A Mobile Instrumented Sensor Platform for Long-Term Terrestrial Ecosystem Analysis: An Example Application in an Arctic Tundra Ecosystem

• Main Authors: Healey, N. C., Oberbauer, S. F., Ahrends, H. E., Dierick, D., Welker, J. M., Leffler, A. J., Hollister, R. D., Vargas, S. A., Tweedie, C. E.
• Format: Article in Journal/Newspaper
• Language: English
• Published: INT SOC ENVIRON INFORM SCI 2014
• Subjects: ddc:no; Arctic; albedo; Climate change; Tundra
• Online Access: https://kups.ub.uni-koeln.de/42968/

To address impacts of climate change on natural ecosystems, researchers need efficient and integrated ground-based sensor systems capable of detecting plant to ecosystem alterations to productivity, species composition, phenology, and structure and function over seasonal, inter-annual, and decadal time scales. Here, we introduce the Mobile Instrumented Sensor Platform (MISP), a versatile robotic sensor system that is suspended above or within the canopy and is designed to be adaptable for both short and long-term observations, and suitable for multiple ecosystems. The system is novel in that it is mobile, rather than static, the suite of sensors can be customized, and installation and operation requires minor surface distur bance relative to comparable systems already in use. MISP was developed as a contribution to the Arctic Observation Network's International Tundra Experiment (AON-ITEX), where at five locations between the low and high Arctic we record observations of different tundra plant communities over a 50 m transect. Observations include air temperature, surface temperature, incoming and outgoing long-and short-wave radiation, albedo, Normalized Difference Vegetation Index (NDVI), three-dimensional video, two and three-dimensional photography, and hyperspectral reflectance. Data analysis has proven the system's suitability for detecting subtle ecosystem changes across small-scale soil moisture and other gradients due to the mobile nature of the sampling. Long-term studies will benefit from this approach because sampling is repeatable with high spatial and temporal resolution and the system can be adapted to incorporate future technologies.