Theoretical Basics

Introduction

Welcome to the first module of the Fundamentals of Remote Sensing course. In this section, you will explore the foundational concepts that define remote sensing. Including its definition, historical evolution, and wide range of applications. Have you ever wondered how data from distant objects without direct contact is captured? Or how is our planet monitored using satellites? These questions will guide you through the core principles of remote sensing, helping you understand how both active and passive sensing work, and how different platforms—ranging from satellites to drones—contribute to Earth observation. 

Introduction

In the second module of the Fundamentals of Remote Sensing course you will explore the building blocks of the electromagnetic spectrum, the foundation upon which remote sensing is based. What is electromagnetic radiation, and why is it central to observing Earth from space? You’ll learn how energy travels through space, how it interacts with matter, and how different wavelengths—from visible light to microwaves—reveal unique surface and atmospheric features. We’ll break down key wave characteristics like wavelength, frequency, and amplitude, and discuss how different materials on Earth absorb, emit, and reflect energy in distinctive ways. These interactions form the basis of how satellites “see” our planet—helping you understand concepts like spectral signatures and the importance of selecting the right wavelengths for specific applications. 

Introduction

This third section takes you deeper into how we detect electromagnetic energy from Earth’s surface and atmosphere. What exactly happens when a satellite or sensor captures an image of our planet? You’ll learn about the fundamental principles of sensing—how remote sensors work, the difference between active and passive systems, and how radiance is measured across spectral bands. We’ll introduce you to concepts like radiometric resolution, spectral resolution, and how various sensors—from multispectral imagers to LIDAR and radar systems—capture and convert light into digital data. You’ll also explore the limits of sensing, such as cloud cover and illumination, and discover how innovative technologies overcome these challenges to keep watch over Earth in all conditions. 

Introduction

Have you ever wondered how satellite data knows where on Earth it comes from? In this module, we shift our focus from sensing energy to locating that energy precisely on a map. Understanding coordinate reference systems (CRS) and projections is essential for working with spatial data—whether you’re mapping land cover, monitoring vegetation, or analyzing urban change. 

You’ll begin by exploring the shape of the Earth—imperfect, oblate, and constantly in motion—and how we approximate it using geoids and ellipsoids. From there, we’ll dive into geographic coordinate systems (like latitude and longitude), and see how these spherical coordinates are transformed into 2D maps through projections. But be warned: projecting a round planet onto a flat surface comes at a cost. You’ll learn how different projections preserve or distort properties like shape, area, and distance—and why choosing the right CRS is crucial when combining data from multiple sources. 

Introduction

Remote sensing data is powerful—but only if we know how to process, interpret, and present it. In this module, you’ll follow the full journey of Earth observation data, starting with the essential step of preprocessing. Raw satellite imagery often carries distortions from the atmosphere, terrain, or the sensor itself. You’ll learn how to correct these issues to ensure your data reflects real surface conditions. 

Once cleaned, the next challenge is to turn reflectance values into meaningful categories—a process known as image classification. You’ll explore both supervised and unsupervised approaches, consider different classification algorithms (from traditional methods to machine learning), and examine how to detect and quantify environmental changes over time. 

Finally, data is only useful if it can be communicated. The last part of the module introduces the art and science of creating scientific maps. You’ll learn how to design clear, purposeful maps that accurately present your findings for fieldwork, reports, or presentations. 

Introduction

In this module you will be introduced to the utilization of Remote Sensing for real-world challenges. In this module, you will explore how Earth observation technologies are used to monitor and manage vital ecosystems—including biodiversity hotspots, forest landscapes, and wetlands. Each of these applications highlights a different aspect of how remote sensing supports conservation, policy, and sustainable land use. 

We’ll begin with biodiversity: how can we detect ecological richness and change from space? You’ll learn about direct and indirect approaches to mapping species distributions, including innovative methods like spectral variability analysis and species distribution models (SDMs). Next, we’ll move into forests—examining how vegetation indices, satellite imagery, and classification techniques are used to track deforestation, assess forest health, and monitor wildfires. Finally, we’ll dive into wetlands, one of the most threatened ecosystems on Earth. You’ll discover how remote sensing helps delineate wetland boundaries, assess their ecological status, and support restoration strategies. 

By the End of this module, you’ll understand how remote sensing supports data-driven decision-making across diverse environmental domains—and how your skills can contribute to sustainable ecosystem management.