Remote Sensing For Wind Farm Site Selection In Gee

Using Google Earth Engine to Identify High-Potential Wind Farm Locations

What you'll learn

Understand how to use remote sensing data to assess wind resources for potential wind farm locations.

Learn to process and analyze geospatial datasets in Google Earth Engine for site suitability analysis.

Develop skills to integrate terrain, land cover, and wind speed data to identify optimal wind farm sites.

Create and interpret suitability maps for wind energy projects using practical coding techniques in Google Earth Engine.

Requirements

No prior experience with Google Earth Engine is required — the course will guide you step-by-step.

Description

This course provides a comprehensive introduction to using remote sensing and geospatial analysis for wind farm site selection with Google Earth Engine (GEE). Students will learn how to leverage satellite-derived data and digital elevation models (DEM) to identify optimal locations for wind energy projects. The core focus is on processing and analyzing key environmental variables such as wind speed, terrain slope, and land cover using GEE’s cloud-based platform.Participants will start by accessing and processing the ERA5 Land dataset to calculate mean annual wind speeds, a critical factor for assessing wind energy potential. They will learn how to work with vector and raster data, including clipping data to country boundaries (e.g., Belgium or Ireland) and normalizing continuous variables for suitability analysis.Next, the course covers terrain analysis using SRTM DEM data to calculate slope, which influences turbine efficiency and construction feasibility. Land cover data from MODIS is used to mask urban and developed areas, ensuring that unsuitable locations are excluded from the final suitability map.Students will learn to combine and normalize multiple layers using geospatial arithmetic operations to generate a composite suitability index, visualized through custom color palettes for clear interpretation. The course also includes guidance on creating interactive legends and visualizations in GEE’s user interface.By the end of this course, learners will be proficient in applying remote sensing datasets and spatial analysis techniques to support wind farm site selection and environmental planning, empowering them with practical skills for renewable energy geospatial applications.

Overview

Section 1: Introduction

Lecture 1 Fundamentals of Remote Sensing

Lecture 2 Wind Energy: Importance and Site Optimization

Lecture 3 Introduction to Google Earth Engine

Lecture 4 Key Datasets for Wind Farm Site Selection

Lecture 5 Getting Started with the Google Earth Engine Interface

Lecture 6 Implementing Wind Farm Site Selection in Google Earth Engine

Lecture 7 Extra: Wind Farm Site Selection for Denmark

Students and researchers in agriculture, environmental science, geography, or remote sensing looking to apply satellite data in real-world scenarios.