Daniele Marinelli received the “Laurea” (B.Sc.) degree in Electronics and Telecommunications Engineering, the “Laurea Magistrale” (M.Sc.) degree in Telecommunications Engineering (cum laude) and the Ph.D. in Information and Communication Technologies (cum laude) from the University of Trento, Italy, in 2013, 2015, 2019, respectively. He is recipient of the prize for the 2015 Best Italian master Thesis in the area of remote sensing awarded by the the Italy Chapter of the IEEE Geoscience and Remote Sensing Society. He got the Second Place in the Student Paper Competition at the 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2018) hold in Valencia (Spain). In 2017 he was a visiting PhD student at the Integrated Remote Sensing Studio, University of British Columbia, Vancouver, Canada working on Change Detection in LiDAR data. From 2017 to 2022 he was a teaching assistant at the Department of Information Engineering and Computer Science of the University of Trento. Currently he is a researcher at the Forest Ecology Unit of Fondazione Edmund Mach. His research interests are related to the multitemporal analysis time-series, Hyperspectral and LiDAR data for forestry applications.
In recent years, several new satellite constellations have been put into service. This, together with the new policies for open data distribution, dramatically increased the availability of time-series with high temporal resolution.
The new widespread availability of high temporal resolution imagery has led to paradigm shift from change detection techniques where pairs of images are compared searching for abrupt changes (e.g. forest fires, forest cuts), to methods capable of tracking changes continuously in time. In particular, time-series allows for the monitoring of subtle and gradual changes for which the definition of a pre and post event date is not straightforward (e.g., vegetation stress caused by drought, bark beetle outbreaks) and anthropogenic processes happening at a finer timescale (e.g. mowing events).
Such data availability, together with increasing ease of access to both offline computing power and to cloud based computing platforms and new tools for data processing, is leading to the development of a wide variety of applications for near real-time monitoring using Earth Observation (EO) data intended to be used in decision making processes (e.g., forest management) by stakeholders such as government agencies. In this context, we present monitoring tools, implemented on the Google Earth Engine platform, that exploit spaceborne EO data to support decision making in Alpine environments affected by two threats connected to global change: pests outbreaks and land use intensification.
After the Vaia storm in 2018, bark beetle outbreaks have become more frequent in the Alps with estimates, at the end of 2022, of 8000 hectares infested by the pests only in the Trento province. Such phenomena must be monitored by detecting both past and new outbreaks. This is critical for the definition of recovery strategies for the affected areas and mitigation strategies to limit the spread of new outbreaks. The developed tool analyzes long Sentinel-2 time-series for bark beetle outbreaks mapping, generating a product that identifies the area hit by an attack and the first year and month of the detection. By processing new images as they are acquired, it performs a near real-time monitoring highlighting new attacks as soon as they are visible from the satellite data. This tool is currently being used by the Forest Service of the Province of Trento that is providing the generated products to the local stations.
The second tool we present uses vegetation indices time-series derived from Sentinel-2 imagery to estimate grassland mowing frequency. Grasslands in Europe are facing management intensification in accessible areas and abandonment in marginal ones, with significant consequences not only for grassland productivity, but also for fodder quality, nitrogen leaching, animal and plant diversity and grassland recreational value. For these reasons the availability of grassland mowing frequency data can contribute to the development of more targeted conservation and management measures. The model is now being used in several research and management contexts, including CAP subsidies conditionality monitoring and habitat suitability for ground nesting endangered bird identification.