MCCN Case Study 1 - Evaluate impact from environmental events/pressures

The MCCN project is to deliver tools to assist the agricultural sector to understand crop-environment relationships, specifically by facilitating generation of data cubes for spatiotemporal data. This repository contains Jupyter notebooks to demonstrate the functionality of the MCCN data cube components.

The dataset contains input files for the case study (source_data), RO-Crate metadata (ro-crate-metadata.json), results from the case study (results), and Jupyter Notebook (MCCN-CASE 1.ipynb)

Research Activity Identifier (RAiD)

RAiD: https://doi.org/10.26292/8679d473

Case Studies

This repository contains code and sample data for the following case studies. Note that the analyses here are to demonstrate the software and result should not be considered scientifically or statistically meaningful. No effort has been made to address bias in samples, and sample data may not be available at sufficient density to warrant analysis. All case studies end with generation of an RO-Crate data package including the source data, the notebook and generated outputs, including netcdf exports of the datacubes themselves.

Case Study 1 - Evaluate impact from environmental events/pressures

Description

Aggregate observations of Caladenia orchids in the ACT so I can analyse the relationship between records and the protection status and vegetation cover of the locations of each species.

This is an example of combining suites of spatial variables at different scales across multiple sites to analyse the factors correlated with a variable of interest. Comparable cases could include:

• Aggregate spatial data for frost and other extreme weather events associated with chickpeas and wheat yields to analyse the effects of such events on different varieties at different stages and advise growers on the best choices
• Aggregate pest data for the same pest across multiple sites and locations to analyse the relationship between population levels and environmental context at the time and over the previous month.

Data sources

• vegetation_cover_northern.tif - GA Landsat Vegetation Cover GeoTIFF at 25m2 for the northern two thirds of the ACT and adjacent NSW in 2020: https://explorer.dea.ga.gov.au/products/ga_ls_landcover_class_cyear_2/datasets/67bb9d38-00c7-46ba-a5e9-b892d9f9ad42 (values defined here: https://knowledge.dea.ga.gov.au/data/product/dea-land-cover-landsat/?tab=details)
• vegetation_cover_southern.tif - GA Landsat Vegetation Cover GeoTIFF at 25m2 for the southern third of the ACT and adjacent NSW in 2020: https://explorer.dea.ga.gov.au/products/ga_ls_landcover_class_cyear_2/datasets/464fd7e2-0554-4095-80e1-42e00f087831
• boundary_act.geojson - 2023 boundary for the Australian Capital Territory from the ACT Government in GeoJSON format: https://actmapi-actgov.opendata.arcgis.com/datasets/ACTGOV::actgov-border/explore
• capad_act.geojson - Protected Area data for the Australian Capital Territory in 2022 from the CAPAD dataset in GeoJSON format: https://fed.dcceew.gov.au/datasets/ec356a872d8048459fe78fc80213dc70_0/explore?filters=eyJTVEFURSI6WyJBQ1QiXX0=&location=-35.437128,149.203518,11.00
• caladenia_act.csv - Distribution records for orchids in the genus Caladenia between 1990 and present from the ALA in CSV format: https://doi.org/10.26197/ala.1e501311-7077-403b-a743-59e096068fa0

Dependencies

• This notebook requires Python 3.10 or higher
• Install relevant Python libraries with: pip install mccn-engine rocrate
• Installing mccn-engine will install other dependencies

Overview

1. Group orchid species records by species
2. Prepare STAC metadata records for each data source (separate records for the distribution data for each orchid species)
3. Load data cube
4. Mask orchid distribution records to boundaries of ACT
5. Calculate the proportion of distribution records for each species occurring inside and outside protected areas
6. Calculate the proportion of distribution records for each species occurring in areas with each class of vegetation cover
7. Report the apparent affinity between each species and protected areas and between each species and different classes of vegetation cover

Notes

• No attempt is made here to compensate for underlying bias in the areas where observers have spent time recording orchids. The analysis should only be considered indicative of relative tendencies.