| File Name: | Remote Sensing for Wind Farm Site Selection in GEE |
| Content Source: | https://www.udemy.com/course/remote-sensing-wind-farm-site-selection-gee |
| Genre / Category: | Other Tutorials |
| File Size : | 497.4 MB |
| Publisher: | Earth’s AI |
| Updated and Published: | July 22, 2025 |
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.
Who this course is for:
- Students, researchers, and professionals in agriculture, environmental science, geography, or remote sensing looking to apply satellite data in real-world water quality monitoring scenarios.
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