DroneSurveySimulator

This project analyses aerial survey data of moose populations using distance sampling methods to estimate population density. The estimated density is then used in a simulated aerial survey. The project is structured to separate different stages of the workflow, ensuring clarity and modularity.

Project Structure

README.md # Project overview and instructions ├── Data/ # Directory for example data ├── Scripts/ # R scripts for different stages of the analysis │ ├── data_preparation.R # Scripts for data cleaning and preparation │ ├── density_analysis.R # Scripts for statistical analysis and modelling │ ├── simulation.R # Script for running simulations │ ├── visualisation/ # Scripts for visualisations and reporting | └── DistanceSampling.rmd # Markdown file demonstrating whole workflow └── Output/ # intermediate files, plots, and reports

Description of Files and Directories

• Data/: Contains raw data (raw/) and processed data (processed/).
• scripts/: Organised by functionality:
  • data_processing/: Includes scripts for cleaning and preparing the raw data (data_cleaning.R).
  • analysis/: Contains scripts for deriving population density using distance sampling (density_analysis.R).
  • simulation/: Includes scripts to simulate aerial surveys using the derived population density (simulation.R).
  • visualisation/: Scripts for generating plots, tables, and reports (visualisation.R).
• Output/: Stores the outputs from analyses, simulations, and visualisations.

Getting Started

1. Install Required Packages: Ensure you have the required R packages installed by running:

   install.packages(c("Distance", "dplyr", "dsims", "dsm", "here", "knitr", "pbapply", "purrr", "sf", , "tidyr", "units")) # List all required packages

2. Running the Scripts: Start with data_cleaning.R to prepare the data.

• Proceed with density_preparation.R for density estimation.
• Run simulation.R to perform the simulated survey.
• Use visualisation.R to generate visual outputs.