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PyGEE_SWToolbox.py
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PyGEE_SWToolbox.py
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import ee
# Initialize the GEE API
# try:
# ee.Initialize()
# except Exception as e:
# ee.Authenticate()
ee.Initialize()
# geemap:A Python package for interactive mapping with Google Earth Engine, ipyleaflet, and ipywidgets
# Documentation: https://geemap.org
import geemap
from geemap import ml
import pickle
# from geemap import ee_basemaps
import matplotlib.pyplot as plt
import numpy as np
#eemont
import eemont
# import developed utilities
# import Utilities as ut
from Utilities import *
# geetols: Google earth engine tools
# https://github.com/gee-community/gee_tools
import geetools
from geetools import tools
# hydrafloods: Hydrologic Remote Sensing Analysis for Floods
#https://github.com/Servir-Mekong/hydra-floods
import hydrafloods as hf
from hydrafloods import geeutils
# Ipywidgets for GUI design
import ipywidgets as ipw
from IPython.display import display
from ipywidgets import HBox, VBox, Layout
# A simple Python file chooser widget for use in Jupyter/IPython in conjunction with ipywidgets
# https://pypi.org/project/ipyfilechooser/
from ipyfilechooser import FileChooser
# Plotly Python API for interactive graphing
import plotly
import plotly.express as px
import plotly.graph_objects as go
# Pandas - Python Data Analysis Library for data analysis and manipulation
import pandas as pd
# Miscellaneous Python modules
from datetime import datetime, timedelta
import os
class Toolbox:
def __init__(self):
#----------------------------------------------------------------------------------------------------
""" UI Design"""
#---------------------------------------------------------------------------------------------------
# Program Title
Title_text = ipw.HTML(
"<h3 class= 'text-center'><font color = 'blue'>Python-GEE Surface Water Analysis Toolbox v.1.0.3</font>")
style = {'description_width': 'initial'}
# Image Processing Tab
#************************************************************************************************
# Image Parameters UI
dataset_description = ipw.HTML(value = f"<b><font color='blue'>{'Satellite Imagery Parameters:'}</b>")
dataset_Label = ipw.Label('Select Dataset:', layout=Layout(margin='5px 0 0 5px')) #top right bottom left
Platform_options = ['Landsat-Collection 2','Sentinel-1', 'Sentinel-2', 'USDA NAIP' ]
self.Platform_dropdown = ipw.Dropdown(options = Platform_options, value = None,
layout=Layout(width='150px', margin='5px 0 0 5px'))
filtering_Label = ipw.Label('Speckle filter:', layout=Layout(margin='5px 0 0 5px'))
filtering_options = ['Refined-Lee', 'Perona-Malik', 'P-median', 'Lee Sigma', 'Gamma MAP','Boxcar Convolution']
self.filter_dropdown = ipw.Dropdown(options = filtering_options, value = 'Refined-Lee',
layout=Layout(width='150px', margin='5px 0 0 15px'))
self.filter_dropdown.disabled = True
PlatformType = HBox([dataset_Label, self.Platform_dropdown])
FilterType = HBox([filtering_Label, self.filter_dropdown])
# Study period definition
#************************************************************************************************
# Start date picker
lbl_start_date = ipw.Label('Start Date:', layout=Layout(margin='5px 0 0 5px'))
self.start_date = ipw.DatePicker(value = datetime.now()-timedelta(7), disabled=False,
layout=Layout(width='150px', margin='5px 0 0 30px'))
start_date_box = HBox([lbl_start_date, self.start_date])
# End date picker
lbl_end_date = ipw.Label('End Date:', layout=Layout(margin='5px 0 0 5px'))
self.end_date = ipw.DatePicker(value = datetime.now(), disabled=False,
layout=Layout(width='150px', margin='5px 0 0 34px'))
end_date_box = HBox([lbl_end_date, self.end_date])
datePickers = VBox([start_date_box, end_date_box])
# Cloud threshold for filtering data
#************************************************************************************************
# Set cloud threshold
self.cloud_threshold = ipw.IntSlider(description = 'Cloud Threshold:', orientation = 'horizontal',
value = 50, step = 5, style = style)
imageParameters = VBox([dataset_description, PlatformType, FilterType, datePickers, self.cloud_threshold],
layout=Layout(width='305px', border='solid 2px black'))
# Study Area definition
#************************************************************************************************
# Option to use a map drawn boundary or upload shapefile
StudyArea_description = ipw.HTML(value = f"<b><font color='blue'>{'Study Area Definition:'}</b>")
self.user_preference = ipw.RadioButtons(options=['Map drawn boundary','Upload boundary'], value='Map drawn boundary')
self.file_selector = FileChooser(description = 'Upload', filter_pattern = ["*.shp","*.kml"], use_dir_icons = True)
# Retrieve and process satellite images
#***********************************************************************************************
# Button to retrieve and process satellite images from the GEE platform
self.imageProcessing_Button = ipw.Button(description = 'Process images',
tooltip='Click to process images', button_style = 'info',
layout=Layout(width='150px', margin='5px 0 0 50px', border='solid 2px black'))
# Study area UI and process button container
# ************************************************************************************************
StudyArea = VBox(children = [StudyArea_description, self.user_preference, self.imageProcessing_Button],
layout=Layout(width='300px', border='solid 2px black', margin='0 0 0 10px'))
# Results UI for displaying number and list of files
#*****************************************************************************************************
lbl_results = ipw.HTML(value = f"<b><font color='blue'>{'Processing Results:'}</b>")
lbl_images = ipw.Label('No. of processed images:')
self.lbl_RetrievedImages = ipw.Label()
display_no_images = HBox([lbl_images, self.lbl_RetrievedImages])
lbl_files = ipw.Label('List of files:')
self.lst_files = ipw.Select(layout=Layout(width='360px', height='100px'))
image_Results = VBox([lbl_results, display_no_images, lbl_files, self.lst_files],
layout=Layout(width='400px', border='solid 2px black', margin='0 0 0 10px'))
# Container for Image Processing Tab
#************************************************************************************************
imageProcessing_tab = HBox([imageParameters, StudyArea, image_Results])
# Water Extraction Tab
#*************************************************************************************************
# Water extraction indices
water_index_options = ['NDWI','MNDWI','DSWE', 'AWEInsh', 'AWEIsh']
lbl_indices = ipw.Label('Water Index:', layout=Layout(margin='5px 0 0 5px'))
self.water_indices = ipw.Dropdown(options = water_index_options, value = 'NDWI',
layout=Layout(width='100px', margin='5px 0 0 63px'))
display_indices = HBox([lbl_indices, self.water_indices])
# Color widget for representing water
lbl_color = ipw.Label('Color:', layout=Layout(margin='5px 0 0 5px'))
self.index_color = ipw.ColorPicker(concise = False, value = 'blue',layout=Layout(width='100px', margin='5px 0 0 101px'))
display_color_widget = HBox([lbl_color, self.index_color])
# Water index threshold selection
threshold_options = ['Simple','Otsu']
lbl_threshold_method = ipw.Label('Thresholding Method:', layout=Layout(margin='5px 0 0 5px'))
self.threshold_dropdown = ipw.Dropdown(options = threshold_options,value = 'Simple',
layout=Layout(width='100px', margin='5px 0 0 10px'))
display_thresholds = HBox([lbl_threshold_method, self.threshold_dropdown])
lbl_threshold = ipw.Label('Threshold value:', layout=Layout(margin='5px 0 5px 5px'))
self.threshold_value = ipw.BoundedFloatText(value=0.000, min = -1.0, max = 1.0, step = 0.050,
layout=Layout(width='100px', margin='5px 0 0 40px'))
display_threshold_widget = HBox([lbl_threshold, self.threshold_value])
water_index_Box = VBox([display_indices, display_thresholds, display_threshold_widget, display_color_widget],
layout=Layout(width='250px', border='solid 2px black'))
self.extractWater_Button = ipw.Button(description = 'Extract Water', tooltip='Click to extract surface water',
button_style = 'info',
layout=Layout(width='150px', margin='5px 0 0 20px', border='solid 2px black'))
Extraction_tab = HBox([water_index_Box, self.extractWater_Button])
self.extractWater_Button.disabled = True
# Spatial Analysis Tab
#**************************************************************************************************
self.water_Frequency_button = ipw.Button(description = 'Compute Water Frequency',
tooltip='Click to compute water occurence frequency',
button_style = 'info',
layout=Layout(width='200px', border='solid 2px black',margin='5 0 0 50px'))
self.water_Frequency_button.disabled = True
self.Depths_Button = ipw.Button(description = 'Compute Depth Map',
tooltip='Click to generate depth maps', button_style = 'info',
layout=Layout(width='200px', border='solid 2px black',margin='5 0 0 50px'))
self.Depths_Button.disabled = True
self.elevData_options = ipw.Dropdown(options=['NED','SRTM','User DEM'], value='NED', description='Elev. Dataset:',
layout=Layout(width='210px', margin='0 0 0 10px'), style = style)
self.elevData_options.disabled = False
self.elev_Methods = ipw.Dropdown(options=['Random Forest','Mod_Stumpf','Mod_Lyzenga','FwDET'], value='Random Forest',
description='Depth method:',
layout=Layout(width='210px', margin='0 0 0 10px'), style = style)
# self.elev_Methods = ipw.Dropdown(options=['Experimental','FwDET'], value='FwDET',
# description='Depth method:',
# layout=Layout(width='210px', margin='0 0 0 10px'), style = style)
self.elev_Methods.disabled = True
self.userDEM = ipw.Dropdown(description='Select GEE asset:',
layout=Layout(width='300px', margin='0 0 0 10px'), style = style)
lbl_Elev = ipw.Label('Elevation Dataset:', layout=Layout(margin='0 0 0 10px'))
elev_Box = HBox([self.Depths_Button, self.elev_Methods, self.elevData_options])
self.zonalAnalysis_Button = ipw.Button(description = 'Zonal Analysis',
tooltip='Click to remove clouds', button_style = 'info',
layout=Layout(width='200px', border='solid 2px black',margin='5 0 0 50px'))
# Spatial_Analysis_Tab = VBox([water_Frequency_button, elev_Box, zonalAnalysis_Button])
Spatial_Analysis_Tab = VBox([self.water_Frequency_button, elev_Box])
# Ploting and Statistics Tab
#***************************************************************************************************
lbl_Area_Plotting = ipw.HTML(value = f"<b><font color='blue'>{'Surface Water Area Computation:'}</b>")
self.area_unit = ipw.Dropdown(options = ['Square m','Square Km', 'Hectares', 'Acre'], value = 'Square m',
description = 'Unit of area:', style=style, tooltip='Select unit for areas', layout=Layout(width='200px'))
self.plot_button = ipw.Button(description = 'Compute and Plot Areas', tooltip='Click to plot graph', button_style = 'info',
layout=Layout(width='170px', margin='10 0 0 200px', border='solid 2px black'))
self.plot_button.disabled = True
lbl_Volume_Plotting = ipw.HTML(value = f"<b><font color='blue'>{'Water Volume Computation:'}</b>")
self.vol_unit = ipw.Dropdown(options = ['Cubic m','Cubic ft', 'Litres', 'ac-ft'], value = 'ac-ft',
description = 'Unit of volume:', style=style, tooltip='Select unit for volumes', layout=Layout(width='200px'))
self.volume_button = ipw.Button(description = 'Compute Volumes', tooltip='Click to plot volumes', button_style = 'info',
layout=Layout(width='170px', margin='10 0 0 200px', border='solid 2px black'))
self.volume_button.disabled = True
# lbl_depth_Plotting = ipw.Label(value ='Plot depth hydrograph at a location:', layout=Layout(margin='10px 0 0 0'))
lbl_depth_Plotting = ipw.HTML(value = f"<b><font color='blue'>{'Plot depth hydrograph at a location:'}</b>")
self.point_preference = ipw.RadioButtons(options=['Map drawn point','Enter coordinates'],
value='Map drawn point')
self.coordinates_textbox = ipw.Text(layout=Layout(width='200px'))
lbl_coordinates = ipw.Label(value='Enter Long, Lat in decimal degrees')
# point_selector = FileChooser(description = 'Upload point', filter_pattern = ["*.shp"], use_dir_icons = True)
self.depth_plot_button = ipw.Button(description = 'Plot depths', tooltip='Click to plot depth hydrograph', button_style = 'info',
layout=Layout(width='170px', margin='10 0 0 100px', border='solid 2px black'))
self.depth_plot_button.disabled = True
depth_box = VBox(children = [lbl_depth_Plotting,self.point_preference, self.depth_plot_button])
plotting_box = VBox([lbl_Area_Plotting, self.area_unit, self.plot_button, lbl_Volume_Plotting,self.vol_unit,self.volume_button, depth_box],
layout=Layout(width='310px', border='solid 2px black'))
lbl_Stats = ipw.HTML(value = f"<b><font color='blue'>{'Summary Statistics:'}</b>")
self.lbl_Max_Area = ipw.Label(value ='', layout=Layout(width='100px')) #top right bottom left
self.lbl_Min_Area = ipw.Label(value ='', layout=Layout(width='100px'))
self.lbl_Avg_Area = ipw.Label(value ='', layout=Layout(width='100px'))
self.lbl_Max_Depth = ipw.Label(value ='', layout=Layout(width='100px'))
self.lbl_Min_Depth = ipw.Label(value ='', layout=Layout(width='100px'))
self.lbl_Avg_Depth = ipw.Label(value ='', layout=Layout(width='100px'))
self.lbl_Max_Volume = ipw.Label(value ='', layout=Layout(width='100px'))
self.lbl_Min_Volume = ipw.Label(value ='', layout=Layout(width='100px'))
self.lbl_Avg_Volume = ipw.Label(value ='', layout=Layout(width='100px'))
self.cap_Max_Area = ipw.Label(value ='Max. Area:')
self.cap_Min_Area = ipw.Label(value ='Min. Area:')
self.cap_Avg_Area = ipw.Label(value ='Avg. Area:')
self.cap_Max_Depth = ipw.Label(value ='Max. Depth:')
self.cap_Min_Depth = ipw.Label(value ='Min. Depth:')
self.cap_Avg_Depth = ipw.Label(value ='Avg. Depth:')
self.cap_Max_Volume = ipw.Label(value ='Max. Volume:')
self.cap_Min_Volume = ipw.Label(value ='Min. Volume:')
self.cap_Avg_Volume = ipw.Label(value ='Avg. Volume:')
max_box = HBox([self.cap_Max_Area,self.lbl_Max_Area, self.cap_Max_Depth,self.lbl_Max_Depth, self.cap_Max_Volume,self.lbl_Max_Volume])
min_box = HBox([self.cap_Min_Area,self.lbl_Min_Area, self.cap_Min_Depth,self.lbl_Min_Depth, self.cap_Min_Volume, self.lbl_Min_Volume])
avg_box = HBox([self.cap_Avg_Area,self.lbl_Avg_Area, self.cap_Avg_Depth,self.lbl_Avg_Depth, self.cap_Avg_Volume, self.lbl_Avg_Volume])
stats_box = VBox([lbl_Stats, max_box, min_box, avg_box])
self.file_selector1 = FileChooser(description = 'Select folder and filename', filter_pattern = "*.csv", use_dir_icons = True)
self.file_selector1.title = 'Select Folder and Filename'
self.file_selector1.default_path = os.getcwd()
self.save_data_button = ipw.Button(description = 'Save Data',tooltip='Click to save computed areas to file',button_style = 'info',
layout=Layout(width='100px', border='solid 2px black',margin='5 0 0 50px'))
lbl_Save = ipw.HTML(value = f"<b><font color='blue'>{'Save Data:'}</b>")
stats_save_box = VBox(children=[stats_box, lbl_Save, self.file_selector1, self.save_data_button],
layout=Layout(width='550px', border='solid 2px black', margin='0 0 0 10px'))
plot_stats_tab = HBox(children=[plotting_box, stats_save_box])
# Downloads Tab
#***************************************************************************************************
self.files_to_download = ipw.RadioButtons(options=['Satellite Images', 'Water Mask', 'Water Frequency', 'Depth Maps',
'DSWE Images'], value='Satellite Images',
description='Files to download:', style = style)
self.download_location = ipw.RadioButtons(options=['Google Drive', 'Local Disk'],
value='Google Drive', description='Download Location:', style = style)
self.folder_name = ipw.Text(description='Folder Name:')
self.folder_selector = FileChooser(description = 'Select Folder', show_only_dirs = True, use_dir_icons = True)
self.folder_selector.title = '<b>Select a folder</b>'
self.folder_selector.default_path = os.getcwd()
self.download_button = ipw.Button(description = 'Download',
tooltip='Click to plot download water images', button_style = 'info')
self.download_button.disabled = True
download_settings = VBox(children=[self.files_to_download, self.download_location, self.folder_name])
download_tab = HBox([download_settings, self.download_button])
# variable to hold the random forest classifier
self.rf_ee_classifier = None
self.WaterMasks = None
self.visParams = None
self.freqParams = None
self.filtered_Collection = None
self.filtered_landsat = None
self.clipped_images = None
self.imageType = None
self.dates = None
self.site = None
self.img_scale = None
self.file_list = None
self.StartDate = None
self.EndDate = None
self.water_images = None
self.dswe_images = None
self.index_images = None
self.dswe_viz = None
self.depth_maps = None
self.filtered_Water_Images = None
self.depthParams = None
# Functions to control UI changes and parameter settings
#****************************************************************************************************
def platform_index_change(change):
"""
Function to set image visualization parameters, hide or show some UI componets and
show water indices that are applicable to the type of satellite image selected
args:
None
returns:
None
"""
try:
if self.Platform_dropdown.value == 'Landsat-Collection 2':
self.visParams = {'bands': ['red', 'green', 'blue'],
'min': 0,
'max': 0.3,
}
self.cloud_threshold.disabled = False
self.water_indices.disabled = False
self.index_color.disabled = False
self.threshold_value.disabled = False
self.water_indices.options = ['NDWI','MNDWI','DSWE','AWEInsh', 'AWEIsh']
self.threshold_dropdown.options = ['Simple','Otsu']
self.filter_dropdown.disabled = True
elif self.Platform_dropdown.value == 'Sentinel-1':
self.visParams = {'min': -25,'max': 0}
self.cloud_threshold.disabled = True
self.water_indices.options = ['VV','VH']#,'NDPI','NVHI', 'NVVI']
self.index_color.disabled = False
self.threshold_value.disabled = True
self.threshold_dropdown.options = ['Otsu']
self.filter_dropdown.disabled = False
elif self.Platform_dropdown.value == 'Sentinel-2':
self.visParams = {'bands': ['red', 'green', 'blue'],
'min': 0.0,
'max': 3000}
self.cloud_threshold.disabled = False
self.water_indices.disabled = False
self.index_color.disabled = False
self.threshold_value.disabled = False
self.water_indices.options = ['NDWI','MNDWI']
self.threshold_dropdown.options = ['Simple','Otsu']
self.filter_dropdown.disabled = True
elif self.Platform_dropdown.value == 'USDA NAIP':
self.visParams = {'bands': ['R', 'G','B'],
'min': 0.0,
'max': 255.0}
self.threshold_value.disabled = False
self.water_indices.disabled = False
self.index_color.disabled = False
self.water_indices.options = ['NDWI']
self.threshold_dropdown.options = ['Simple','Otsu']
self.filter_dropdown.disabled = True
except Exception as e:
print(e)
# Link widget to function
self.Platform_dropdown.observe(platform_index_change, 'value')
def showFileSelector(button):
"""
Function to show or hide shapefile upload widget
args:
None
returns:
None
"""
if button['new']:
StudyArea.children = [StudyArea_description, self.user_preference, self.file_selector, self.imageProcessing_Button]
else:
StudyArea.children = [StudyArea_description, self.user_preference,self.imageProcessing_Button]
# Link widget to file selector function
self.user_preference.observe(showFileSelector, names='index')
def showLocationSelector(button):
"""
Function to show or hide folder selector
args:
None
returns:
None
"""
if button['new']:
download_settings.children = [self.files_to_download, self.download_location, self.folder_selector]
else:
download_settings.children = [self.files_to_download, self.download_location, self.folder_name]
# Link widget to folder selector function
self.download_location.observe(showLocationSelector, names='index')
def pointOptions_selector(button):
"""
Function to show or hide folder selector
args:
None
returns:
None
"""
if button['new']:
depth_box.children = [lbl_depth_Plotting,self.point_preference, lbl_coordinates, self.coordinates_textbox,
self.depth_plot_button]
else:
depth_box.children = [lbl_depth_Plotting,self.point_preference, self.depth_plot_button]
# Link widget to folder selector function
self.point_preference.observe(pointOptions_selector, names='index')
def indexSelection(change):
if self.water_indices.value =='DSWE':
self.threshold_value.min = 1.0
self.threshold_value.max = 4.0
self.threshold_value.step = 1.0
self.threshold_value.value = 4.0
self.threshold_dropdown.options = ['Simple']
else:
self.threshold_value.min = -1.0
self.threshold_value.max = 1.0
self.threshold_value.step = 0.050
self.threshold_value.value = 0.0
self.threshold_dropdown.options = ['Simple','Otsu']
self.water_indices.observe(indexSelection, 'value')
def thresholdSelection(change):
if self.threshold_dropdown.value =='Otsu':
self.threshold_value.disabled = True
else:
self.threshold_value.disabled = False
# Link widget to threshold method selection function
self.threshold_dropdown.observe(thresholdSelection, 'value')
def depthMethodSelection(change):
if self.elev_Methods.value == 'FwDET' or self.elev_Methods.value == 'Experimental':
self.elevData_options.disabled = False
else:
self.elevData_options.disabled = True
elev_Box.children = [self.Depths_Button, self.elev_Methods, self.elevData_options]
# Link widget to threshold method selection function
self.elev_Methods.observe(depthMethodSelection, 'value')
def demSelection(change):
if self.elevData_options.value == 'User DEM':
folder = ee.data.getAssetRoots()[0]['id']
assets = ee.data.listAssets({'parent':folder})
# filter only image assets
filtered_asset = list(filter(lambda asset: asset['type'] == 'IMAGE', assets['assets']))
# create a list of image assets
list_assets = [sub['id'] for sub in filtered_asset]
elev_Box.children = [self.Depths_Button, self.elev_Methods, self.elevData_options, self.userDEM]
self.userDEM.options = list_assets # set dropdown options to list of image assets
else:
elev_Box.children = [self.Depths_Button, self.elev_Methods, self.elevData_options]
# Link widget to function
self.elevData_options.observe(demSelection, 'value')
#****************************************************************************************************
# Full UI
#***************************************************************************************************
tab_children = [imageProcessing_tab, Extraction_tab, Spatial_Analysis_Tab, plot_stats_tab, download_tab]
tab = ipw.Tab()
tab.children = tab_children
# changing the title of the first and second window
tab.set_title(0, 'Image Processing')
tab.set_title(1, 'Water Extraction')
tab.set_title(2, 'Spatial Analysis')
tab.set_title(3, 'Plotting & Stats')
tab.set_title(4, 'Download & Export')
# Plotting outputs and feedback to user
#***************************************************************************************************
self.feedback = ipw.Output()
# OUTPUTS = VBox([self.feedback])
# create map instance
self.Map = geemap.Map()
self.Map.add_basemap('HYBRID')
GUI = VBox([Title_text,tab,self.Map])
display(GUI)
self.fig = go.FigureWidget()
self.fig.update_layout(title = '<b>Surface Water Area Hydrograph<b>',
title_x = 0.5, title_y = 0.90, title_font=dict(family="Arial",size=24),
template = "plotly_white",
xaxis =dict(title ='<b>Date<b>', linecolor = 'Black'),
yaxis=dict(title='Area (sq m)', linecolor = 'Black'),
font_family="Arial")
# display plotly figure
display(self.fig)
display(self.feedback)
# Widget-Function connections
self.imageProcessing_Button.on_click(self.process_images)
self.extractWater_Button.on_click(self.Water_Extraction)
self.plot_button.on_click(self.plot_areas)
self.save_data_button.on_click(self.save_data)
self.Depths_Button.on_click(self.calc_depths)
self.download_button.on_click(self.dowload_images)
self.water_Frequency_button.on_click(self.water_frequency)
self.depth_plot_button.on_click(self.plot_depths)
self.volume_button.on_click(self.plot_volumes)
# Function to clip images
def clipImages(self,img):
"""
Function to clip images
args:
Image
returns:
Clipped image
"""
orig = img
clipped_image = img.clip(self.site).copyProperties(orig, orig.propertyNames())
return clipped_image
def process_images(self, b):
"""
Function to retrieve and process satellite images from GEE platform
args:
None
returns:
None
"""
with self.feedback:
self.feedback.clear_output()
try:
self.fig.data = [] # clear existing plot
self.lbl_RetrievedImages.value = 'Processing....'
cloud_thresh = self.cloud_threshold.value
# Define study area based on user preference
if self.user_preference.index == 1:
file = self.file_selector.selected
self.site = load_boundary(file)
self.Map.addLayer(self.site, {}, 'AOI')
self.Map.center_object(self.site)
else:
self.site = ee.FeatureCollection(self.Map.draw_last_feature)
# get widget values
self.imageType = self.Platform_dropdown.value
filterType = self.filter_dropdown.value
self.StartDate = ee.Date.fromYMD(self.start_date.value.year,self.start_date.value.month,self.start_date.value.day)
self.EndDate = ee.Date.fromYMD(self.end_date.value.year,self.end_date.value.month,self.end_date.value.day)
boxcar = ee.Kernel.circle(**{'radius':3, 'units':'pixels', 'normalize':True})
def filtr(img):
return img.convolve(boxcar)
# filter image collection based on date, study area and cloud threshold(depends of datatype)
if self.imageType == 'Landsat-Collection 2':
self.filtered_landsat = load_Landsat_Coll_2(self.site, self.StartDate, self.EndDate, cloud_thresh)
self.filtered_Collection = self.filtered_landsat.map(maskLandsatclouds)
elif self.imageType == 'Sentinel-2':
Collection_before = load_Sentinel2(self.site, self.StartDate, self.EndDate, cloud_thresh)
self.filtered_Collection = Collection_before.map(maskS2clouds)
elif self.imageType == 'Sentinel-1':
Collection_before = load_Sentinel1(self.site, self.StartDate, self.EndDate)
# apply speckle filter algorithm or smoothing
if filterType == 'Gamma MAP':
corrected_Collection = Collection_before.map(slope_correction)
self.filtered_Collection = corrected_Collection.map(hf.gamma_map)
elif filterType == 'Refined-Lee':
corrected_Collection = Collection_before.map(slope_correction)
self.filtered_Collection = corrected_Collection.map(hf.refined_lee)
elif filterType == 'Perona-Malik':
corrected_Collection = Collection_before.map(slope_correction)
self.filtered_Collection = corrected_Collection.map(hf.perona_malik)
elif filterType == 'P-median':
corrected_Collection = Collection_before.map(slope_correction)
self.filtered_Collection = corrected_Collection.map(hf.p_median)
elif filterType == 'Boxcar Convolution':
corrected_Collection = Collection_before.map(slope_correction)
self.filtered_Collection = corrected_Collection.map(filtr)
elif filterType == 'Lee Sigma':
# corrected_Collection = Collection_before.map(ut.slope_correction) # slope correction before lee_sigma fails
self.filtered_Collection = Collection_before.map(hf.lee_sigma)
elif self.imageType == 'USDA NAIP':
self.filtered_Collection = load_NAIP(self.site, self.StartDate, self.EndDate)
# Clip images to study area
self.clipped_images = self.filtered_Collection.map(self.clipImages)
# Mosaic same day images
self.clipped_images = tools.imagecollection.mosaicSameDay(self.clipped_images)
# Add first image in collection to Map
first_image = self.clipped_images.first()
if self.imageType == 'Sentinel-1':
self.img_scale = first_image.select(0).projection().nominalScale().getInfo()
else:
bandNames = first_image.bandNames().getInfo()
self.img_scale = first_image.select(str(bandNames[0])).projection().nominalScale().getInfo()
self.Map.addLayer(self.clipped_images.first(), self.visParams, self.imageType)
# Get no. of processed images
no_of_images = self.filtered_Collection.size().getInfo()
# Display number of images
self.lbl_RetrievedImages.value = str(no_of_images)
# List of files
self.file_list = self.filtered_Collection.aggregate_array('system:id').getInfo()
# display list of files
self.lst_files.options = self.file_list
self.extractWater_Button.disabled = False # enable the water extraction button
self.download_button.disabled = False
except Exception as e:
print(e)
print('An error occurred during processing.')
def Water_Extraction(self, b):
"""
Function to extract surface water from satellite images
args:
None
returns:
None
"""
with self.feedback:
self.feedback.clear_output()
try:
color_palette = self.index_color.value
# Function to extract water using NDWI or MNDWI from multispectral images
def water_index(img):
"""
Function to extract surface water from Landsat and Sentinel-2 images using
water extraction indices: NDWI, MNDWI, and AWEI
args:
Image
returns:
Image with water mask
"""
index_image = ee.Image(1)
if self.water_indices.value == 'NDWI':
if self.imageType == 'Landsat-Collection 2' or self.imageType == 'Sentinel-2':
bands = ['green', 'nir']
elif self.imageType == 'USDA NAIP':
bands = ['G', 'N']
index_image = img.normalizedDifference(bands).rename('waterIndex')\
.copyProperties(img, ['system:time_start'])
elif self.water_indices.value == 'MNDWI':
if self.imageType == 'Landsat-Collection 2':
bands = ['green', 'swir1']
index_image = img.normalizedDifference(bands).rename('waterIndex')\
.copyProperties(img, ['system:time_start'])
elif self.imageType == 'Sentinel-2':
# Resample the swir bands from 20m to 10m
resampling_bands = img.select(['swir1','swir2'])
img = img.resample('bilinear').reproject(**
{'crs': resampling_bands.projection().crs(),
'scale':10
})
bands = ['green', 'swir1']
index_image = img.normalizedDifference(bands).rename('waterIndex')\
.copyProperties(img, ['system:time_start'])
elif self.water_indices.value == 'AWEInsh':
index_image = img.expression(
'(4 * (GREEN - SWIR1)) - ((0.25 * NIR)+(2.75 * SWIR2))', {
'NIR': img.select('nir'),
'GREEN': img.select('green'),
'SWIR1': img.select('swir1'),
'SWIR2': img.select('swir2')
}).rename('waterIndex').copyProperties(img, ['system:time_start'])
elif self.water_indices.value == 'AWEIsh':
index_image = img.expression(
'(BLUE + (2.5 * GREEN) - (1.5 * (NIR + SWIR1)) - (0.25 * SWIR2))', {
'BLUE':img.select('blue'),
'NIR': img.select('nir'),
'GREEN': img.select('green'),
'SWIR1': img.select('swir1'),
'SWIR2': img.select('swir2')
}).rename('waterIndex').copyProperties(img, ['system:time_start'])
return img.addBands(index_image)
def water_thresholding(img):
# Compute threshold
if self.threshold_dropdown.value == 'Simple': # Simple value no dynamic thresholding
nd_threshold = self.threshold_value.value
water_image = img.select('waterIndex').gt(nd_threshold).rename('water')\
.copyProperties(img, ['system:time_start'])
elif self.threshold_dropdown.value == 'Otsu':
reducers = ee.Reducer.histogram(255,2).combine(reducer2=ee.Reducer.mean(), sharedInputs=True)\
.combine(reducer2=ee.Reducer.variance(), sharedInputs= True)
histogram = img.select('waterIndex').reduceRegion(
reducer=reducers,
geometry=self.site.geometry(),
scale=self.img_scale,
bestEffort=True)
nd_threshold = otsu(histogram.get('waterIndex_histogram')) # get threshold from the nir band
water_image = img.select('waterIndex').gt(nd_threshold).rename('water')
water_image = water_image.copyProperties(img, ['system:time_start'])
return img.addBands(water_image)
# Function to extract water from SAR Sentinel 1 images
def add_S1_waterMask(band):
"""
Function to extract surface water from Sentinel-1 images Otsu algorithm
args:
Image
returns:
Image with water mask
"""
def wrap(img):
reducers = ee.Reducer.histogram(255,2).combine(reducer2=ee.Reducer.mean(), sharedInputs=True)\
.combine(reducer2=ee.Reducer.variance(), sharedInputs= True)
histogram = img.select(band).reduceRegion(
reducer=reducers,
geometry=self.site.geometry(),
scale=self.img_scale,
bestEffort=True)
# Calculate threshold via function otsu (see before)
threshold = otsu(histogram.get(band+'_histogram'))
# get watermask
waterMask = img.select(band).lt(threshold).rename('water')
# waterMask = waterMask.updateMask(waterMask) #Remove all pixels equal to 0
return img.addBands(waterMask)
return wrap
def maskDSWE_Water(img):
nd_threshold = self.threshold_value.value+1
waterImage = img.select('dswe').rename('water')
water = waterImage.gt(0).And(waterImage.lt(nd_threshold)).copyProperties(img, ['system:time_start'])
return img.addBands(water)
def mask_Water(img):
waterMask = img.select('water').selfMask().rename('waterMask').copyProperties(img, ['system:time_start'])
return img.addBands(waterMask)
if self.imageType == 'Sentinel-1':
band = self.water_indices.value
self.water_images = self.clipped_images.map(add_S1_waterMask(band))#.select('water')
self.WaterMasks = self.water_images.map(mask_Water)
# self.visParams = {'min': 0,'max': 1, 'palette': color_palette}
self.Map.addLayer(self.WaterMasks.select('waterMask').max(), {'palette': color_palette}, 'Water')
elif self.imageType == 'Landsat-Collection 2':
if self.water_indices.value == 'DSWE':
dem = ee.Image('USGS/SRTMGL1_003')
self.dswe_images = DSWE_2(self.filtered_landsat, dem, self.site)
# Viz parameters: classes: 0, 1, 2, 3, 4, 9
self.dswe_viz = {'min':0, 'max': 9, 'palette': ['000000', '002ba1', '6287ec', '77b800', 'c1bdb6',
'000000', '000000', '000000', '000000', 'ffffff']}
self.water_images = self.dswe_images.map(maskDSWE_Water)
self.WaterMasks = self.water_images.map(mask_Water)
# Map.addLayer(dswe_images.max(), dswe_viz, 'DSWE')
else:
self.index_images = self.clipped_images.map(water_index)
self.water_images = self.index_images.map(water_thresholding)
self.WaterMasks = self.water_images.map(mask_Water)
self.Map.addLayer(self.WaterMasks.select('waterMask').max(), {'palette': color_palette}, 'Water')
else:
self.index_images = self.clipped_images.map(water_index)
self.water_images = self.index_images.map(water_thresholding)
self.WaterMasks = self.water_images.map(mask_Water)
self.Map.addLayer(self.WaterMasks.select('waterMask').max(), {'palette': color_palette}, 'Water')
self.water_Frequency_button.disabled = False
self.Depths_Button.disabled = False
self.elev_Methods.disabled = False
self.plot_button.disabled = False
except Exception as e:
print(e)
print('An error occurred during computation.')
def calc_area(self, img):
"""
Function to calculate area of water pixels
args:
Water mask image
returns:
Water image with calculated total area of water pixels
"""
global area_unit_symbol
unit = self.area_unit.value
divisor = 1
if unit =='Square Km':
divisor = 1e6
area_unit_symbol = 'Sq km'
elif unit =='Hectares':
divisor = 1e4
area_unit_symbol = 'Ha'
elif unit =='Square m':
divisor = 1
area_unit_symbol = 'Sq m'
else:
divisor = 4047
area_unit_symbol = 'acre'
pixel_area = img.select('waterMask').multiply(ee.Image.pixelArea()).divide(divisor)
img_area = pixel_area.reduceRegion(**{
'geometry': self.site.geometry(),
'reducer': ee.Reducer.sum(),
'scale': self.img_scale,
'maxPixels': 1e13
})
return img.set({'water_area': img_area})
def plot_areas(self, b):
"""
Function to plot a time series of calculated water area for each water image
and to cycle through
args:
None
returns:
None
"""
with self.feedback:
self.feedback.clear_output()
try:
global df
global save_water_data
save_water_data = 1
# Compute water areas
water_areas = self.WaterMasks.map(self.calc_area)
water_stats = water_areas.aggregate_array('water_area').getInfo()
self.dates = self.WaterMasks.aggregate_array('system:time_start')\
.map(lambda d: ee.Date(d).format('YYYY-MM-dd')).getInfo()
dates_lst = [datetime.strptime(i, '%Y-%m-%d') for i in self.dates]
y = [item.get('waterMask') for item in water_stats]
df = pd.DataFrame(list(zip(dates_lst,y)), columns=['Date','Area'])
self.fig.data = []
self.fig.add_trace(go.Scatter(x=df['Date'], y=df['Area'], name='Water Hydrograph',
mode='lines+markers', line=dict(dash = 'solid', color ='Blue', width = 0.5)))
self.fig.layout.title = '<b>Surface Water Area Hydrograph<b>'
self.fig.layout.titlefont = dict(family="Arial",size=24)
self.fig.layout.title.x = 0.5
self.fig.layout.title.y = 0.9
self.fig.layout.yaxis.title = 'Area ('+area_unit_symbol+')'
scatter = self.fig.data[0] # set figure data to scatter for click function
color_palette = self.index_color.value
max_Area_value = df['Area'].max()
min_Area_value = df['Area'].min()
avg_Area_value = df['Area'].mean()
self.lbl_Max_Area.value = str(round(max_Area_value, 3))
self.lbl_Min_Area.value = str(round(min_Area_value, 3))
self.lbl_Avg_Area.value = str(round(avg_Area_value, 3))
# Function to select and show images on clicking the graph
def update_point(trace, points, selector):
global wImage
global selected_sat
date = df['Date'].iloc[points.point_inds].values[0]
date = pd.to_datetime(str(date))
selected_image = self.WaterMasks.closest(date).first()
wImage = selected_image.select('waterMask')
self.Map.addLayer(selected_image, self.visParams, self.imageType)
if self.water_indices.value == 'DSWE':