Industrial Site Suitability Mapping with Remote Sensing

This lecture provides an essential introduction to remote sensing principles, covering the electromagnetic spectrum, satellite sensors, spatial/spectral/temporal resolutions, and data types. Students will explore how satellites collect data, the role of different bands (such as infrared for vegetation or thermal for surface temperature), and how to interpret imagery. By the end, learners will have a clear understanding of how remote sensing supports environmental monitoring, urban planning, and natural resource management—laying the groundwork for practical applications in later lectures.

Students will learn how to apply remote sensing techniques for identifying suitable land for industrial development. This includes interpreting land cover datasets, using DEMs for elevation and slope analysis, and avoiding restricted or unsuitable areas like forests and water bodies. The lecture focuses on criteria-based analysis to evaluate spatial data and determine optimal site locations. By the end, learners will understand how satellite imagery and GIS-based layers contribute to evidence-driven decision-making in land use planning.

This lecture introduces Google Earth Engine, a powerful cloud-based platform for processing satellite data. Students will learn how to navigate the GEE code editor, access global datasets, and write basic JavaScript code for image analysis. Key concepts include filtering imagery, calculating indices, and visualizing outputs. With hands-on examples, learners will build the skills needed to process large datasets efficiently and begin developing geospatial workflows that are scalable, accurate, and reproducible.

This section introduces the Google Earth Engine platform, guiding learners through accessing the Code Editor, understanding its interface, and exploring key panels like the script editor, map viewer, inspector, and data catalog. It helps beginners get comfortable navigating GEE before writing any code or performing analysis.

In this practical session, learners implement industrial site suitability mapping using Google Earth Engine. The lecture walks through integrating elevation, slope, land cover, proximity to infrastructure, and exclusion zones into a weighted scoring system. Students will create composite suitability maps, apply logical masking, and export results. By the end, participants will have a complete end-to-end workflow—from data acquisition to final suitability output—tailored for real-world industrial planning using remote sensing and cloud geospatial analysis.

In this lecture, learners will integrate forest cover and water body data into their site suitability analysis using satellite-derived land cover datasets. You'll learn how to isolate forested areas and water bodies from ESA WorldCover, apply distance transformations, and create suitability scores based on proximity. This practical exercise helps ensure industrial development planning considers environmental constraints and sustainable land use. By the end, you’ll effectively add and process these critical landscape layers in Google Earth Engine (GEE).