Spaces:
Sleeping
Sleeping
| # -*- coding: utf-8 -*- | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import leafmap.foliumap as leafmap | |
| import rioxarray | |
| import geopandas as gpd | |
| import streamlit as st | |
| import altair as alt | |
| import ibis | |
| from ibis import _ | |
| import ibis.selectors as s | |
| from streamlit_folium import st_folium | |
| import json | |
| def extract_geom(gdf, cog): | |
| x = (rioxarray. | |
| open_rasterio('/vsicurl/'+cog, masked=True). | |
| rio.clip(gdf.geometry.values, gdf.crs, from_disk=True) | |
| ) | |
| return x | |
| def read_polygon(polygon): | |
| geojson_str = json.dumps(polygon) | |
| gdf = gpd.read_file(geojson_str, driver='GeoJSON') | |
| gdf.set_crs('epsg:4326') | |
| return gdf | |
| def area_hectares(gdf): | |
| area = gdf.to_crs("EPSG:9822").area / 10000. | |
| return area | |
| # + | |
| st.set_page_config(layout="wide", page_title="Paradise Valley", page_icon="⚡") | |
| st.title("Land Use change in Paradise Valley") | |
| DESCRIPTION=''' | |
| Pan and zoom to the desired location on the map. Then, use the map tools to draw a polygon (pentagon tool), bounding box (square tool) or other shape anywhere on the map. | |
| (use esc key to exit drawing mode). Map will display acres of land converted. | |
| ''' | |
| built = "https://huggingface.co/datasets/boettiger-lab/yellowstone/resolve/main/diffs_cog.tif" | |
| m = leafmap.Map(center=(-110.6, 45.45), zoom=8) | |
| m.add_basemap("Esri.NatGeoWorldMap") | |
| ## Map controls sidebar | |
| with st.sidebar: | |
| st.markdown(DESCRIPTION) | |
| cog_layers = { | |
| "Expanded Built Land, 2017-2023": built, | |
| } | |
| selection = st.radio("Data", cog_layers) | |
| cog = cog_layers[selection] | |
| m.add_cog_layer(cog, name=selection, palette="reds", | |
| opacity = 0.3, | |
| transparent_bg = True, | |
| zoom_to_layer=True) | |
| st_data = m.to_streamlit(height=400, bidirectional=True) | |
| polygon = st_data["last_active_drawing"] | |
| # Here we actually compute the total carbon in the requested polygon | |
| if polygon is not None: | |
| gdf = read_polygon(polygon) | |
| x = extract_geom(gdf, cog).fillna(0) | |
| count = x.count() | |
| impact_pixels = x.sum() | |
| area = round(float(area_hectares(gdf))) | |
| impact_area = area * impact_pixels / count | |
| carbon_total = round(float(x.mean()) * area) # no, mean does not include zeros | |
| col2, col3 = st.columns(2) | |
| col2.metric(label=f"Area", value=f"{impact_area:,.2f} Hectares") | |
| col3.metric(label=f"pixels", value=f"{count:,}") | |
| # pixel sums instead of means | |
| # value = round(float(x.sum())) | |
| # if(selection in ["Vulnerable Carbon (2018)", | |
| # "Manageable Carbon (2018)", | |
| # "Irrecoverable Carbon (2018)"]): | |
| # value = value * 9 # 300m pixels, each pixel is 9 hectres | |
| st.divider() | |
| ''' | |
| Note: this is just a proof-of-principle demonstration of these tools, and these calculations have not been validated. | |
| ## Credits | |
| ### Data sources | |
| - <https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02> | |
| ### Software stack | |
| - Streamlit (python) app hosted on free-tier HuggingFace spaces ([source code](https://huggingface.co/spaces/boettiger-lab/leafmap/blob/main/app.py)). | |
| - Cloud-optimized geotifs hosted on [Source.Coop](https://source.coop) | |
| - Mapping with Leafmap, calculations with rasterio | |
| ''' | |