Abstract
Soil erosion in fired areas is one of the main environmental problem involves degrading the quality of the soil and reducing the productivity of the affected lands. The aim of this work is to implement a procedure that analyzes the change detection of the potential soil eroded in a burned area, and discriminate the amount of potential soil loss. As part of the MESARIP project (in agreement with the Regional Civil Protection) in order to implement the analyses of soil erosion pre and post fire event, using Sentinel 2 data and with the RUSLE (Revised Universal Soil Loss Equation) method in a GIS open source environment, a graphical model has been developed. The application of the RUSLE requires a series of consequential spatial analysis elaborations and, according to this scheme, the model has been developed with the Graphical Modeler. QGIS contains in a single environment a multiplicity of tools and algorithms native to other open source GIS software, such as, for example, SAGA GIS and GRASS GIS. The user interface is very simple and requires basic and thematic input data such as DEM, MASK areas or vegetation indices etc. The advantages in the construction of the model can be identified in the standardization of map algebra operations and also in the speed of execution of the steps. Currently the model has been tested in some burned areas in 2019 located in the northern part of the Apulia Region and will be tested in operational mode during the 2020 summer season.
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References
Chuvieco, E., Martin, M.P., Palacios, A.: Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. Int. J. Remote Sens. 23(23), 5103–5110 (2002)
Fu, B.J., et al.: Assessment of soil erosion at large watershed scale using RUSLE and GIS: a case study in the Loess Plateau of China. Land Degrad. Dev. 16(1), 73–85 (2005)
García-Ruiz, J.M., Nadal-Romero, E., Lana-Renault, N., Beguería, S.: Erosion in Mediterranean landscapes: changes and future challenges. Geomorphology 198, 20–36 (2013)
García-Ruiz, J.M., et al.: Flood generation and sediment transport in experimental catchments affected by land use changes in the central Pyrenees. J. Hydrol. 356(1–2), 245–260 (2008)
García, M.L., Caselles, V.: Mapping burns and natural reforestation using Thematic Mapper data. Geocarto Int. 6(1), 31–37 (1991)
Giovannini, G., Vallejo, R., Lucchesi, S., Bautista, S., Ciompi, S., Llovet, J.: Effects of land use and eventual fire on soil erodibility in dry Mediterranean conditions. For. Ecol. Manage. 147(1), 15–23 (2001)
Hall, R.J., Freeburn, J.T., De Groot, W.J., Pritchard, J.M., Lynham, T.J., Landry, R.: Remote sensing of burn severity: experience from western Canada boreal fires. Int. J. Wildland Fire 17(4), 476–489 (2008)
Hartigan, J.A., Wong, M.A.: Algorithm AS 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100–108 (1979)
Heredia, Á., Martínez, S., Quintero, E., Piñeros, W., Chuvieco, E.: Comparación de distintas técnicas de análisis digital para la cartografía de áreas quemadas con imágenes LANDSAT ETM+. GeoFocus. Revista Internacional de Ciencia y Tecnología de la Información Geográfica 3, 216–234 (2003)
Holden, Z.A., Evans, J.S.: Using fuzzy C-means and local autocorrelation to cluster satellite-inferred burn severity classes. Int. J. Wildland Fire 19(7), 853–860 (2010)
Kuo, K.T., Sekiyama, A., Mihara, M.: Determining C factor of universal soil loss equation (USLE) based on remote sensing. Int. J. Environ. Rural Dev. 7(2), 154–161 (2016)
Lanorte, A., Danese, M., Lasaponara, R., Murgante, B.: Multiscale mapping of burn area and severity using multisensor satellite data and spatial autocorrelation analysis. Int. J. Appl. Earth Obs. Geoinf. 20, 42–51 (2013)
Larsen, I.J., MacDonald, L.H.: Predicting postfire sediment yields at the hillslope scale: testing RUSLE and Disturbed WEPP. Water Resour. Res. 43(11) (2007)
Lasaponara, R., Lanorte, A.: Multispectral fuel type characterization based on remote sensing data and Prometheus model. For. Ecol. Manage. 234, S226 (2006)
Lee, S.: Soil erosion assessment and its verification using the universal soil loss equation and geographic information system: a case study at Boun Korea. Environ. Geol. 45(4), 457–465 (2004)
Lisle, T.E., Napolitano, M.B.: Effects of recent logging on the main channel of North Fork Caspar Creek. In: Ziemer, R.R. [Tech. Coord.]. Proceedings of the conference on coastal watersheds: The Caspar Creek story. PSW-GTR-168. Pacific Southwest Research Station. USDA Forest Service, Albany, CA, pp. 81–85, May 1998
Miller, J.D., Nyhan, J.W., Yool, S.R.: Modeling potential erosion due to the Cerro Grande Fire with a GIS-based implementation of the Revised Universal Soil Loss Equation. Int. J. Wildland Fire 12(1), 85–100 (2003)
Mitasova, H., Hofierka, J., Zlocha, M., Iverson, L.R.: Modelling topographic potential for erosion and deposition using GIS. Int. J. Geogr. Inf. Syst. 10(5), 629–641 (1996)
Odeh, I.O.A., McBratney, A.B., Chittleborough, D.J.: Soil pattern recognition with fuzzy-c-means: application to classification and soil-landform interrelationships. Soil Sci. Soc. Am. J. 56(2), 505–516 (1992)
Panagos, P., et al.: Rainfall erosivity in Europe. Sci. Total Environ. 511, 801–814 (2015)
Panagos, P., Borrelli, P., Meusburger, K.: A new European slope length and steepness factor (LS-Factor) for modeling soil erosion by water. Geosciences 5(2), 117–126 (2015)
Panagos, P., Meusburger, K., Ballabio, C., Borrelli, P., Alewell, C.: Soil erodibility in Europe: a high-resolution dataset based on LUCAS. Sci. Total Environ. 479, 189–200 (2014)
Rauste, Y., Herland, E., Frelander, H., Soini, K., Kuoremaki, T., Ruokari, A.: Satellite-based forest fire detection for fire control in boreal forests. Int. J. Remote Sens. 18(12), 2641–2656 (1997)
Renard, K.G.: Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). United States Government Printing (1997)
Renard, K.G., Foster, G.R., Weesies, G.A., Porter, J.P.: RUSLE: revised universal soil loss equation. J. Soil Water Conserv. 46(1), 30–33 (1991)
Richards, G.D.: A general mathematical framework for modeling two-dimensional wildland fire spread. Int. J. Wildland Fire 5(2), 63–72 (1995)
Shakesby, R.A.: Post-wildfire soil erosion in the Mediterranean: review and future research directions. Earth Sci. Rev. 105(3–4), 71–100 (2011)
Sorriso-Valvo, M., Bryan, R.B., Yair, A., Iovino, F., Antronico, L.: Impact of afforestation on hydrological response and sediment production in a small Calabrian catchment. CATENA 25(1–4), 89–104 (1995)
Surfleet, C.G., Ziemer, R.R.: Effects of forest harvesting on large organic debris in coastal streams. In: LeBlanc, J. (ed.) Conference on Coast Redwood Forest Ecology and Management, 18–20 June 1996, Arcata, California. Humboldt State University, pp. 134–136 (1996)
Terranova, O., Antronico, L., Coscarelli, R., Iaquinta, P.: Soil erosion risk scenarios in the Mediterranean environment using RUSLE and GIS: an application model for Calabria (southern Italy). Geomorphology 112(3–4), 228–245 (2009)
Wischmeier, W.H., Smith, D.D.: Predicting rainfall erosion losses - a guide for conservation planning. U.S. Department of Agriculture, Agriculture Handbook No 537, p. 58, Hyattsville (MD) (1978)
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Nolè, G. et al. (2020). Model of Post Fire Erosion Assessment Using RUSLE Method, GIS Tools and ESA Sentinel DATA. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12253. Springer, Cham. https://doi.org/10.1007/978-3-030-58814-4_36
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