With R Satellite Monitoring, organizations can use satellite data to track vegetation growth, drought, water levels, land use, and climate change over time. In this blended learning course, you will learn how to analyze, visualize, and forecast satellite and remote sensing time series.
R Satellite Monitoring focuses on analyzing, visualizing, and predicting trends in satellite and remote sensing data using the R programming language. Within Geo-ICT, this is important for issues related to vegetation, drought, water, climate change, land use, and spatial monitoring over time.
With R, satellite images and derived indicators can be converted into time series. Examples include tracking vegetation development, identifying seasonal patterns, analyzing drought periods, monitoring water bodies, and predicting trends in landscape change. This creates a powerful environment for monitoring, policy analysis, and remote sensing workflows.
What makes R so powerful is the combination of time series analysis, statistics, visualization, and reproducible workflows. This allows satellite measurements to be systematically analyzed and compared over longer periods. Within Geo-ICT, R is increasingly being used not only to map spatial changes but also to explain and predict them over time.
In this blended learning course, you will work with key packages such as zoo, xts, tsibble, forecast, and prophet. You will learn to structure time series, analyze trends, recognize seasonal patterns, and make predictions based on satellite and monitoring data.
In addition, R offers extensive capabilities for combining satellite monitoring with GIS data, climate data, visualization, and reporting. This makes this blended learning course particularly relevant for GIS specialists, remote sensing specialists, data analysts, policy analysts, ecologists, and Geo-ICT professionals who wish to track and substantiate spatial developments over time.
In this blended learning course, you will be introduced to the key capabilities of R for satellite monitoring and time series analysis. You will learn how to convert satellite data and derived indicators into usable time series and how to analyze trends, patterns, and anomalies. You will work with packages such as zoo, xts, tsibble, forecast, and prophet.
Attention is given to time series structures, seasonal patterns, trend analysis, forecasting, and monitoring of spatial changes. You will learn how to track and interpret developments in vegetation, drought, water, land use, and climate data within Geo-ICT projects.
In addition, you will learn how R can be used to make predictions based on historical satellite and monitoring data. Using packages such as forecast and prophet, you will discover how future trends can be estimated and how uncertainties and patterns can be visualized.
During the blended learning course, you will work with practical datasets and learn how to set up reproducible satellite monitoring workflows in R. Upon completion, you will be able to independently analyze satellite data over time, identify trends, and translate results into actionable insights for GIS, remote sensing, and policy projects.
Do you already have experience with R Google Earth Engine, R Hydrology, or R Visualization? Then this blended learning course is a logical next step toward time series analysis, monitoring, and predictive analysis using satellite data.
Blended learning combines independent online learning with practical, interactive sessions, allowing you to understand both the fundamentals of time series analysis and its application to satellite data. In the online modules, you’ll learn how to structure monitoring data, analyze trends, and make predictions using modern R packages.
You’ll discover how to work with satellite indicators, seasonal patterns, trend breaks, and forecasting. You’ll also learn how to set up analyses in a reproducible way, ensuring results are transparent and repeatable across different areas and time periods. With unlimited access to the course materials, you can review and practice the material at your own pace.
During the hands-on online sessions, you’ll immediately apply the theory to realistic datasets and familiar Geo-ICT challenges. You’ll receive guidance from experienced instructors and learn how to execute satellite monitoring workflows using packages such as zoo, xts, tsibble, forecast, and prophet.
The combination of online learning and interactive hands-on experience ensures that you not only learn how to technically analyze satellite data, but also how to interpret changes over time in terms of content. After completing the blended learning program, you will be able to use R professionally for satellite monitoring, time series analysis, forecasting, and spatial policy analysis.
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.
A basic understanding of GIS and R is helpful, but experience with satellite monitoring or remote sensing is not required. During the blended learning course, time-series analysis, satellite data, and monitoring are explained step by step using practical Geo-ICT examples.
During the blended learning program, you will work on monitoring vegetation, drought, water bodies, climate indicators, land use, and spatial changes using satellite and remote sensing data.
R is used for vegetation monitoring, climate adaptation, drought analysis, water management, monitoring of spatial changes, time series analysis, forecasting, and policy analysis based on satellite data.
Time series analysis makes it possible to visualize changes over time. This allows for better analysis and prediction of trends, seasonal patterns, anomalies, and future developments within Geo-ICT and remote sensing workflows.
During the blended learning program, you will work with tools such as zoo, xts, tsibble, forecast, and prophet for time series analysis, trend analysis, forecasting, and satellite monitoring in R.
