MCCN Case Study 3 - Select optimal survey locality

Full description

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 3.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 3 - Select optimal survey locality

Given a set of existing survey locations across a variable landscape, determine the optimal site to add to increase the range of surveyed environments. This study demonstrates: 1) Loading heterogeneous data sources into a cube, and 2) Analysis and visualisation using numpy and matplotlib.

Data Sources

The primary goal for this case study is to demonstrate being able to import a set of environmental values for different sites and then use these to identify a subset that maximises spread across the various environmental dimensions.

This is a simple implementation that uses four environmental attributes imported for all Australia (or a subset like NSW) at a moderate grid scale:

1. Digital soil maps for key soil properties over New South Wales, version 2.0 - SEED - see https://esoil.io/TERNLandscapes/Public/Pages/SLGA/ProductDetails-SoilAttributes.html
2. ANUCLIM Annual Mean Rainfall raster layer - SEED - see https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-rainfall-raster-layer
3. ANUCLIM Annual Mean Temperature raster layer - SEED - see https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-temperature-raster-layer

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. Generate STAC metadata for layers from predefined configuratiion
2. Load data cube and exclude nodata values
3. Scale all variables to a 0.0-1.0 range
4. Select four layers for comparison (soil organic carbon 0-30 cm, soil pH 0-30 cm, mean annual rainfall, mean annual temperature)
5. Select 10 random points within NSW
6. Generate 10 new layers representing standardised environmental distance between one of the selected points and all other points in NSW
7. For every point in NSW, find the lowest environmental distance to any of the selected points
8. Select the point in NSW that has the highest value for the lowest environmental distance to any selected point - this is the most different point
9. Clean up and save results to RO-Crate