MCCN Case Study 6 - Environmental Correlates for Productivity

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 6.ipynb)<b>Research Activity Identifier (RAiD)</b>RAiD: https://doi.org/10.26292/8679d473<b>Case Studies</b>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.<b>Case Study 6 - Environmental Correlates for Productivity</b><b>Description</b>Analyse relationship between different environmental drivers and plant yield. This study demonstrates: 1) Loading heterogeneous data sources into a cube, and 2) Analysis and visualisation of drivers. This study combines a suite of spatial variables at different scales across multiple sites to analyse the factors correlated with a variable of interest.<b>Data Sources</b>The dataset includes the Gilbert site in Queensland which has multiple standard sized plots for three years. We are using data from 2022. The source files are part pf the larger collection - Chapman, Scott and Smith, Daniel (2023). INVITA Core site UAV dataset. The University of Queensland. Data Collection. https://doi.org/10.48610/951f13cBoundary file - This is a shapefile defining the boundaries of all field plots at the Gilbert site. Each polygon represents a single plot and is associated with a unique Plot ID (e.g., 03_03_1). These plot IDs are essential for joining and aligning data across the orthomosaics and plot-level measurements.https://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/shp.zip.Orthomosaics - The site was imaged by UAV flights multiple times throughout the 2022 growing season, spanning from June to October. Each flight produced an orthorectified mosaic image using RGB and Multispectral (MS) sensors.https://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/2022-09-18.tifhttps://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/UQ_GilbertN_danNVT_2022-07-28_10-00-00_Altum_bgren_20m_transparent_reflectance_packed.tifhttps://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/UQ_GilbertN_danNVT_2022-08-08_10-00-00_Altum_bgren_20m_transparent_reflectance_packed.tifPlot level measurements - Multispectral Traits: Calculated from MS sensor imagery and include indices NDVI, NDRE, SAVI and Biomass Cuts: Field-measured biomass sampled during different growth stages (used as a proxy for yield).https://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/filtered_biomass_updated.csvhttps://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/filtered_multispec_aggregated.csv<br>

MCCN项目旨在提供工具，助力农业领域理解作物-环境关系，具体而言，通过促进时空数据立方体的生成实现这一目标。本仓库包含Jupyter笔记本（Jupyter notebooks），用于演示MCCN数据立方体组件的功能。该数据集包含案例研究的输入文件（source_data）、RO-Crate元数据（RO-Crate metadata，ro-crate-metadata.json）、案例研究结果（results）及Jupyter笔记本（MCCN-CASE 6.ipynb）。<b>研究活动标识符（Research Activity Identifier，RAiD）</b>RAiD：https://doi.org/10.26292/8679d473<b>案例研究</b>本仓库包含以下案例研究的代码和样本数据。请注意，此处的分析仅用于演示软件功能，其结果不具备科学或统计意义。未针对样本偏差进行校正，且样本数据密度可能不足以支撑有效分析。所有案例研究均以生成RO-Crate数据包收尾，该数据包包含源数据、笔记本及生成的输出，包括数据立方体本身的netCDF格式导出文件。<b>案例研究6——生产力的环境关联因素</b><b>描述</b>分析不同环境驱动因子与植物产量之间的关系。本研究展示：1）将异构数据源加载至数据立方体；2）驱动因子的分析与可视化。本研究结合多站点不同尺度的空间变量，分析与目标变量相关的因素。<b>数据来源</b>该数据集包含昆士兰州吉尔伯特站点的数据，该站点拥有三年的多个标准尺寸地块。本研究使用2022年的数据。源文件属于更大的数据集集合——Chapman, Scott与Smith, Daniel（2023）。INVITA核心站点无人机数据集。昆士兰大学。数据集合。https://doi.org/10.48610/951f13c边界文件——这是一个定义吉尔伯特站点所有田间地块边界的形状文件（shapefile）。每个多边形代表一个地块，并关联唯一的Plot ID（例如03_03_1）。这些Plot ID对于正射影像与地块级测量数据的关联和对齐至关重要。https://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/shp.zip。正射影像——2022年生长季（6月至10月）期间，该站点通过无人机飞行进行了多次成像。每次飞行使用RGB和多光谱（MS）传感器（Multispectral sensors）生成正射校正镶嵌影像。https://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/2022-09-18.tifhttps://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/UQ_GilbertN_danNVT_2022-07-28_10-00-00_Altum_bgren_20m_transparent_reflectance_packed.tifhttps://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/UQ_GilbertN_danNVT_2022-08-08_10-00-00_Altum_bgren_20m_transparent_reflectance_packed.tif地块级测量数据——多光谱特征（Multispectral Traits）：由多光谱传感器影像计算得出，包括NDVI、NDRE、SAVI指数；生物量采样（Biomass Cuts）：不同生长阶段采集的田间实测生物量（用作产量的替代指标）。https://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/filtered_biomass_updated.csvhttps://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/filtered_multispec_aggregated.csv<br>