dea_tools.wetlands

This module is for processing DEA wetlands data.

License: The code in this notebook is licensed under the Apache License,Version 2.0 (https://www.apache.org/licenses/LICENSE-2.0). Digital Earth Australia data is licensed under the Creative Commons by Attribution 4.0 license (https://creativecommons.org/licenses/by/4.0/).

Contact: If you need assistance, please post a question on the Open Data Cube Slack channel (http://slack.opendatacube.org/) or on the GIS Stack Exchange (https://gis.stackexchange.com/questions/ask?tags=open-data-cube)using the open-data-cube tag (you can view previously asked questions here: https://gis.stackexchange.com/questions/tagged/open-data-cube).

If you would like to report an issue with this script, file one on GitHub: GeoscienceAustralia/dea-notebooks#new

Last modified: July 2024

Functions

display_wit_stack_with_df(polygon_base_df[, ...])

This functions produces WIT plots.

generate_low_quality_data_periods(df)

This function generates low quality data periods, including the SLC off period: https://www.usgs.gov/faqs/what-landsat-7-etm-slc-data and periods with an observation density of less than four observations within a twelve month (365 days) period.

normalise_wit(polygon_base_df)

This function is to normalise the Fractional Cover vegetation
dea_tools.wetlands.display_wit_stack_with_df(polygon_base_df, polygon_name='your_wetland_name', png_name='your_file_name', width=32, height=6, x_axis_labels='years')[source]

This functions produces WIT plots. Function displays a stack plot and saves as a png.

Last modified: July 2023

Parameters:

  • polygon_base_df (pandas DataFrame with columns including:) –

  • ['date' – ‘wet’, ‘water’, ‘norm_bs’, ‘norm_pv’, ‘norm_npv’] polygon_name : string png_name : string x_axis_labels : string with options of “years” or “months” to set either years or months on the x axis as labels

:param‘wet’,

‘water’, ‘norm_bs’, ‘norm_pv’, ‘norm_npv’] polygon_name : string png_name : string x_axis_labels : string with options of “years” or “months” to set either years or months on the x axis as labels

dea_tools.wetlands.generate_low_quality_data_periods(df)[source]

This function generates low quality data periods, including the SLC off period: https://www.usgs.gov/faqs/what-landsat-7-etm-slc-data and periods with an observation density of less than four observations within a twelve month (365 days) period. Off value is 100 where there is low data quality and 0 for good data.

Last modified: July 2023

Parameters:

  • df (pandas DataFrame with columns including:) –

  • ['date'] –

Returns:

  • df (pandas DataFrame with additional column:)

  • [‘off_value’]

dea_tools.wetlands.normalise_wit(polygon_base_df)[source]

This function is to normalise the Fractional Cover vegetation components so users can choose to display the WIT plot in a more readable way. Normalising vegetation components so they total to 1. Normalised values are returned as additional columns.

Last modified: July 2023

polygon_base_df : pandas DataFrame with columns: [‘date’, ‘pv’, ‘npv’, ‘bs’, ‘wet’, ‘water’]

polygon_base_df with columns: [‘index’,

‘date’, ‘pv’, ‘npv’, ‘bs’, ‘wet’, ‘water’, ‘veg_areas’, ‘overall_veg_num’, ‘norm_bs’, ‘norm_pv’, ‘norm_npv’]

A polygon has 11 pixels

[cloud][water][wet][wet][wet][wet][wet][wet][wet][wet][vegetation]
| | |
| | |
| |__> wet = 8/10 = 80% |__> pv/npv/bs == 1/10 = 10%
|
|__> water = 1/10 = 10%

|__> pc_missing = 1/11 ~+ 9.1%

The vegetation pixel relative np, npv, and bs values

[vegetation]

|__> [pv] [npv] [bs]

[ 5] [ 4] [ 2]

Assume vegetation relative values are:

water = 0.1 wet = 0.8

pv = 0.05 npv = 0.04 bs = 0.02

vegetation_area = 1 - water - wet

vegetation_overall_value = pv + npv + bs

print(f”The pv is {pv}

The npv is {npv} The bs is {bs}

The overall number is {water + wet + pv + npv + bs}”)

The pv is 0.05 The npv is 0.04 The bs is 0.02 The overall number is 1.01

The overall number is greater than 1. Let us normalise the result. The water and wet are pixel classification result, so we should not touch them.

pv = pv/vegetation_overall_value*vegetation_area npv = npv/vegetation_overall_value*vegetation_area bs = bs/vegetation_overall_value*vegetation_area

print(f”The normalised pv is {pv}

The normalised npv is {npv}

The normalised bs is {bs} The normalised overall number is

{water + wet + pv + npv + bs}”)

The normalised pv is 0.04545454545454545 The normalised npv is 0.036363636363636355 The normalised bs is 0.018181818181818177 The normalised overall number is 1.0