Assessing of Soil Erosion Risk Through Geoinformation Sciences and Remote Sensing -- A Review
| Autor: | Filchev, Lachezar, Kolev, Vasil |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Rai, P.K., Singh, P., Mishra, V.N. (eds), Recent Technologies for Disaster Management and Risk Reduction, Earth and Environmental Sciences Library, Springer, 2021. https://link.springer.com/chapter/10.1007/978-3-030-76116-5_21 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1007/978-3-030-76116-5_21 |
| Popis: | During past decades a marked manifestation of widespread erosion phenomena was studied worldwide. Global conservation community has launched campaigns at local, regional and continental level in developing countries for preservation of soil resources in order not only to stop or mitigate human impact on nature but also to improve life in rural areas introducing new approaches for soil cultivation. After the adoption of Sustainable Development Goals of UNs and launching several world initiatives such as the Land Degradation Neutrality (LDN) the world came to realize the very importance of the soil resources on which the biosphere relies for its existence. The main goal of the chapter is to review different types and structures erosion models as well as their applications. Several methods using spatial analysis capabilities of geographic information systems (GIS) are in operation for soil erosion risk assessment, such as Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation (RUSLE) in operation worldwide and in the USA and MESALES model. These and more models are being discussed in the present work alongside more experimental models and methods for assessing soil erosion risk such as Artificial Intelligence (AI), Machine and Deep Learning, etc. At the end of this work, a prospectus for the future development of soil erosion risk assessment is drawn. Comment: Chapter 21 (pages 54) |
| Databáze: | arXiv |
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