How do you identify changes in the landscape and determine whether they are temporary or structural? In this Blended Learning course, you’ll learn to compare satellite images from different periods and analyze time series. You’ll examine changes in vegetation, water, land use, and built-up areas, among other things, and learn how to translate these into reliable information for monitoring and spatial planning issues.
During this Blended Learning course on Change Detection and Time Series, you’ll learn how to use satellite imagery from different periods to identify, analyze, and monitor changes on the Earth’s surface. You’ll work with time series from sources such as Sentinel and Landsat and learn to distinguish changes from normal variation. This will enable you to systematically track developments in landscape, vegetation, water, nature, and built-up areas.
Change detection begins by comparing observations of the same area at different times. It is important that differences in satellite images are actually the result of changes on the Earth’s surface and are not caused by, for example, cloud cover, shadows, seasons, differences between sensors, or imaging conditions.
You will therefore learn which factors influence the comparability of satellite images and how to distinguish between actual changes and temporary variations. You will also be introduced to various forms of change detection, such as comparing images, spectral bands, indices, and land-cover maps.
Comparing two points in time can reveal what has changed between two images. A time series goes further and reveals how an area evolves over months, years, or even decades. This allows you not only to detect changes but also to determine when they occur and whether they represent a temporary anomaly or a structural trend.
You will learn to organize satellite observations chronologically and recognize patterns over time. In doing so, you will pay attention to seasonal influences, trends, sudden changes, and deviations from normal patterns. Longer time series, for example, make it possible to distinguish gradual changes in vegetation, urbanization, or land use from short-term events such as droughts, floods, or wildfires. Time series analysis therefore plays an increasingly important role in continuous monitoring using remote sensing.
For change detection, satellite programs with regular and long-term observations are particularly valuable. You will therefore work with open-access satellite data from sources such as Sentinel and Landsat and learn to select appropriate images and time periods for various monitoring questions.
You will explore how spectral bands, indices, and classified images can be compared over time. This allows you, for example, to track changes in vegetation, surface water, land cover, and urban areas. You’ll also learn how existing European land-cover products and change layers can be used as supplementary information and references. The Copernicus Land Monitoring Service, for example, offers land-cover products and specific change layers for analyzing changes over time.
After covering the theoretical foundation, you’ll analyze changes yourself. You’ll learn to visually identify differences between successive satellite images and explore how to determine the extent, direction, and rate of change.
For longer time series, you’ll analyze developments by combining multiple observations. You’ll examine trends, recurring patterns, anomalies, and potential turning points. This provides a more complete picture than comparing just two points in time. Modern change-detection methods therefore increasingly rely on dense time series to identify complex changes more reliably.
The analyses are performed using open data and open-source techniques. QGIS serves as a key working environment for combining, analyzing, and visualizing spatial and temporal data. The focus is not on the software itself, but on determining which analysis method is needed to reliably identify a change.
During the Blended Learning course, you’ll work with realistic real-world examples and satellite data from different time periods. You’ll not only investigate where changes are occurring but also attempt to explain what the observed patterns mean and how this information can be used for monitoring.
You’ll work on assignments such as:
Upon completion, you will be able to independently compare satellite images from different periods and use time series to identify changes, trends, and anomalies. You will be able to distinguish between temporary and structural changes and translate the results into actionable information for monitoring, management, and spatial decision-making.
You’ll receive 1-on-1 guidance. After signing up, our course coordinator will contact you to schedule your first session.
Do you have questions about the course content? Or are you unsure whether the course aligns with your learning goals or preferences? Would you prefer an in-house or private course? We’d be happy to help.
Change Detection involves comparing satellite observations of the same area from different time periods. This allows you, for example, to identify and spatially map the expansion of built-up areas, changes in surface water, loss of vegetation, or changes in land use. By quantifying these differences, you can determine not only where a change has occurred, but also the extent of that change.
A comparison of two satellite images shows that something has changed, but does not always reveal whether that change is temporary or structural. A time series analysis allows you to analyze multiple observations over months or years. This reveals seasonal patterns, normal variations, and long-term trends, enabling you, for example, to distinguish between temporary drought and long-term decline in vegetation.
Satellite imagery makes it possible to compare an area before, during, and after an event. This allows you to determine the extent of a flood, analyze vegetation damage after a wildfire, or track the progression of a drought. With longer time series, you can then investigate how quickly an area recovers and whether the situation returns to its original pattern.
By comparing satellite images taken over several years, you can track changes in land use and development. This makes it possible to detect new residential areas, industrial parks, infrastructure, deforestation, or changes in agricultural land. Time series make it possible not only to document the current situation, but also to analyze the pace and direction of developments and to monitor areas over the long term.
