Sessions will commence in between Dashain and Tihar
Formal lectures have not started yet. Before our first live practical session, please review the course orientation below and complete the required software installations on your personal laptop.
Computational Methods for Forestry Research
Equipping future ecologists and forest researchers with quantitative rigor, computational workflows, and reproducible science practices to turn raw field observations into trusted scientific evidence.
Course Orientation
Hello everyone, and welcome to Computational Methods for Forestry Research! Over the period of this course, we will bridge the gap between traditional forestry fieldwork and modern computational research.
Throughout the session, we will build a continuous research project centered on The X Province Community Forest Observatory. You will learn to use R for statistical modeling, forest allometry, biomass estimation, and ecological analysis. Simultaneously, we will dive into Python for batch data automation, spatial GIS workflows, canopy LiDAR processing, Google Earth Engine satellite pipelines, and predictive machine learning.
Required Software Stack & Pre-Installation Checklist
Please download, install, and configure each tool on your laptop prior to the first meeting.
R (v4.3+) & RStudio Desktop
Our primary statistical computing, allometry modeling, and reporting environment.
Python (v3.10+) with VS Code or Jupyter
Used for data pipeline automation, spatial raster workflows, and applied machine learning.
Quarto CLI
Integrated with RStudio & VS Code to render reproducible scientific documents uniting narrative, live code, and figures.
Git Version Control
Distributed tracking system to manage script history, collaborate, and prevent irreversible code loss.
Google Earth Engine (GEE) Account
Sign up with your institutional email for planetary-scale geospatial analysis and remote sensing API access.
Standard Project Workspace Architecture
We strictly avoid hard-coded absolute paths (e.g. C:/Users/Student/Desktop/...) so your work runs reliably on any machine.
From Day 1, you will organize all course analysis using a standardized RStudio Project repository layout:
X_observatory/
├── data/
│ ├── raw/ # Original field CSVs (NEVER edited manually)
│ └── processed/ # Cleaned tables generated by code
├── scripts/ # Numbered R and Python workflow scripts
├── figures/ # Exported high-resolution plots
├── outputs/ # Summary tables and model predictions
├── reports/ # Quarto (.qmd) executable reports
├── notebooks/ # Exploratory Jupyter / R notebooks
└── X_observatory.Rprojdata/raw/ must never be edited manually. Every standardisation, species code cleanup, or unit conversion must happen transparently and reproducibly via executable code scripts.Core Workflow & Computational Mindset
A structured 7-step sequence replacing error-prone spreadsheets with programmatic reproducibility.
We are moving away from manual spreadsheet click-and-drag edits toward writing explicit, re-executable logic that runs consistently across thousands of tree inventory plots, climate raster tiles, and satellite pixels.
Course AI-Assisted Coding Policy
Harnessing modern computational aids while ensuring genuine programmatic literacy.
Lab Practical Exercises
No AI assistance permitted. You must write, test, debug, and reason through statistical queries and data structures independently to cement foundational programming fluency.
Mini-Projects & Weekend Labs
AI tools allowed with formal documentation. You must submit a tool-and-purpose disclosure statement alongside detailed prompt logs. You remain 100% accountable for all statistical calculations, biological units, and ecological interpretations.
Books, Slides & Session Materials
All lecture slides, Quarto source documents, sample datasets, and reading lists will be continuously uploaded and indexed at this permanent URL:
https://www.prabeshgouli.com/course/Computational_Methods_for_Forestry_Research/Please complete your software setup prior to Day 1. If you encounter any installation errors or permission issues, capture the terminal error logs and bring them to our first onboarding lab.
We look forward to meeting everyone and exploring the future of computational forestry science together!