Prediction of Erosion-Prone Areas in the Catchments of Big Lowland Rivers: Implementation of Maximum Entropy Modelling—Using the Example of the Lower Vistula River (Poland)
It is common knowledge that erosion depends on environmental factors modified by human activity. Erosion within a catchment area can be defined by local lithological, morphometric, hydrological features, etc., and land cover, with spatial distribution described by means of remote sensing tools. The...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/23/4775 |
| _version_ | 1797507325126246400 |
|---|---|
| author | Marta Brzezińska Dawid Szatten Zygmunt Babiński |
| author_facet | Marta Brzezińska Dawid Szatten Zygmunt Babiński |
| author_sort | Marta Brzezińska |
| collection | DOAJ |
| description | It is common knowledge that erosion depends on environmental factors modified by human activity. Erosion within a catchment area can be defined by local lithological, morphometric, hydrological features, etc., and land cover, with spatial distribution described by means of remote sensing tools. The study relied on spatial data for the catchment of the Lower Vistula—the biggest river in Poland. GIS (SAGA, QGIS) tools were used to designate the spatial distribution of independent environmental variables that determined the process of erosion according to land cover types within the Lower Vistula catchment (Corine Land Cover). In addition, soil loss in the catchment area was calculated using the USLE model (Universal Soil Loss Equation). The spatial data was used to determine the predictive power of variables for the process of erosion by applying the maximum entropy model (MaxEnt) commonly used in fields of science unrelated to fluvial hydrology. The results of the study pointed directly to environmental features strongly connected with the process of erosion, identifying areas susceptible to intensified erosion, and in addition positively verified by USLE. This testifies to the correct selection of the proposed method, which is a strong point of the presented study. The proposed interdisciplinary approach to predict erosion within the catchment area (MaxEnt), widely supported by GIS tools, will allow the identification of environmental pressures to support the decision-making process in erosion-prone areas. |
| first_indexed | 2024-03-10T04:46:56Z |
| format | Article |
| id | doaj.art-e80075eb82594a21bfb0bd636c7fc3fb |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T04:46:56Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e80075eb82594a21bfb0bd636c7fc3fb2023-11-23T02:56:10ZengMDPI AGRemote Sensing2072-42922021-11-011323477510.3390/rs13234775Prediction of Erosion-Prone Areas in the Catchments of Big Lowland Rivers: Implementation of Maximum Entropy Modelling—Using the Example of the Lower Vistula River (Poland)Marta Brzezińska0Dawid Szatten1Zygmunt Babiński2Department of Inland Waterways Revitalization, Institute of Geography, Kazimierz Wielki University, 85-064 Bydgoszcz, PolandDepartment of Inland Waterways Revitalization, Institute of Geography, Kazimierz Wielki University, 85-064 Bydgoszcz, PolandDepartment of Inland Waterways Revitalization, Institute of Geography, Kazimierz Wielki University, 85-064 Bydgoszcz, PolandIt is common knowledge that erosion depends on environmental factors modified by human activity. Erosion within a catchment area can be defined by local lithological, morphometric, hydrological features, etc., and land cover, with spatial distribution described by means of remote sensing tools. The study relied on spatial data for the catchment of the Lower Vistula—the biggest river in Poland. GIS (SAGA, QGIS) tools were used to designate the spatial distribution of independent environmental variables that determined the process of erosion according to land cover types within the Lower Vistula catchment (Corine Land Cover). In addition, soil loss in the catchment area was calculated using the USLE model (Universal Soil Loss Equation). The spatial data was used to determine the predictive power of variables for the process of erosion by applying the maximum entropy model (MaxEnt) commonly used in fields of science unrelated to fluvial hydrology. The results of the study pointed directly to environmental features strongly connected with the process of erosion, identifying areas susceptible to intensified erosion, and in addition positively verified by USLE. This testifies to the correct selection of the proposed method, which is a strong point of the presented study. The proposed interdisciplinary approach to predict erosion within the catchment area (MaxEnt), widely supported by GIS tools, will allow the identification of environmental pressures to support the decision-making process in erosion-prone areas.https://www.mdpi.com/2072-4292/13/23/4775erosion predictionmaximum entropy modelUSLE modelVistula River |
| spellingShingle | Marta Brzezińska Dawid Szatten Zygmunt Babiński Prediction of Erosion-Prone Areas in the Catchments of Big Lowland Rivers: Implementation of Maximum Entropy Modelling—Using the Example of the Lower Vistula River (Poland) Remote Sensing erosion prediction maximum entropy model USLE model Vistula River |
| title | Prediction of Erosion-Prone Areas in the Catchments of Big Lowland Rivers: Implementation of Maximum Entropy Modelling—Using the Example of the Lower Vistula River (Poland) |
| title_full | Prediction of Erosion-Prone Areas in the Catchments of Big Lowland Rivers: Implementation of Maximum Entropy Modelling—Using the Example of the Lower Vistula River (Poland) |
| title_fullStr | Prediction of Erosion-Prone Areas in the Catchments of Big Lowland Rivers: Implementation of Maximum Entropy Modelling—Using the Example of the Lower Vistula River (Poland) |
| title_full_unstemmed | Prediction of Erosion-Prone Areas in the Catchments of Big Lowland Rivers: Implementation of Maximum Entropy Modelling—Using the Example of the Lower Vistula River (Poland) |
| title_short | Prediction of Erosion-Prone Areas in the Catchments of Big Lowland Rivers: Implementation of Maximum Entropy Modelling—Using the Example of the Lower Vistula River (Poland) |
| title_sort | prediction of erosion prone areas in the catchments of big lowland rivers implementation of maximum entropy modelling using the example of the lower vistula river poland |
| topic | erosion prediction maximum entropy model USLE model Vistula River |
| url | https://www.mdpi.com/2072-4292/13/23/4775 |
| work_keys_str_mv | AT martabrzezinska predictionoferosionproneareasinthecatchmentsofbiglowlandriversimplementationofmaximumentropymodellingusingtheexampleofthelowervistulariverpoland AT dawidszatten predictionoferosionproneareasinthecatchmentsofbiglowlandriversimplementationofmaximumentropymodellingusingtheexampleofthelowervistulariverpoland AT zygmuntbabinski predictionoferosionproneareasinthecatchmentsofbiglowlandriversimplementationofmaximumentropymodellingusingtheexampleofthelowervistulariverpoland |