Google Earth Engine: Supervised Classification For LULC

Dive into remote sensing and geospatial analysis with this comprehensive guide to Land-Use and Land-Cover (LULC) mapping and Change Detection. This hands-on course empowers students, researchers, and GIS professionals to harness the power of Google Earth Engine (GEE) for impactful environmental insights.

Learn supervised classification techniques within Google Earth Engine to generate highly accurate land-use mapping. We'll walk you through the entire process:

• Data Preprocessing: Preparing your remote sensing data for analysis.
• Training Sample Creation: Developing robust training datasets for classification.
• Classification Algorithms: Exploring various algorithms within GEE for optimal results.
• Accuracy Assessment: Evaluating the reliability of your LULC maps.

Go beyond static maps! Master Change Detection analysis using ArcMap (ArcGIS Desktop). Identify and quantify significant environmental shifts, including:

• Urban Growth
• Deforestation
• Agricultural Expansion
• Water Body Variations

Communicate your findings effectively with data visualization in Excel. Develop compelling charts, tables, and comparative statistics to clearly present your LULC maps and change detection results.

By the end of this course, you'll possess a strong understanding of:

• Supervised Classification in Google Earth Engine
• Land-Use Land-Cover Mapping Techniques
• Change Detection using ArcMap
• Accuracy Assessment for LULC Results
• Data Visualization and Reporting in Excel

Gain a practical, real-world workflow for completing LULC mapping and change detection projects. Develop the skills to apply remote sensing and GIS to critical fields like environmental monitoring, urban planning, agriculture, and natural resource management. Unlock valuable insights from satellite imagery and contribute to a more sustainable future.