Monitoring blue carbon ecosystem restoration using drones and object-based classification - Data

These data were used as part of wetland restoration monitoring research, using drone imagery and object-based image classification.

Drone flights were conducted over a restoration site (Fish Fry Flat, Kooragang Island, Hunter River estuary, NSW, Australia) at seven time points over a 46-month period. The raw multispectral and elevation drone data were used along with training data to classify saltmarsh and other land cover types at a saltmarsh restoration site at seven temporal points over a 46-month period. The Google Earth Engine code for this classification is available here: https://code.earthengine.google.com/f2a4ea73b8542e1e7cb4ca04ef9b8bf4.

This Mendeley repository includes the classified images produced in Google Earth Engine and other associated files used to analyse the classified images in RStudio. The change tiffs were generated in QGIS by subtracting the classified images from one another. The accuracy data ("training_validation_2_alldata.xlsx") are a summary of the confusion matrix data produced in Google Earth Engine, and the variable importance data ("variable_importance_allvars.xlsx") were exported from column charts of variable importance in Google Earth Engine. The growth/loss data ("growth_loss.xlsx") refer to growth, loss and species transitions between the two major saltmarsh species at our site (see which code refers to which growth/loss transition in the "growth_loss" script on GitHub), and were generated in QGIS by reclassifying and summing classified images. The "classmetrics" and "landmetrics" csvs were produced in the "patch_metrics" R script and allow users to skip midway into the patch analysis, because the initial patch analysis takes hours to run.

The data can be analysed in RStudio using the code in this GitHub repository: https://github.com/dlanceman/kooragang. The data can be used to investigate temporal and spatial changes in saltmarsh species cover over time and relationships with elevation. They can also be used to look at the importance of different variables for classification and for exploring classification accuracy between classes and over time.