NANO Alumni Bennet Atsu K. Foli and colleagues published the following article in the Remote Sensing in Earth Systems Sciences Journal
Development of a Raspberry Pi–Based Remote Station Prototype for Coastal Environment Monitoring
Kwame, B. et al. (2021) Remote Sensing in Earth Systems Sciences Journal. DOI 10.1007/s41976-021-00053-2
Monitoring of the marine and coastal environment using standard measuring equipment is not without incurring a significant amount of cost. This study was geared at prospecting relatively inexpensive environmental monitoring instrument using the Raspberry Pi computer in combination with commonly available sensors. Atmospheric temperature, humidity, and sea surface temperature (SST) were monitored using locally assembled low-cost measuring equipment with a subsequent comparison with data from a standard weather station. The developed instrument was consequently evaluated for its efficacy and various functionalities in coastal environmental monitoring. DHT11 and DHT22 sensors are relatively cheap and both measure atmospheric temperature and humidity, while a DS19B20 waterproof digital thermometer measures water temperature. These sensors were incorporated in a locally built in situ measuring equipment interfaced by a Python-programmed Raspberry Pi for acquiring data. A successful assemblage and deployment of the device in a near-shore coastal marine environment yielded efficient and accurate data recorded by the DHT22 and DS19B20 sensors. A comparison of the DS18B20-measured SST to SST from Sentinel-3 satellite revealed no significant difference for a simple T-test and with R2 and root mean square error (RMSE) values of 0.172 and 2.15 °C respectively. Similarly, a comparison of atmospheric temperature and humidity between the developed equipment using
DHT22 sensor, and the standard weather station yielded strong positive correlations (0.92 and 0.93) and with R2 of 0.71 and 0.58, and RMSE of 0.92 °C and 3.1% respectively. A transformation of the data from the developed equipment with respective regression equations yielded further significant improvements in the results with R2 values of 0.93, 0.84 and 0.87, and RMSE values of 0.63 °C, 0.68 °C and 1.74% respectively for SST (DS19B20), atmospheric temperature (DHT22) and humidity (DHT22). Although the DHT11 sensor recorded higher errors in atmospheric temperature and humidity data due to its low operating tolerance ranges, an application of respective regression equations also yielded improved results. This study has successfully demonstrated the potential of developing and using locally assembled relatively low-cost equipment for environmental monitoring where funding is a constraint for small-scale research and operational in situ observations.
- Atmospheric temperature and humidity
- GMES & Africa
- Open-source coastal weather station
- Raspberry Pi .
- Sea surface temperature .
Link for the publication here