Aerial surveys – whether with pen and paper or a camera on an airplane – have been around for a long time and are a tradition in the FZS’s major project areas. Today, we can precisely direct satellites that provide us with images accurate to within half a meter resolution on a projected time. This generates enormous amounts of data for large landscapes. For computer – based evaluation, we therefore rely on machine learning to analyze the images – for example, to identify where open terrain, dense tree canopies, or rivers are located. It becomes particularly exciting when we link this data with climate data and other secondary sources and results from our monitoring efforts, such as the movement patterns of tagged animals or species records from camera traps, acoustic, or eDNA sensors.

Digital remote sensing is a game – changer for nature conservation. It gives us a comprehensive overview even of vast areas like the Peruvian Amazon. By comparing images taken at different times, we can identify where changes are occurring – whether due to natural processes like seasonal flooding or human activities such as deforestation, road construction, tourism, or gold mining. This information helps us better contextualize our field data. Based on this, we can plan and prioritize conservation measures more effectively. This is particularly crucial in very large areas.

One of the biggest challenges is selecting the right technologies from the vast array of technical developments. The possibilities today are enormous, but not every application truly advances our conservation efforts. Many are expensive, complex, and less useful than they initially appear. That’s why it’s important to test new technologies in a targeted manner and compare them with field experience – we’re currently doing this with new analytical methods in various African landscapes. Ultimately, what matters is that technologies make a real contribution to the protection of landscapes and species.

I grew up in Pakistan near second largest mangrove forest in the world. There, I witnessed firsthand how human pressure and state negligence of land has severely damaged this unique ecosystem. That experience shaped my view of nature and our responsibility toward it. Today, I’m motivated to use my knowledge as a data engineer to help preserve nature. I want to help ensure that the landscapes I analyze on my screen today will still exist 20 years from now – no matter what technologies we’re using to analyze them then.