innovative urban climate in-situ measurements for Earth Observation

innovative urban climate in-situ measurements for Earth Observation

February 10, 2021

Bikair is a project aiming at measuring urban climate parameters with in-situ and Earth Observation. It focuses on testing low-cost Arduino-based sensors in an urban environment such as the city of Würzburg. Eventually, the project aims to correlate in-situ data with relevant Remote Sensing-based parameters. It takes advantage of the recent advancements in the Internet of Things (IoT), which allows the collection of data on specific locations and its further transportation using wireless network protocols. Bikair team designed static and mobile devices using sensors that measure environmental parameters like temperature, humidity, UV radiation, particulate matter (PM1, PM 2.5, PM 10), and soil moisture. Through microcontrollers and communication protocols, the data is stored in cloud-based database management systems, and it is analyzed to determine trends. The final product would allow users to access, visualize, or download the data using application software or APIs. These measurements could strengthen a wide range of applications such as monitoring the relationship between urban green and temperature, identifying air pollution hotspots, or supporting the downscaling of satellite remote sensing products.

you may also like:

Successful MSc defense by Sonja Maas

Successful MSc defense by Sonja Maas

Big congratulations to Sonja Maas, who successfully defended her Master thesis today on the highly relevant and increasingly pressing topic: LiDAR-Based Acquisition Strategies for Forest Management Planning in a Mature Beech Stand Supervised by Dr. Julian Fäth and...

Visit at the Institute for Geoinformatics (IFGI) at University of Münster

Visit at the Institute for Geoinformatics (IFGI) at University of Münster

Two days ago, our PostDoc Dr. Jakob Schwalb-Willmann visited the Institute for Geoinformatics at University of Münster to give a talk at IFGI’s GI Forum titled “Can animals be used to classify land use? Employing movement-tracked animals as environmental informants using deep learning”.