Understanding spatio-temporal complexity of vegetation using drones, what could we improve?

Unraveling the intricate spatial and temporal complexities of vegetation represents a crucial key to understanding ecosystem functioning. Drones, as cutting-edge technology, hold immense potential in bridging the gap between on-ground measurements and satellite remote sensing data. Nonetheless, a multitude of challenges still looms, with one of the foremost being the nuanced identification of scales that strike a balance between capturing maximum complexity while minimizing measurement errors.

Which environmental factors control extreme thermal events in rivers? A multi-scale approach (Wallonia, Belgium).

Managers need to know how to mitigate rising stream water temperature (WT) due to climate change. This requires identifying the environmental drivers that influence thermal regime and determining the spatial area where interventions are most effective. We hypothesized that (i) extreme thermal events can be influenced by a set of environmental factors that reduce thermal sensitivity and (ii) the role played by those factors varies spatially.

Using remote sensing to characterize riparian vegetation: A review of available tools and perspectives for managers.

Riparian vegetation is a central component of the hydrosystem. As such, it is often subject to management practices that aim to influence its ecological, hydraulic or hydrological functions. Remote sensing has the potential to improve knowledge and management of riparian vegetation by providing cost-effective and spatially continuous data over wide extents.

Multi-temporal monitoring of a regional riparian buffer network (>12,000 km) with LiDAR and photogrammetric point clouds.

Riparian buffers are of major concern for land and water resource managers despite their relatively low spatial coverage. In Europe, this concern has been acknowledged by different environmental directives which recommend multi-scale monitoring (from local to regional scales). Remote sensing methods could be a cost-effective alternative to field-based monitoring, to build replicable "wall-to-wall" monitoring strategies of large river networks and associated riparian buffers.

Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system.

Riparian forests are critically endangered many anthropogenic pressures and natural hazards. The importance of riparian zones has been acknowledged by European Directives, involving multi-scale monitoring. The use of this very-high-resolution and hyperspatial imagery in a multi-temporal approach is an emerging topic.

Discrimination of Deciduous Tree Species from Time Series of Unmanned Aerial System Imagery.

Technology advances can revolutionize Precision Forestry by providing accurate and fine forest information at tree level. This paper addresses the question of how and particularly when Unmanned Aerial System (UAS) should be used in order to efficiently discriminate deciduous tree species. The goal of this research is to determine when is the best time window to achieve an optimal species discrimination.