This lecture introduces land use and land cover (LULC) classification using Sentinel-2 imagery, distinguishing land cover (the physical material on the Earth's surface, mapped via remote sensing) from land use (how people utilize that land) — both key baselines for LGU zoning, disaster risk reduction, and environmental planning. It explains image classification as categorizing pixels into thematic classes, compares supervised, unsupervised (K-Means), and hybrid approaches with their trade-offs and accuracy assessment, and concludes with Exercise 4: performing an unsupervised land cover classification using the Semi-Automatic Classification Plugin (SCP) in QGIS.
Teacher: Alvin Baloloy