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app.py
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import streamlit as st
import ee
from ee import oauth
from google.oauth2 import service_account
import folium
from streamlit_folium import folium_static
from streamlit_elements import elements, mui
from streamlit_elements import nivo
from datetime import datetime, timedelta
import json
st.set_page_config(
page_title="Wildfire Burn Severity Analysis",
page_icon="https://cdn-icons-png.flaticon.com/512/7204/7204183.png",
layout="wide",
initial_sidebar_state="expanded",
menu_items={
'Get help': "https://github.com/IndigoWizard/wildfire-burn-severity",
'Report a bug': "https://github.com/IndigoWizard/wildfire-burn-severity/issues",
'About': "This app was developped by [IndigoWizard](https://github.com/IndigoWizard/wildfire-burn-severity) (original author) for the purpose of environmental monitoring and geospatial analysis. Give proper credit when forking/using the open source code/piece of code."
}
)
### CSS STYLING
st.markdown(
"""
<style>
/* Header*/
/* Dark theme version */
.st-emotion-cache-h4xjwg.ezrtsby2 {
height: 1rem;
background: none;
}
/* Light theme version */
.st-emotion-cache-12fmjuu.ezrtsby2 {
height: 1rem;
background: none;
}
/* Smooth scrolling*/
.main {
scroll-behavior: smooth;
}
/* main app body with less padding*/
.st-emotion-cache-1jicfl2.ea3mdgi5 {
padding-block: 0;
position: relative;
}
/* ******* Sidebar ******* */
/* Main container */
.st-emotion-cache-qeahdt.eczjsme9 {
padding: 0 1rem;
}
.st-emotion-cache-1mi2ry5.eczjsme6 {
height: 0;
}
.st-emotion-cache-12skds7 {
height: 0;
}
.st-emotion-cache-1gv3huu.eczjsme16 {
background-color: rgb(240, 242, 246);
}
/* Logo */
.st-emotion-cache-1kyxreq.e115fcil2 {
justify-content: center;
}
/* Sidebar : inside container */
.css-ge7e53 {
width: fit-content;
}
/*Sidebar : image*/
.css-1kyxreq {
display: block !important;
}
/*Sidebar : Navigation list*/
div.element-container:nth-child(4) > div:nth-child(1) > div:nth-child(1) > ul:nth-child(1) {
margin: 0;
padding: 0;
list-style: none;
}
div.element-container:nth-child(4) > div:nth-child(1) > div:nth-child(1) > ul:nth-child(1) > li {
padding: 0;
margin: 0;
padding: 0;
font-weight: 600;
}
div.element-container:nth-child(4) > div:nth-child(1) > div:nth-child(1) > ul:nth-child(1) > li > a {
text-decoration: none;
transition: 0.2s ease-in-out;
padding-inline: 10px;
}
div.element-container:nth-child(4) > div:nth-child(1) > div:nth-child(1) > ul:nth-child(1) > li > a:hover {
color: rgb(46, 206, 255);
transition: 0.2s ease-in-out;
background: #131720;
border-radius: 4px;
}
/* Sidebar: socials*/
div.css-rklnmr:nth-child(6) > div:nth-child(1) > div:nth-child(1) > p {
display: flex;
flex-direction: row;
gap: 1rem;
}
/* ******* Upload Section ******* */
/* ***** Upload info box */
/* Light theme version */
.st-emotion-cache-1gulkj5 {
background-color: rgb(215, 210, 225);
color: rgb(40, 40, 55);
}
/* ***** Upload SVG: Mobile view */
@media (max-width: 576px) {
/* Dark theme version*/
.st-emotion-cache-wn8ljn.e1b2p2ww13 {
display: unset;
}
/* Light theme version*/
.st-emotion-cache-nwtri.e1b2p2ww13 {
display: unset;
}
}
/* ***** Upload button: dark theme*/
.st-emotion-cache-1erivf3.e1b2p2ww15 {
display: flex;
flex-direction: column;
align-items: inherit;
font-size: 14px;
}
.st-emotion-cache-19rxjzo.ef3psqc12 {
display: flex;
flex-direction: row;
margin-inline: 0;
}
/* ***** Upload button: light theme*/
.st-emotion-cache-1gulkj5.e1b2p2ww15 {
display: flex;
flex-direction: column;
align-items: inherit;
font-size: 14px;
}
.st-emotion-cache-7ym5gk.ef3psqc12 {
display: flex;
flex-direction: row;
margin-inline: 0;
background: rgba(0, 3, 172, 0.15);
}
/* ******* Form Submit ******* */
/* ***** Generate Map */
/* Dark theme version */
.st-emotion-cache-19rxjzo.ef3psqc7 {
width: 100%;
}
/* Light Theme Version */
.st-emotion-cache-7ym5gk.ef3psqc7 {
width: 100%;
background: rgba(0, 3, 172, 0.25);
}
/* Buttons */
/* Light theme verison; hober effect */
.st-emotion-cache-7ym5gk:hover {
border-color: rgb(255, 0, 110);
color: rgb(255, 0, 110);
}
/* ******* Legend style ******* */
.ndwilegend {
transition: 0.2s ease-in-out;
border-radius: 5px;
box-shadow: 0 0 5px rgba(0, 0, 0, 0.2);
background: rgba(0, 0, 0, 0.05);
}
.ndwilegend:hover {
transition: 0.3s ease-in-out;
box-shadow: 0 0 5px rgba(0, 0, 0, 0.8);
background: rgba(0, 0, 0, 0.12);
cursor: pointer;
}
.reclassifieddNBR {
transition: 0.2s ease-in-out;
border-radius: 5px;
box-shadow: 0 0 5px rgba(0, 0, 0, 0.2);
background: rgba(0, 0, 0, 0.05);
}
.reclassifieddNBR:hover {
transition: 0.3s ease-in-out;
box-shadow: 0 0 5px rgba(0, 0, 0, 0.8);
background: rgba(0, 0, 0, 0.12);
cursor: pointer;
}
</style>
""", unsafe_allow_html=True)
# Initializing the Earth Engine library
# GEE Servuce Account Auth+init for cloud deployment
@st.cache_data(persist=True)
def ee_authenticate():
# Check for json key in Streamlit Secrets
if "json_key" in st.secrets:
json_creds = st.secrets["json_key"]
service_account_info = json.loads(json_creds)
# Catching eventual email related error
if "client_email" not in service_account_info:
raise ValueError("Service account email address missing in json key")
creds = service_account.Credentials.from_service_account_info(service_account_info, scopes=oauth.SCOPES)
# Initializing gee for each run of the app
ee.Initialize(creds)
else:
# Fallback to normal init method if no json key/st secrets available. (local machine)
ee.Initialize()
# Earth Engine drawing method setup
def add_ee_layer(self, ee_image_object, vis_params, name):
map_id_dict = ee.Image(ee_image_object).getMapId(vis_params)
layer = folium.raster_layers.TileLayer(
tiles=map_id_dict['tile_fetcher'].url_format,
attr='Map Data © <a href="https://earthengine.google.com/">Google Earth Engine</a>',
name=name,
overlay=True,
control=True
)
layer.add_to(self)
return layer
# Configuring Earth Engine display rendering method in Folium
folium.Map.add_ee_layer = add_ee_layer
# Defining a function to create and filter a GEE image collection for results
def satCollection(cloudRate, initialDate, updatedDate, aoi):
collection = ee.ImageCollection('COPERNICUS/S2_SR') \
.filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE", cloudRate)) \
.filterDate(initialDate, updatedDate) \
.filterBounds(aoi)
# Defining a function to clip the colleciton to the area of interst
def clipCollection(image):
return image.clip(aoi).divide(10000)
# clipping the collection
collection = collection.map(clipCollection)
return collection
# Upload function
last_uploaded_centroid = None
def upload_files_proc(upload_files):
# A global variable to track the latest geojson uploaded
global last_uploaded_centroid
# Setting up a variable that takes all polygons/geometries within the same/different geojson
geometry_aoi_list = []
for upload_file in upload_files:
bytes_data = upload_file.read()
geojson_data = json.loads(bytes_data)
if 'features' in geojson_data and isinstance(geojson_data['features'], list):
# Handle GeoJSON files with a 'features' list
features = geojson_data['features']
elif 'geometries' in geojson_data and isinstance(geojson_data['geometries'], list):
# Handle GeoJSON files with a 'geometries' list
features = [{'geometry': geo} for geo in geojson_data['geometries']]
else:
# handling cases of unexpected file format or missing 'features' or 'geometries'
continue
for feature in features:
if 'geometry' in feature and 'coordinates' in feature['geometry']:
coordinates = feature['geometry']['coordinates']
geometry = ee.Geometry.Polygon(coordinates) if feature['geometry']['type'] == 'Polygon' else ee.Geometry.MultiPolygon(coordinates)
geometry_aoi_list.append(geometry)
# Update the last uploaded centroid
last_uploaded_centroid = geometry.centroid(maxError=1).getInfo()['coordinates']
if geometry_aoi_list:
geometry_aoi = ee.Geometry.MultiPolygon(geometry_aoi_list)
else:
geometry_aoi = ee.Geometry.Point([16.25, 36.65])
return geometry_aoi
# Time input processing function
def date_input_proc(input_date, time_range):
end_date = input_date
start_date = input_date - timedelta(days=time_range)
str_start_date = start_date.strftime('%Y-%m-%d')
str_end_date = end_date.strftime('%Y-%m-%d')
return str_start_date, str_end_date
# Raster Area calculation function
def calculate_class_area(classified_image, geometry_aoi, class_value):
class_pixel_area = classified_image.eq(class_value).multiply(ee.Image.pixelArea())
class_area = class_pixel_area.reduceRegion(
reducer=ee.Reducer.sum(),
geometry=geometry_aoi,
scale=10,
maxPixels=1e12
)
area_value = class_area.getInfo()
return area_value.get(list(area_value.keys())[0], 0) # Get the value dynamically
# Geojson Area calculation function
def geojson_area(aoi):
# geojson area: (geometry area)
aoi_area_sqm = aoi.area()
# Convert the area to square kilometers
aoi_area_info = aoi_area_sqm.getInfo()/1e6
aoi_area_rounded = round(aoi_area_info, 4)
return aoi_area_rounded
# Main function to run the Streamlit app
def main():
# initialize gee
ee_authenticate()
# sidebar
with st.sidebar:
st.logo(image="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAAC0lEQVQIW2NgAAIAAAUAAR4f7BQAAAAASUVORK5CYII=", link=None, icon_image="https://cdn-icons-png.flaticon.com/512/7204/7204183.png")
st.image("https://cdn-icons-png.flaticon.com/512/7204/7204183.png", width=90)
st.markdown("#### Wildfire Burn Severity Analysis")
st.subheader("Navigation:")
st.markdown(
"""
- [Wildfire Map](#wildfire-burn-severity-analysis)
- [Map Legend](#map-legend)
- [Analysis Report](#analysis-report)
- [Interpreting the Results](#interpreting-the-results)
- [Environmental Index](#usage-the-environmental-index-nbr-dnbr)
- [Data](#data)
- [Credit](#credit)
""")
st.subheader("Contact:")
st.markdown("[](https://linkedin.com/in/ahmed-islem-mokhtari) [](https://github.com/IndigoWizard) [](https://medium.com/@Indigo.Wizard/mt-chenoua-forest-fires-analysis-with-remote-sensing-614681f468e9)")
st.caption("ʕ •ᴥ•ʔ Star⭐the [project on GitHub](https://github.com/IndigoWizard/wildfire-burn-severity/)!")
with st.container():
st.title("Wildfire Burn Severity Analysis")
st.markdown("**Evaluate Wildfire Burn Severity through NBR Analysis: Assess the Impact of Wildfires Through Delta NBR Index Values Using Sentinel-2 Satellite Images!**")
#### User input section - START
# columns for input - map
with st.form("input_form"):
c1, c2 = st.columns([3, 1])
with st.container():
with c2:
## Cloud coverage input
st.info("Cloud Coverage 🌥️")
cloud_pixel_percentage = st.slider(label="cloud pixel rate", min_value=5, max_value=100, step=5, value=75 , label_visibility="collapsed")
## File upload
# User input GeoJSON file
st.info("Upload Area Of Interest file:")
upload_files = st.file_uploader("Crete a GeoJSON file at: [geojson.io](https://geojson.io/)", accept_multiple_files=True)
# calling upload files function
geometry_aoi = upload_files_proc(upload_files)
## Accessibility: Color palette input
st.info("Custom Color Palettes")
accessibility = st.selectbox("Accessibility: Colorblind-friendly Palettes", ["Normal", "Deuteranopia", "Protanopia", "Tritanopia", "Achromatopsia"])
# Define default color palettes: used in map layers & map legend
default_dnbr_palette = ["#ffffe5", "#f7fcb9", "#78c679", "#41ab5d", "#238443", "#005a32"]
default_dNBR_classified_palette = ['#1c742c', '#2aae29', '#a1d574', '#f8ebb0', '#f7a769', '#e86c4e', '#902cd6']
default_ndwi_palette = ["#caf0f8", "#00b4d8", "#023e8a"]
# a copy of default colors that can be reaffected
ndwi_palette = default_ndwi_palette.copy()
dnbr_palette = default_dnbr_palette.copy()
dNBR_classified_palette = default_dNBR_classified_palette.copy()
if accessibility == "Deuteranopia":
dnbr_palette = ["#fffaa1","#f4ef8e","#9a5d67","#573f73","#372851","#191135"]
dNBR_classified_palette = ["#95a600","#92ed3e","#affac5","#78ffb0","#69d6c6","#22459c","#000e69"]
elif accessibility == "Protanopia":
dnbr_palette = ["#a6f697","#7def75","#2dcebb","#1597ab","#0c677e","#002c47"]
dNBR_classified_palette = ["#95a600","#92ed3e","#affac5","#78ffb0","#69d6c6","#22459c","#000e69"]
elif accessibility == "Tritanopia":
dnbr_palette = ["#cdffd7","#a1fbb6","#6cb5c6","#3a77a5","#205080","#001752"]
dNBR_classified_palette = ["#ed4700","#ed8a00","#e1fabe","#99ff94","#87bede","#2e40cf","#0600bc"]
elif accessibility == "Achromatopsia":
dnbr_palette = ["#407de0", "#2763da", "#394388", "#272c66", "#16194f", "#010034"]
dNBR_classified_palette = ["#004f3d", "#338796", "#66a4f5", "#3683ff", "#3d50ca", "#421c7f", "#290058"]
with st.container():
## Time range input
with c1:
col1, col2 = st.columns(2)
col1.warning("Pre-Fire NBR Date 📅")
initial_date = col1.date_input("initial", datetime(2023, 7, 12), label_visibility="collapsed")
col2.success("Post-Fire NBR Date 📅")
updated_date = col2.date_input("updated", datetime(2023, 7, 27), label_visibility="collapsed")
time_range = 7
# Process initial date
str_initial_start_date, str_initial_end_date = date_input_proc(initial_date, time_range)
# Process updated date
str_updated_start_date, str_updated_end_date = date_input_proc(updated_date, time_range)
#### User input section - END
#### Map section - START
global last_uploaded_centroid
# Create the initial map
if last_uploaded_centroid is not None:
latitude = last_uploaded_centroid[1]
longitude = last_uploaded_centroid[0]
m = folium.Map(location=[latitude, longitude], tiles=None, zoom_start=11, control_scale=True)
else:
latitude=36.60
longitude=16.00
# Default location if no file is uploaded
m = folium.Map(location=[36.60, 16.00], tiles=None, zoom_start=5, control_scale=True)
## Primary basemap
# OSM
b0 = folium.TileLayer('OpenStreetMap', name="Open Street Map", attr="OSM")
b0.add_to(m)
b1 = folium.TileLayer('cartodbdark_matter', name='Dark Basemap', attr='CartoDB')
b1.add_to(m)
#### Satellite imagery Processing Section - START
## Defining and clipping image collections for both dates:
# Pre-fire
pre_fire_collection = satCollection(cloud_pixel_percentage, str_initial_start_date, str_initial_end_date, geometry_aoi)
# Post-fire
post_fire_collection = satCollection(cloud_pixel_percentage, str_updated_start_date, str_updated_end_date, geometry_aoi)
# setting a sat_imagery variable that could be used for various processes later on (tci, NBR... etc)
pre_fire = pre_fire_collection.median()
post_fire = post_fire_collection.median()
#################### Remote Sensing Index ####################
# Satellite image
pre_fire_satImg = pre_fire
post_fire_satImg = post_fire
# Sat image visual parameters
satImg_params = {
'bands': ['B12', 'B11', 'B4'],
'min': 0,
'max': 1,
'gamma': 1.1
}
# NDWI (Normalized Difference Water Index)
def get_NDWI(image):
return image.normalizedDifference(['B3', 'B11'])
pre_fire_ndwi = get_NDWI(pre_fire)
post_fire_ndwi = get_NDWI(post_fire)
ndwi_params = {
'min': -1,
'max': 0,
'palette': ndwi_palette
}
# NBR (Normalized Burn Ratio)
def get_NBR(image):
return image.normalizedDifference(['B8', 'B12'])
# claculating NBR for pre/post fire
pre_fire_NBR = get_NBR(pre_fire_satImg)
post_fire_NBR = get_NBR(post_fire_satImg)
# Delta NBR (dNBR)
dNBR = pre_fire_NBR.subtract(post_fire_NBR)
dNBR_params = {
'min': -0.5,
'max': 1.3,
'palette': dnbr_palette
}
img_classifier = dNBR
dNBR_classified = ee.Image(img_classifier) \
.where(img_classifier.gte(-0.5).And(img_classifier.lt(-0.251)), 1) \
.where(img_classifier.gte(-0.250).And(img_classifier.lt(-0.101)), 2) \
.where(img_classifier.gte(-0.100).And(img_classifier.lt(0.99)), 3) \
.where(img_classifier.gte(0.100).And(img_classifier.lt(0.269)), 4) \
.where(img_classifier.gte(0.270).And(img_classifier.lt(0.439)), 5) \
.where(img_classifier.gte(0.440).And(img_classifier.lt(0.659)), 6) \
.where(img_classifier.gte(0.660).And(img_classifier.lte(1.300)), 7) \
# Classified dNBR visual parameters
dNBR_classified_params = {
'min': 1,
'max': 7,
'palette': dNBR_classified_palette
}
## Image masking
# making the NDWI show only water part on NDWI layer
masked_pre_fire_ndwi = pre_fire_ndwi.updateMask(pre_fire_ndwi.gt(-0.12))
# post_fire_ndwi = post_fire_ndwi.updateMask(post_fire_ndwi.gt(-0.12))
# The following masks are not depandant/tied to the masked_pre_fire_ndwi variable/layer
# Creating a binary mask based on original NDWI: water = black = 0 | land = white = 1
binaryMask = pre_fire_ndwi.lt(-0.1)
# Creating a water mask based on NDWI binarmy mask using the land area (1)
waterMask = binaryMask.selfMask()
## Clipping raster images to the water mask
# masked_dNBR = dNBR.updateMask(waterMask)
masked_dNBR_classified = dNBR_classified.updateMask(waterMask)
### Burn scar area - vector
# Define arbitrary thresholds on the classified dNBR image.
dNBR_classified = dNBR_classified.gte(4)
dNBR_classified = dNBR_classified.updateMask(dNBR_classified.neq(0))
# Convert the zones of the thresholded burn areas to vectors.
vectors = dNBR_classified.addBands(dNBR_classified).reduceToVectors(
**{
'geometry': geometry_aoi,
'crs': dNBR_classified.projection(),
'scale': 10,
'geometryType': 'polygon',
'eightConnected': False,
'labelProperty': 'zone',
'reducer': ee.Reducer.mean(),
'bestEffort': True
})
# Burn scar based on converted rasters to vectors> Is displayed as its own layer
burn_scar = ee.Image(0).updateMask(0).paint(vectors, '000000', 2)
#### Satellite imagery Processing Section - END
### Layers section - START
# Check if the initial and updated dates are the same
if initial_date == updated_date:
m.add_ee_layer(post_fire_satImg, satImg_params, 'Satellite Imagery')
else:
m.add_ee_layer(pre_fire_satImg, satImg_params, f'Pre-Fire Satellite Imagery: {initial_date}')
m.add_ee_layer(post_fire_satImg, satImg_params, f'Post-Fire Satellite Imagery: {updated_date}')
# m.add_ee_layer(dNBR_classified, dNBR_classified_params, 'dNBR Classes')
m.add_ee_layer(masked_pre_fire_ndwi, ndwi_params, f'NDWI: {initial_date}')
# m.add_ee_layer(updateMask, dNBR_params, 'NBR "_masked')
# m.add_ee_layer(binaryMask, {}, 'binaryMask')
# m.add_ee_layer(waterMask, {}, 'SelfMak')
m.add_ee_layer(masked_dNBR_classified, dNBR_classified_params, 'Reclassified dNBR')
m.add_ee_layer(burn_scar, {'palette': '#87043b'}, 'Burn Scar')
#### Layers section - END
#### Map result display - START
# Folium Map Layer Control: we can see and interact with map layers
folium.LayerControl(collapsed=True).add_to(m)
# Display the map
submitted = c2.form_submit_button("Generate map")
if submitted:
with c1:
folium_static(m)
else:
with c1:
folium_static(m)
#### Map result display - END
#### Legend - START
with st.container():
st.subheader("Map Legend:")
col3, col4, col5 = st.columns([1,2,1])
with col3:
# Create an HTML legend for NDWI classes
ndwi_legend_html = """
<div class="ndwilegend">
<h5>NDWI</h5>
<div style="display: flex; flex-direction: row; align-items: flex-start; gap: 1rem; width: 100%;">
<div style="width: 30px; height: 200px; background: linear-gradient({0},{1},{2}); border-radius: 2px;"></div>
<div style="display: flex; flex-direction: column; justify-content: space-between; height: 200px;">
<span>-1</span>
<span style="align-self: flex-end;">1</span>
</div>
</div>
</div>
""".format(*ndwi_palette)
# Display the NDWI legend using st.markdown
st.markdown(ndwi_legend_html, unsafe_allow_html=True)
with col4:
# Create an HTML legend for dNBR classes
reclassified_dNBR_legend_html = """
<div class="reclassifieddNBR">
<h5>Reclassified Delta NBR</h5>
<ul style="list-style-type: none; padding: 0;">
<li style="margin: 0.2em 0px; padding: 0;"><span style="background-color: {0}; opacity: 0.75; display: inline-block; width: 15px; height: 15px; border-radius: 50%; margin-right: 5px;"></span> Enhanced Regrowth (High).</li>
<li style="margin: 0.2em 0px; padding: 0;"><span style="background-color: {1}; opacity: 0.75; display: inline-block; width: 15px; height: 15px; border-radius: 50%; margin-right: 5px;"></span> Enhanced Regrowth (Low).</li>
<li style="margin: 0.2em 0px; padding: 0;"><span style="background-color: {2}; opacity: 0.75; display: inline-block; width: 15px; height: 15px; border-radius: 50%; margin-right: 5px;"></span> Unburned.</li>
<li style="margin: 0.2em 0px; padding: 0;"><span style="background-color: {3}; opacity: 0.75; display: inline-block; width: 15px; height: 15px; border-radius: 50%; margin-right: 5px;"></span> Low Severity Burns.</li>
<li style="margin: 0.2em 0px; padding: 0;"><span style="background-color: {4}; opacity: 0.75; display: inline-block; width: 15px; height: 15px; border-radius: 50%; margin-right: 5px;"></span> Moderate-Low Severity Burns.</li>
<li style="margin: 0.2em 0px; padding: 0;"><span style="background-color: {5}; opacity: 0.75; display: inline-block; width: 15px; height: 15px; border-radius: 50%; margin-right: 5px;"></span> Moderate-High Severity Burns.</li>
<li style="margin: 0.2em 0px; padding: 0;"><span style="background-color: {6}; opacity: 0.75; display: inline-block; width: 15px; height: 15px; border-radius: 50%; margin-right: 5px;"></span> High Severity Burns.</li>
</ul>
</div>
""".format(*dNBR_classified_palette)
# Display the Reclassified dNBR legend using st.markdown
st.markdown(reclassified_dNBR_legend_html, unsafe_allow_html=True)
#### Legend - END
#### Area Calculation - START
st.write("#### Analysis Report")
with st.form("report_form"):
# geojson area: (geometry area)
geometry_area = geojson_area(geometry_aoi)
# Calculate and display the areas of each dNBR class
dNBR_class_areas = []
for i in range(1, 8):
area = calculate_class_area(masked_dNBR_classified, geometry_aoi, i)
dNBR_class_areas.append(area / 1e6) # Convert to square kilometers
class_names = [ # dNBR class names
"Enhanced Regrowth (High)",
"Enhanced Regrowth (Low)",
"Unburned",
"Low Severity Burns",
"Moderate-Low Severity Burns",
"Moderate-High Severity Burns",
"High Severity Burns",
]
# Report submit button
report_form = st.form_submit_button("Generate report", type="primary")
if report_form:
st.write("#### Wildfire Burn Severity Analysis Report:")
# Stats layout
col1, col2 = st.columns([1,1])
col3, col4 = st.columns([1.5,2])
# setting up stats to print
centroid_info = f"**ROI Location:** [:blue[{round(latitude, 4)}], :blue[{round(longitude, 4)}]]"
area_of_interest = f"**Surface Area of Region of Interest: ~:blue[{geometry_area}] (Km²)**"
initial_date_range = f"**Pre-Fire date range:** :blue-background[{str_initial_start_date}], :blue-background[{str_initial_end_date}]"
updated_date_range = f"**Post-Fire date range:** :blue-background[{str_updated_start_date}], :blue-background[{str_updated_end_date}]"
col1.success(centroid_info) # location
col1.success(area_of_interest) # size of aoi
col2.success(initial_date_range) # pre-fire date range
col2.success(updated_date_range) # post-fire date range
# print area of individual dNBR classes
for i, area in enumerate(dNBR_class_areas, start=1):
class_sq = f"**{class_names[i-1]}: ~** :green[{round(area, 4)}] **(Km²)**"
col3.info(class_sq)
# Display Interactive Pie Chart
with col4:
# Display stat visuals
DATA_PIE = [
{ "id": class_names[i-1], "label": class_names[i-1], "value": round(area, 4), "color": dNBR_classified_palette[i-1] }
for i, area in enumerate(dNBR_class_areas, start=1)
]
# Render the nivo.Pie component with the defined theme
with elements("nivo_pie_chart"):
with mui.Box(sx={"height": 500}):
nivo.Pie(
data=DATA_PIE,
margin={"top": 20, "right": 100, "bottom": 150, "left": 100},
innerRadius=0.5,
padAngle=0.7,
cornerRadius=3,
activeOuterRadiusOffset=8,
borderWidth=1,
borderColor={"from": "color", "modifiers": [["darker", 0.8]]},
arcLinkLabelsSkipAngle=2,
arcLinkLabelsTextColor={"from": "color"},
arcLinkLabelsColor={"from": "color"},
colors={"datum": 'data.color'},
arcLinkLabel="value",
arcLinkLabelsThickness=2,
arcLabelsSkipAngle=10,
arcLinkLabelsDiagonalLength=10,
arcLinkLabelsStraightLength=10,
arcLinkLabelsTextOffset=4,
arcLabelsTextColor={"from": "color", "modifiers": [["darker", 4]]},
defs = [
{
"id": "EnhancedRegrowthHigh",
"type": "patternLines",
# "color": "#1c742cbf",
"color": f"{dNBR_classified_palette[0]}",
"background": f"{dNBR_classified_palette[0]}bf",
"rotation": 105,
"lineWidth": 3,
"spacing": 10,
},
{
"id": "EnhancedRegrowthLow",
"type": "patternLines",
# "color": "#2aae29bf",
"color": f"{dNBR_classified_palette[1]}",
"background": f"{dNBR_classified_palette[1]}bf",
"rotation": -15,
"lineWidth": 4,
"spacing": 9,
},
{
"id": "Unburned",
"type": "patternSquares",
# "color": "#a1d574bf",
"color": f"{dNBR_classified_palette[2]}",
"background": f"{dNBR_classified_palette[2]}bf",
"size": 4,
"padding": 1.5,
"stagger": True,
},
{
"id": "LowSeverityBurns",
"type": "patternSquares",
# "color": "#f8ebb0bf",
"color": f"{dNBR_classified_palette[3]}",
"background": f"{dNBR_classified_palette[3]}bf",
"size": 5,
"padding": 3,
"stagger": True,
},
{
"id": "ModerateLowSeverityBurns",
"type": "patternDots",
# "color": "#f7a769bf",
"color": f"{dNBR_classified_palette[4]}",
"background": f"{dNBR_classified_palette[4]}bf",
"size": 4.5,
"padding": 4.5,
"stagger": True,
},
{
"id": "ModerateHighSeverityBurns",
"type": "patternDots",
# "color": "#e86c4ebf",
"color": f"{dNBR_classified_palette[5]}",
"background": f"{dNBR_classified_palette[5]}bf",
"size": 4,
"padding": 3,
"stagger": True,
},
{
"id": "HighSeverityBurns",
"type": "patternDots",
# "color": "#902cd6bf",
"color": f"{dNBR_classified_palette[6]}",
"background": f"{dNBR_classified_palette[6]}bf",
"size": 3,
"padding": 2,
"stagger": True,
},
],
fill=[
{"match": {"id": "Enhanced Regrowth (High)"}, "id": "EnhancedRegrowthHigh"},
{"match": {"id": "Enhanced Regrowth (Low)"}, "id": "EnhancedRegrowthLow"},
{"match": {"id": "Unburned"}, "id": "Unburned"},
{"match": {"id": "Low Severity Burns"}, "id": "LowSeverityBurns"},
{"match": {"id": "Moderate-Low Severity Burns"}, "id": "ModerateLowSeverityBurns"},
{"match": {"id": "Moderate-High Severity Burns"}, "id": "ModerateHighSeverityBurns"},
{"match": {"id": "High Severity Burns"}, "id": "HighSeverityBurns"},
],
theme={
"tooltip": {
"container": { # container within the tooltip
"background": "white", # background of the tooltip inside container
"fontSize": 14,
"font-family": "sans-serif",
"padding": 2,
"border-radius": 4
},
"basic": { # the box within the container within the tooltip
"whiteSpace": "pre",
"display": "flex",
"flex-direction": "row",
"alignItems": "center",
"justify-content": "space-around",
"background": "#0e1117",
"margin": 1,
"padding": 5,
"width": "fit-content",
"height": "fit-content",
"color": "white",
},
}
}
)
#### Area Calculation - END
##### Miscs Infos - START
with st.container():
st.divider()
# Results interpretation
st.write("#### Interpreting the Results")
st.write("This app is designed to provide an accessible tool for both technical and non-technical users to explore and interpret burn severity and land surface changes.")
st.write("The burn severity map is a valuable tool, its interpretation requires consideration of various factors. When exploring the dNBR map, keep in mind:")
st.write("- Clouds, atmospheric conditions, and water bodies can affect the map's appearance and so the surface area.")
st.write("- Satellite sensors have limitations in distinguishing surface types, leading to color variations.")
st.write("- NBR/dNBR values may subtley vary with type of vegetation and land cover changes.")
st.write("- The map provides visual insights rather than precise representations.")
st.write("Understanding these factors will help you interpret the results more effectively. This application aims to provide you with an informative visual aid for vegetation burn severity analysis.")
## NBR/Environmental Index
st.write("#### Usage the Environmental Index: NBR / dNBR")
st.write("The [Normalized Burn Ratio (NBR)](https://www.earthdatascience.org/courses/earth-analytics/multispectral-remote-sensing-modis/normalized-burn-index-dNBR/) is used to emphasize charred areas after a fire. The NBR vegetation index equation takes into account observations at both NIR and SWIR wavelengths: healthy vegetation has a high reflectance in the NIR spectrum, whereas recently burned sections of vegetation reflect strongly in the SWIR spectrum.")
st.write("NBR is calculated using satellite imagery that captures both Near-Infrared **(NIR)** and Short-Wave Infrared **(SWIR)** wavelengths. The formula is:")
st.latex(r'''
\text{NBR} = \frac{\text{NIR} - \text{SWIR}}{\text{NIR} + \text{SWIR}}
''')
st.write("dNBR (Difference NBR) is calculated by the difference of Pre-Fire NBR and Post-Fire-NBR values. The formula is:")
st.latex(r'''
\text{dNBR} = \text{NBR}_{pre-fire} - \text{NBR}_{post-fire}
''')
st.write("NBR values range from **[-1** to **1]**, with higher values indicating higher severity burns. Lower values represent unburned vegetated surfaces or enhanced regrowth.")
## Data
st.write("#### Data")
st.write("This app utilizes **Sentinel-2 Level-2A atmospherically corrected Surface Reflectance images**. The [Sentinel-2 satellite constellation](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/applications) consists of twin satellites (Sentinel-2A and Sentinel-2B) that capture high-resolution multispectral imagery of the Earth's surface.")
st.write("The [Level-2A](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/product-types/level-2a) products have undergone atmospheric correction, enhancing the accuracy of surface reflectance values. These images are suitable for various land cover and vegetation analyses, including NBR calculations.")
## Credits
st.write("##### Credit:")
st.caption("""The app was developped by [IndigoWizard](https://github.com/IndigoWizard) using; [Streamlit](https://streamlit.io/), [Google Earth Engine](https://github.com/google/earthengine-api) Python API and [Folium](https://github.com/python-visualization/folium). Wildfire icons created by <a href="https://www.flaticon.com/free-icons/wildfire" title="wildfire icons">Pomicon - Flaticon</a>""", unsafe_allow_html=True)
#### Miscs Info - END
##### Custom Styling
st.markdown(
"""
<style>
/*Map iframe*/
iframe {
width: 100%;
}
</style>
""", unsafe_allow_html=True)
# Run the app
if __name__ == "__main__":
main()