=0)
ndvi2014[ndvi2014 > 1] = 1 #for water bodies

ndvi2019 = np.divide(B52019_Data - B42019_Data, B52019_Data+ B42019_Data,where=(B52019_Data - B42019_Data)!=0)
ndvi2019[ndvi2019 > 1] = 1

#plot the array
plt.imshow(ndvi2014*100)

<matplotlib.image.AxesImage at 0x19d3a36cfd0>

def saveRaster(dataset,datasetPath,cols,rows,projection):
    rasterSet = rasterio.open(datasetPath,
                             'w',
                             driver='GTiff',
                             height=rows,
                             width=cols,
                             count=1,
                             dtype=np.float32,
                             crs=projection,
                             transform=transform,)
    rasterSet.write(dataset,1)
    rasterSet.close()

saveRaster(ndvi2014.astype('float32'),path_NDVI_2014,cols,rows,projection)

saveRaster(ndvi2019.astype('float32'),path_NDVI_2019,cols,rows,projection)

Plot NDVI Images

We create a function to plot the resulting NDVI images with a colobar

def plotNDVI(ndviImage,cmap):
    src = rasterio.open(ndviImage,'r')
    fig, ax = plt.subplots(1, figsize=(12, 12))
    show(src, cmap=cmap, ax=ax)
    ax.set_xlabel('Este')
    ax.set_ylabel('Norte')
    plt.show()

def plotNDVIContour(ndviImage,cmap):
    src = rasterio.open(ndviImage,'r')
    fig, ax = plt.subplots(1, figsize=(12, 12))
    show(src, cmap=cmap, contour=True, ax=ax)
    ax.set_xlabel('Este')
    ax.set_ylabel('Norte')
    plt.show()

plotNDVI(path_NDVI_2014,'YlGn')

plotNDVIContour(path_NDVI_2014,'YlGn')

plotNDVI(path_NDVI_2019,'YlGn')

Create a land cover change image

We create a land cover change image based on the differences in NDVI from 2019 and 2014 imagery. The image will be stored as a separate file.

ndviChange = ndvi2019-ndvi2014
ndviChange = np.where((ndvi2014>-999) & (ndvi2019>-999),ndviChange,-999)
ndviChange

array([[ 0.        ,  0.03547079,  0.03795126, ...,  0.03159038,
         0.03800274,  0.02669263],
       [ 0.        ,  0.03765402,  0.05892348, ..., -0.00586917,
         0.01892243,  0.0195063 ],
       [ 0.        ,  0.03218401,  0.03739506, ..., -0.06377307,
        -0.0306757 ,  0.03894268],
       ...,
       [ 0.        , -0.01659706,  0.01140219, ...,  0.00272184,
         0.02552676,  0.06663901],
       [ 0.        ,  0.00599447, -0.00407705, ...,  0.05750174,
         0.00467577,  0.08448431],
       [ 0.        ,  0.        ,  0.        , ...,  0.        ,
         0.        ,  0.        ]])

saveRaster(ndviChange.astype('float32'),path_NDVIChange_19_14,cols,rows,projection)

plotNDVI(path_NDVIChange_19_14,'Spectral')

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