Mauro Maesano
Biographic Data
| ID | 6933651 |
|---|---|
| NAME | Mauro Maesano |
| GIVEN NAMES | Mauro |
| FAMILY NAME | Maesano |
| SIGNATURE | MAESANO M |
| AFFILIATIONS | Università degli Studi della Tuscia |
| ORCID | 0000-0002-4325-951X |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Evaluating Shallow Landslide Prediction Mapping by Using Two Different GIS-Based Models
Landslides affecting soil layers up to 1–2 m deep pose a significant hazard in mountainous and hilly regions, particularly in the Mediterranean, where intense precipitation is increasing. Identifying landslide-prone areas is crucial for risk assessment and mitigation, as landslides can severely impact land surfaces, infrastructure, and inhabited areas. Forest cover and management play a fundamental role in stabilizing soil and reducing landslide …
Urban Greenprint
Urban forests (UFs) play a crucial role in mitigating climate change, but their management presents complex trade-offs between environmental, economic, and social aspects. We developed a Decision Support Tool (DST) to simulate 27-year UF dynamics under six different management strategies, aiming to maximize socio-economic and environmental benefits while considering costs. Business as Usual (BaU), Yielding Scenario (YS), High Management (HM), For…
No prominent works on this page.
Evaluating Shallow Landslide Prediction Mapping by Using Two Different GIS-Based Models
Landslides affecting soil layers up to 1–2 m deep pose a significant hazard in mountainous and hilly regions, particularly in the Mediterranean, where intense precipitation is increasing. Identifying landslide-prone areas is crucial for risk assessment and mitigation, as landslides can severely impact land surfaces, infrastructure, and inhabited areas. Forest cover and management play a fundamental role in stabilizing soil and reducing landslide …
Urban Greenprint
Urban forests (UFs) play a crucial role in mitigating climate change, but their management presents complex trade-offs between environmental, economic, and social aspects. We developed a Decision Support Tool (DST) to simulate 27-year UF dynamics under six different management strategies, aiming to maximize socio-economic and environmental benefits while considering costs. Business as Usual (BaU), Yielding Scenario (YS), High Management (HM), For…
Computer Science (2 works) · Geography (2 works) · Artificial Intelligence (1 work) · Business (1 work) · Cartography (1 work) · Decision support system (1 work) · Environmental planning (1 work) · Environmental resource management (1 work) · Environmental Science (1 work) · Forestry (1 work)