{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NDVI Phenology\n",
"\n",
"### Background\n",
"\n",
"Phenology is the study of how plant and animal life varies both with the season, and the climate more broadly. \n",
"\n",
"This notebook calculates vegetation phenology changes using Landsat-7 and/or Landsat-8 data. To detect changes in plant life, the algorithm uses the normalised difference vegetation index NDVI as a common proxy for vegetation growth and health. The output from this notebook can be used to assess differences in agriculture fields over time or space and also allow the assessment of growing states such as planting and harvesting. \n",
"\n",
"The output product is a statistical time series plot of NDVI with the data \"binned\" into weeks or months. The timeline can be changed from a single year (all data binned with the 12-month period) or spread out over the entire time window of the analysis. \n",
"\n",
"See this website for more information: https://phenology.cr.usgs.gov/ndvi_foundation.php"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preliminary steps"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Supress Warning \n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Load Data Cube Configuration\n",
"import datacube\n",
"dc = datacube.Datacube(app = 'my_app')\n",
"\n",
"# Import Data Cube API\n",
"import utils.data_cube_utilities.data_access_api as dc_api \n",
"api = dc_api.DataAccessApi()\n",
"\n",
"# Import other required packages\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np \n",
"import xarray as xr \n",
"import pandas as pd\n",
"import datetime as dt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define product and extent"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Available extents\n",
"\n",
"We've listed the available ingested data that you can explore in the ODC Sandbox. The latitude, longitude and time ranges correspond to the boundaries of the ingested data cubes. You'll be able to explore sub-samples of these cubes. You'll also need to provide the platform, product and resolution information for the cube you're subsampling.\n",
"\n",
"#### LS8 Caqueta\n",
"Platform: `'LANDSAT_8'` \n",
"Product: `'ls8_collection1_AMA_ingest'` \n",
"Latitude: `(0.000134747292617865, 1.077843593651382)` \n",
"Longitude: `(-74.91935994831539, -73.30266193148462)` \n",
"Time: `('2013-04-13', '2018-03-26')` \n",
"Resolution: `(-0.000269494585236, 0.000269494585236)`\n",
"\n",
"#### LS8 Vietnam\n",
"Platform: `'LANDSAT_8'` \n",
"Product: `'ls8_collection1_AMA_ingest'` \n",
"Latitude: `(10.513927001104687, 12.611133863411238)` \n",
"Longitude: `(106.79005909290998, 108.91906631627438)` \n",
"Time: `('2014-01-14', '2016-12-21')` \n",
"Resolution: `(-0.000269494585236, 0.000269494585236)` \n",
"\n",
"#### LS7 Caqueta\n",
"Platform: `'LANDSAT_7'` \n",
"Product: `'ls7_collection1_AMA_ingest'` \n",
"Latitude: `(0.000134747292617865, 1.077843593651382)` \n",
"Longitude: `(-74.91935994831539, -73.30266193148462)` \n",
"Time: `('1999-08-21', '2018-03-25')` \n",
"Resolution: `(-0.000269494585236, 0.000269494585236)`\n",
"\n",
"#### LS7 Lake Baringo\n",
"Platform: `'LANDSAT_7'` \n",
"Product: `'ls7_collection1_AMA_ingest'` \n",
"Latitude: `(0.4997747685, 0.7495947795)` \n",
"Longitude: `(35.9742163305, 36.473586859499996)` \n",
"Time: `('2005-01-08', '2016-12-24')` \n",
"Resolution: `(-0.000269493, 0.000269493)`"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# CHANGE HERE >>>>>>>>>>>>>>>>>\n",
"\n",
"# Select a product and platform\n",
"platform = \"LANDSAT_7\"\n",
"product = 'ls7_collection1_AMA_ingest'\n",
"resolution = (-0.000269494585236, 0.000269494585236)\n",
"output_crs = 'EPSG:4326'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Set extent information\n",
"\n",
"You can change the values in this cell to specify the extent of the data cube you wish to analyse.\n",
"\n",
"You should select a sub-sample from one of the four data cubes listed above. When subsampling, keep in mind that:\n",
"* Your latitude and longitude bounds should be within the extents given.\n",
"* Your area should be small to keep load times reasonable (less than 0.5 square degrees).\n",
"* Your time period should be within the extents given.\n",
"\n",
"You should format the variables as:\n",
"* `latitude = (min_latitude, max_latitude)`\n",
"* `longitude = (min_longitude, max_longitude)`\n",
"* `time_extents = (min_time, max_time)`, where each time has the format: `'YYYY-MM-DD'`."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# CHANGE HERE >>>>>>>>>>>>>>>>>>\n",
"\n",
"# Select a sub-region to analyse\n",
"latitude = (0.49964002, 0.74964002)\n",
"longitude = (36.0, 36.3)\n",
"time_extents = ('2007-01-01', '2009-01-01')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### View the region before loading\n",
"\n",
"The next cell will allow you to view the area you'll be analysing by displaying a red bounding box on an interactive map. You can change the extents in the previous cell and rerun the `display_map()` command to see the resulting bounding box."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The code below renders a map that can be used to view the analysis region.\n",
"from utils.data_cube_utilities.dc_display_map import display_map\n",
"\n",
"display_map(latitude, longitude)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the dataset and the required spectral bands or other parameters\n",
"The data is loaded by passing the product and area information to the `dc.load()` function. As a part of this load, we also specify the measurements we want in the form of the Landsat bands.\n",
"\n",
"The load can take up to a few minutes, so please be patient."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"measurements = ['red', 'blue', 'green', 'nir', 'swir1', 'swir2', 'pixel_qa']\n",
"\n",
"landsat_dataset = dc.load(\n",
" latitude=latitude,\n",
" longitude=longitude,\n",
" platform=platform,\n",
" time=time_extents,\n",
" product=product,\n",
" output_crs=output_crs,\n",
" resolution=resolution,\n",
" measurements=measurements\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is often useful to print the loaded data to check the dimensions and data variables\n",
"\n",
"When looking at the dimensions, the numbers for latitude and longitude correspond to the number of pixels in each dimension and the number for time corresponds to the number of time steps. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Dimensions: (latitude: 928, longitude: 1114, time: 27)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2007-02-15T07:39:23 2007-03-03T07:39:27 ...\n",
" * latitude (latitude) float64 0.7496 0.7493 0.7491 0.7488 0.7485 0.7483 ...\n",
" * longitude (longitude) float64 36.0 36.0 36.0 36.0 36.0 36.0 36.0 36.0 ...\n",
"Data variables:\n",
" red (time, latitude, longitude) int16 -9999 -9999 -9999 -9999 ...\n",
" blue (time, latitude, longitude) int16 -9999 -9999 -9999 -9999 ...\n",
" green (time, latitude, longitude) int16 -9999 -9999 -9999 -9999 ...\n",
" nir (time, latitude, longitude) int16 -9999 -9999 -9999 -9999 ...\n",
" swir1 (time, latitude, longitude) int16 -9999 -9999 -9999 -9999 ...\n",
" swir2 (time, latitude, longitude) int16 -9999 -9999 -9999 -9999 ...\n",
" pixel_qa (time, latitude, longitude) int32 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ...\n",
"Attributes:\n",
" crs: EPSG:4326\n"
]
}
],
"source": [
"# Displays an overview of the loaded data\n",
"print(landsat_dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Masking out clouds\n",
"\n",
"As part of the utilities for the Open Data Cube, we have defined a function to mask clouds based on the quality assurance information for Landsat. The function returns an `xarray.DataArray` object containing the mask. This can then be passed to the `where()` function, which masks the data."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from utils.data_cube_utilities.clean_mask import landsat_qa_clean_mask\n",
"\n",
"cloud_mask = landsat_qa_clean_mask(landsat_dataset, platform=platform)\n",
"\n",
"cleaned_dataset = landsat_dataset.where(cloud_mask)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create the NDVI Phenology Product\n",
"\n",
"The first step is to caculate the normalised difference vegetation index (NDVI). Here, we use the `NDVI()` function, defined in the utilities. This is applied to the clean dataset, and then the result is addedd as a data variable."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"from utils.data_cube_utilities.dc_ndvi_anomaly import NDVI\n",
"\n",
"ndvi_arr = NDVI(cleaned_dataset)\n",
"\n",
"cleaned_dataset['ndvi'] = ndvi_arr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next step is to generate a box-and-whisker plot for the NDVI. The box-and-whisker plot is calculated by getting the relevant statistics for binned data. \n",
"\n",
"Set the binning variable `bin_size` to one of the following: \n",
"`'fortnight'`, which bins the data by each fortnight in the timeframe \n",
"`'month'`, which bins the data by each month in the timeframe \n",
"`'weekofyear'`, which aggregates the data over all years and bins it by week \n",
"`'monthofyear'`, which aggregates the data over all years and bins it by month\n",
"\n",
"*Note:* the `'fortnight'` and `'month'` settings may cause the notebook to crash if the timespan of the data is greater than two years.\n",
"\n",
"It's also possible to plot a polynomial curve fit to the mean of the NDVI over time. Set the `plot_curve_fit_ndvi_mean` variable to one of the following: \n",
"`True`, which includes the fit in the plot \n",
"`False`, which excludes the fit from the plot\n",
"\n",
"Depending on the timeframe, you may need to change the degree of the polynomial you're fitting. Set the polynomial degree by changing the value for `polynomial_degree`: \n",
"`polynomial_degree = 3` will fit the peaks and troughs over a single year \n",
"`polynomial_degree = 5` will fit the peaks and troughs over two years"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# CHANGE HERE >>>>>>>>>>>>>>>>> \n",
"\n",
"# Import function and determine settings for box-and-whisker plot\n",
"from utils.data_cube_utilities.plotter_utils import xarray_time_series_plot\n",
"\n",
"bin_size = 'month'\n",
"plot_curve_fit_ndvi_mean = True\n",
"polynomial_degree = 5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next step is to resample the data based on the binning setting chosen above. The following code also sets up the plotting parameters."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# Resample the data based on bin_size prior to plotting\n",
"for bin_by in [bin_size]:\n",
" aggregated_by_str = None\n",
" if bin_by is None:\n",
" plotting_data = cleaned_dataset\n",
" elif bin_by == 'fortnight':\n",
" plotting_data = cleaned_dataset.resample(time='2w').mean()\n",
" aggregated_by_str = 'Fortnight'\n",
" elif bin_by == 'month':\n",
" plotting_data = cleaned_dataset.resample(time='1m').mean()\n",
" aggregated_by_str = 'Month'\n",
" elif bin_by == 'weekofyear':\n",
" plotting_data = cleaned_dataset.groupby('time.week').mean(dim=('time'))\n",
" aggregated_by_str = 'Week of Year'\n",
" elif bin_by == 'monthofyear':\n",
" plotting_data = cleaned_dataset.groupby('time.month').mean(dim=('time'))\n",
" aggregated_by_str = 'Month of Year'\n",
"\n",
"# Set any parameters for the plot\n",
"params = dict(dataset=plotting_data, plot_descs={'ndvi':{'none':[\n",
" {'box':{'boxprops':{'facecolor':'forestgreen'}}}]}})\n",
"params['scale_params'] = 'norm'\n",
"\n",
"# Set parameters for plotting the polynomial fit\n",
"if plot_curve_fit_ndvi_mean:\n",
" params['plot_descs']['ndvi']['mean'] = [{'poly': {'degree': polynomial_degree}}]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The final step is to make the plot."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NDVI (Aggregated by Month)\n"
]
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Make the plot \n",
"xarray_time_series_plot(**params, fig_params=dict(figsize=(8,4), dpi=150))\n",
"plt.title('Box-and-Whisker Plot of NDVI with a curvefit of mean NDVI')\n",
"\n",
"print(\"NDVI {}\".format(\"(Aggregated by {})\".format(aggregated_by_str) if aggregated_by_str is not None else \"\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}