Water-induced erosion potentiality and vulnerability assessment in Kangsabati river basin, eastern India
Author:
Publisher
Springer Science and Business Media LLC
Subject
Management, Monitoring, Policy and Law,Economics and Econometrics,Geography, Planning and Development
Link
https://link.springer.com/content/pdf/10.1007/s10668-021-01576-w.pdf
Reference108 articles.
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2. Alejandro, M., & Omasa, K. (2007). Estimation of vegetation parameter for modeling soil erosion using linear spectral mixture analysis of landsat ETM data. ISPRS Journal of Photogrammetry and Remote Sensing, 62, 309–324.
3. Arabameri, A., Asadi Nalivan, O., Saha, S., et al. (2020). Novel ensemble approaches of machine learning techniques in modeling the gully erosion susceptibility. Remote Sensing, 12, 1890. https://doi.org/10.3390/rs12111890
4. Arabameri, A., Chandra Pal, S., Costache, R., Saha, A., Rezaie, F., Seyed Danesh, A., & Hoang, N. D. (2021). Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms. Geomatics, Natural Hazards and Risk, 12(1), 469–498.
5. Avinash, K., Jayappa, K. S., & Deepika, B. (2011). Prioritization of sub-basins based on geomorphology and morphometricanalysis using remote sensing and geographic informationsystem (GIS) techniques. Geocarto International. https://doi.org/10.1080/10106049.2011.606925
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