Global change has many faces: Earth observation satellites measure daily how glaciers are shrinking and cities are booming, how vegetation is changing and humans are encroaching on natural areas. Using satellite images, Sarah Asam gains valuable insights into these processes. The geographer, who holds a doctorate, researches long-term processes on the earth's surface in the German Remote Sensing Data Center (DFD). The DFD is an institute of the German Aerospace Center ( DLR ). In Oberpfaffenhofen near Munich, Sarah Asam evaluates the data from national and international satellite missions.
What does globalization look like from above?
Satellites allow us to observe natural and human-caused changes to the Earth. We investigate and quantify the dynamics of the land surface. This is much more than climate change! This also includes urban sprawl, pollution, natural disasters and the loss of biodiversity.
What phenomena do you investigate?
I focus on agroecosystems and phenology, i.e. the recurring seasonal development of nature. I compare historical and current earth observation data in order to make developments measurable. For example, I can record how forest populations, droughts, biomass or growing seasons change over the years. Using satellite images, I analyze, among other things, the density, mass, area and growth phases of vegetation. Important conclusions can be drawn from this for sustainable land and water management.
What are you currently working on ?
I have been leading the TIMELINE project since the beginning of the year. Creeping changes in our environment only become visible when you compare images from several decades. I use satellite data time series to identify patterns and try to determine whether they are long-term trends or perhaps just temporary fluctuations. Around 15 scientists from different departments from all seven DFD departments work on TIMELINE. This guarantees an exciting exchange, well-coordinated processor developments and efficient data evaluation.
What do you base your analysis on?
I use recordings from the AVHRR sensor. It is the only satellite that has been imaging the entire Earth every day since 1979. This is extraordinary because most satellites have only been flying for 15 years or less. Older material is therefore rare, and the time series are a real treasure for me. Because of the volume of data - we are in the petabyte range - I concentrate my research on Europe and North Africa. That’s why the support of the system engineers in the project team is so important.
What information do satellite images provide?
They reproduce the earth's surface with a spatial resolution of around one kilometer: one square kilometer on earth is a point on the satellite image - Lake Starnberg can be clearly seen. The sensor records visual and thermal information, i.e. colors and temperatures. For example, I investigate when vegetation begins to grow in a particular location. Climate change is causing phenological shifts. I have been able to make nuanced statements about this over many years.
How do you analyze the raw material?
As soon as individual maps have been created from the satellite images, I place them on top of each other on the computer for the period of time that I want to examine. Then I define a specific image point to puncture through all the cards. For this point, exactly one value remains on each card. To compare the individual numerical values in the series, I use simple statistical methods, but also complex machine learning methods. However, many steps are necessary before I can evaluate a card. For example, I work closely with the atmospheric scientists at the institute who extract cloud information from the data. I can only quantify dynamics on the land surface on maps without clouds.
What can your insights achieve?
Our results serve to protect the environment, use resources responsibly and identify risks. We look at phenomena such as strongly fluctuating snow cover, postponed cultivation periods and the increase in surface temperature, which become relevant in global change, with great temporal depth. This allows us to provide authorities with the basis for decision-making for sensible flood protection or advise farmers on optimal management.
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