Spaces:
Sleeping
Sleeping
File size: 158,749 Bytes
deddcd3 4ee8332 d29ffc2 deddcd3 d29ffc2 deddcd3 20ee84e deddcd3 c299472 deddcd3 a4fd617 deddcd3 932bd84 deddcd3 97a1254 deddcd3 74f0508 deddcd3 7fe8a52 deddcd3 16cd080 deddcd3 b6276fd f819288 b6276fd deddcd3 8985c20 deddcd3 8985c20 deddcd3 8985c20 deddcd3 5fdf120 deddcd3 fb51a82 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 |
import os
import streamlit as st
import os
os.system('pip install geemap')
import geemap.foliumap as geemap
import ee
os.system('pip install geopandas')
import geopandas as gpd
import tempfile
import uuid
os.system('pip install fiona')
import fiona
from datetime import datetime
import base64
os.system('pip install rasterio')
import rasterio
from rasterio.plot import show
import numpy as np
os.system('pip install opencv-python')
import cv2
import matplotlib.pyplot as plt
from matplotlib import pyplot as plt
import pyproj
from shapely.geometry import Polygon
from rasterio.features import shapes
import pandas as pd
os.system('pip install scikit-image')
from skimage import measure
from shapely.geometry import Polygon, box, MultiPolygon
import matplotlib.pyplot as plt
import requests
from io import StringIO
from rasterio.warp import calculate_default_transform, reproject, Resampling
import json
# Set page configuration
st.set_page_config(layout="wide")
# Define the custom CSS style for the title and subtitle
custom_css = """
<style>
@import url('https://fonts.googleapis.com/css2?family=Open+Sans:wght@400;700&display=swap');
.title-custom-style {
font-family: 'SpaceGrotesk-Light';
font-size: 64px;
font-weight: 500;
color: #fff;
margin-bottom: 25px;
margin-top: 125px;
margin-left: 280px;
text-shadow: 2px 2px 4px rgba(1, 1, 1, 1);
}
.subtitle-custom-style {
font-family: 'SpaceGrotesk-Medium';
max-width: 620px;
margin-left: 280px;
font-weight: 20;
text-transform: uppercase;
color: #fff;
font-size: 15px;
}
</style>
"""
import streamlit as st
os.system('pip install streamlit-navigation-bar')
from streamlit_navigation_bar import st_navbar
pages = ["Home","Worldwide Analysis"]
styles = {
"nav": {
"background-color": "rgba(0, 0, 0, 0.5)",
# Add 50% transparency
},
"div": {
"max-width": "32rem",
},
"span": {
"border-radius": "0.26rem",
"color": "rgb(255 ,255, 255)",
"margin": "0 0.225rem",
"padding": "0.375rem 0.625rem",
},
"active": {
"background-color": "rgba(0 ,0, 200, 0.95)",
},
"hover": {
"background-color": "rgba(255, 255, 255, 0.95)",
},
}
page = st_navbar(pages, styles=styles)
page = st.sidebar.selectbox("",pages)
# Apply the custom CSS style and HTML title using Markdown
st.markdown(f"{custom_css}<h1 class='title-custom-style'>Real-Time Reservoir Monitoring Platform</h1>", unsafe_allow_html=True)
st.markdown("<h2 class='subtitle-custom-style'>This software allows you to monitorize the volume storage of almost any water body at your choice. It is still in beta version.</h2>", unsafe_allow_html=True)
import psutil
import streamlit as st
# Authenticate to the Earth Engine servers
#ee.Authenticate()
#use this line if necessary to authenticate on Google Earth Engine API project
# Access the secret as an environment variable
gee_secret_service_account = os.getenv("gee_secret_service_account")
if gee_secret_service_account:
# Parse the JSON string
service_account_info_dict = json.loads(gee_secret_service_account)
# Authenticate with Google Earth Engine
try:
service_account_email = service_account_info_dict["client_email"]
# Create a temporary JSON file for the credentials
with open("temp_service_account.json", "w") as temp_file:
json.dump(service_account_info_dict, temp_file)
# Authenticate using the temporary file
credentials = ee.ServiceAccountCredentials(service_account_email, "temp_service_account.json")
ee.Initialize(credentials)
print("Authenticated successfully with Google Earth Engine!")
except Exception as e:
print(f"Error authenticating with Google Earth Engine: {e}")
else:
print("Error: 'gee_secret_service_account' environment variable not found.")
# Function to process uploaded GeoJSON or KML file and return a GeoDataFrame
def process_uploaded_file(data):
_, file_extension = os.path.splitext(data.name)
file_id = str(uuid.uuid4())
file_path = os.path.join(tempfile.gettempdir(), f"{file_id}{file_extension}")
with open(file_path, "wb") as file:
file.write(data.read()) # Use data.read() to write file content
if file_extension.lower() == ".kml":
fiona.drvsupport.supported_drivers["KML"] = "rw"
gdf = gpd.read_file(file_path, driver="KML")
elif file_extension.lower() in [".geojson", ".json"]:
gdf = gpd.read_file(file_path)
else:
raise ValueError(f"Unsupported file format: {file_extension}")
return gdf
import streamlit as st
# Sidebar customization
st.sidebar.title("About")
st.sidebar.markdown(
"""
This Beta version allows you to visualize the volume storage, water surface elevation and other infor of the majority of reservoirs and lakes worlddwide, in real time using remote sensing,
created by João Pimenta
"""
)
# Create unique keys for each st.radio widget
world_key = "Worldwide anaysis"
if page == 'Home':
# Video URL (ensure it's accessible)
video_url = "https://raw.githubusercontent.com/joao862/BLU/main/1851190-uhd_3840_2160_25fps.mp4"
# Fetch the video from the URL
response = requests.get(video_url)
# Check if the request was successful
if response.status_code == 200:
video_bytes = response.content
# Convert the video bytes to Base64
video_base64 = base64.b64encode(video_bytes).decode("utf-8")
# Set the background video using CSS
st.markdown(
f"""
<style>
.stApp {{
background-image: url('data:video/mp4;base64,{video_base64}');
background-size: cover;
}}
</style>
""",
unsafe_allow_html=True
)
else:
st.error("Failed to load video. Please check the URL or your internet connection.")
elif page == "Worldwide Analysis":
st.title("Worldwide Analysis")
# File uploader for GeoJSON or KML
uploaded_file = st.file_uploader("Upload a GeoJSON or KML File")
HydroLakes = ee.FeatureCollection('projects/ee-joaopedromateusp/assets/HydroLAKES')
# List of feature collections
datasets = [
ee.FeatureCollection('projects/ee-joaopedromateusp/assets/SWOT_files/SWOT_EU_21'),
ee.FeatureCollection('projects/ee-joaopedromateusp/assets/SWOT_files/SWOT_EU_22'),
ee.FeatureCollection('projects/ee-joaopedromateusp/assets/SWOT_files/SWOT_EU_23'),
ee.FeatureCollection('projects/ee-joaopedromateusp/assets/SWOT_files/SWOT_EU_24'),
ee.FeatureCollection('projects/ee-joaopedromateusp/assets/SWOT_files/SWOT_EU_25'),
ee.FeatureCollection('projects/ee-joaopedromateusp/assets/SWOT_files/SWOT_EU_26'),
ee.FeatureCollection('projects/ee-joaopedromateusp/assets/SWOT_files/SWOT_EU_27'),
ee.FeatureCollection('projects/ee-joaopedromateusp/assets/SWOT_files/SWOT_EU_29'),
# Add more datasets
]
# Initialize with the first dataset in the list
merged_dataset = datasets[0]
# Loop through and merge each subsequent dataset
for dataset in datasets[1:]:
merged_dataset = merged_dataset.merge(dataset)
night_mode = 'CartoDB.DarkMatter'
normal_mode = 'HYBRID'
# Options for confirming the reservoir selection
modes = ['day theme', 'night theme']
# Select box to confirm selection
confirmation_mode = st.sidebar.selectbox("Choose this lake/reservoir", modes)
if confirmation_mode == 'day theme':
mode = normal_mode
else:
mode = normal_mode
# Step 1: Create a geemap Map object with the required plugins
m = geemap.Map(
basemap=mode,
plugin_Draw=True,
Draw_export=True,
locate_control=True,
plugin_LatLngPopup=True
)
m.set_center(13.5352, 48.8069, 5)
os.system('pip install streamlit-folium')
from streamlit_folium import st_folium
import folium
import json
# Define visualization parameters to color the polygons blue or yellow
vis_params = {'color': 'Blue'}
#m.addLayer(HydroLakes.style(**vis_params), {}, 'HydroLakes')
# Add the HydroLakes layer to the map
m.addLayer(merged_dataset.style(**vis_params), {}, 'Europe')
# JavaScript for click events to set session state
click_js = """
function addClickHandler(map) {
map.on('click', function(e) {
const latlng = e.latlng;
const coords = [latlng.lat, latlng.lng];
window.parent.postMessage(coords, '*');
});
}
addClickHandler(window.map);
"""
# Add JavaScript to the map
m.add_child(folium.Element(f'<script>{click_js}</script>'))
# Display the map in Streamlit
st_data = st_folium(m, height=800, width=1600)
# Initialize session state for the selected ROI
if 'roi' not in st.session_state:
st.session_state['roi'] = None
if st_data['last_clicked']:
lat, lng = st_data['last_clicked']['lat'], st_data['last_clicked']['lng']
point = ee.Geometry.Point([lng, lat])
filtered = merged_dataset.filterBounds(point)
info = filtered.getInfo()
features = info['features']
if features:
properties = features[0]['properties']
coordinates = features[0]['geometry']['coordinates']
# Extracting the required fields
lake_name = properties.get('names', 'N/A')
lake_id = properties.get('lake_id', 'N/A')
latitude = properties.get('lat', 'N/A')
longitude = properties.get('lon', 'N/A')
ref_area = properties.get('ref_area', 'N/A')
storage = properties.get('storage', 'N/A')
# Display metrics in Streamlit
st.title("Lake Information")
# Using Streamlit columns for a clean layout
#col1, col2 = st.columns(2)
#with col1:
#st.metric("Lake Name", lake_name)
#st.metric("Lake ID", lake_id)
#with col2:
#st.metric("Latitude", round(latitude, 4))
#st.metric("Longitude", round(longitude, 4))
# Display reference area with one decimal for clarity
#st.metric("Reference Area (sq km)", f"{ref_area:.1f}")
# Ensure coordinates are in the correct format for GeoJSON (swapping lon, lat to lat, lon)
#if isinstance(coordinates[0][0], list): # Check if it's a nested list (MultiPolygon or Polygon)
# Swap lon and lat if necessary
#coordinates = [list(map(lambda coord: [coord[1], coord[0]], sub_coord)) for sub_coord in coordinates]
# Create the Earth Engine geometry (assuming Polygon type)
aoi = ee.Geometry.Polygon(coordinates)
roi = aoi
globathy_dataset = ee.FeatureCollection("projects/ee-joaopedromateusp/assets/HydroLAKES")
# Add the HydroLakes layer to the map
m.addLayer(globathy_dataset.style(**vis_params), {}, 'Globathy')
point = ee.Geometry.Point([lng, lat])
filtered = globathy_dataset.filterBounds(point)
info = filtered.getInfo()
features = info['features']
if features:
properties = features[0]['properties']
hydrolakes_id = properties.get('Hylak_id', 'N/A')
Vol_res = properties.get('Vol_res','N/A')
Grand_id = properties.get('Grand_id','N/A')
# Using Streamlit columns for a clean layout
col1, col2 = st.columns(2)
with col1:
st.metric("Hydrolakes ID", hydrolakes_id)
st.metric("Maximum Volume", Vol_res)
with col2:
st.metric("GranD ID", Grand_id)
import ee
import geemap
import os
import matplotlib.pyplot as plt
import rasterio
from rasterio.plot import show
from skimage import measure
from shapely.geometry import Polygon, box
from shapely.ops import transform
import numpy as np
import json
# Filter Sentinel-2 images
sentinelImageCollection = ee.ImageCollection('COPERNICUS/S2') \
.filterBounds(roi) \
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 5)) \
.sort('system:time_start', False) # Sort by time_start in descending order
# Get the latest (first) image from the sorted collection
latest_image = sentinelImageCollection.first()
previous_image = sentinelImageCollection.toList(sentinelImageCollection.size()).get(1)
previous_image = ee.Image(previous_image)
# Define a function to calculate NDWI and mask
def calculate_ndwi_and_mask(image):
ndwi = image.normalizedDifference(['B3', 'B8']).rename('NDWI')
ndwi_threshold = ndwi.gte(0.0)
ndwi_mask = ndwi_threshold.updateMask(ndwi_threshold)
return ndwi_mask
# Apply the function to the latest image to calculate NDWI mask
ndwi_mask = calculate_ndwi_and_mask(latest_image)
ndwi_prev_mask = calculate_ndwi_and_mask(previous_image)
# Define a function to calculate water area
def calculate_water_area(image):
water_area = image.multiply(ee.Image.pixelArea()).reduceRegion(
reducer=ee.Reducer.sum(),
geometry=roi,
scale=5
).get('NDWI')
return image.set('water_area', water_area)
# Calculate water area for the NDWI mask
ndwi_mask_with_area = calculate_water_area(ndwi_mask)
ndwi_pre_mask_with_area = calculate_water_area(ndwi_prev_mask)
m.add_marker(lat=lat, lon=lng, location=[lat, lng])
m.set_center(lng, lat, 14)
globathy_dataset = ee.FeatureCollection("projects/ee-joaopedromateusp/assets/HydroLAKES")
# Add the HydroLakes layer to the map
m.addLayer(globathy_dataset.style(**vis_params), {}, 'Globathy')
point = ee.Geometry.Point([lng, lat])
filtered = globathy_dataset.filterBounds(point)
info = filtered.getInfo()
features = info['features']
if features:
properties = features[0]['properties']
hydrolakes_id = properties.get('Hylak_id', 'N/A')
Vol_res = properties.get('Vol_res','N/A')
Grand_id = properties.get('Grand_id','N/A')
Country = properties.get('Country','N/A')
try:
# Get the water area information
water_area_info = ndwi_mask_with_area.get('water_area').getInfo()
pre_water_area_info = ndwi_pre_mask_with_area.get('water_area').getInfo()
prev = round((pre_water_area_info / 1e6), 2)
water_area_km2 = round((water_area_info / 1e6), 2)
variance = round(((water_area_km2 - prev) / prev) * 100, 2) # Calculate variance as a percentage
import netCDF4 as nc
import numpy as np
# Open the NetCDF file
nc_file = nc.Dataset('/Users/joaopimenta/Downloads/Master thesis/GLOBathy_hAV_relationships.nc')
# Specify the lake ID you want to search for
target_lake_id = hydrolakes_id # Replace this with the actual lake ID you're interested in
# Find the index of the lake based on the lake ID
lake_ids = nc_file.variables['lake_id'][:]
# Check if the target lake ID exists in the lake_id variable
lake_index = np.where(lake_ids == target_lake_id)[0]
if len(lake_index) == 0:
st.write("Lake not found in the dataset.")
else:
lake_index = lake_index[0] # Use the first match if found
# Extract coefficients of the area-storage equation for the identified lake
area_storage_coeffs = nc_file.variables['f_hA'][lake_index, :]
lon_lat = nc_file.variables['lon_lat'][lake_index, :]
import numpy as np
# Coefficients obtained from the NetCDF dataset
a = area_storage_coeffs[0]
b = area_storage_coeffs[1]
# Calculate the volume using the area-storage equation
volume = ((water_area_info/1e6) / a) ** (1 / b)
volume_prev = ((pre_water_area_info/1e6) / a) ** (1 / b)
vol_variance = round(((volume - volume_prev) / volume_prev) * 100, 2)
except Exception as e:
st.write("Error retrieving water area information:", e)
# Using Streamlit columns for a clean layout
col1, col2 = st.columns(2)
with col1:
st.metric("Lake Name", lake_name)
st.metric("Lake ID", lake_id)
st.metric("Hydrolakes ID", hydrolakes_id)
st.metric("Maximum Volume(10⁸ m³)", Vol_res/10)
with col2:
# Display metric with variance as delta
st.metric(
label="Current Water Area",
value=f"{water_area_km2} km²",
delta=f"{variance}%", # Add percentage change as delta
delta_color="normal",
help=None,
label_visibility="visible",
)
st.metric(
label="Current Water Volume",
value=f"{round(volume,2)} x10⁸m³",
delta=f"{vol_variance}%", # Add percentage change as delta
delta_color="normal",
help=None,
label_visibility="visible",
)
st.metric("Country",Country)
st.metric("GranD ID", Grand_id)
else:
st.write("No features were selected")
# Highlight the selected lake
m.addLayer(ee.Image().paint(aoi, 1, 3), {'palette': 'red'}, 'Selected Lake')
else:
st.write('No polygon found at clicked location.')
# Function to export ROI as GeoJSON
def export_roi_as_geojson(roi):
if roi:
roi_geojson = roi.getInfo()
if roi_geojson.get('type') == 'Polygon':
geojson_str = json.dumps(roi_geojson)
return geojson_str
else:
st.error("GeoJSON type is not supported.")
return None
else:
st.error("No ROI available.")
return None
geojson_str = export_roi_as_geojson(aoi)
if geojson_str:
st.download_button(
label="Download ROI as GeoJSON",
data=geojson_str,
file_name="roi.geojson",
mime="application/geo+json"
)
# Options for confirming the reservoir selection
box_reservoir = ['No', 'Yes']
# Select box to confirm selection
confirmation = st.selectbox("Choose this lake/reservoir", box_reservoir)
# Handle the selection
if confirmation == 'Yes':
st.session_state['roi'] = aoi # Store the selected ROI in session state
roi = aoi # Set the roi for further processing
st.success("Reservoir selected successfully!")
else:
st.warning("No reservoir selected yet.")
# If a region of interest (ROI) is available, provide download
if uploaded_file is not None:
try:
gdf = process_uploaded_file(uploaded_file)
if not gdf.empty:
roi_fc = geemap.geopandas_to_ee(gdf)
roi_geometry = roi_fc.geometry()
aoi = roi_geometry
st.session_state['roi'] = aoi # Store the selected ROI in session state
roi = aoi # Set the roi for further processing
st.success("Reservoir selected successfully!")
# Add markers for each feature in the GeoDataFrame
for index, row in gdf.iterrows():
latitude, longitude = row.geometry.centroid.coords[0] # Get centroid coordinates
m.add_marker(location =[lng,lat])
# Set the map center and zoom level based on the selected location
m.set_center(lat, lng, 12)
globathy_dataset = ee.FeatureCollection("projects/ee-joaopedromateusp/assets/HydroLAKES")
# Add the HydroLakes layer to the map
m.addLayer(globathy_dataset.style(**vis_params), {}, 'Globathy')
point = ee.Geometry.Point([lng, lat])
filtered = globathy_dataset.filterBounds(point)
info = filtered.getInfo()
features = info['features']
if features:
properties = features[0]['properties']
hydrolakes_id = properties.get('Hylak_id', 'N/A')
Vol_res = properties.get('Vol_res','N/A')
Grand_id = properties.get('Grand_id','N/A')
# Using Streamlit columns for a clean layout
col1, col2 = st.columns(2)
with col1:
st.metric("Hydrolakes ID", hydrolakes_id)
st.metric("Maximum Volume", Vol_res)
with col2:
st.metric("GranD ID", Grand_id)
m.addLayer(roi_fc, {}, "Uploaded Data")
except Exception as e:
st.write(f"Error processing uploaded file: {e}")
if 'roi' in st.session_state and 'aoi' in locals():
roi = aoi # Use the selected ROI
# Create a select box for choosing the area-volume relationship method
opt = ["Don't have that info", "Write the A-V function of your reservoir", "upload excel sheet", "upload the DEM"]
method = st.sidebar.selectbox(
"Choose the area-volume relationship input",
opt,
key="method")
if method == ("Write the A-V function of your reservoir"):
volumes =[]
column1, column2 = st.sidebar.columns(2)
with column1:
a = st.number_input("Coefficient a")
with column2 :
b = st.number_input("Coefficient b")
elif method == ("upload excel sheet"):
# File uploader for Excel files
uploaded_file = st.file_uploader("Upload an Excel File", type=["xlsx"])
if uploaded_file is not None:
# Add an input box for the user to enter a sheet index number
sheet_index = int(st.number_input("Enter the index of the Excel sheet (first sheet is 0)", min_value=0))
st.write("You entered sheet index:", sheet_index)
try:
# Load the Excel file into a DataFrame from the specified sheet
df = pd.read_excel(uploaded_file, sheet_name=sheet_index)
# Check if the required columns are present
if 'ÁREA (m2)' not in df.columns or 'VOLUME (m3)' not in df.columns:
st.error("Required columns 'ÁREA (m2)' or 'VOLUME (m3)' not found in the sheet.")
else:
# Drop rows with NaN values in the required columns
df = df.dropna(subset=['ÁREA (m2)', 'VOLUME (m3)'])
# Initialize a dictionary
dictionary = {}
# Populate the dictionary with 'ÁREA (m2)' as keys and 'VOLUME (m3)' as values
for index, row in df.iterrows():
area = row['ÁREA (m2)']
volume = row['VOLUME (m3)']
dictionary[area] = volume
# Display the created dictionary
st.write("dictionary =", dictionary)
except ValueError as e:
st.error(f"Error reading sheet index {sheet_index}: {e}")
except Exception as e:
st.error(f"An error occurred: {e}")
elif method == "upload the DEM":
import rasterio
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
# File uploader for GeoTIFF
dem_file = st.file_uploader("Upload a GeoTIFF File")
if dem_file is not None:
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
tmp_file.write(dem_file.getbuffer())
tmp_file_path = tmp_file.name
# Load the raster data
lakeRst = rasterio.open(tmp_file_path)
st.write("Number of bands:", lakeRst.count)
# Raster resolution
resolution = lakeRst.res
st.write("Resolution:", resolution)
# Read the first band (assuming single band raster)
lakeBottom = lakeRst.read(1)
st.write("Sample raster data:\n", lakeBottom[:5, :5])
# Replace no-data value with np.nan
noDataValue = np.copy(lakeBottom[0, 0])
lakeBottom = lakeBottom.astype(float)
lakeBottom[lakeBottom == noDataValue] = np.nan
# Display the raster data
plt.figure(figsize=(12, 12))
plt.imshow(lakeBottom, cmap='viridis')
plt.title('Lake Bottom Elevation')
plt.colorbar(label='Elevation (masl)')
st.pyplot(plt)
# Calculate minimum and maximum elevation
minElev = np.nanmin(lakeBottom)
maxElev = np.nanmax(lakeBottom)
st.write('Min bottom elevation: %.2f m, Max bottom elevation: %.2f m' % (minElev, maxElev))
# Define the number of steps for calculation
nSteps = 20
# Generate elevation steps
elevSteps = np.round(np.linspace(minElev, maxElev, nSteps), 2)
st.write("Elevation steps:", elevSteps)
# Define function to calculate volume at a given elevation step
def calculateVol(elevStep, elevDem, lakeRst):
tempDem = elevStep - elevDem[elevDem < elevStep]
tempVol = tempDem.sum() * lakeRst.res[0] * lakeRst.res[1]
return tempVol
# Define function to calculate inundated area for a given elevation
def calculateArea(elevStep, elevDem):
inundated_mask = np.where(elevDem <= elevStep, 1, 0)
area = np.sum(inundated_mask) * resolution[0] * resolution[1]
return area
# Calculate volumes and areas for each elevation step
volArray = []
areaArray = []
for elev in elevSteps:
tempVol = calculateVol(elev, lakeBottom, lakeRst)
tempArea = calculateArea(elev, lakeBottom)
volArray.append(tempVol)
areaArray.append(tempArea)
st.write(f"Elevation: {elev}, Area: {tempArea}, Volume: {tempVol / 1e6} MCM")
# Convert volumes to million cubic meters
volArrayMCM = [round(vol / 1e6, 2) for vol in volArray]
# Print results
st.write("Elevation steps (m):", elevSteps)
st.write("Volumes (MCM):", volArrayMCM)
# Plot elevation vs volume
fig, ax = plt.subplots(figsize=(12, 5))
ax.plot(volArrayMCM, elevSteps, label='Lake Volume Curve')
ax.grid(True)
ax.legend()
ax.set_xlabel('Volume (MCM)')
ax.set_ylabel('Elevation (masl)')
st.pyplot(fig)
# Plot lake bottom elevation and volume curve side by side
fig, [ax1, ax2] = plt.subplots(1, 2, figsize=(20, 8), gridspec_kw={'width_ratios': [2, 1]})
ax1.set_title('Lake Bottom Elevation')
botElev = ax1.imshow(lakeBottom, cmap='viridis')
divider = make_axes_locatable(ax1)
cax = divider.append_axes('bottom', size='5%', pad=0.5)
fig.colorbar(botElev, cax=cax, orientation='horizontal', label='Elevation (masl)')
ax2.plot(volArrayMCM, elevSteps, label='Lake Volume Curve')
ax2.grid(True)
ax2.legend()
ax2.set_xlabel('Volume (MCM)')
ax2.set_ylabel('Elevation (masl)')
st.pyplot(fig)
# Print elevation and corresponding inundated area
st.write("Elevation (m) Inundated Area (sq. meters)")
for elev, area in zip(elevSteps, areaArray):
st.write("{:.2f} {:.2f}".format(elev, area))
st.write("Inundated Area (sq. meters) Volume (MCM)")
for area, vol in zip(areaArray, volArrayMCM):
st.write("{:.2f} {:.2f}".format(area, vol))
# Plot the inundated area-volume curve
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(areaArray, volArrayMCM, label='Inundated Area-Volume Curve')
ax.set_xlabel('Inundated Area (square meters)')
ax.set_ylabel('Volume (MCM)')
ax.grid(True)
ax.legend()
plt.title('Inundated Area-Volume Curve')
st.pyplot(fig)
# Create and display area-volume curve dictionary
area_volume_curve = {}
for area,vol in zip(areaArray, volArrayMCM):
area_volume_curve[float(area)]= vol
st.write(area_volume_curve)
import datetime
# Date input for filtering Sentinel-2 images
startDate = st.sidebar.date_input("Start Date", value=None, min_value=None, max_value=None, key=None, help=None, on_change=None, args=None, kwargs=None, format="YYYY/MM/DD", disabled=False, label_visibility="visible")
endDate = st.sidebar.date_input("End Date", value=datetime.datetime.now(), min_value=None, max_value=None, key=None, help=None, on_change=None, args=None, kwargs=None, format="YYYY/MM/DD", disabled=False, label_visibility="visible")
# Sidebar selection for output
output = st.sidebar.multiselect("Select the output",
["Water Area", "Water Surface Elevation", "Water Volume",
"Bathymetry file", "Timelapse", "Storage-Capacity curve"
])
st.sidebar.info("Choose the cloud coverage percentage of the satellite images")
threshold = st.sidebar.slider("Cloud Percentage Threshold", 0, 20, 5)
if st.sidebar.button("Start computing") and startDate and endDate and threshold:
if "Timelapse" in output:
with st.spinner('Creating Timelapse...'):
# Export the GIF
import geemap
gif_path = "/Users/joaopimenta/Downloads/Master thesis/Python scripts/Test_gee/ndwi_timelapse.gif"
Map = geemap.Map()
Map.add_landsat_ts_gif(layer_name='Timelapse', roi=roi, label=f'{lat}, {lng}', start_year=2021, end_year=2024, start_date='06-10', end_date='09-20', bands=['SWIR1', 'NIR', 'Red'], vis_params=None, dimensions=768, frames_per_second=2, font_size=30, font_color='white', add_progress_bar=True, progress_bar_color='white', progress_bar_height=5, out_gif=gif_path, download=True, apply_fmask=True, nd_bands=None, nd_threshold=0, nd_palette=['black', 'blue'])
file_ = open(gif_path, "rb")
contents = file_.read()
data_url = base64.b64encode(contents).decode("utf-8")
file_.close()
st.markdown(
f'<img src="data:image/gif;base64,{data_url}" alt="timelapse gif">',
unsafe_allow_html=True,
)
# Convert the date objects to strings in the format expected by EE
start_date_str = startDate.strftime('%Y-%m-%d')
end_date_str = endDate.strftime('%Y-%m-%d')
sentinel_image_collection = ee.ImageCollection('COPERNICUS/S2') \
.filterBounds(roi) \
.filterDate(start_date_str, end_date_str)
sentinel_image = sentinel_image_collection \
.sort('CLOUDY_PIXEL_PERCENTAGE') \
.first() \
.clip(roi)
# Visualize using RGB
m.addLayer(sentinel_image,
{'min': 0.0, 'max': 2000, 'bands': ['B4', 'B3', 'B2']},
'RGB')
ndwi = sentinel_image.normalizedDifference(['B3', 'B8']).rename('NDWI')
m.addLayer(ndwi,
{'palette': ['red', 'yellow', 'green', 'cyan', 'blue']},
'NDWI')
# Create NDWI mask
ndwi_threshold = ndwi.gte(0.0)
ndwi_mask = ndwi_threshold.updateMask(ndwi_threshold)
m.addLayer(ndwi_threshold,
{'palette': ['black', 'white']},
'NDWI Binary Mask')
m.addLayer(ndwi_mask,
{'palette': ['blue']},
'NDWI Mask')
if "Water Surface Elevation" in output:
# Send a request to the Hydrocron API to get lake data in CSV format
url = (
"https://soto.podaac.earthdatacloud.nasa.gov/hydrocron/v1/timeseries?"
"feature=PriorLake"
f"&feature_id={lake_id}"
f"&start_time={start_date_str}T00:00:00Z"
f"&end_time={end_date_str}T00:00:00Z"
"&output=csv"
"&fields=time_str,wse"
)
# Request the data
hydrocron_response = requests.get(url).json()
# Extract CSV data from the response
csv_str = hydrocron_response['results']['csv']
# Convert the CSV string into a pandas DataFrame
df = pd.read_csv(StringIO(csv_str))
# Prepare to plot water surface elevation (WSE) and area
# Convert 'time_str' column to datetime format
df['time_str'] = pd.to_datetime(df['time_str'], errors='coerce')
# Filter and store dates and elevations
df_filtered = df.dropna(subset=['time_str', 'wse'])
df_filtered['wse'] = pd.to_numeric(df_filtered['wse'], errors='coerce')
df_filtered = df_filtered.dropna(subset=['wse'])
# Plot water surface elevation (WSE) over time
plt.figure(figsize=(10, 5))
plt.plot(df_filtered['time_str'], df_filtered['wse'], marker='o', linestyle='-')
plt.xlabel('Date')
plt.ylabel('Water Surface Elevation (m)')
plt.title(f'Water Surface Elevation for Lake {lake_name}')
plt.xticks(rotation=45)
plt.grid(True)
# Show the plot in Streamlit
st.pyplot(plt)
# Display the filtered DataFrame
st.write(df_filtered[['time_str', 'wse']])
with st.spinner('Retrieving satilite images...'):
#Define a function to calculate NDWI
def calculate_ndwi(image):
ndwi = image.normalizedDifference(["B8", "B3"]) # B8 is NIR and B3 is green
return ndwi
# Filter Sentinel-2 images
sentinelImageCollection = ee.ImageCollection('COPERNICUS/S2') \
.filterBounds(roi) \
.filterDate(start_date_str, end_date_str) \
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', threshold)) \
# Check if images are available
num_images = sentinelImageCollection.size().getInfo()
st.write("Number of images:", num_images)
volumes = []
# Alternatively, convert acquisition times to readable format (if needed)
acquisition_times = sentinelImageCollection.aggregate_array('system:time_start').getInfo()
acquisition_dates = [datetime.datetime.utcfromtimestamp(time / 1000).strftime('%Y-%m-%d') for time in acquisition_times]
if num_images == 0:
st.warning("No images available within the specified date range.")
else:
if threshold >= 15:
st.write("CLoudless Algorithm will identify and remove the effects of clouds and shadows")
START_DATE = start_date_str
END_DATE = end_date_str
CLOUD_FILTER = 40
CLD_PRB_THRESH = 70
NIR_DRK_THRESH = 0.15
CLD_PRJ_DIST = 2
BUFFER = 100
# Function to get Sentinel-2 surface reflectance and cloud probability collections
def get_s2_sr_cld_col(aoi, start_date, end_date):
s2_sr_col = (ee.ImageCollection('COPERNICUS/S2_SR')
.filterBounds(aoi)
.filterDate(start_date, end_date)
.filter(ee.Filter.lte('CLOUDY_PIXEL_PERCENTAGE', CLOUD_FILTER)))
s2_cloudless_col = (ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY')
.filterBounds(aoi)
.filterDate(start_date, end_date))
return ee.ImageCollection(ee.Join.saveFirst('s2cloudless').apply(**{
'primary': s2_sr_col,
'secondary': s2_cloudless_col,
'condition': ee.Filter.equals(**{
'leftField': 'system:index',
'rightField': 'system:index'
})
}))
# Apply the function to build the collection
s2_sr_cld_col = get_s2_sr_cld_col(roi, START_DATE, END_DATE)
# Function to get cloud cover percentage for an image
def get_cloud_cover_percentage(image):
cloud_cover = ee.Image(image).get('CLOUDY_PIXEL_PERCENTAGE')
return ee.Feature(None, {'cloud_cover': cloud_cover, 'image_id': image.id()})
# Apply the function to the collection
image_list = s2_sr_cld_col.map(get_cloud_cover_percentage).getInfo()
# Debug: Print the properties of the first image to inspect the available properties
print("Inspecting the first image's properties:")
print(image_list['features'][0]['properties'])
# Extract the image ids, cloud covers, and dates (if available)
image_info = []
for f in image_list['features']:
image_id = f['properties'].get('image_id', 'Unknown')
cloud_cover = f['properties'].get('cloud_cover', 'Unknown')
timestamp = f['properties'].get('system:time_start', None)
# If timestamp is None, we'll set it to 'Unknown'
if timestamp:
date = datetime.utcfromtimestamp(timestamp / 1000).strftime('%Y-%m-%d')
else:
date = 'Unknown'
image_info.append((image_id, cloud_cover, date))
print("Available images and their cloud cover percentages:")
for idx, (image_id, cloud_cover, date) in enumerate(image_info):
print(f"{idx}: Image ID: {image_id}, Date: {date}, Cloud Cover: {cloud_cover}%")
water_area_info = []
# Count the number of images in the collection
num_images = s2_sr_cld_col.size().getInfo()
print(f"Total number of images in the collection: {num_images}")
# Loop through each image in the collection and print its cloud cover
for i in range(num_images):
selected_idx = i
selected_image_id = image_info[selected_idx][0]
cloud_cover = image_info[selected_idx][1] # Get the cloud cover for the selected image
selected_image = ee.Image(s2_sr_cld_col.filter(ee.Filter.eq('system:index', selected_image_id)).first())
print(f"Image ID: {selected_image_id}, Cloud Cover: {cloud_cover}%")
if cloud_cover >= 15:
# Define functions to add cloud and shadow bands
def add_cloud_bands(img):
cld_prb = ee.Image(img.get('s2cloudless')).select('probability')
is_cloud = cld_prb.gt(CLD_PRB_THRESH).rename('clouds')
return img.addBands(ee.Image([cld_prb, is_cloud]))
def add_shadow_bands(img):
not_water = img.select('SCL').neq(6)
SR_BAND_SCALE = 1e4
dark_pixels = img.select('B8').lt(NIR_DRK_THRESH * SR_BAND_SCALE).multiply(not_water).rename('dark_pixels')
shadow_azimuth = ee.Number(90).subtract(ee.Number(img.get('MEAN_SOLAR_AZIMUTH_ANGLE')))
cld_proj = (img.select('clouds').directionalDistanceTransform(shadow_azimuth, CLD_PRJ_DIST * 10)
.reproject(crs=img.select(0).projection(), scale=100)
.select('distance').mask().rename('cloud_transform'))
shadows = cld_proj.multiply(dark_pixels).rename('shadows')
return img.addBands(ee.Image([dark_pixels, cld_proj, shadows]))
def add_cld_shdw_mask(img):
img_cloud = add_cloud_bands(img)
img_cloud_shadow = add_shadow_bands(img_cloud)
is_cld_shdw = img_cloud_shadow.select('clouds').add(img_cloud_shadow.select('shadows')).gt(0)
is_cld_shdw = (is_cld_shdw.focalMin(2).focalMax(BUFFER * 2 / 20)
.reproject(crs=img.select([0]).projection(), scale=20)
.rename('cloudmask'))
return img_cloud_shadow.addBands(is_cld_shdw)
# Define the function to apply the cloud and shadow mask
def apply_cld_shdw_mask(img):
not_cld_shdw = img.select('cloudmask').Not()
return img.select('B.*').updateMask(not_cld_shdw)
# Add cloud and shadow bands, apply the mask
selected_image_with_mask = add_cld_shdw_mask(selected_image)
cloud_free_image = apply_cld_shdw_mask(selected_image_with_mask)
# Define a function to calculate NDWI and mask
def calculate_ndwi_and_mask(image):
ndwi = image.normalizedDifference(['B3', 'B8']).rename('NDWI')
ndwi_threshold = ndwi.gte(0.0)
ndwi_mask = ndwi_threshold.updateMask(ndwi_threshold)
return ndwi_mask
# Apply the function to the latest image to calculate NDWI mask
ndwi_mask = calculate_ndwi_and_mask(selected_image)
# Define a function to calculate water area
def calculate_water_area(image):
water_area = image.multiply(ee.Image.pixelArea()).reduceRegion(
reducer=ee.Reducer.sum(),
geometry=roi,
scale=5
).get('NDWI')
return image.set('water_area', water_area)
# Calculate water area for the NDWI mask
ndwi_mask_with_area = calculate_water_area(ndwi_mask)
waterarea = ndwi_mask_with_area.get('water_area').getInfo()
w = waterarea
#print(f"This is the water area of the NDWI image:{w}")
# Load the bathymetry dataset from Earth Engine
globathy = ee.Image("projects/sat-io/open-datasets/GLOBathy/GLOBathy_bathymetry")
# Export the data as an image
out_dir = "/Users/joaopimenta/Desktop/GEE_test" # Specify the output directory
if not os.path.exists(out_dir):
os.makedirs(out_dir)
out_image_path = os.path.join(out_dir, "globathy_bathymetry.tif") # Specify the output image path
# Export the image
geemap.ee_export_image(globathy, filename=out_image_path, scale=10, region=roi)
# Load Bathymetry image
bathymetry_path = out_image_path
bathymetry_dataset = rasterio.open(bathymetry_path)
#print(f"This is the ndwi area of the lake {ndwi_masked_area}")
# Export the binary water mask to a GeoTIFF file
folder_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + "_dam_volume_images_tif"
directory = "/Users/joaopimenta/Desktop"
folder_path = os.path.join(directory, folder_name)
os.makedirs(folder_path)
geemap.ee_export_image(
ndwi_mask,
filename=os.path.join(folder_path, "binary_NDWI.tif"),
region=roi,
scale=10
)
file_name = "binary_NDWI.tif"
file_path = os.path.join(folder_path, file_name)
# Load NDWI image
ndwi_path = file_path # Update this path
ndwi_dataset = rasterio.open(ndwi_path)
ndwi = ndwi_dataset.read(1)
with rasterio.open(file_path) as src:
ndwi_data = src.read(1) # Read the first band
transform = src.transform
# Convert NDWI to binary format for visualization
binary_ndwi = np.where(ndwi_data == 1, 255, 0).astype(np.uint8)
# Calculate the area of the detected water bodies from binary mask
def calculate_area(image, transform):
# Mask the image to include only water
water_mask = image == 0
# Compute the area in square meters
pixel_area = abs(transform[0] * transform[4]) # pixel size (in square meters)
water_area_pixels = np.sum(water_mask)
total_area_m2 = water_area_pixels * pixel_area
return total_area_m2
# Calculate the area using the converted binary mask
total_area_m2 = calculate_area(binary_ndwi, transform)/ 1e3
#print(f"Total area calculated from binary mask: {total_area_m2 :.2f} km²")
# Plot the results
plt.figure(figsize=(15, 10))
# Binary NDWI (K-means method)
plt.subplot(1, 2, 1)
plt.imshow(binary_ndwi, cmap='gray')
plt.title('Binary NDWI (K-means)')
# Identified contour (K-means method)
plt.subplot(1, 2, 2)
contour_image = np.zeros_like(binary_ndwi)
contours, _ = cv2.findContours(binary_ndwi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if contours:
cv2.drawContours(contour_image, [max(contours, key=cv2.contourArea)], -1, (255), 2)
plt.imshow(contour_image, cmap='gray')
plt.title('Identified Dam Contour (K-means)')
plt.show()
# Check for cloud pixels within the dam (ROI)
cloud_pixels_in_roi = selected_image_with_mask.select('cloudmask').reduceRegion(
reducer=ee.Reducer.sum(),
geometry=roi,
scale=10
).get('cloudmask').getInfo()
print(f"This is the cloud pixels in the ROI:{cloud_pixels_in_roi}")
# Export the cloud mask
geemap.ee_export_image(
selected_image_with_mask.select('cloudmask'),
filename=os.path.join(folder_path, "cloud_mask.tif"),
region=roi,
scale=10
)
cloud_mask_path = os.path.join(folder_path, "cloud_mask.tif")
cloud_mask_dataset = rasterio.open(cloud_mask_path)
cloud_mask = cloud_mask_dataset.read(1)
# Reproject the cloud mask to match NDWI resolution
resampled_cloud_mask = np.empty_like(ndwi)
reproject(
source=cloud_mask,
destination=resampled_cloud_mask,
src_transform=cloud_mask_dataset.transform,
src_crs=cloud_mask_dataset.crs,
dst_transform=ndwi_dataset.transform,
dst_crs=ndwi_dataset.crs,
resampling=Resampling.nearest)
# Mask the NDWI image by removing cloud pixels
ndwi_masked = np.where(resampled_cloud_mask == 0, ndwi, np.nan)
# Load Bathymetry image
bathymetry_dataset = rasterio.open(bathymetry_path)
# Reproject Bathymetry to the NDWI CRS
dst_crs = ndwi_dataset.crs
transform, width, height = calculate_default_transform(
bathymetry_dataset.crs, dst_crs, bathymetry_dataset.width,
bathymetry_dataset.height, *bathymetry_dataset.bounds)
kwargs = bathymetry_dataset.meta.copy()
kwargs.update({
'crs': dst_crs,
'transform': transform,
'width': width,
'height': height
})
reprojected_bathymetry = np.empty((height, width), dtype=np.float32)
reproject(
source=rasterio.band(bathymetry_dataset, 1),
destination=reprojected_bathymetry,
src_transform=bathymetry_dataset.transform,
src_crs=bathymetry_dataset.crs,
dst_transform=transform,
dst_crs=dst_crs,
resampling=Resampling.nearest)
# Resample Bathymetry to match NDWI resolution
resampled_bathymetry = np.empty_like(ndwi)
reproject(
source=reprojected_bathymetry,
destination=resampled_bathymetry,
src_transform=transform,
src_crs=dst_crs,
dst_transform=ndwi_dataset.transform,
dst_crs=dst_crs,
resampling=Resampling.bilinear)
# Plot the images
fig, ax = plt.subplots(figsize=(10, 10))
# Plot the NDWI image
ndwi_extent = (ndwi_dataset.bounds.left, ndwi_dataset.bounds.right,
ndwi_dataset.bounds.bottom, ndwi_dataset.bounds.top)
cax_ndwi = ax.imshow(ndwi_masked, cmap='Blues', extent=ndwi_extent,
alpha=0.6)
# Overlay the Bathymetry image
bathy_extent = (ndwi_dataset.bounds.left, ndwi_dataset.bounds.right,
ndwi_dataset.bounds.bottom, ndwi_dataset.bounds.top)
cax_bathy = ax.imshow(resampled_bathymetry, cmap='viridis',
extent=bathy_extent, alpha=0.4)
fig.colorbar(cax_bathy, ax=ax, fraction=0.046, pad=0.04,
label='Bathymetry')
# Plot the NDWI cloud-removed image
fig, ax = plt.subplots(figsize=(10, 10))
# Plot the NDWI image
ndwi_extent = (ndwi_dataset.bounds.left, ndwi_dataset.bounds.right, ndwi_dataset.bounds.bottom, ndwi_dataset.bounds.top)
cax_ndwi = ax.imshow(ndwi_masked, cmap='Blues', extent=ndwi_extent)
fig.colorbar(cax_ndwi, ax=ax, fraction=0.046, pad=0.04, label='NDWI')
# Get the cloud mask from the selected image
cloud_mask = selected_image_with_mask.select('cloudmask')
# Apply cloud mask to the NDWI mask
ndwi_cloud_removed_mask = ndwi_mask.updateMask(cloud_mask.Not())
# Calculate the pixel area for the masked NDWI image
pixel_area = ndwi_cloud_removed_mask.multiply(ee.Image.pixelArea())
# Reduce the region to calculate the total water area
water_area = pixel_area.reduceRegion(
reducer=ee.Reducer.sum(),
geometry=roi,
scale=10, # Adjust the scale as needed
maxPixels=1e10
)
# Assuming water_area is the result from reduceRegion
water = water_area.getInfo().get('NDWI')
print(water)
# Get the total water area in square meters
total_water_area_m2 = total_area_m2
# Convert the area to square kilometers
total_water_area_km2 = total_water_area_m2 / 1e6# Convert the area to square kilometers
total_water_area_adjusted = total_water_area_m2
area_cloud_aftected = w - total_water_area_adjusted
cloud_affect_percentage = area_cloud_aftected/ cloud_pixels_in_roi
print(f"The total NDWI water area is:{w}")
print(f"The adjusted water area is: {total_water_area_adjusted}")
print(f"The total amount of pixels covering the reservoir is:{area_cloud_aftected}")
print(f"This is the area cloud pixels in the ROI:{cloud_pixels_in_roi*10}")
print(f"The percentage of pixels which affect the reservoir's area are :{cloud_affect_percentage}")
if cloud_pixels_in_roi > 0:
import rasterio
import numpy as np
import matplotlib.pyplot as plt
from skimage import measure
from shapely.geometry import Polygon
from pyproj import Transformer
import rasterio.transform
from scipy.ndimage import binary_fill_holes # To fill inside polygons
from rasterio.warp import reproject, Resampling, calculate_default_transform
# Path to bathymetry raster file
path_bathymetry = "/Users/joaopimenta/Desktop/GEE_test/globathy_bathymetry.tif"
# Path to NDWI raster file (the one with the projection you want)
path_ndwi = ndwi_path
path_cloud_mask = cloud_mask_path
# Load Bathymetry image
bathymetry_dataset = rasterio.open(path_bathymetry)
cloud_mask_dataset = rasterio.open(path_cloud_mask)
ndwi_dataset = rasterio.open(path_ndwi)
# Reproject Bathymetry to the NDWI CRS if necessary
dst_crs = ndwi_dataset.crs
transform, width, height = calculate_default_transform(
bathymetry_dataset.crs, dst_crs, bathymetry_dataset.width, bathymetry_dataset.height, *bathymetry_dataset.bounds)
kwargs = bathymetry_dataset.meta.copy()
kwargs.update({
'crs': dst_crs,
'transform': transform,
'width': width,
'height': height
})
reprojected_bathymetry = np.empty((height, width), dtype=np.float32)
reproject(
source=rasterio.band(bathymetry_dataset, 1),
destination=reprojected_bathymetry,
src_transform=bathymetry_dataset.transform,
src_crs=bathymetry_dataset.crs,
dst_transform=transform,
dst_crs=dst_crs,
resampling=Resampling.nearest)
# Reproject cloud mask to the bathymetry CRS if necessary
if bathymetry_dataset.crs != cloud_mask_dataset.crs:
print("CRS misalignment detected. Reprojecting cloud mask to bathymetry CRS.")
reprojected_cloud_mask = np.empty_like(reprojected_bathymetry)
reproject(
source=rasterio.band(cloud_mask_dataset, 1),
destination=reprojected_cloud_mask,
src_transform=cloud_mask_dataset.transform,
src_crs=cloud_mask_dataset.crs,
dst_transform=transform,
dst_crs=dst_crs,
resampling=Resampling.nearest
)
else:
reprojected_cloud_mask = cloud_mask_dataset.read(1)
# Resample Bathymetry and cloud mask to match NDWI resolution if necessary
resampled_bathymetry = np.empty_like(ndwi_dataset.read(1))
resampled_cloud_mask = np.empty_like(ndwi_dataset.read(1))
reproject(
source=reprojected_bathymetry,
destination=resampled_bathymetry,
src_transform=transform,
src_crs=dst_crs,
dst_transform=ndwi_dataset.transform,
dst_crs=dst_crs,
resampling=Resampling.bilinear)
reproject(
source=reprojected_cloud_mask,
destination=resampled_cloud_mask,
src_transform=transform,
src_crs=dst_crs,
dst_transform=ndwi_dataset.transform,
dst_crs=dst_crs,
resampling=Resampling.nearest)
# Load bathymetry raster data
lakeBottom = resampled_bathymetry
resolution = bathymetry_dataset.res
lake_crs = bathymetry_dataset.crs
lake_transform = bathymetry_dataset.transform
# Load NDWI raster data (to get CRS and extent)
ndwi_extent = (ndwi_dataset.bounds.left, ndwi_dataset.bounds.right, ndwi_dataset.bounds.bottom, ndwi_dataset.bounds.top)
# Replace no-data value with np.nan for the bathymetry raster
noDataValue = lakeBottom[0, 0]
lakeBottom = lakeBottom.astype(float)
lakeBottom[lakeBottom == noDataValue] = np.nan
# Calculate minimum and maximum elevation
minElev = np.nanmin(lakeBottom)
maxElev = np.nanmax(lakeBottom)
# Define number of steps for calculation
nSteps = 50
elevSteps = np.round(np.linspace(minElev, maxElev, nSteps), 2)
# Define function to create a mask for a specific elevation
def createMaskForElevation(elevation, elevDem, cloud_mask):
# Create a mask based on the elevation
mask = np.where(elevDem <= elevation, 1, 0)
# Fill holes inside the polygon
filled_mask = binary_fill_holes(mask) * mask # Ensures it's a binary mask
waterarea = np.sum(mask) * (resolution[0] * resolution[1])
area_Array.append(waterarea)
# Apply the cloud mask, setting cloud-covered pixels to 0
filled_mask[cloud_mask == 1] = 0
return filled_mask
# Set up transformation to match NDWI CRS
transformer = Transformer.from_crs(lake_crs, ndwi_dataset.crs, always_xy=True)
# Arrays to store the areas for each elevation step
areaArray = []
area_Array = []
# Plot setup
fig, ax = plt.subplots(figsize=(12, 10))
colors = plt.cm.viridis(np.linspace(0, 1, len(elevSteps)))
for i, elev in enumerate(elevSteps):
# Create a mask for the current elevation and apply cloud mask
mask = createMaskForElevation(elev, lakeBottom, resampled_cloud_mask)
# Calculate water area by summing valid pixels (non-cloud, non-zero)
water_area = np.sum(mask) * (resolution[0] * resolution[1]) # Pixel resolution area
areaArray.append(water_area)
# Find contours (polygons) from the mask
contours = measure.find_contours(mask, 0.5)
# Reproject and plot each contour as a polygon
for contour in contours:
lon_lat_coords = rasterio.transform.xy(lake_transform, contour[:, 0], contour[:, 1])
x_coords, y_coords = np.array(lon_lat_coords[0]), np.array(lon_lat_coords[1])
# Reproject coordinates to NDWI CRS
x_proj, y_proj = transformer.transform(x_coords, y_coords)
# Plot the reprojected contour
ax.plot(x_proj, y_proj, color=colors[i], label=f'Elevation {elev} m' if i == 0 else "")
# Set the same extent as the NDWI image
ax.set_xlim(ndwi_extent[0], ndwi_extent[1])
ax.set_ylim(ndwi_extent[2], ndwi_extent[3])
# Plot the elevation vs area
areaArraySqM = [area * 1e8 for area in areaArray] # Convert to square meters
area_ArraySqM = [area * 1e8 for area in area_Array]
# Paths to the raster file
path = "/Users/joaopimenta/Desktop/GEE_test/globathy_bathymetry.tif"
# Load the raster data
lakeRst = rasterio.open(path)
lakeBottom = lakeRst.read(1)
# Raster resolution (in meters, assuming UTM projection)
resolution = lakeRst.res
print("Resolution:", resolution)
# Replace no-data value with np.nan
noDataValue = np.copy(lakeBottom[0, 0])
lakeBottom = lakeBottom.astype(float)
lakeBottom[lakeBottom == noDataValue] = np.nan
# Get the pixel size from raster resolution (in meters)
pixelArea = lakeRst.res[0] * lakeRst.res[1] # in square meters
# Calculate the area of the detected water bodies from binary mask
def calculate_area(image, transform):
# Mask the image to include only water
water_mask = image == 0
# Compute the area in square meters
pixel_area = abs(transform[0] * transform[4]) # pixel size (in square meters)
water_area_pixels = np.sum(water_mask)
total_area_m2 = water_area_pixels * pixel_area
return total_area_m2
# Define function to create mask for a specific elevation
def createMaskForElevation(elevation, elevDem):
mask = np.where(elevDem <= elevation, 1, 0) # White pixels for inundated area
return mask
# Arrays to store the areas for each elevation step
area_normal_Array = []
# Plot all polygons representing water area for each elevation step
fig, ax = plt.subplots(figsize=(12, 10))
# Colors for different elevation levels
colors = plt.cm.viridis(np.linspace(0, 1, len(elevSteps)))
# Loop over each elevation step, calculate area, and plot polygons
for i, elev in enumerate(elevSteps):
# Create a mask for the current elevation step
mask = createMaskForElevation(elev, lakeBottom)
# Calculate the water area at this elevation
waterArea = np.sum(mask) * pixelArea # sum of all '1' pixels * pixel area
area_normal_Array.append(waterArea) # Store the area for this elevation
# Find contours (polygons) from the mask
contours = measure.find_contours(mask, 0.5)
# Plot each contour as a polygon
for contour in contours:
# Transform contour coordinates to UTM coordinates using the raster transform
utm_coords = rasterio.transform.xy(lakeRst.transform, contour[:, 0], contour[:, 1])
x_coords, y_coords = np.array(utm_coords[0]), np.array(utm_coords[1])
# Plot the polygon for the current elevation step
ax.plot(x_coords, y_coords, color=colors[i], label=f'Elevation {elev} m' if i == 0 else "")
# Plot the elevation vs area
# Multiply the area by 1,000,000 to convert from km² to m² if necessary
area_normal_ArraySqM = [area * 1e8 for area in area_normal_Array] # Convert to square meters
# Function to create a binary mask for the chosen elevation
def createMaskForElevation(elevation, elevDem, resolution):
# Step 1: Generate the initial binary mask (1 for water, 0 for no water)
mask = np.where(elevDem <= elevation, 1, 0)
# Step 2: Fill the holes inside the lake region
filled_mask = binary_fill_holes(mask) * mask # Fill holes only inside the mask
# Step 3: Create a mask for the lake region (anything inside the boundary is considered lake)
lake_mask = np.where(np.isnan(elevDem), 1, 0) # NaN represents outside the lake
# Step 4: Assign a value of 1 to everything outside the lake region
result_mask = np.where(lake_mask == 1, 1, filled_mask)
# Step 5: Calculate the inundated area for the white pixels inside the lake
area = np.sum(filled_mask) * resolution[0] * resolution[1]
return result_mask, area
# Ensure differences, areaArraySqM, and elev are arrays or lists
differences = []
for area in areaArraySqM:
dif = abs(water - area) # Absolute difference
differences.append(dif)
print(f"The water area without the cloud pixels is: {water}")
# Find the index of the smallest difference
best_match_index = differences.index(min(differences))
best_match = area_ArraySqM[best_match_index]
# Reverse the elevation steps and convert to a list to allow indexing
step_elevation_reversed = list(reversed(elevSteps))
# Allow user to input a specific elevation based on the best match index
specificElevation = step_elevation_reversed[best_match_index]
# Generate the binary mask and calculate the area for the selected elevation
maskForSpecificElevation, specificArea = createMaskForElevation(specificElevation, lakeBottom, resolution)
# Load NDWI image and bathymetry mask
ndwi_dataset = rasterio.open(path_ndwi) # Path to NDWI image
ndwi_crs = ndwi_dataset.crs
ndwi_transform = ndwi_dataset.transform
ndwi_res = ndwi_dataset.res
# Ensure the mask for the specific elevation is reprojected to the NDWI's CRS, extent, and resolution
mask_for_elevation_reprojected = np.empty_like(ndwi_dataset.read(1))
reproject(
source=maskForSpecificElevation, # Mask to reproject
destination=mask_for_elevation_reprojected,
src_transform=lake_transform, # Transform from the bathymetry mask
src_crs=lake_crs, # CRS of the mask
dst_transform=ndwi_transform, # NDWI transform
dst_crs=ndwi_crs, # NDWI CRS
resampling=Resampling.nearest # Nearest neighbor interpolation for binary masks
)
# Now overlay the NDWI image with the mask
ndwi_image = ndwi_dataset.read(1) # Read the NDWI image (band 1)
# Assign value 1 to NDWI where mask is 1
ndwi_image[mask_for_elevation_reprojected == 0] = 1
# Save the NDWI image locally as a GeoTIFF
output_path = '/Users/joaopimenta/Desktop/GEE_test/reconstructed_plygon.tif' # Define the output path for the saved image
# Retrieve the metadata from the NDWI dataset to use it for saving the file
meta = ndwi_dataset.meta.copy()
# Update metadata for a single band output
meta.update({
'dtype': 'float32', # or 'uint8' depending on the NDWI data type
'count': 1, # Number of bands
'driver': 'GTiff', # Save as a GeoTIFF file
'crs': ndwi_crs, # Coordinate reference system
'transform': ndwi_transform # Affine transform for georeferencing
})
# Save the NDWI image with the mask applied as a GeoTIFF
with rasterio.open(output_path, 'w', **meta) as dst:
dst.write(ndwi_image.astype('float32'), 1) # Write the NDWI data to band 1
print(f'Saved NDWI image as {output_path}')
# Create a figure with 2 subplots side by side
fig, axes = plt.subplots(1, 2, figsize=(12, 6)) # 1 row, 2 columns
# Plot the first contour: Identified contour (K-means method) on binary_ndwi
contour_image_binary = np.zeros_like(binary_ndwi)
contours_binary, _ = cv2.findContours(binary_ndwi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if contours_binary:
cv2.drawContours(contour_image_binary, [max(contours_binary, key=cv2.contourArea)], -1, (255), 2)
axes[0].imshow(contour_image_binary, cmap='gray')
axes[0].set_title('Polygon of the NDWI affected by clouds')
# Plot the second contour: Identified contour (K-means method) on ndwi_image
contour_image_ndwi = np.zeros_like(ndwi_image)
contours_ndwi, _ = cv2.findContours(ndwi_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if contours_ndwi:
cv2.drawContours(contour_image_ndwi, [max(contours_ndwi, key=cv2.contourArea)], -1, (255), 2)
axes[1].imshow(contour_image_ndwi, cmap='gray')
axes[1].set_title('Polygon of the reconstructed Image')
# Show the plots
plt.tight_layout()
plt.show()
# Create contour image for binary NDWI
contour_image_binary = np.zeros_like(binary_ndwi)
contours_binary, _ = cv2.findContours(binary_ndwi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if contours_binary:
cv2.drawContours(contour_image_binary, [max(contours_binary, key=cv2.contourArea)], -1, (255), 2)
# Create contour image for NDWI image
contour_image_ndwi = np.zeros_like(ndwi_image)
contours_ndwi, _ = cv2.findContours(ndwi_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if contours_ndwi:
# Draw only the largest contour for the NDWI image
largest_contour = max(contours_ndwi, key=cv2.contourArea)
cv2.drawContours(contour_image_ndwi, [max(contours_ndwi, key=cv2.contourArea)], -1, (255), 2)
# Stack the binary contour and NDWI contour into a 3-channel image for overlay (Red, Green, Blue)
overlay_image = np.zeros((contour_image_binary.shape[0], contour_image_binary.shape[1], 3), dtype=np.uint8)
overlay_image[..., 0] = contour_image_binary # Red channel for binary NDWI contour
overlay_image[..., 1] = contour_image_ndwi # Green channel for NDWI image contour
# Create a mask to fill the inside of the largest NDWI contour
mask = np.zeros_like(ndwi_image, dtype=np.uint8)
if contours_ndwi:
cv2.drawContours(mask, [largest_contour], -1, (255), thickness=cv2.FILLED)
# Create a new output image, initialized to zeros (0 for a black image)
output_image = np.zeros_like(ndwi_image, dtype=np.uint8)
# Set the inside of the largest contour to one (255)
output_image[mask == 255] = 1 # Change to fill with pixel value 1
# Optionally convert to uint8 range for visualization
output_image *= 255 # If you need the output image to be in the 0-255 range
# Save the NDWI image locally as a GeoTIFF
output_path = '/Users/joaopimenta/Desktop/GEE_test/reconstructed_water_mask.tif' # Define the output path for the saved image
# Retrieve the metadata from the NDWI dataset to use it for saving the file
meta = ndwi_dataset.meta.copy()
# Update metadata for a single band output
meta.update({
'dtype': 'float32', # or 'uint8' depending on the NDWI data type
'count': 1, # Number of bands
'driver': 'GTiff', # Save as a GeoTIFF file
'crs': ndwi_crs, # Coordinate reference system
'transform': ndwi_transform # Affine transform for georeferencing
})
# Save the NDWI image with the mask applied as a GeoTIFF
with rasterio.open(output_path, 'w', **meta) as dst:
dst.write(output_image.astype('float32'), 1) # Write the NDWI data to band 1
print(f'Saved NDWI image as {output_path}')
# If resolution is a tuple (x_resolution, y_resolution)
x_resolution, y_resolution = resolution
pixel_area = x_resolution * y_resolution # Area of one pixel in square meters
# Count water pixels
water_pixels = ndwi_image > 0
water_pixel_count = np.sum(water_pixels)
# Calculate total water area
total_water_area = water_pixel_count * pixel_area*1e8
# Print the result
print(f'Total water area: {total_water_area} square meters')
water_area_info.append(total_water_area)
# Optionally, save the modified NDWI image as a new file
out_meta = ndwi_dataset.meta.copy()
with rasterio.open('ndwi_with_elevation_mask.tif', 'w', **out_meta) as dst:
dst.write(ndwi_image, 1) # Write the new image to disk
# Close datasets
ndwi_dataset.close()
else:
print("There are no cloud pixels inside the reservoir's area.")
water_area_info.append(waterarea)
st.write(water_area_info)
else:
# Options for confirming the reservoir selection
water_method = ['Fixed thershold', 'Dynamic']
# Select box to confirm selection
water = st.selectbox("Choose this method of identifying water pixels", water_method)
def extract_bbox_from_aoi(aoi):
# Get the bounding box of the AOI
bounds = aoi.bounds().getInfo()
# Extract the coordinates based on the observed structure
try:
lon_min = bounds['coordinates'][0][0][0] # First point's longitude
lat_min = bounds['coordinates'][0][0][1] # First point's latitude
lon_max = bounds['coordinates'][0][2][0] # Third point's longitude
lat_max = bounds['coordinates'][0][2][1] # Third point's latitude
return lat_min, lon_min, lat_max, lon_max
except (IndexError, KeyError, TypeError):
print("Unexpected bounds structure:", bounds)
raise ValueError("Unable to extract bounding box; check structure of bounds data.")
lat_min, lon_min, lat_max, lon_max = extract_bbox_from_aoi(roi)
# Function to query bridges
def check_bridge_in_area(lat_min, lon_min, lat_max, lon_max):
overpass_url = "http://overpass-api.de/api/interpreter"
overpass_query = f"""
[out:json];
(
way["man_made"="bridge"]({lat_min},{lon_min},{lat_max},{lon_max});
node(w);
);
out body qt;
"""
print(f"Querying Overpass API with:\n{overpass_query}")
response = requests.get(overpass_url, params={'data': overpass_query})
if response.status_code == 200:
print("Received response from Overpass API.")
return response.json()
else:
print(f"Error: {response.status_code}")
return None
# Query the Overpass API for bridges in the bounding box
bridge_data = check_bridge_in_area(lat_min, lon_min, lat_max, lon_max)
# Initialize lists for GeoDataFrame
bridge_names = []
elem = None
# Parse and display bridges with their shapes and names
if bridge_data and 'elements' in bridge_data:
node_coords = { # Store node coordinates for reference
int(node['id']): (node['lat'], node['lon'])
for node in bridge_data['elements']
if node['type'] == 'node'
}
if len(node_coords) > 0:
print(f"Node coordinates found: {node_coords}")
else:
print("No node coordinates found.")
for element in bridge_data['elements']:
if element['type'] == 'way':
elem = element['type']
bridge_name = element.get('tags', {}).get('name')
if bridge_name: # Check if bridge has a name
st.write(f"It was detected the bridge {bridge_name} inside the ROI")
coords = [node_coords.get(node_id) for node_id in element.get('nodes', [])]
coords = [coord for coord in coords if coord is not None] # Remove invalid coordinates
else:
st.write(f"Bridge ID: {element['id']} has no name and will be excluded.")
else:
st.write("No bridge data or 'elements' not in the response.")
if water == 'Fixed thershold':
# Define a function to calculate NDWI and mask for each image
def calculate_ndwi_and_mask(image):
ndwi = image.normalizedDifference(['B3', 'B8']).rename('NDWI')
ndwi_threshold = ndwi.gte(0.0)
ndwi_mask = ndwi_threshold.updateMask(ndwi_threshold)
return ndwi_mask
# Map the function over the image collection to get NDWI masks for each image
ndwi_masks = sentinelImageCollection.map(calculate_ndwi_and_mask)
# Perform erosion (shrinking the mask slightly to remove small gaps and noise)
eroded_ndwi = ndwi_masks.map(lambda img: img.focal_min(radius=1, kernelType='circle', iterations=1))
# Perform dilation after erosion (expanding the mask back to restore shape)
closed_ndwi = eroded_ndwi.map(lambda img: img.focal_max(radius=1, kernelType='circle', iterations=1))
# Now, closed_ndwi contains the NDWI masks that have been eroded and then dilated for each image in the collection.
# Define a function to calculate water area
def calculate_water_area(image):
water_area = image.multiply(ee.Image.pixelArea()).reduceRegion(
reducer=ee.Reducer.sum(),
geometry=roi,
bestEffort=True,
scale=5
).get('NDWI')
return image.set('water_area', water_area)
if elem is not None:
# Map the function over the NDWI masks to calculate water area for each image
ndwi_masks_with_area = closed_ndwi.map(calculate_water_area)
else:
# Map the function over the NDWI masks to calculate water area for each image
ndwi_masks_with_area = ndwi_masks.map(calculate_water_area)
# Get the water area information
water_area_info = ndwi_masks_with_area.aggregate_array('water_area').getInfo()
# Display the list of water areas
#st.write(water_area_info)
# Get acquisition dates in human-readable format
dates = ndwi_masks_with_area.aggregate_array('system:time_start') \
.map(lambda d: ee.Date(d).format('YYYY-MM-dd')).getInfo()
# Display the dates
#st.write("Acquisition dates for each image:", dates)
# Alternatively, convert acquisition times to readable format (if needed)
acquisition_times = sentinelImageCollection.aggregate_array('system:time_start').getInfo()
acquisition_dates = [datetime.datetime.utcfromtimestamp(time / 1000).strftime('%Y-%m-%d') for time in acquisition_times]
#st.write("Alternative acquisition dates:", acquisition_dates)
else:
# Define a function to calculate NDWI
def calculate_ndwi(image):
ndwi = image.normalizedDifference(['B3', 'B8']).rename('NDWI')
return image.addBands(ndwi)
# Define a function to sample NDWI values for clustering
def sample_ndwi(image):
ndwi = image.select('NDWI')
sampled_ndwi = ndwi.sample(
region=roi_geometry,
scale=10,
numPixels=10000,
seed=0
).select('NDWI')
return sampled_ndwi
# Define a function to perform K-means clustering
def cluster_ndwi(sampled_ndwi):
clusterer = ee.Clusterer.wekaKMeans(2).train(sampled_ndwi)
return clusterer
# Define a function to determine the water cluster
def get_water_cluster(clustered_image):
mean_ndwi_per_cluster = clustered_image.reduceRegion(
reducer=ee.Reducer.mean(),
geometry=roi_geometry,
scale=10
)
mean_values = ee.List(mean_ndwi_per_cluster.values())
water_cluster = mean_values.indexOf(mean_values.reduce(ee.Reducer.max()))
return water_cluster
# Define a function to create a binary water mask based on the cluster
def create_water_mask(clustered_image, water_cluster):
water_mask = clustered_image.eq(water_cluster).rename('water_mask')
return water_mask
# Define a function to compute the area of water bodies in square meters
def compute_water_area(water_mask):
water_area = water_mask.reduceRegion(
reducer=ee.Reducer.sum(),
geometry=roi_geometry,
scale=10
).get('water_mask')
water_area = ee.Number(water_area).multiply(100).divide(1e4) # Convert to square kilometers
return water_area
if elem is not None:
# Combine all functions into one for mapping
def process_image(image):
# Calculate NDWI
image = calculate_ndwi(image)
# Sample and cluster NDWI for water detection
sampled_ndwi = sample_ndwi(image)
clusterer = cluster_ndwi(sampled_ndwi)
clustered_image = image.select('NDWI').cluster(clusterer).rename('cluster')
# Determine which cluster represents water
water_cluster = get_water_cluster(clustered_image)
water_mask = create_water_mask(clustered_image, water_cluster)
# Perform morphological operations (closing)
eroded_ndwi = water_mask.focal_min(radius=1, kernelType='circle', iterations=1)
closed_ndwi = eroded_ndwi.focal_max(radius=1, kernelType='circle', iterations=1)
water_area = compute_water_area(closed_ndwi)
return image.set('water_area_km2', water_area)
else:
# Combine all functions into one for mapping
def process_image(image):
image = calculate_ndwi(image)
sampled_ndwi = sample_ndwi(image)
clusterer = cluster_ndwi(sampled_ndwi)
clustered_image = image.select('NDWI').cluster(clusterer).rename('cluster')
water_cluster = get_water_cluster(clustered_image)
water_mask = create_water_mask(clustered_image, water_cluster)
water_area = compute_water_area(water_mask)
return image.set('water_area_km2', water_area)
# Apply the processing function to each image in the collection
processed_images = sentinelImageCollection.map(process_image)
# Extract the water area and date information
water_area_info = processed_images.aggregate_array('water_area_km2').getInfo()
dates = processed_images.aggregate_array('system:time_start').map(lambda d: ee.Date(d).format('YYYY-MM-dd')).getInfo()
# Get acquisition times of the images
acquisition_times = sentinelImageCollection.aggregate_array('system:time_start').getInfo()
# Convert acquisition times to human-readable dates
acquisition_dates = [datetime.datetime.utcfromtimestamp(time / 1000).strftime('%Y-%m-%d') for time in acquisition_times]
if method == ("Write the A-V function of your reservoir"):
for area in water_area_info:
volume = None
# Calculate the volume using the area-storage equation
volume = (area / a) ** (1 / b)
if volume is not None:
volumes.append(volume)
else:
st.write("Error with the coefficients")
st.write(f"The list of the volumes in cubic meters for the chosen dates is: {volumes}")
elif method == ("upload excel sheet"):
if dictionary:
for area in water_area_info:
volume = None
keys = sorted(dictionary.keys())
for i in range(len(keys)):
key = keys[i]
if key >= area:
if i == 0:
volume = dictionary[key]
st.write(f"This is the volume {volume/10**6}km³")
volumes.append(volume)
else:
prev_key = keys[i - 1]
delta_volume = dictionary[key] - dictionary[prev_key]
delta_key = key - prev_key
delta_area = area - prev_key
interpolated_volume = dictionary[prev_key] + (delta_volume * delta_area / delta_key)
volume = (interpolated_volume/10**6)
st.write(f"This is the volume {volume}km³")
volumes.append(volume)
break
else:
# This else block belongs to the for loop, not the if condition
st.write("Dam value not found in the dictionary ")
elif method == ("upload the DEM"):
if dictionary:
for area in water_area_info:
volume = None
keys = sorted(dictionary.keys())
for i in range(len(keys)):
key = keys[i]
if key >= area:
if i == 0:
volume = dictionary[key]
st.write(f"This is the volume {volume/10**6}km³")
volumes.append(volume)
else:
prev_key = keys[i - 1]
delta_volume = dictionary[key] - dictionary[prev_key]
delta_key = key - prev_key
delta_area = area - prev_key
interpolated_volume = dictionary[prev_key] + (delta_volume * delta_area / delta_key)
volume = (interpolated_volume/10**6)
st.write(f"This is the volume {volume}km³")
volumes.append(volume)
break
else:
# This else block belongs to the for loop, not the if condition
st.write("Dam value not found in the dictionary ")
elif method == "Don't have that info":
import netCDF4 as nc
import numpy as np
volumes =[]
# Open the NetCDF file
nc_file = nc.Dataset('/Users/joaopimenta/Downloads/Master thesis/GLOBathy_hAV_relationships.nc')
# Specify the lake ID you want to search for
target_lake_id = hydrolakes_id # Replace this with the actual lake ID you're interested in
# Find the index of the lake based on the lake ID
lake_ids = nc_file.variables['lake_id'][:]
# Check if the target lake ID exists in the lake_id variable
lake_index = np.where(lake_ids == target_lake_id)[0]
if len(lake_index) == 0:
st.write("Lake not found in the dataset.")
else:
lake_index = lake_index[0] # Use the first match if found
# Extract coefficients of the area-storage equation for the identified lake
area_storage_coeffs = nc_file.variables['f_hA'][lake_index, :]
lon_lat = nc_file.variables['lon_lat'][lake_index, :]
# Print the lake's ID, coordinates, and area-storage equation coefficients
st.write("Coordinates (Lon, Lat):", lon_lat)
# Print the coefficients
st.write("Area-Storage equation coefficients:")
st.write("a:", area_storage_coeffs[0])
st.write("b:", area_storage_coeffs[1])
st.write("R^2:", area_storage_coeffs[2])
import numpy as np
for area in water_area_info:
# Coefficients obtained from the NetCDF dataset
a = area_storage_coeffs[0]
b = area_storage_coeffs[1]
# Calculate the volume using the area-storage equation
volume = ((area/1e6) / a) ** (1 / b)
volumes.append(volume)
from io import BytesIO
# Function to generate the sample data DataFrame
def generate_sample_data():
date = acquisition_dates
area = water_area_info
vol = volumes
return pd.DataFrame({'Date': date, 'Volume (10⁸m³)': vol, 'Area (km²)': area })
# Generate the sample data DataFrame
df = generate_sample_data()
# Save the DataFrame to an Excel file in memory
excel_buffer = BytesIO()
import pandas as pd
import io
# Assuming excel_buffer and output, area_storage_coeffs are defined elsewhere in your code
excel_buffer = io.BytesIO()
# Use a single `ExcelWriter` for writing all sheets
with pd.ExcelWriter(excel_buffer, engine='xlsxwriter') as writer:
# Check if 'Water Surface Elevation' is in output and write relevant data
if 'Water Surface Elevation' in output:
# Convert date to timezone-unaware if necessary
elevation_dates = pd.to_datetime(df_filtered['time_str']).dt.tz_localize(None)
elevations = df_filtered['wse']
df_2 = pd.DataFrame({'Date': elevation_dates, 'Water Surface Elevations': elevations})
df_2.to_excel(writer, sheet_name='Elevations Data', index=False)
# Check if 'Storage-Capacity curve' is in output and write relevant data
if 'Storage-Capacity curve' in output:
# Assume `area_storage_coeffs` contains appropriate data in tuple or list format
df_3 = pd.DataFrame({
'a': [area_storage_coeffs[0]],
'b': [area_storage_coeffs[1]],
'R^2': [area_storage_coeffs[2]]
})
df_3.to_excel(writer, sheet_name='Storage_capacity_curve', index=False)
# Assuming `df` is a base DataFrame you want to write to a default sheet
df.to_excel(writer, sheet_name='Reservoir Data', index=False)
# Reset buffer position to the start for reading/download
excel_buffer.seek(0)
# Create a download button for the Excel file
st.download_button(
label="Download Excel file",
data=excel_buffer,
file_name="reservoir_data.xlsx",
mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
)
st.write("Click the button above to download the data as an Excel file.")
import tempfile
# Load the bathymetry dataset from Earth Engine
globathy = ee.Image("projects/sat-io/open-datasets/GLOBathy/GLOBathy_bathymetry")
# Define the function to export the image and return the path
def export_image_for_download(image, roi, scale=10):
# Use a temporary directory for saving the file
with tempfile.NamedTemporaryFile(delete=False, suffix=".tif") as temp_file:
out_image_path = temp_file.name
geemap.ee_export_image(image, filename=out_image_path, scale=scale, region=roi)
return out_image_path
if 'Bathymetry file' in output:
# Call the function and set up the download button
if st.button("Download Image"):
# Assuming `globathy` is your Earth Engine image and `roi` is the region of interest
image_path = export_image_for_download(globathy, roi)
# Read the file as bytes for download
with open(image_path, "rb") as file:
file_bytes = file.read()
st.download_button(
label="Click here to download the image",
data=file_bytes,
file_name="exported_image.tif",
mime="image/tiff"
)
# Display the bar charts
col1, col2= st.columns([7,3])
# Combine acquisition dates and volumes into a list of tuples
with col1:
import pandas as pd
import altair as alt
# Function to generate sample data
def generate_sample_data():
date = acquisition_dates
vol= volumes
return pd.DataFrame({'Date': date, 'Volume': vol})
# Sample data
df = generate_sample_data()
if 'Water Surface Elevation' in output:
st.subheader("WSE over the chosen date range")
# Convert 'time_str' to timezone-unaware and set up DataFrame for plotting
elevation_dates = pd.to_datetime(df_filtered['time_str']).dt.tz_localize(None)
elevations = df_filtered['wse']
df_wse = pd.DataFrame({'Date': elevation_dates, 'Water Surface Elevations': elevations})
# Create the line chart for water surface elevations
wse_chart = alt.Chart(df_wse).mark_line(
color='#00FFFF'
).encode(
x=alt.X('Date:T', title='Date'),
y=alt.Y('Water Surface Elevations:Q', title='Water Surface Elevation (m)')
)
# Display the WSE chart in Streamlit
st.altair_chart(wse_chart, use_container_width=True)
st.subheader("Volume over the chosen date range")
# Ensure that 'Date' and 'Volume' columns are available in df
volume_chart = alt.Chart(df).mark_line(
color='#00FFFF'
).encode(
x=alt.X('Date:T', title='Date'),
y=alt.Y('Volume:Q', title='Volume (10⁶ m³)',scale=alt.Scale(zero=False))
)
# Display the volume chart in Streamlit
st.altair_chart(volume_chart, use_container_width=True)
with col2:
import pandas as pd
# Donut chart function
def make_donut(input_response, input_text, input_color):
if input_color == 'green':
chart_color = ['#27AE60', '#12783D']
elif input_color == 'red':
chart_color = ['#E74C3C', '#781F16']
elif input_color == 'yellow':
chart_color = ['#FFFF00', '#FFD700'] # Yellow colors
elif input_color == 'orange':
chart_color = ['#FFA500', '#FF4500'] # Orange colors
elif input_color == 'light green':
chart_color = ['#90EE90', '#006400'] # Light green colors
else:
raise ValueError("Invalid color. Please choose either 'green' or 'red'.")
source = pd.DataFrame({
"Topic": ['', input_text],
"% value": [100-input_response, input_response]
})
source_bg = pd.DataFrame({
"Topic": ['', input_text],
"% value": [100, 0]
})
plot = alt.Chart(source).mark_arc(innerRadius=45, cornerRadius=25).encode(
theta="% value",
color=alt.Color("Topic:N",
scale=alt.Scale(
domain=[input_text, ''],
range=chart_color),
legend=None),
).properties(width=130, height=130)
text = plot.mark_text(align='center', color=chart_color[0], font="sans-serif", fontSize=20, fontWeight=500, fontStyle="italic").encode(text=alt.value(f'{input_response} %'))
plot_bg = alt.Chart(source_bg).mark_arc(innerRadius=45, cornerRadius=20).encode(
theta="% value",
color=alt.Color("Topic:N",
scale=alt.Scale(
domain=[input_text, ''],
range=chart_color), # 31333F
legend=None),
).properties(width=130, height=130)
return plot_bg + plot + text
def get_color(value):
"""Helper function to determine the color based on percentage."""
if value < 25:
return 'red'
elif 25 <= value < 50:
return 'orange'
elif value == 50:
return 'yellow'
elif 50 < value < 75:
return 'light green'
else:
return 'green'
# Check if storage is not None, not an empty string, and can be converted to a float
if Vol_res is not None:
try:
storage_float = Vol_res/10
if storage_float > 0:
total_volume = storage_float
worst = (min(volumes) / total_volume) * 100
best = (max(volumes) / total_volume) * 100
# Colors for worst and best day
wrst_color = get_color(worst)
bst_color = get_color(best)
# Display donut charts
st.subheader("Lower storage")
st.altair_chart(make_donut(round(worst, 2), 'Worst day', wrst_color), use_container_width=True)
st.subheader("Higher storage")
st.altair_chart(make_donut(round(best, 2), 'Best day', bst_color), use_container_width=True)
except ValueError:
st.write("Invalid storage value; cannot convert to float.")
# Fallback if storage is invalid or not provided, and ref_area is available
elif properties and ref_area is not None:
ref_area_float = (float(ref_area)*1e6)
worst = (min(water_area_info) / ref_area_float) * 100
best = (max(water_area_info) / ref_area_float) * 100
# Colors for worst and best day
wrst_color = get_color(worst)
bst_color = get_color(best)
# Display fallback donut charts
st.subheader("Lower storage")
st.altair_chart(make_donut(round(worst, 2), 'Worst day', wrst_color), use_container_width=True)
st.subheader(" Higher storage")
st.altair_chart(make_donut(round(best, 2), 'Best day', bst_color), use_container_width=True)
def calculate_max_percentage_variation(volumes, acquisition_dates):
max_variation = 0
max_variation_index = None
for i in range(1, len(volumes)):
# Calculate percentage variation
percentage_variation = abs((volumes[i] - volumes[i - 1]) / volumes[i - 1]) * 100
# Update max variation and index if current variation is greater
if percentage_variation > max_variation:
max_variation = percentage_variation
max_variation_index = i - 1 # Store index of the first date in the pair
# Get the dates corresponding to the max variation
date1 = acquisition_dates[max_variation_index]
date2 = acquisition_dates[max_variation_index + 1]
return max_variation, date1, date2
max_variation, date1, date2 = calculate_max_percentage_variation(volumes, acquisition_dates)
import statistics
std_dev = round(statistics.stdev(volumes),2)
mean_vol = round(statistics.mean(volumes),2)
mean_area = round(statistics.mean(water_area_info),2)
max_variation_area, date1, date2 = calculate_max_percentage_variation(water_area_info, acquisition_dates)
#st.write(" The greates variation occured between:", date1, "and", date2)
if max_variation >=0:
delta = 1
else:
delta = -1
max_area = max(water_area_info)
max_volume = max(volumes)
current_volume = round(volumes[-1],2)
current_area = round(water_area_info[-1],2)
#create column span
col1, col2, col3 = st.columns(3)
#Customize metric style to have white text color
metric_style = "color: black;"
col1.metric(label="Max variation", value="%" + " " + f"{max_variation:,.2f}", delta=delta)
col2.metric(label="Mean area", value="km²" + " " + f"{mean_area/1e6:,.2f}", delta=round(max_variation_area,2))
col3.metric(label="standard deviation", value = "km³ " + " " + f"{std_dev:,.2f}")
st.write(" The greates variation occured between:", date1, "and", date2)
# Create a select box for asking ChatGPT about this reservoir
opt = ["No","Yes"]
info = st.selectbox(
f"Ask ChatGPT more information about this lake {lake_name}",
opt,
key="info")
if info == "Yes":
import streamlit as st
from openai import OpenAI
# Define the reservoir information retrieval function
def get_reservoir_info(lake_name, country, lng, lat):
client = OpenAI(api_key="sk-proj-uK1IbwMXiNImV7RFDxr3T3BlbkFJXjBzHIIJYRoQbiJE6Kc5")
prompt = (
f"Provide detailed information about the dam/reservoir {lake_name}, "
f"located in {country}, with coordinates (longitude: {lng}, latitude: {lat}). "
f"Use short sentences and split the response into concise, clear lines."
f"Specify the type of use of that reservoir or lake and when it wast built. "
)
stream = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": prompt}],
stream=True,
)
response_content = ""
for chunk in stream:
if chunk.choices[0].delta.content is not None:
response_content += chunk.choices[0].delta.content
return response_content
st.subheader(f"Information about the reservoir {lake_name}")
info_text = get_reservoir_info(lake_name, Country, lng, lat)
# Displaying the information with line breaks
for line in info_text.split(". "): # Split into sentences
st.text(line.strip() + ".")
else:
st.write("Please select the date range and cloud coverage thershold for the analysis.")
else:
st.write("Please search on the map the lake you want to analyse and click on it to select it") |