Oil pollution remains a critical environmental challenge with extensive impacts on marine and coastal ecosystems. Remote sensing offers a powerful, non-invasive means to detect, quantify and monitor ...

The geographer is the first from UB to receive the honor, which recognizes exceptional contributions to the field through research, teaching or outreach.

Watershed morphometric analysis constitutes a vital methodology for deciphering the physical character and behaviour of drainage basins. By quantifying parameters such as drainage density, bifurcation ...

The environment is changing, particularly along our coasts where sea levels are rising. Knowing how to mitigate these changes requires a deeper understanding of how the coastal ocean responds to ...

In a recent systematic review published in the International Journal of Environmental Research and Public Health, researchers examined the use of geospatial tools, methods, and techniques such as ...

This Research Topic is part of a series of article collections published within Frontiers in Water including: - Emerging Themes in Socio-Hydrology: Digital, ...

This study introduces a comprehensive analysis of various soil-resistant vegetation indices (VIs) designed to mitigate the ...

Analyze images and other data to solve problems across disciplines. Imagery and other data collected by satellites, crewed aircraft, and uncrewed aerial systems (UAS) are increasingly important for ...

The Remote Sensing for Natural Resources certificate focuses on using satellite and aerial imagery to monitor, measure, and model natural systems. Learn how to interpret remote sensing data to support ...

No audio available for this content. NV5 Geospatial’s distribution network data shows pole usage. (Image: Nv5 Geospatial) Asset and vegetation management applications help utilities minimize risk and ...

The field of Earth Observation (EO) is continuously advancing with the development of new optical remote sensing techniques and instrument concepts. These ...

a multidimensional poverty index (MPI). Using state-of-the-art machine learning algorithms, findings suggest that the model had relatively low levels of predictive accuracy, insufficient to provide ...