NDVI after the Las Conchas fire in New Mexico¶

This notebook will be used to download NDVI data using the NASA appeeaars api.

In [1]:
# Import Libraries

import pathlib
import os

import earthpy
import hvplot.pandas
import hvplot.xarray
import matplotlib

import geopandas as gpd
import pandas as pd
import panel as pn
import rioxarray as rxr
import xarray as xr
import holoviews as hv
import earthpy.api.appeears as eaapp
In [2]:
# Make a project and a directory

project = earthpy.Project('Las Conchas Vegetation', dirname='fire_boundary')
In [3]:
# Read in Fire Boundary shapefile

fire_bound_gdf = gpd.read_file(project.project_dir / 'nm3581210654120110626_20110624_20120618_burn_bndy.shp')
fire_bound_gdf
Out[3]:
Event_ID irwinID Incid_Name Incid_Type Map_ID Map_Prog Asmnt_Type BurnBndAc BurnBndLat BurnBndLon ... Perim_ID dNBR_offst dNBR_stdDv NoData_T IncGreen_T Low_T Mod_T High_T Comment geometry
0 NM3581210654120110626 None LAS CONCHAS Wildfire 3962 MTBS Extended 150874 35.87 -106.423 ... 503403520110928 -5 -9999 -970 -150 5 177 380 None POLYGON ((-917592.246 1492208.61, -917565.51 1...

1 rows × 23 columns

In [4]:
# Check CRS
fire_bound_gdf.crs # ESRI:102039

# Change CRS to EPSG: 4326
fire_bound_gdf = fire_bound_gdf.to_crs(4326)
print(fire_bound_gdf.crs)

EPSG:4326
In [5]:
# Quick plot to make sure the boundary looks right

fire_bound_plot = fire_bound_gdf.hvplot(
    geo=True, 
    tiles='EsriNatGeo', 
    frame_width=400,
    legend=False, 
    fill_color='red', 
    fill_alpha=0.4,
    edge_color='white',
    title='Boundary of Las Conchas Fire (2011)',
    xlabel='Longitude',
    ylabel='Latitude'
)

# Save fire boundary plot
panel_plot = pn.panel(fire_bound_plot)
panel_plot.save('Las_Conchas_fire_bound.html', embed=True)

# Show the plot
fire_bound_plot

WARNING:param.main: edge_color option not found for polygons plot with bokeh; similar options include: ['muted_color', 'bgcolor', 'line_color']
WARNING:bokeh.core.validation.check:W-1005 (FIXED_SIZING_MODE): 'fixed' sizing mode requires width and height to be set: figure(id='p1076', ...)
Out[5]:

This looks like what we want, so we're set to download MODIS NDVI data now!

In [6]:
# Initialize AppeearsDownloader for MODIS NDVI data

# set parameters
ndvi_downloader = eaapp.AppeearsDownloader(

    # give download a name
    download_key = "las-conchas-fire-ndvi",

    # put data in the directory we defined
    project = project,

    # specify MODIS product we want
    product = 'MOD13Q1.061',
    layer = '_250m_16_days_NDVI',

    # choose a start date and end data
    start_date = "05-01",
    end_date = "09-30",

    # recurring means I want those dates over multiple years
    recurring = True,

    # specify the range of years
    year_range = [2006, 2021],

    # specify the polygon I want to get NDVI data for
    polygon = fire_bound_gdf
)
In [7]:
# download the prepared download - this can take a while!
ndvi_downloader.download_files(cache=True)
Out[7]:
<generator object Path.rglob at 0x7cb8cf2f1140>
In [8]:
# get a sorted list of NDVI file paths and check the file names
ndvi_paths = sorted(list(project.project_dir.rglob('*NDVI*.tif')))

print(len(ndvi_paths))
print(ndvi_paths[:3])

176
[PosixPath('/workspaces/data/fire_boundary/las-conchas-fire-ndvi/MOD13Q1.061_2006106_to_2021273/MOD13Q1.061__250m_16_days_NDVI_doy2006113000000_aid0001.tif'), PosixPath('/workspaces/data/fire_boundary/las-conchas-fire-ndvi/MOD13Q1.061_2006106_to_2021273/MOD13Q1.061__250m_16_days_NDVI_doy2006129000000_aid0001.tif'), PosixPath('/workspaces/data/fire_boundary/las-conchas-fire-ndvi/MOD13Q1.061_2006106_to_2021273/MOD13Q1.061__250m_16_days_NDVI_doy2006145000000_aid0001.tif')]

These results look like what we want - we've successfully downloaded NDVI rasters for the area we're interested in from NASA!

In [9]:
# Store variables to be used in plotting notebooks

%store fire_bound_gdf ndvi_paths project

Stored 'fire_bound_gdf' (GeoDataFrame)
Stored 'ndvi_paths' (list)
Stored 'project' (Project)