PhD Students

Elias Berra

The monitoring offorest phenology in a cost-effective manner at a fine spatial scale over relatively larger areas remains a significant challenge. To address this issue, unmanned aerial vehicles (UAVs) appear as a potential new option for forest phenology monitoring. The aim of this research is to develop an approach which employs optical cameras attached on a fixed-wing UAV to monitor forest phenology during the growing season, in order to detect the seasonal changes of the canopy leaves and to allow scaling-up to make comparisons with optical satellite data. The effects of heterogeneity on phenology studies using satellite images can then be investigated. Vegetation indices (VI) will be calculated from the UAV imagery generating a VI time series, with values fitted by logistic models. Then, a selected model will estimate the day of year corresponding to beginning and end of growing season (phenological metrics). The same phenological metrics will be estimated from MODIS imagery. In order to compare the results between the UAV and satellite, the UAV images’ spatial resolution will be scaled up to the satellite spatial resolution. Additionally, the scaling up process in the UAV images will be used to investigate the relationship between phenological patterns and spatial scale in order to reveal an optimal spatial resolution to monitor forest phenology. Therefore, it is expected that this study can answer the following research questions: Can low-cost cameras attached on an UAV acquire time series imagery suitable for forest phenology monitoring? Do UAVs provide a basis for validating satellite-derived land surface phenology products? 

   

Training Flights with the UAV

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