# Step 0: Libraries
import os
from pathlib import Path
import geopandas as gpd
import rasterio
import rioxarray as rxr
from rasterio.plot import show
import matplotlib.pyplot as plt
import zipfile

# Step 0.1: Add parameters

plt.rcParams['figure.figsize'] = (8, 8)

# Current notebook directory, e.g. .../ramona-fire-recovery/notebooks/02_fire_perimeters
NOTEBOOK_DIR = Path().resolve()

# Project root is two levels up: 02_fire_perimeters -> notebooks -> ramona-fire-recovery
PROJECT_ROOT = NOTEBOOK_DIR.parents[1]

DATA_DIR = PROJECT_ROOT / 'data'
BOUNDARY_DIR = DATA_DIR / 'boundaries'
IMAGERY_DIR = DATA_DIR / 'raw' / 'imagery'
FIG_DIR = Path(r"C:\Users\kayle\Desktop\earth-analytics\Final project\ramona-fire-recovery\figures\perimeters")
FIG_DIR.mkdir(parents=True, exist_ok=True)

# Project to California Albers
target_crs = 'EPSG:3310'

print("NOTEBOOK_DIR:", NOTEBOOK_DIR)
print("PROJECT_ROOT:", PROJECT_ROOT)
print("DATA_DIR:", DATA_DIR, "exists?", DATA_DIR.exists())
print("BOUNDARY_DIR:", BOUNDARY_DIR, "exists?", BOUNDARY_DIR.exists())
print("FIG_DIR:", FIG_DIR, "exists?", FIG_DIR.exists())

# Project to California Albers
target_crs = 'EPSG:3310'

PROJECT_ROOT, DATA_DIR

NOTEBOOK_DIR: C:\Users\kayle\Desktop\earth-analytics\Final project\ramona-fire-recovery\notebooks\02_fire_perimeters
PROJECT_ROOT: C:\Users\kayle\Desktop\earth-analytics\Final project\ramona-fire-recovery
DATA_DIR: C:\Users\kayle\Desktop\earth-analytics\Final project\ramona-fire-recovery\data exists? True
BOUNDARY_DIR: C:\Users\kayle\Desktop\earth-analytics\Final project\ramona-fire-recovery\data\boundaries exists? True
FIG_DIR: C:\Users\kayle\Desktop\earth-analytics\Final project\ramona-fire-recovery\figures\perimeters exists? True

(WindowsPath('C:/Users/kayle/Desktop/earth-analytics/Final project/ramona-fire-recovery'),
 WindowsPath('C:/Users/kayle/Desktop/earth-analytics/Final project/ramona-fire-recovery/data'))

# Step 1: Load Ramona CDP boundary from CA CDP shapefile

# Path to the statewide CDP ZIP shapefile
cdp_zip = BOUNDARY_DIR / "California_Census_Designated_Places_(CDPs).zip"


cdp_zip = BOUNDARY_DIR / "California_Census_Designated_Places_(CDPs).zip"
print("cdp_zip:", cdp_zip)
print("cdp_zip exists?", cdp_zip.exists())  # should be True

cdp_gdf = gpd.read_file(cdp_zip)

# Looking at the column headings will help us identify where Ramona CDP is. 
# In the table below we can see we will need to filter by 'NAMESLAD'
cdp_gdf.head()

cdp_zip: C:\Users\kayle\Desktop\earth-analytics\Final project\ramona-fire-recovery\data\boundaries\California_Census_Designated_Places_(CDPs).zip
cdp_zip exists? True

#### CONT ####
# Step 1: Load Ramona CDP boundary from CA CDP shapefile

# Filter for Ramona CDP using the NAMELSAD column
# We will use .copy() to ensure that our original dataframe cdp_gdf remains untouched and we can essentially 'view' Ramona instead
ramona_cdp = cdp_gdf[cdp_gdf["NAMELSAD"] == "Ramona CDP"].copy()

# Give us a count value of what we have filtered, it should be 1 as 'Ramona CDP' is unique in the shapefile
print("Number of features found:", len(ramona_cdp))

# Call the ramona_cdp dataframe copy of cdp_gdf to ensure we only have Ramona CDP left
ramona_cdp

Number of features found: 1

#### CONT ####
# Step 1: Load Ramona CDP boundary from CA CDP shapefile

# We set our target crs earlier, so now we are just going to reproject to that here
ramona_cdp = ramona_cdp.to_crs(target_crs)

# Double check that the reprojection worked, the output should say EPSG:3310
print("Reprojected CRS:", ramona_cdp.crs)

Reprojected CRS: EPSG:3310

#### CONT ####
# Step 1: Load Ramona CDP boundary from CA CDP shapefile

fig, ax = plt.subplots(figsize=(8, 8))

# Now we can display the clipped boundary we created from our copied dataframe
# Using ';' at the end of the ax.set_title ensures we just get the figure and we truncate the value of the last expression

ramona_cdp.plot(
    ax=ax,
    edgecolor="black",
    facecolor="none",
    linewidth=1
)

ax.set_title("Ramona CDP Boundary (EPSG:3310)")
ax.set_xlabel("Easting (meters)")
ax.set_ylabel("Northing (meters)")

fig.savefig(FIG_DIR / "ramona_CDP_boundary.png",
            dpi=300,
            bbox_inches="tight")

# Step 2: Load and extract Cedar (2003) and Witch (2007) fire perimeters

# So just like with the Ramona, CA shapefile, we have a 'California_Historic_Fire_Perimeters.zip' we can use for each fire perimeter
# We can reuse the code from when we made the Ramona, CA boundary.

# Path to the statewide historic fire perimeters ZIP shapefile
fire_perims_zip = BOUNDARY_DIR / 'California_Historic_Fire_Perimeters.zip'

# Read directly from the zipped shapefile
fires_gdf = gpd.read_file(f"zip://{fire_perims_zip}")

# Looking at the column headings will help us identify where the Cedar and Witch fires are.
### IMPORTANT ###
# Fire names are often reused over time, so we will need to specify both the name and year of fire we want sorted (most likely)
# We can check by calling the rows and columns of our dataframe

fires_gdf.head(5)

c:\Users\kayle\miniconda3\envs\earth-analytics-python\Lib\site-packages\pyogrio\raw.py:198: RuntimeWarning: /vsizip/C:\Users\kayle\Desktop\earth-analytics\Final project\ramona-fire-recovery\data\boundaries\California_Historic_Fire_Perimeters.zip/California_Fire_Perimeters_(1950+).shp contains polygon(s) with rings with invalid winding order. Autocorrecting them, but that shapefile should be corrected using ogr2ogr for example.
  return ogr_read(

#### CONT ####
# Step 2: Load and extract Cedar (2003) and Witch (2007) fire perimeters

# Filter for Cedar and Witch using the FIRE_NAME and YEAR columns
# We will use .copy() to ensure that our original dataframe fires_gdf remains untouched and we can essentially 'view' these fires instead

# For the cedar 2003 fire, there are actually two in 2003, one named CEDAR and one named CEDAR 3, so for the cedar 2003 sorting we need to:
# use == for CEDAR so that it only pulls that exact match. For the witch 2007 fire, because there is no other witch fire we can
# just set a general 'contains' parameter instead.

cedar_2003 = fires_gdf[
    (fires_gdf["FIRE_NAME"].str.upper() == "CEDAR") &
    (fires_gdf["YEAR_"] == 2003)
].copy()


witch_2007 = fires_gdf[
    (fires_gdf["FIRE_NAME"].str.contains("WITCH", case=False, na=False)) &
    (fires_gdf["YEAR_"] == 2007)
].copy()

# Give us a count value of what we have filtered, it should be 2
print(len(cedar_2003), "Cedar 2003 feature(s) found")
print(len(witch_2007), "Witch 2007 feature(s) found")

# Call the cedar_2003 and witch_2007 dataframe copies of fires_gdf to ensure we only have Witch and Cedar left
cedar_2003

1 Cedar 2003 feature(s) found
1 Witch 2007 feature(s) found

####CONT####
# Call the cedar_2003 and witch_2007 dataframe copies of fires_gdf to ensure we only have Witch and Cedar left
witch_2007

#### CONT ####
# Step 2: Load and extract Cedar (2003) and Witch (2007) fire perimeters

# We set our target crs earlier, so now we are just going to reproject to that here so all our boundaries use the same projection
cedar_2003 = cedar_2003.to_crs(target_crs)
witch_2007 = witch_2007.to_crs(target_crs)

# Double check that the reprojection worked, the output should say EPSG:3310
print("Cedar 2003 CRS:", cedar_2003.crs)
print("Witch 2007 CRS:", witch_2007.crs)

Cedar 2003 CRS: EPSG:3310
Witch 2007 CRS: EPSG:3310

# Step 3: Plot Ramona with the two fire perimeters

# Now we will create and display our boundaries combined with the Ramona, CA boundary we made earlier

fig, ax = plt.subplots(figsize=(8, 8))

# Plot Ramona boundary
ramona_cdp.boundary.plot(ax=ax, edgecolor="black", linewidth=1, label="Ramona CDP")

# Plot Cedar Fire (2003)
cedar_2003.boundary.plot(ax=ax, edgecolor="red", linewidth=1, label="Cedar Fire 2003")

# Plot Witch Fire (2007)
witch_2007.boundary.plot(ax=ax, edgecolor="blue", linewidth=1, label="Witch Fire 2007")

ax.set_title("Ramona CDP with Cedar (2003) and Witch (2007) Fire Perimeters")
ax.legend()
ax.set_xlabel("Easting (meters)")
ax.set_ylabel("Northing (meters)")
fig.savefig(FIG_DIR / "ramona_CDP_with_Cedar_Witch_boundaries.png",
            dpi=300,
            bbox_inches="tight")

# Special side quest
# Lets investigate the 'green ring' of Ramona further using CPAD data. 

# This collection includes geospatial data on California lands which are protected by under ownership or easement. 
# These include the California Protected Areas Database (CPAD) and the California Conservation Easement Database (CCED).

cpad_zip = BOUNDARY_DIR / "cpad_2025a.zip"

cpad_super = gpd.read_file(f"zip://{cpad_zip}!CPAD_2025a_SuperUnits.shp")

# Inspect the dataset
cpad_super.head(5)

# So we have way too many rows to inspect for the areas around Ramona. lets do a clip-buffer instead. 
# We are going to essentially "spatially filter" rather than sorting by any particular column in the shapefile.

# Reproject CPAD superunits to EPSG:3310
cpad_super_3310 = cpad_super.to_crs(target_crs)

cpad_super_3310.crs

<Projected CRS: EPSG:3310>
Name: NAD83 / California Albers
Axis Info [cartesian]:
- X[east]: Easting (metre)
- Y[north]: Northing (metre)
Area of Use:
- name: United States (USA) - California.
- bounds: (-124.45, 32.53, -114.12, 42.01)
Coordinate Operation:
- name: California Albers
- method: Albers Equal Area
Datum: North American Datum 1983
- Ellipsoid: GRS 1980
- Prime Meridian: Greenwich

# Ensure Ramona is in the same CRS. We already had a reprojection variable for the Ramona boundary but lets keep these seperate. 
# So we will create another ramona var that has this same project (which means for our variables we have 2 with the same parameters).
ramona_3310 = ramona_cdp.to_crs(target_crs)

# Create a 20 km buffer around Ramona
ramona_buffer = ramona_3310.buffer(10000)  # 10,000 meters = 10 km

# Quick plot to check
ax = ramona_buffer.boundary.plot(edgecolor="black", figsize=(6, 6))
ramona_3310.boundary.plot(ax=ax, edgecolor="red")
ax.set_title("Ramona CDP with 10 km Buffer");
ax.set_xlabel("Easting (meters)")
ax.set_ylabel("Northing (meters)")

Text(-5.152777777777784, 0.5, 'Northing (meters)')

# Filter CPAD superunits to only those that intersect the Ramona buffer
# Use union_all to dissolve the buffer geometry
ramona_buffer_union = ramona_buffer.union_all()

# Keep only CPAD features intersecting this buffer
cpad_near_ramona = cpad_super_3310[
    cpad_super_3310.intersects(ramona_buffer_union)
].copy()

len(cpad_near_ramona)

78

nearby_parks = (
    cpad_near_ramona["PARK_NAME"]
    .drop_duplicates()
    .sort_values()
)

for name in nearby_parks:
    print(name)

# View as scrollable element to see all 69 areas. We dropped duplicates so we went from 78 to 69 entries of green spaces.

Aubrey Park
BLM
Barnett Ranch Preserve
Blue Sky Ecological Reserve
Boden Canyon Ecological Reserve
Boulder Oaks Open Space Preserve
CWA Open Space
California State Lands Commission
Canada de San Vicente Ecological Reserve
City of Poway Open Space
City of San Diego Open Space
Cleveland Corridor Conservation Bank
Cleveland National Forest
Collier Park
Corral Mountain Open Space
Dos Picos Park
El Cajon Mountain Preserve
El Capitan Preserve
El Capitan Reservoir
Garden Road Neighborhood Park
Goodan Ranch Sycamore Canyon Preserve
Helix Water District Open Space
Hilleary Park
Holly Oaks Park
Iron Mountain Open Space
Iron Mountain Preserve
Kent Hill Open Space
Lake Poway Park
Lake Poway Recreation Area
Lake Ramona
Luelf Pond Preserve
Montecito Ranch
Mt. Gower Preserve
Mt. Woodson Gateway County Preserve
Oakoasis Preserve
Old Poway Park and Railroad
Pamo Valley
Pamo Watershed
Poway Creek Riparian Preserve
Ramona Community Park
Ramona Grasslands Preserve
Ramona Municipal Water District
Rattlesnake Canyon Open Space
Rock Haven Spring Open Space
SR67 County Preserve
Saddle Club Estates Open Space
San Diego Safari Park
San Dieguito River Park
San Pasqual Battlefield State Historic Park
San Pasqual Trails Open Space
San Pasqual Valley Agricultural and Open Space Preserve
San Vicente Highlands
San Vicente Reservoir
Santa Maria Creek County Park
Santa Maria Creek Greenway
Santa Ysabel Preserve
Silverset Neighborhood Park
Silverwood Wildlife Sanctuary
Simon Preserve
Sutherland Reservoir
Sycamore Canyon Open Space
Sycamore Creek Neighborhood Park - East
Sycamore Creek Neighborhood Park - West
The Vineyard at Escondido Golf Course
Twin Peaks Open Space
Unnamed Site - Poway, City of
Valle Verde Neighborhood Park
Veteran's Park
West Ridge Trail

# Lets filter some of the main 'green spaces' around ramona

# List of key parks I picked
key_parks = [
    "Barnett Ranch Preserve",
    "Boden Canyon Ecological Reserve",
    "Canada de San Vicente Ecological Reserve",
    "Cleveland National Forest",
    "Iron Mountain Open Space",
    "Iron Mountain Preserve",
    "Mt. Gower Preserve",
    "Mt. Woodson Gateway County Preserve",
    "Ramona Grasslands Preserve",
    "Rock Haven Spring Open Space",
]

# Filter to only those parks (exact name matches)
key_parks_gdf = cpad_near_ramona[
    cpad_near_ramona["PARK_NAME"].isin(key_parks)
].copy()

print("Number of superunits for selected parks:", len(key_parks_gdf))
key_parks_gdf[["PARK_NAME"]].drop_duplicates()

Number of superunits for selected parks: 11

# Clip these parks even further before plotting

# Start from your selected parks in 3310
parks_3310 = key_parks_gdf.to_crs(target_crs)

# Clip parks to within the 5 km buffer around Ramona
parks_clipped = gpd.clip(parks_3310, ramona_buffer_union)

import matplotlib.patches as mpatches
from matplotlib.lines import Line2D

# Make sure everything else is still in the right reprojection

# Make sure everything is in the same CRS (defensive, but safe)
ramona_3310 = ramona_cdp.to_crs(target_crs)
cedar_3310 = cedar_2003.to_crs(target_crs)
witch_3310 = witch_2007.to_crs(target_crs)

fig, ax = plt.subplots(figsize=(12, 10))

# 1. Protected open space (clipped)
parks_clipped.plot(
    ax=ax,
    color="#c7e9c0",
    alpha=0.65,
    edgecolor="none"
)

# 2. Ramona (subtle fill)
ramona_3310.plot(
    ax=ax,
    facecolor="#f0f0f0",
    edgecolor="black",
    linewidth=2
)

# 3. Fire perimeters
cedar_3310.boundary.plot(ax=ax, edgecolor="red", linewidth=1.5)
witch_3310.boundary.plot(ax=ax, edgecolor="blue", linewidth=1.5)

# ---- NUMBERED LABELS ----
# Dissolve by park name so we get one centroid per park
label_df = parks_clipped.dissolve(by="PARK_NAME", as_index=False)
label_df["ID"] = range(1, len(label_df) + 1)

for _, row in label_df.iterrows():
    cx, cy = row.geometry.centroid.x, row.geometry.centroid.y
    ax.text(
        cx, cy,
        str(row["ID"]),          # just the number
        fontsize=9,
        ha="center",
        va="center",
        color="black",
        weight="bold",
        alpha=0.9,
    )

# Legend for feature types only (not each park)
legend_elements = [
    mpatches.Patch(facecolor="#c7e9c0", edgecolor="none", label="Protected Open Space"),
    Line2D([0], [0], color="black", lw=2, label="Ramona CDP"),
    Line2D([0], [0], color="red", lw=1.5, label="Cedar Fire 2003"),
    Line2D([0], [0], color="blue", lw=1.5, label="Witch Fire 2007"),
]

ax.legend(
    handles=legend_elements,
    loc="center left",
    bbox_to_anchor=(1, 0.5),
    title="Features"
)

ax.set_title("Ramona CDP Surrounded by Open Space and Historic Fires", fontsize=16)
ax.set_xlabel("Easting (meters)")
ax.set_ylabel("Northing (meters)")
ax.set_aspect("equal")
plt.tight_layout()

label_df[["ID", "PARK_NAME"]].sort_values("ID")

# Look at all variables
%whos

Variable              Type            Data/Info
-----------------------------------------------
BOUNDARY_DIR          WindowsPath     C:\Users\kayle\Desktop\ea<...>-recovery\data\boundaries
DATA_DIR              WindowsPath     C:\Users\kayle\Desktop\ea<...>ramona-fire-recovery\data
IMAGERY_DIR           WindowsPath     C:\Users\kayle\Desktop\ea<...>recovery\data\raw\imagery
Line2D                type            <class 'matplotlib.lines.Line2D'>
NOTEBOOK_DIR          WindowsPath     C:\Users\kayle\Desktop\ea<...>ebooks\02_fire_perimeters
PROJECT_ROOT          WindowsPath     C:\Users\kayle\Desktop\ea<...>ject\ramona-fire-recovery
Path                  type            <class 'pathlib.Path'>
ax                    Axes            Axes(0.0909606,0.19583;0.720023x0.608339)
cdp_gdf               GeoDataFrame           PLACENS    GEOID  <...>n[1136 rows x 12 columns]
cdp_zip               WindowsPath     C:\Users\kayle\Desktop\ea<...>ignated_Places_(CDPs).zip
cedar_2003            GeoDataFrame          YEAR_ STATE AGENCY <...> -571822.043, 297555...  
cedar_3310            GeoDataFrame          YEAR_ STATE AGENCY <...> -571822.043, 297555...  
cpad_near_ramona      GeoDataFrame                  ACCESS_TYP <...>n\n[78 rows x 19 columns]
cpad_super            GeoDataFrame                 ACCESS_TYP  <...>[16213 rows x 19 columns]
cpad_super_3310       GeoDataFrame                 ACCESS_TYP  <...>[16213 rows x 19 columns]
cpad_zip              WindowsPath     C:\Users\kayle\Desktop\ea<...>boundaries\cpad_2025a.zip
cx                    float           283883.7815186097
cy                    float           -553247.2766576202
fig                   Figure          Figure(1200x1000)
fire_perims_zip       WindowsPath     C:\Users\kayle\Desktop\ea<...>toric_Fire_Perimeters.zip
fires_gdf             GeoDataFrame           YEAR_ STATE AGENCY<...>[17540 rows x 19 columns]
gpd                   module          <module 'geopandas' from <...>\geopandas\\__init__.py'>
key_parks             list            n=10
key_parks_gdf         GeoDataFrame                 ACCESS_TYP  <...> -549337.786, 285701...  
label_df              GeoDataFrame                             <...>  242.0      242.00  10  
legend_elements       list            n=4
mpatches              module          <module 'matplotlib.patch<...>\matplotlib\\patches.py'>
name                  str             West Ridge Trail
nearby_parks          Series          Shape: (69,)
os                    module          <module 'os' (frozen)>
parks_3310            GeoDataFrame                 ACCESS_TYP  <...> -549337.786, 285701...  
parks_clipped         GeoDataFrame                 ACCESS_TYP  <...>542966.725, 290352.6...  
plt                   module          <module 'matplotlib.pyplo<...>\\matplotlib\\pyplot.py'>
ramona_3310           GeoDataFrame          PLACENS    GEOID   <...>75.975, 284493.414 -...  
ramona_buffer         GeoSeries       620    POLYGON ((274497.8<...>346 -...\ndtype: geometry
ramona_buffer_union   Polygon         POLYGON ((274497.88211981<...>8167 -549481.9775004998))
ramona_cdp            GeoDataFrame          PLACENS    GEOID   <...>75.975, 284493.414 -...  
rasterio              module          <module 'rasterio' from '<...>\\rasterio\\__init__.py'>
row                   Series          Shape: (20,)
rxr                   module          <module 'rioxarray' from <...>\rioxarray\\__init__.py'>
show                  function        <function show at 0x0000022BBC7C80E0>
target_crs            str             EPSG:3310
witch_2007            GeoDataFrame          YEAR_ STATE AGENCY <...>93.394, 296370.072 -...  
witch_3310            GeoDataFrame          YEAR_ STATE AGENCY <...>93.394, 296370.072 -...  
zipfile               module          <module 'zipfile' from 'c<...>python\\Lib\\zipfile.py'>

# Store variables 
%store PROJECT_ROOT  DATA_DIR  BOUNDARY_DIR  ramona_cdp cedar_2003  cedar_3310  witch_2007  witch_3310  fires_gdf ramona_3310  ramona_buffer  ramona_buffer_union target_crs

Stored 'PROJECT_ROOT' (WindowsPath)
Stored 'DATA_DIR' (WindowsPath)
Stored 'BOUNDARY_DIR' (WindowsPath)
Stored 'ramona_cdp' (GeoDataFrame)
Stored 'cedar_2003' (GeoDataFrame)
Stored 'cedar_3310' (GeoDataFrame)
Stored 'witch_2007' (GeoDataFrame)
Stored 'witch_3310' (GeoDataFrame)
Stored 'fires_gdf' (GeoDataFrame)
Stored 'ramona_3310' (GeoDataFrame)
Stored 'ramona_buffer' (GeoSeries)
Stored 'ramona_buffer_union' (Polygon)
Stored 'target_crs' (str)

	PLACENS	GEOID	GEOIDFQ	NAMELSAD	CLASSFP	FUNCSTAT	ALAND	AWATER	INTPTLAT	INTPTLON	GlobalID	geometry
0	02582926	0600135	1600000US0600135	Acalanes Ridge CDP	U1	S	1193569.0	0.0	+37.9047250	-122.0785674	1d2479d9-6bf9-48bd-a01b-e99b830d63a1	MULTIPOLYGON (((-183171.923 -11022.94, -183169...
1	02629758	0600156	1600000US0600156	Acampo CDP	U1	S	2428474.0	0.0	+38.1735286	-121.2798915	0271a90c-05b1-4b9e-8064-a0229bfc9258	POLYGON ((-112818.505 18883.406, -112079.991 1...
2	02407697	0600212	1600000US0600212	Acton CDP	U1	S	101679451.0	60418.0	+34.4960703	-118.1838970	3c1128e0-0038-4209-af12-514a10751293	POLYGON ((159739.983 -389681.743, 159918.46 -3...
3	02582928	0600450	1600000US0600450	Agua Dulce CDP	U1	S	59181047.0	14646.0	+34.5016784	-118.3206796	2af5f189-989d-4432-9491-97457a519e95	POLYGON ((148865.702 -393026.019, 148866.07 -3...
4	02583084	0600535	1600000US0600535	Airport CDP	U2	S	451729.0	0.0	+37.6338833	-120.9720056	8da67b1e-d92b-43c7-89e3-c82dab78031b	MULTIPOLYGON (((-85289.121 -41976.591, -85288....

	YEAR_	STATE	AGENCY	UNIT_ID	FIRE_NAME	INC_NUM	ALARM_DATE	CONT_DATE	CAUSE	C_METHOD	OBJECTIVE	GIS_ACRES	COMMENTS	COMPLEX_NA	IRWINID	FIRE_NUM	COMPLEX_ID	DECADES	geometry
0	2025	CA	CDF	LDF	PALISADES	00000738	2025-01-07	2025-01-31	14	7	1	23448.900	None	None	{A7EA5D21-F882-44B8-BF64-44AB11059DC1}	None	None	2020-January 2025	MULTIPOLYGON (((-13193558.265 4032826.468, -13...
1	2025	CA	CDF	LAC	EATON	00009087	2025-01-08	2025-01-31	14	7	1	14056.300	None	None	{72660ADC-B5EF-4D96-A33F-B4EA3740A4E3}	None	None	2020-January 2025	MULTIPOLYGON (((-13146936.686 4051222.067, -13...
2	2025	CA	CDF	ANF	HUGHES	00250270	2025-01-22	2025-01-28	14	7	1	10396.800	None	None	{994072D2-E154-434A-BB95-6F6C94C40829}	None	None	2020-January 2025	MULTIPOLYGON (((-13197885.239 4107084.744, -13...
3	2025	CA	CCO	VNC	KENNETH	00003155	2025-01-09	2025-02-04	14	2	1	998.738	from OES Intel 24	None	{842FB37B-7AC8-4700-BB9C-028BF753D149}	None	None	2020-January 2025	POLYGON ((-13211054.577 4051508.758, -13211051...
4	2025	CA	CDF	LDF	HURST	00003294	2025-01-07	2025-01-09	14	7	1	831.385	None	None	{F4E810AD-CDF3-4ED4-B63F-03D43785BA7B}	None	None	2020-January 2025	POLYGON ((-13187991.688 4073306.403, -13187979...

	ACCESS_TYP	PARK_NAME	PARK_URL	SUID_NMA	MNG_AG_ID	MNG_AGENCY	MNG_AG_LEV	MNG_AG_TYP	AGNCY_WEB	LAYER	ACRES	LABEL_NAME	GAP2_acres	GAP3_acres	GAP4_acres	GAP_tot_ac	geometry
0	Open Access	Deer Canyon Park	http://www.anaheim.net/comm_svc/parks/parkDeta...	129	1006	Anaheim, City of	City	City Agency	https://www.anaheim.net/comm_svc/parks/	City	286.396931	Deer Canyon Park	0.00	0.0	286.0	286.00	POLYGON ((207932.86 -458338.406, 207905.623 -4...
1	Open Access	Arcata Community Forest	http://www.cityofarcata.org/departments/enviro...	295	1432	Arcata, City of	City	City Agency	https://www.cityofarcata.org/rec	City	654.491867	Arcata Community Forest	0.00	653.0	0.0	653.00	POLYGON ((-343521.833 324576.822, -343521.918 ...
2	Open Access	Modini Mayacamas Preserves	http://egret.org/visit_modini_mayacamas	383	3005	Audubon Canyon Ranch	Non Profit	Non Profit - Conservation	https://www.egret.org	Non Governmental Organization	2863.749714	Modini Mayacamas Preserves	2859.00	0.0	0.0	2859.00	MULTIPOLYGON (((-240507.377 79636.678, -240467...
3	Open Access	Wright's Field	https://www.facebook.com/WrightsField	399	3007	Back Country Land Trust	Non Profit	Non Profit - Land Trust	https://www.bclt.org/	Non Governmental Organization	232.148088	Wright's Field	0.00	0.0	228.0	228.00	MULTIPOLYGON (((303285.394 -572236.826, 303512...
4	Open Access	Kern River Parkway Park	http://www.bakersfieldcity.us/recreation/Kern_...	425	1019	Bakersfield, City of	City	City Agency	http://www.bakersfieldcity.us/recreation/	City	1115.695945	Kern River Parkway Park	0.04	0.0	1090.4	1090.44	MULTIPOLYGON (((78899.227 -295825.489, 78895.0...

Using this notebook¶

Step 0: Libraries¶

Step 1: Load Ramona Census Designated Places (CDP) boundary from CA CDP shapefile¶

Step 2: Load and extract Cedar (2003) and Witch (2007) fire perimeters¶

Step 3: Plot Ramona with the two fire perimeters¶

Step 4: (Optional side-quest) Creating a buffer around Ramona to clip CDP green space data to¶

Remember: Save the clipped imagery after steps 1-3 in the plotted cells¶

WARNING¶

Step 1: Load Ramona CDP boundary from CA CDP shapefile¶

Accessing NDVI data¶

Step 2: Load and extract Cedar and Witch fire perimeters¶

How else can we know these are the correct fires we are sorting?¶

Step 3: Plot Ramona with the two fire perimeters¶

The ring of fire: Ramona, CA (2003–2007)¶

Step 4: (Optional) Side quest¶

Why create a 10 km Buffer Around Ramona?¶

Preparing codes for storing¶

Storing variables for later notebooks¶

	PARK_NAME
424	Iron Mountain Open Space
579	Cleveland National Forest
7373	Rock Haven Spring Open Space
10122	Barnett Ranch Preserve
10842	Mt. Gower Preserve
11617	Ramona Grasslands Preserve
12091	Boden Canyon Ecological Reserve
14148	Canada de San Vicente Ecological Reserve
15568	Iron Mountain Preserve
15726	Mt. Woodson Gateway County Preserve