-
Notifications
You must be signed in to change notification settings - Fork 6
/
app.py
614 lines (512 loc) · 28.8 KB
/
app.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
import streamlit as st
import ee
import geemap
import folium
from folium import WmsTileLayer
from streamlit_folium import folium_static
from datetime import datetime, timedelta
import json
st.set_page_config(
page_title="NDVI Viewer",
page_icon="https://cdn-icons-png.flaticon.com/512/2516/2516640.png",
layout="wide",
initial_sidebar_state="expanded",
menu_items={
'Get help': "https://github.com/IndigoWizard/NDVI-Viewer",
'Report a bug': "https://github.com/IndigoWizard/NDVI-Viewer/issues",
'About': "This app was developped by [IndigoWizard](https://github.com/IndigoWizard/NDVI-Viewer) for the purpose of environmental monitoring and geospatial analysis"
}
)
st.markdown(
"""
<style>
/* Header*/
.st-emotion-cache-1avcm0n{
height: 1rem;
}
/* Smooth scrolling*/
.main {
scroll-behavior: smooth;
}
/* main app body with less padding*/
.st-emotion-cache-z5fcl4 {
padding-block: 0;
}
/*Sidebar*/
.st-emotion-cache-10oheav {
padding: 0 1rem;
}
/*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 info box */
/*Upload button: dark theme*/
.st-emotion-cache-1erivf3 {
display: flex;
flex-direction: column;
align-items: inherit;
font-size: 14px;
}
.css-u8hs99.eqdbnj014 {
display: flex;
flex-direction: row;
margin-inline: 0;
}
/*Upload button: light theme*/
.st-emotion-cache-1gulkj5 {
display: flex;
flex-direction: column;
align-items: inherit;
font-size: 14px;
}
.st-emotion-cache-u8hs99 {
display: flex;
flex-direction: row;
margin-inline: 0;
}
/*Legend style*/
.ndvilegend {
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);
}
.ndvilegend: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;
}
.reclassifiedndvi {
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);
}
.reclassifiedndvi: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;
}
/*Form submit button: generate map*/
button.st-emotion-cache-19rxjzo:nth-child(1) {
width: 100%;
}
</style>
""", unsafe_allow_html=True)
# Initializing the Earth Engine library
# Use ee.Initialize() only on local machine! Comment back before deployement (Unusable on deployment > use geemap init+auth bellow)
#ee.Initialize()
# geemap auth + initialization for cloud deployment
@st.cache_data(persist=True)
def ee_authenticate(token_name="EARTHENGINE_TOKEN"):
geemap.ee_initialize(token_name=token_name)
# 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
# Define a global variable to store the centroid of the last uploaded geometry
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([27.98, 36.13])
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
# Main function to run the Streamlit app
def main():
# initiate gee
ee_authenticate(token_name="EARTHENGINE_TOKEN")
# sidebar
with st.sidebar:
st.title("NDVI Viewer App")
st.image("https://cdn-icons-png.flaticon.com/512/2516/2516640.png", width=90)
st.subheader("Navigation:")
st.markdown(
"""
- [NDVI Map](#ndvi-viewer)
- [Map Legend](#map-legend)
- [Process workflow](#process-workflow-aoi-date-range-and-classification)
- [Interpreting the Results](#interpreting-the-results)
- [Environmental Index](#using-an-environmental-index-ndvi)
- [Data](#data-sentinel-2-imagery-and-l2a-product)
- [Contribution](#contribute-to-the-app)
- [About](#about)
- [Credit](#credit)
""")
st.subheader("Contact:")
st.markdown("[![LinkedIn](https://static.licdn.com/sc/h/8s162nmbcnfkg7a0k8nq9wwqo)](https://linkedin.com/in/ahmed-islem-mokhtari) [![GitHub](https://github.com/githubassets/favicons/favicon-dark.png)](https://github.com/IndigoWizard) [![Medium](https://miro.medium.com/1*m-R_BkNf1Qjr1YbyOIJY2w.png)](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/NDVI-Viewer/)!")
with st.container():
st.title("NDVI Viewer")
st.markdown("**Monitor Vegetation Health by Viewing & Comparing NDVI Values Through Time and Location with Sentinel-2 Satellite Images on The Fly!**")
# columns for input - map
with st.form("input_form"):
c1, c2 = st.columns([3, 1])
#### User input section - START
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=85 , 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_ndvi_palette = ["#ffffe5", "#f7fcb9", "#78c679", "#41ab5d", "#238443", "#005a32"]
default_reclassified_ndvi_palette = ["#a50026","#ed5e3d","#f9f7ae","#f4ff78","#9ed569","#229b51","#006837"]
# a copy of default colors that can be reaffected
ndvi_palette = default_ndvi_palette.copy()
reclassified_ndvi_palette = default_reclassified_ndvi_palette.copy()
if accessibility == "Deuteranopia":
ndvi_palette = ["#fffaa1","#f4ef8e","#9a5d67","#573f73","#372851","#191135"]
reclassified_ndvi_palette = ["#95a600","#92ed3e","#affac5","#78ffb0","#69d6c6","#22459c","#000e69"]
elif accessibility == "Protanopia":
ndvi_palette = ["#a6f697","#7def75","#2dcebb","#1597ab","#0c677e","#002c47"]
reclassified_ndvi_palette = ["#95a600","#92ed3e","#affac5","#78ffb0","#69d6c6","#22459c","#000e69"]
elif accessibility == "Tritanopia":
ndvi_palette = ["#cdffd7","#a1fbb6","#6cb5c6","#3a77a5","#205080","#001752"]
reclassified_ndvi_palette = ["#ed4700","#ed8a00","#e1fabe","#99ff94","#87bede","#2e40cf","#0600bc"]
elif accessibility == "Achromatopsia":
ndvi_palette = ["#407de0", "#2763da", "#394388", "#272c66", "#16194f", "#010034"]
reclassified_ndvi_palette = ["#004f3d", "#338796", "#66a4f5", "#3683ff", "#3d50ca", "#421c7f", "#290058"]
with st.container():
## Time range input
with c1:
col1, col2 = st.columns(2)
# Creating a 2 days delay for the date_input placeholder to be sure there are satellite images in the dataset on app start
today = datetime.today()
delay = today - timedelta(days=2)
# Date input widgets
col1.warning("Initial NDVI Date 📅")
initial_date = col1.date_input("initial", value=delay, label_visibility="collapsed")
col2.success("Updated NDVI Date 📅")
updated_date = col2.date_input("updated", value=delay, label_visibility="collapsed")
# Setting up the time range variable for an image collection
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=12, control_scale=True)
else:
# Default location if no file is uploaded
m = folium.Map(location=[36.45, 10.85], tiles=None, zoom_start=4, control_scale=True)
### BASEMAPS - START
## Primary basemaps
# OSM
b0 = folium.TileLayer('Open Street Map', name="Open Street Map")
b0.add_to(m)
# CartoDB Dark Matter basemap
b1 = folium.TileLayer('cartodbdark_matter', name='Dark Basemap')
b1.add_to(m)
#### Satellite imagery Processing Section - START
## Defining and clipping image collections for both dates:
# initial Image collection
initial_collection = satCollection(cloud_pixel_percentage, str_initial_start_date, str_initial_end_date, geometry_aoi)
# updated Image collection
updated_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, ndvi... etc)
initial_sat_imagery = initial_collection.median()
updated_sat_imagery = updated_collection.median()
## TCI (True Color Imagery)
# Clipping the image to the area of interest "aoi"
initial_tci_image = initial_sat_imagery
updated_tci_image = updated_sat_imagery
# TCI image visual parameters
tci_params = {
'bands': ['B4', 'B3', 'B2'], #using Red, Green & Blue bands for TCI.
'min': 0,
'max': 1,
'gamma': 1
}
## Other imagery processing operations go here
# NDVI
def getNDVI(collection):
return collection.normalizedDifference(['B8', 'B4'])
# clipping to AOI
initial_ndvi = getNDVI(initial_sat_imagery)
updated_ndvi = getNDVI(updated_sat_imagery)
# NDVI visual parameters:
ndvi_params = {
'min': 0,
'max': 1,
'palette': ndvi_palette
}
# Masking NDVI over the water & show only land
def satImageMask(sat_image):
masked_image = sat_image.updateMask(sat_image.gte(0))
return masked_image
# Mask NDVI images
initial_ndvi = satImageMask(initial_ndvi)
updated_ndvi = satImageMask(updated_ndvi)
# ##### NDVI classification: 7 classes
def classify_ndvi(masked_image): # better use a masked image to avoid water bodies obstracting the result as possible
ndvi_classified = ee.Image(masked_image) \
.where(masked_image.gte(0).And(masked_image.lt(0.15)), 1) \
.where(masked_image.gte(0.15).And(masked_image.lt(0.25)), 2) \
.where(masked_image.gte(0.25).And(masked_image.lt(0.35)), 3) \
.where(masked_image.gte(0.35).And(masked_image.lt(0.45)), 4) \
.where(masked_image.gte(0.45).And(masked_image.lt(0.65)), 5) \
.where(masked_image.gte(0.65).And(masked_image.lt(0.75)), 6) \
.where(masked_image.gte(0.75), 7) \
return ndvi_classified
# Classify masked NDVI
initial_ndvi_classified = classify_ndvi(initial_ndvi)
updated_ndvi_classified = classify_ndvi(updated_ndvi)
# Classified NDVI visual parameters
ndvi_classified_params = {
'min': 1,
'max': 7,
'palette': reclassified_ndvi_palette
# each color corresponds to an NDVI class.
}
#### Satellite imagery Processing Section - END
#### Layers section - START
# Check if the initial and updated dates are the same
if initial_date == updated_date:
# Only display the layers based on the updated date without dates in their names
m.add_ee_layer(updated_tci_image, tci_params, 'Satellite Imagery')
m.add_ee_layer(updated_ndvi, ndvi_params, 'Raw NDVI')
m.add_ee_layer(updated_ndvi_classified, ndvi_classified_params, 'Reclassified NDVI')
else:
# Show both dates in the appropriate layers
# Satellite image
m.add_ee_layer(initial_tci_image, tci_params, f'Initial Satellite Imagery: {initial_date}')
m.add_ee_layer(updated_tci_image, tci_params, f'Updated Satellite Imagery: {updated_date}')
# NDVI
m.add_ee_layer(initial_ndvi, ndvi_params, f'Initial Raw NDVI: {initial_date}')
m.add_ee_layer(updated_ndvi, ndvi_params, f'Updated Raw NDVI: {updated_date}')
# Add layers to the second map (m.m2)
# Classified NDVI
m.add_ee_layer(initial_ndvi_classified, ndvi_classified_params, f'Initial Reclassified NDVI: {initial_date}')
m.add_ee_layer(updated_ndvi_classified, ndvi_classified_params, f'Updated Reclassified NDVI: {updated_date}')
#### 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 NDVI classes
ndvi_legend_html = """
<div class="ndvilegend">
<h5>Raw NDVI</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},{3},{4},{5});"></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(*ndvi_palette)
# Display the NDVI legend using st.markdown
st.markdown(ndvi_legend_html, unsafe_allow_html=True)
with col4:
# Create an HTML legend for NDVI classes
reclassified_ndvi_legend_html = """
<div class="reclassifiedndvi">
<h5>NDVI Classes</h5>
<ul style="list-style-type: none; padding: 0;">
<li style="margin: 0.2em 0px; padding: 0;"><span style="color: {0};">■</span> Absent Vegetation. (Water/Clouds/Built-up/Rocks/Sand Surfaces..)</li>
<li style="margin: 0.2em 0px; padding: 0;"><span style="color: {1};">■</span> Bare Soil.</li>
<li style="margin: 0.2em 0px; padding: 0;"><span style="color: {2};">■</span> Low Vegetation.</li>
<li style="margin: 0.2em 0px; padding: 0;"><span style="color: {3};">■</span> Light Vegetation.</li>
<li style="margin: 0.2em 0px; padding: 0;"><span style="color: {4};">■</span> Moderate Vegetation.</li>
<li style="margin: 0.2em 0px; padding: 0;"><span style="color: {5};">■</span> Strong Vegetation.</li>
<li style="margin: 0.2em 0px; padding: 0;"><span style="color: {6};">■</span> Dense Vegetation.</li>
</ul>
</div>
""".format(*reclassified_ndvi_palette)
# Display the Reclassified NDVI legend using st.markdown
st.markdown(reclassified_ndvi_legend_html, unsafe_allow_html=True)
#### Legend - END
#### Miscs Infos - START
st.subheader("Information")
## How It Works
st.write("#### Process workflow: AOI, Date Range, and Classification")
st.write("This app provides a simple interface to explore NDVI changes over time for a specified Area of Interest (AOI). Here's how it works:")
st.write("1. **Upload GeoJSON AOI:** Start by uploading a GeoJSON file that outlines your Area of Interest. This defines the region where NDVI analysis will be performed. You can create any polygon-shaped area of interest at [geojson.io](https://geojson.io).")
st.write("2. **Select Date Range:** Choose a date, this input triggers the app to gather images from a **7-days range** leading to that date. These images blend into a mosaic that highlights vegetation patterns while minimizing disruptions like clouds. ")
st.write("3. **Select Cloud Coverate Rate:** Choose a value for cloud coverage, this input triggers the app to gather images with relevant value of clouds covering the images. A higher value will gather more images but may be of poor quality, lower cloud coverage value gathers clearer images, but may have less images in the collection.")
st.write("4. **Image Collection and Processing:** Once the date range is established, the app collects satellite images spanning that period. These images are then clipped to your chosen Area of Interest (AOI) and undergo processing to derive raw NDVI values using wavelength calculations. This method ensures that the resulting NDVI map accurately reflects the vegetation status within your specific region of interest.")
st.write("5. **NDVI Classification:** The raw NDVI results are classified into distinct vegetation classes. This classification provides a simplified visualization of vegetation density, aiding in interpretation.")
st.write("6. **Map Visualization:** The results are displayed on an interactive map, allowing you to explore NDVI patterns and changes within your AOI.")
st.write("This app is designed to provide an accessible tool for both technical and non-technical users to explore and interpret vegetation health and density changes.")
st.write("Keep in mind that while the NDVI map is a valuable tool, its interpretation requires consideration of various factors. Enjoy exploring the world of vegetation health and density!")
# Results interpretation
st.write("#### Interpreting the Results")
st.write("When exploring the NDVI map, keep in mind:")
st.write("- Clouds, atmospheric conditions, and water bodies can affect the map's appearance.")
st.write("- Satellite sensors have limitations in distinguishing surface types, leading to color variations.")
st.write("- NDVI values vary with seasons, growth stages, 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 analysis.")
## NDVI/Environmental Index
st.write("#### Using an Environmental Index - NDVI:")
st.write("The [Normalized Difference Vegetation Index (NDVI)](https://eos.com/make-an-analysis/ndvi/) is an essential environmental index that provides insights into the health and density of vegetation. It is widely used in remote sensing and geospatial analysis to monitor changes in land cover, vegetation growth, and environmental conditions.")
st.write("NDVI is calculated using satellite imagery that captures both Near-Infrared **(NIR)** and Red **(R)** wavelengths. The formula is:")
st.latex(r'''
\text{NDVI} = \frac{\text{NIR} - \text{R}}{\text{NIR} + \text{R}}
''')
st.write("NDVI values range from **[-1** to **1]**, with higher values indicating denser and healthier vegetation. Lower values represent non-vegetated surfaces like water bodies, bare soil, or built-up areas.")
## Data
st.write("#### Data: Sentinel-2 Imagery and L2A Product")
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 NDVI calculations.")
#### Miscs Info - END
#### Contributiuon - START
st.header("Contribute to the App")
con1, con2 = st.columns(2)
con1.image("https://www.pixenli.com/image/SoL3iZMG")
con2.markdown("""
Contributions are welcome from the community to help improve this app! Whether you're interested in fixing bugs 🐞, implementing a new feature 🌟, or enhancing the user experience 🪄, your contributions are valuable.
The project is listed under **Hacktoberfest** lalbel for those of you [Hacktoberfest](https://hacktoberfest.com/) enthusiasts! Since the reward for contributing 4 PRs is getting a tree planted in your name through [TreeNation](https://tree-nation.com/), I see it fits the theme of this project.
""")
st.markdown("""
#### Ways to Contribute
- **Report Issues**: If you come across any bugs, issues, or unexpected behavior, please report them in the [GitHub Issue Tracker](https://github.com/IndigoWizard/NDVI-Viewer/issues).
- **Suggest Enhancements**: Have an idea to make the app better? Share your suggestions in the [GitHub Issue Tracker](https://github.com/IndigoWizard/NDVI-Viewer/issues).
- **Code Contributions**: If you're comfortable with coding, you can contribute by submitting pull requests against the `dev` branch of the [Project's GitHub repository](https://github.com/IndigoWizard/NDVI-Viewer/).
""")
#### Contributiuon - START
#### About App - START
st.subheader("About:")
st.markdown("This project was first developed by me ([IndigoWizard](https://github.com/IndigoWizard)) and [Emmarie-Ahtunan](https://github.com/Emmarie-Ahtunan) as a submission to the **Environemental Data Challenge** of [Global Hack Week: Data](https://ghw.mlh.io/) by [Major League Hacking](https://mlh.io/).<br> I continued developing the base project to make it a feature-complete app. Check the project's GitHub Repo here: [IndigoWizard/NDVI-Viewer](https://github.com/IndigoWizard/NDVI-Viewer)", unsafe_allow_html=True)
st.image("https://www.pixenli.com/image/Hn1xkB-6")
#### About App - END
#### Credit - START
st.subheader("Credit:")
st.markdown("""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, [geemap](https://github.com/gee-community/geemap), [Folium](https://github.com/python-visualization/folium). Agriculture icons created by <a href="https://www.flaticon.com/free-icons/agriculture" title="agriculture icons">dreamicons - Flaticon</a>""", unsafe_allow_html=True)
#### Credit - END
##### Custom Styling
st.markdown(
"""
<style>
/*Map iframe*/
iframe {
width: 100%;
}
.css-1o9kxky.e1f1d6gn0 {
border: 2px solid #ffffff4d;
border-radius: 4px;
padding: 1rem;
}
</style>
""", unsafe_allow_html=True)
# Run the app
if __name__ == "__main__":
main()