You will explore how to spatially constrain rasters to reduce noise and prepare data for focused analysis.

You will explore how to prepare multiband rasters required for vegetation indices like NDVI.

Before modifying raster data, you need to understand what’s inside it. In this module, you will review why inspecting raster metadata is a critical first step in any geospatial workflow.

Most raster datasets are delivered in projections that do not match your analysis or cloud requirements. Reprojection is a core GDAL skill you will use frequently. In this module, you will review how gdalwarp performs reprojection, resampling, and extent control for raster data.

You will understand what truly makes a GeoTIFF cloud-optimized by focusing on how internal structure, tiling, and overviews affect performance and usability in cloud and web-based workflows.

In this module, you will explore how Earth-observation satellites collect data and compare Landsat and Sentinel sensors to understand when and why each is used for vegetation monitoring and forest analysis.

In this module, you will apply spectral concepts to calculate NDVI, a widely used vegetation index, and interpret what NDVI values reveal about plant health.

In this module, you will learn why atmospheric effects distort raw satellite imagery and apply basic atmospheric correction to prepare surface reflectance data suitable for vegetation analysis and NDVI calculation in a forest health context.

In this module, you are introduced to SAR as a practical disaster-response data source that remains available even when optical imagery is blocked by clouds. You focus on one core barrier to using SAR confidently: speckle. Rather than treating speckle as a vague “noise problem,” you learn what it looks like, why it occurs, and how filtering changes interpretability. The module is designed to develop judgment: you learn to apply speckle filtering, compare outcomes, and reason about trade-offs because aggressive smoothing can hide meaningful edges while weak filtering may leave the scene unreadable.

In this module, you move from preprocessing to analysis. Using multispectral imagery captured across time, you perform change detection to identify where surface conditions shifted after a storm event. The module emphasizes interpretive reasoning: you learn that a “change map” is not automatically a flood map, and you must think about what the change signal could represent. You also learn how to structure your outputs so you can communicate results clearly, highlighting what changed, where confidence is higher, and what limitations remain.

The final module teaches you the habit that makes your work trustworthy: evaluation. You learn that classification outputs can look convincing while still being wrong in critical ways. The module introduces beginner-friendly accuracy evaluation concepts and asks you to make judgment calls: is this accurate enough for the decision at hand, and what would you disclose as limitations? This module ties directly to operational credibility because in real flood response, the cost of being confidently wrong is high.

In this project, you will build a Python workflow to analyze vegetation change using satellite raster data. You will compute NDVI for two time periods, handle data quality issues, and detect areas of increase or decline in vegetation. You will generate output rasters and interpret the results to identify environmental patterns. This project demonstrates how remote sensing and raster processing techniques are applied in real-world environmental analysis. You will also practice core remote sensing analysis skills such as verifying sensor and band information, considering temporal comparability between image dates, and clearly stating assumptions, sources of uncertainty, and limitations of your analysis.