EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Integration of TreeTalker proximal data with remote sensing for monitoring seasonal phenological dynamics at the species level in Italy

Alexander Cotrina-Sánchez1,2, Gaia Vaglio Laurin1, Jerzy Piotr Kabala3, Francesco Niccoli3, Jim Yates1, Riccardo Valentini1, and Giovanna Battipaglia3
Alexander Cotrina-Sánchez et al.
  • 1Department for Innovation in Biological, Agro-food and Forest Systems (DIBAF), Tuscia University, Viterbo (VT), Italy (
  • 2Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas 01001, Peru (
  • 3Department of Environmental Biological and Pharmaceutical Sciences and Technologies, University of Campania –L. Vanvitelli”, Caserta, Italy

Over the years, remote sensing and spectroscopy have contributed significantly to vegetation monitoring, primarily to understand the interaction of plants with solar radiation. However, due to spatial and temporal heterogeneity, light availability below and within tree canopies is challenging to estimate or time-consuming. Therefore, it is essential to know the structure of the tree canopy through vertical light transmission profiles, which will allow estimating plant development, biophysical properties throughout the canopy and the seasonal phenology of the species.

Currently, technologies based on the Internet of Things (IoT) are constituted as efficient and low-cost tools for monitoring forest ecology at the individual and species levels. A device applied to ecology and based on IoT is the "TreeTalker" (TT +), which allows measuring in semi-real time, in addition to water transport in trees, diametrical growth, the spectral transmittance of light through the canopy in 12 spectral bands, using spectrometers in the visible range (VIS) between 450 – 650 nm and 610 – 860 nm in the near-infrared (NIR). These parameters are acquired and stored by each TT+ every hour, sent to a node (TT-Cloud), and transmitted and stored on a server.

In this context, our study integrates remote sensing data and those obtained through IoT to evaluate the variability of the spectral response at the population level in forest species: Quercus cerris, Fagus Sylvatica and Pinus pinaster. Cloud computing was used through Google Earth Engine (GEE) to extract multitemporal values from the Sentinel 2 satellite and its subsequent integration with data from the TreeTalker spectrometer, devices installed in trees of 05 plots located in central and southern Italy, precisely in the Rocarespampani sector in Viterbo, Vesuvio National Park and Matese Regional Park in Campania Region.

Preliminary results show the ability of the TT+ spectrometer to store daily information at different wavelengths during the year. The spectral response of the near-infrared (NIR) bands is the most susceptible to foliage changes for deciduous species, mainly in the summer and spring seasons. In the case of visible bands (VIS), it is more susceptible to energy input in coniferous species and in the winter and autumn seasons. Finally, a higher correlation was obtained between the NIR bands of Sentinel 2 and TT+, mainly for the deciduous species Q. cerris and F. Sylvatica.

The seasonal assessment of the species will be continued during the following years at local and regional scales to understand their responses to climate change. Also, light transmission through the forest canopy will contribute to identifying and complementing knowledge of forest-climate interactions, allowing a more detailed understanding of the ecophysiological parameters of forest vegetation and phenological changes at the species and ecosystem level.

How to cite: Cotrina-Sánchez, A., Vaglio Laurin, G., Kabala, J. P., Niccoli, F., Yates, J., Valentini, R., and Battipaglia, G.: Integration of TreeTalker proximal data with remote sensing for monitoring seasonal phenological dynamics at the species level in Italy, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-4657,, 2023.