File size: 3,865 Bytes
7d1d8b9 |
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 |
# -*- 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=[35, -100], zoom=3)
## 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, palette="reds", name=selection,
transparent_bg=True, opacity = 0.8,
zoom_to_layer=False)
"### python code for map layer:"
"adjust options or add additional layers using leafmap"
code = st.text_area(
label = "code:",
value = code_ex,
height = 400)
# run whatever python code is in the python box, just for fun
eval(compile(code, "<string>", "exec"))
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()
area = round(float(area_hectares(gdf)))
carbon_total = round(float(x.mean()) * area) # no, mean does not include zeros
col2, col3 = st.columns(3)
col2.metric(label=f"Area", value=f"{area:,} 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
'''
|