def ChangeDetection(bands_1993, bands_2000, band_name='SWIR', ndi_of_ndvi=False):
""" Calculates And Plot Change Detections.
Band 1 : Blue
- Bathymetric mapping, distinguishing soil from vegetation and deciduous from coniferous vegetation
Band 2 : Green
- Emphasizes peak vegetation, which is useful for assessing plant vigor
Band 3 : Red
- Discriminates vegetation slopes
Band 4 : Near Infrared (NIR)
- Emphasizes biomass content and shorelines
Band 5 : Short-wave Infrared (SWIR)
- Discriminates moisture content of soil and vegetation (penetrates thin clouds)
"""
if band_name == 'SWIR':
before, after = bands_1993[4], bands_2000[4]
cmap_diff = 'seismic_r'
cmap_rat = 'seismic'
elif band_name == 'NIR':
before, after = bands_1993[3], bands_2000[3]
cmap_diff = 'Greens'
elif band_name == 'Red':
before, after = bands_1993[2], bands_2000[2]
cmap_diff = 'RdGy_r'
cmap_rat = 'seismic'
elif band_name == 'Green':
before, after = bands_1993[1], bands_2000[1]
cmap_diff = 'YlGn'
elif band_name == 'Blue':
before, after = bands_1993[0], bands_2000[0]
cmap_diff = 'PuOr'
else:
print("Invalid band_number, use one from ['Blue', 'Green', 'Red', 'NIR', 'SWIR']")
Difference(after, before, title='%s difference [1993 - 2000]' %band_name, cmap=cmap_diff)
Ratio(after, before, title='%s ratio [1993 - 2000]' %band_name, cmap=cmap_rat) #only defined cmap_rat for red
NDI(after, before, title='%s NDI [1993 - 2000]' %band_name, cmap=cmap_diff)
if ndi_of_ndvi:
# suppress RuntimeWarning (probably because of NaN)
np.seterr(divide='ignore', invalid='ignore')
ndvi_before = es.normalized_diff(bands_1993[3], bands_1993[2])
ndvi_after = es.normalized_diff(bands_2000[3], bands_2000[2])
NDI_of_NDVI(ndvi_after, ndvi_before, title='NDI of NDVI', cmap='RdYlGn_r')
def test_normalized_diff_shapes(b1_b2_arrs):
"""Test that two arrays with different shapes returns a ValueError."""
# Test data
b1, b2 = b1_b2_arrs
b2 = b2[0]
# Check ValueError
with pytest.raises(ValueError,
match="Both arrays should have the same dimensions"):
es.normalized_diff(b1=b1, b2=b2)
def calcola_ndvi(nir, red, metadatas):
bands, rows, cols = red.shape
ndvi = es.normalized_diff(b1=nir[0, :, :], b2=red[0, :, :])
# ndvi2=np.zeros((rows,cols),dtype="float64")
#
for i in range(0, rows):
for j in range(0, cols):
appo = ndvi[i, j]
if math.isnan(appo):
ndvi[i, j] = 0
#print(ndvi[i,j])
#sopra=int(b4[0,i,j]-b8[0,i,j])
#sotto=int(b4[0,i,j]+b8[0,i,j])
# if sotto > 0 :
# diff=sopra/sotto
# ndvi2[i,j]=diff
# if math.isnan(diff) is True:
# diff=0
#else:
# ndvi2[i,j]=0
metadatas['dtype'] = "float64"
metadatas['driver'] = "GTiff"
metadatas['count'] = 1
metadatas['nodata'] = 0
print(metadatas)
# with rasterio.open('/Users/glicciardi/Desktop/ndvi.tif','w',**metadatas) as dst:
# dst.write(ndvi,1)
#
# dst.close()
#
## with rasterio.open('/Users/glicciardi/Desktop/ndvi2.tif','w',**metadatas) as dst:
## dst.write(ndvi2,1)
#
# dst.close()
return ndvi, metadatas
def NDVI(bands, year):
"""Normalized Difference Vegetation Index (NDVI)"""
#ndvi = es.normalized_diff(band4, band3) # (band4-band3)/(band4+band3)
ndvi = es.normalized_diff(bands[3], bands[2]) # (band4-band3)/(band4+band3)
titles = ['Normalized Difference Vegetation Index (NDVI) Year: %s' %year]
# Turn off bytescale scaling due to float values for NDVI
ep.plot_bands(ndvi, cmap="RdYlGn", cols=1, title=titles, vmin=-1, vmax=1)
# Create classes and apply to NDVI results
ndvi_class_bins = [-np.inf, 0, 0.25, 0.5, 0.75, np.inf]
ndvi_landsat_class = np.digitize(ndvi, ndvi_class_bins)
# Define color map and class names
ndvi_colors = ListedColormap(['gray', 'y', 'yellowgreen', 'g', 'darkgreen'])
ndvi_names = ['0.00 No Vegetation','0.25 Bare Area','0.50 Low Vegetation','0.75 Moderate Vegetation','1.00 High Vegetation']
# Get list of classes
classes = np.unique(ndvi_landsat_class).tolist()
fig, ax = plt.subplots(figsize=(8, 8))
im = ax.imshow(ndvi_landsat_class, cmap=ndvi_colors)
ep.draw_legend(im_ax=im, classes=classes, titles=ndvi_names)
ax.set_title('Normalized Difference Vegetation Index (NDVI) Classes. \nYear: %s' %yr,fontsize=14)
ax.set_axis_off(); plt.tight_layout()
plt.show()
def computeNDVI(nirBand, redBand):
"""
This function computes the ndvi and plot the results in a scale of -1 to 1
"""
ndvi = es.normalized_diff(nirBand, redBand)
plt.imshow(ndvi, vmin = -1, vmax = 1, cmap="RdYlGn")
plt.colorbar()
plt.show()
def test_normalized_diff_no_mask(b1_b2_arrs):
"""Test that if result does not include nan values,
the array is returned as unmasked."""
# Test data
b1, b2 = b1_b2_arrs
n_diff = es.normalized_diff(b1=b1, b2=b2)
# Output array unmasked
assert not ma.is_masked(n_diff)
def test_normalized_diff_mask(b1_b2_arrs):
"""Test that if result does include nan values,
the array is returned as masked."""
# Test data
b1, b2 = b1_b2_arrs
b2 = b2.astype(float)
b2[1:, 4:] = np.nan
n_diff = es.normalized_diff(b1=b1, b2=b2)
# Output array masked
assert ma.is_masked(n_diff)
def test_normalized_diff_inf(b1_b2_arrs):
"""Test that inf values in result are set to nan and
that array is returned as masked."""
# Test data
b1, b2 = b1_b2_arrs
b2[1:, 4:] = -20
# Check warning
with pytest.warns(Warning,
match="Divide by zero produced infinity values"):
n_diff = es.normalized_diff(b1=b1, b2=b2)
# Inf values set to nan
assert not np.isinf(n_diff).any()
# Output array masked
assert ma.is_masked(n_diff)
def main():
all_landsat_post_bands = glob('AOI/cana_band*.tif')
#'AOI/arroz_band*.tif')
all_landsat_post_bands.sort()
landsat_img = []
landsat_data = []
band_vectors = []
cana_bottom_limits = [181, 243, 531, 339, 2281, 1339, 558]
cana_top_limits = [567, 658, 960, 1081, 3812, 2644, 1982]
arroz_bottom_limits = [27, 158, 354, 168, 696, 175, 115]
arroz_top_limits = [720, 830, 1123, 1430, 4060, 3363, 2236]
for i in range(len(all_landsat_post_bands)):
landsat_img.append(Image.open(all_landsat_post_bands[i]))
landsat_data.append(np.array(landsat_img[i]))
#band_vectors.append(matrix2vector(landsat_data[i], cana_bottom_limits[i], cana_top_limits[i]))
#band_vectors.append(matrix2vector(landsat_data[i], arroz_bottom_limits[i], arroz_top_limits[i]))
NIR = matrix3D2matrix2D(landsat_data[3], cana_bottom_limits[3],
cana_top_limits[3])
VIS = matrix3D2matrix2D(landsat_data[0], cana_bottom_limits[0],
cana_bottom_limits[0])
naip_ndvi = es.normalized_diff(NIR, VIS)
ep.plot_bands(naip_ndvi, cmap="PiYG", scale=False, vmin=-1, vmax=1)
plt.show()
import rasterio as rio
import geopandas as gpd
import earthpy as et
import earthpy.spatial as es
import earthpy.plot as ep
# Download data and set working directory
data = et.data.get_data('cold-springs-fire')
os.chdir(os.path.join(et.io.HOME, 'earth-analytics'))
naip_data_path = os.path.join("data", "cold-springs-fire", "naip",
"m_3910505_nw_13_1_20150919", "crop",
"m_3910505_nw_13_1_20150919_crop.tif")
with rio.open(naip_data_path) as src:
naip_data = src.read()
# View shape of the data
naip_data.shape
naip_ndvi = es.normalized_diff(naip_data[3], naip_data[0])
ep.plot_bands(
naip_ndvi,
cmap='PiYG',
scale=False,
vmin=-1,
vmax=1,
title="NAIP Derived NDVI\n 19 September 2015 - Cold Springs Fire, Colorado"
)
plt.show()
#sub_candidates = fs.ls(link)
#print(sub_candidates)
#dev_s3_client.list_objects(link)
s3 = boto3.resource('s3')
s3_client = boto3.client('s3')
bucket=link.partition('/')[0]
my_bucket = s3.Bucket(bucket)
for my_bucket_object in my_bucket.objects.filter(Prefix=link.partition('/')[2]):
#print(my_bucket_object)
print('{0}:{1}'.format(my_bucket.name, my_bucket_object.key))
with rio.open('s3://{0}/{1}'.format(my_bucket.name, my_bucket_object.key)) as src:
print("key",my_bucket_object.key)
print(src.meta)
# # Load red and NIR bands - note all PlanetScope 4-band images have band order BGRN
planet_ndvi = es.normalized_diff(src.read(3), src.read(4))
aug_pla_meta = src.profile
# Change the count or number of bands from 4 to 5
aug_pla_meta['count'] = 5
# Change the data type to float rather than integer
aug_pla_meta['dtype'] = "float64"
aug_pla_meta
#convert to float64
#ndvi_64 = np.array(planet_ndvi, dtype=numpy.float64)
t = src.transform
# rescale the metadata
transform = Affine(t.a * scale, t.b, t.c, t.d, t.e * scale, t.f)
height = int(src.height / scale)
width = int(src.width / scale)
from matplotlib.colors import ListedColormap
import earthpy as et
import earthpy.spatial as es
import earthpy.plot as ep
"""Call Landsat 5 input image (a tiff file) with rasterio """
image = rasterio.open(
'/test_images/subset_0_of_LT05_L1TP_180032_20060727_20180311_01_T1_reprojected.tif'
)
"""Assign names of bands from test image"""
green = image.read(2)
nir = image.read(4)
"""Normalized difference water index"""
ndwi = es.normalized_diff(green, nir)
"""Display output"""
titles = ["Landsat 5 Normalized Difference Water Index"]
ep.plot_bands(ndwi, cmap="gray", cols=1, title=titles)
plt.show()
"""Create output ass a tiff file (one band)"""
meta = image.profile
meta.update(driver='GTiff')
meta.update(count=1)
with rasterio.open('NDWI.tif', 'w', **meta) as file:
file.write(ndwi, 1)
file.close()
) landsat_path.sort() arr_st, meta = es.stack(landsat_path, nodata=-9999) ############################################################################### # Calculate Normalized Difference Vegetation Index (NDVI) # ------------------------------------------------------- # # You can calculate NDVI for your dataset using the # ``normalized_diff`` function from the ``earthpy.spatial`` module. # Math will be calculated (b1-b2) / (b1 + b2). # Landsat 8 red band is band 4 at [3] # Landsat 8 near-infrared band is band 5 at [4] ndvi = es.normalized_diff(arr_st[4], arr_st[3]) ############################################################################### # Plot NDVI With Colorbar Legend of Continuous Values # ---------------------------------------------------- # # You can plot NDVI with a colorbar legend of continuous values using the # ``plot_bands`` function from the ``earthpy.plot`` module. titles = ["Landsat 8 - Normalized Difference Vegetation Index (NDVI)"] # Turn off bytescale scaling due to float values for NDVI ep.plot_bands(ndvi, cmap="RdYlGn", cols=1, title=titles, vmin=-1, vmax=1) ###############################################################################
# it will only plot that single band. You can turn off the # colorbar using the ``cbar`` parameter (``cbar=False``). ep.plot_bands(array_stack[4], cbar=False) plt.show() ################################################################################## # Turn Off Scaling # ----------------- # # ``ep.plot_bands()`` scales the imagery to a 0-255 scale by default. This range # of values makes it easier for matplotlib to plot the data. To turn off # scaling, set the scale parameter to ``False``. Below you # plot NDVI with scaling turned off in order for the proper range of values # (-1 to 1) to be displayed. You can use the ``cmap=`` parameter to adjust # the colormap for the plot NDVI = es.normalized_diff(array_stack[4], array_stack[3]) ep.plot_bands(NDVI, scale=False, cmap="RdYlGn") plt.show() ################################################################################## # Adjust the Number of Columns for a Multi Band Plot # --------------------------------------------------- # # The number of columns used while plotting multiple bands can be changed in order # to change the arrangement of the images overall. ep.plot_bands(array_stack, cols=2) plt.show()
