D-RUSLE: a dynamic model to estimate potential soil erosion with satellite time series in the Italian Alps
Soil erosion is addressed as one of the main hydrogeological risks in the European Union. Since the average annual soil loss rate exceeds the annual average formation rate, soil is considered as a non-renewable resource. Besides, human activities, human-induced forces and climate change have further...
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| Veröffentlicht in: | European journal of remote sensing 2019-12, Vol.52 (sup4), p.34-53 |
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| creator | Gianinetto, Marco Aiello, Martina Polinelli, Francesco Frassy, Federico Rulli, Maria Cristina Ravazzani, Giovanni Bocchiola, Daniele Chiarelli, Davide Danilo Soncini, Andrea Vezzoli, Renata |
| description | Soil erosion is addressed as one of the main hydrogeological risks in the European Union. Since the average annual soil loss rate exceeds the annual average formation rate, soil is considered as a non-renewable resource. Besides, human activities, human-induced forces and climate change have further accelerated the erosion processes. Therefore, understanding soil erosion spatial and temporal trends could provide important information for supporting government land-use policies and strategies for its reduction. This paper describes the Dynamic Revised Universal Soil Loss Equation (D-RUSLE) model, a modified version of the well-known RUSLE model. The RUSLE model formulation was modified to include variations in rainfall erosivity and land-cover to provide more accurate estimates of the potential soil erosion in the Italian Alps. Specifically, the modelling of snow occurrence and the inclusion of Earth Observation data allow dynamic estimation of both spatial and temporal land-cover changes. Results obtained in Val Camonica (Italy) show that RUSLE model tends to overestimate erosion rates in Autumn/Winter because not considering snow cover and vegetation dynamics. The assimilation of satellite-derived information in D-RUSLE allows a better representation of soil erosion forcing, thus proving a more accurate erosion estimate for supporting government land-use policies and strategies for reducing this phenomenon. |
| doi_str_mv | 10.1080/22797254.2019.1669491 |
| format | Article |
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Since the average annual soil loss rate exceeds the annual average formation rate, soil is considered as a non-renewable resource. Besides, human activities, human-induced forces and climate change have further accelerated the erosion processes. Therefore, understanding soil erosion spatial and temporal trends could provide important information for supporting government land-use policies and strategies for its reduction. This paper describes the Dynamic Revised Universal Soil Loss Equation (D-RUSLE) model, a modified version of the well-known RUSLE model. The RUSLE model formulation was modified to include variations in rainfall erosivity and land-cover to provide more accurate estimates of the potential soil erosion in the Italian Alps. Specifically, the modelling of snow occurrence and the inclusion of Earth Observation data allow dynamic estimation of both spatial and temporal land-cover changes. Results obtained in Val Camonica (Italy) show that RUSLE model tends to overestimate erosion rates in Autumn/Winter because not considering snow cover and vegetation dynamics. The assimilation of satellite-derived information in D-RUSLE allows a better representation of soil erosion forcing, thus proving a more accurate erosion estimate for supporting government land-use policies and strategies for reducing this phenomenon.</description><identifier>ISSN: 2279-7254</identifier><identifier>EISSN: 2279-7254</identifier><identifier>DOI: 10.1080/22797254.2019.1669491</identifier><language>eng</language><publisher>Cagiari: Taylor & Francis</publisher><subject>Accelerated erosion ; Alpine basin ; Climate and human activity ; Climate change ; Dynamic models ; Erosion processes ; Erosion rates ; Geology ; Human influences ; Hydrogeology ; Land cover ; Land use ; natural hazards ; NDVI ; Nonrenewable resources ; Policies ; Rainfall ; Renewable resources ; RUSLE model ; Satellite time series ; Snow cover ; Soil dynamics ; Soil erosion ; Soil loss</subject><ispartof>European journal of remote sensing, 2019-12, Vol.52 (sup4), p.34-53</ispartof><rights>2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. 2019</rights><rights>2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution – Non-Commercial License http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). 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Since the average annual soil loss rate exceeds the annual average formation rate, soil is considered as a non-renewable resource. Besides, human activities, human-induced forces and climate change have further accelerated the erosion processes. Therefore, understanding soil erosion spatial and temporal trends could provide important information for supporting government land-use policies and strategies for its reduction. This paper describes the Dynamic Revised Universal Soil Loss Equation (D-RUSLE) model, a modified version of the well-known RUSLE model. The RUSLE model formulation was modified to include variations in rainfall erosivity and land-cover to provide more accurate estimates of the potential soil erosion in the Italian Alps. Specifically, the modelling of snow occurrence and the inclusion of Earth Observation data allow dynamic estimation of both spatial and temporal land-cover changes. Results obtained in Val Camonica (Italy) show that RUSLE model tends to overestimate erosion rates in Autumn/Winter because not considering snow cover and vegetation dynamics. The assimilation of satellite-derived information in D-RUSLE allows a better representation of soil erosion forcing, thus proving a more accurate erosion estimate for supporting government land-use policies and strategies for reducing this phenomenon.</description><subject>Accelerated erosion</subject><subject>Alpine basin</subject><subject>Climate and human activity</subject><subject>Climate change</subject><subject>Dynamic models</subject><subject>Erosion processes</subject><subject>Erosion rates</subject><subject>Geology</subject><subject>Human influences</subject><subject>Hydrogeology</subject><subject>Land cover</subject><subject>Land use</subject><subject>natural hazards</subject><subject>NDVI</subject><subject>Nonrenewable resources</subject><subject>Policies</subject><subject>Rainfall</subject><subject>Renewable resources</subject><subject>RUSLE model</subject><subject>Satellite time series</subject><subject>Snow cover</subject><subject>Soil dynamics</subject><subject>Soil erosion</subject><subject>Soil loss</subject><issn>2279-7254</issn><issn>2279-7254</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>0YH</sourceid><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><sourceid>DOA</sourceid><recordid>eNp9UU1LAzEQXURBUX-CEPC8Nclusoknxc9CQfDjHGaTrKakm5pEpP_e1Fbx5FwyTN68NzOvqk4InhAs8BmlnewoaycUEzkhnMtWkp3qYF2v1x-7f_L96jilOS4hMO6EPKjm1_Xjy9Ps5hwBMqsRFk6jRTDWoxyQTdktIFu0DNmO2YFHKTiPbAzJhRF9uvyGUgF47wqqgC1KNjqbkBtRfrNomsE7GNGlX6ajam8An-zx9j2sXm5vnq_u69nD3fTqclbrlpFcG9FZiwcqteREdgYIM0QzDqTrQWNquISe98KwnurOtJSX_YH1XcsxbgQ0h9V0w2sCzNUylhXiSgVw6rsQ4quCmJ32VmnWN5wZSlpu214PguqGkCLT9A0t4oXrdMO1jOH9o9xDzcNHHMv4irZcMCYkZgXFNihdDpOiHX5VCVZrl9SPS2rtktq6VPouNn1uHEJcwGeI3qgMKx_iEGHULqnmf4ovuDeX8Q</recordid><startdate>20191218</startdate><enddate>20191218</enddate><creator>Gianinetto, Marco</creator><creator>Aiello, Martina</creator><creator>Polinelli, Francesco</creator><creator>Frassy, Federico</creator><creator>Rulli, Maria Cristina</creator><creator>Ravazzani, Giovanni</creator><creator>Bocchiola, Daniele</creator><creator>Chiarelli, Davide Danilo</creator><creator>Soncini, Andrea</creator><creator>Vezzoli, Renata</creator><general>Taylor & Francis</general><general>Taylor & Francis Ltd</general><general>Taylor & Francis Group</general><scope>0YH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TG</scope><scope>7XB</scope><scope>8FD</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H8D</scope><scope>KL.</scope><scope>L7M</scope><scope>M2O</scope><scope>MBDVC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-6850-0883</orcidid><orcidid>https://orcid.org/0000-0001-9011-616X</orcidid></search><sort><creationdate>20191218</creationdate><title>D-RUSLE: a dynamic model to estimate potential soil erosion with satellite time series in the Italian Alps</title><author>Gianinetto, Marco ; Aiello, Martina ; Polinelli, Francesco ; Frassy, Federico ; Rulli, Maria Cristina ; Ravazzani, Giovanni ; Bocchiola, Daniele ; Chiarelli, Davide Danilo ; Soncini, Andrea ; Vezzoli, Renata</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c451t-d87ee0f29c96197da15d1c56a17bac02d69ab6b8d5b2c7d426972a5b7460038a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Accelerated erosion</topic><topic>Alpine basin</topic><topic>Climate and human activity</topic><topic>Climate change</topic><topic>Dynamic models</topic><topic>Erosion processes</topic><topic>Erosion rates</topic><topic>Geology</topic><topic>Human influences</topic><topic>Hydrogeology</topic><topic>Land cover</topic><topic>Land use</topic><topic>natural hazards</topic><topic>NDVI</topic><topic>Nonrenewable resources</topic><topic>Policies</topic><topic>Rainfall</topic><topic>Renewable resources</topic><topic>RUSLE model</topic><topic>Satellite time series</topic><topic>Snow cover</topic><topic>Soil dynamics</topic><topic>Soil erosion</topic><topic>Soil loss</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gianinetto, Marco</creatorcontrib><creatorcontrib>Aiello, Martina</creatorcontrib><creatorcontrib>Polinelli, Francesco</creatorcontrib><creatorcontrib>Frassy, Federico</creatorcontrib><creatorcontrib>Rulli, Maria Cristina</creatorcontrib><creatorcontrib>Ravazzani, Giovanni</creatorcontrib><creatorcontrib>Bocchiola, Daniele</creatorcontrib><creatorcontrib>Chiarelli, Davide Danilo</creatorcontrib><creatorcontrib>Soncini, Andrea</creatorcontrib><creatorcontrib>Vezzoli, Renata</creatorcontrib><collection>Taylor & Francis Open Access</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Technology Research Database</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>Aerospace Database</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>European journal of remote sensing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gianinetto, Marco</au><au>Aiello, Martina</au><au>Polinelli, Francesco</au><au>Frassy, Federico</au><au>Rulli, Maria Cristina</au><au>Ravazzani, Giovanni</au><au>Bocchiola, Daniele</au><au>Chiarelli, Davide Danilo</au><au>Soncini, Andrea</au><au>Vezzoli, Renata</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>D-RUSLE: a dynamic model to estimate potential soil erosion with satellite time series in the Italian Alps</atitle><jtitle>European journal of remote sensing</jtitle><date>2019-12-18</date><risdate>2019</risdate><volume>52</volume><issue>sup4</issue><spage>34</spage><epage>53</epage><pages>34-53</pages><issn>2279-7254</issn><eissn>2279-7254</eissn><abstract>Soil erosion is addressed as one of the main hydrogeological risks in the European Union. Since the average annual soil loss rate exceeds the annual average formation rate, soil is considered as a non-renewable resource. Besides, human activities, human-induced forces and climate change have further accelerated the erosion processes. Therefore, understanding soil erosion spatial and temporal trends could provide important information for supporting government land-use policies and strategies for its reduction. This paper describes the Dynamic Revised Universal Soil Loss Equation (D-RUSLE) model, a modified version of the well-known RUSLE model. The RUSLE model formulation was modified to include variations in rainfall erosivity and land-cover to provide more accurate estimates of the potential soil erosion in the Italian Alps. Specifically, the modelling of snow occurrence and the inclusion of Earth Observation data allow dynamic estimation of both spatial and temporal land-cover changes. Results obtained in Val Camonica (Italy) show that RUSLE model tends to overestimate erosion rates in Autumn/Winter because not considering snow cover and vegetation dynamics. The assimilation of satellite-derived information in D-RUSLE allows a better representation of soil erosion forcing, thus proving a more accurate erosion estimate for supporting government land-use policies and strategies for reducing this phenomenon.</abstract><cop>Cagiari</cop><pub>Taylor & Francis</pub><doi>10.1080/22797254.2019.1669491</doi><tpages>20</tpages><orcidid>https://orcid.org/0000-0002-6850-0883</orcidid><orcidid>https://orcid.org/0000-0001-9011-616X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accelerated erosion Alpine basin Climate and human activity Climate change Dynamic models Erosion processes Erosion rates Geology Human influences Hydrogeology Land cover Land use natural hazards NDVI Nonrenewable resources Policies Rainfall Renewable resources RUSLE model Satellite time series Snow cover Soil dynamics Soil erosion Soil loss |
| title | D-RUSLE: a dynamic model to estimate potential soil erosion with satellite time series in the Italian Alps |
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