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Assessment and Monitoring of Soil Erosion Risk and Land Degradation in Arable Land Combining Remote Sensing Methodologies and RUSLE Factors

Authors :
Francesco Scorza
Giuseppe Cillis
Biagio Tucci
Gabriele Nolè
Antonio Lanorte
Valentina Santarsiero
Beniamino Murgante
Source :
Computational Science and Its Applications – ICCSA 2021 ISBN: 9783030869786, ICCSA (6), 21st International Conference on Computational Science and Its Applications, ICCSA 2021, pp. 704–716, Virtual, Online, 13 September 2021 through 16 September 2021, info:cnr-pdr/source/autori:Tucci, Biagio; Nole, Gabriele; Lanorte, Antonio; Santarsiero, Valentina; Cillis, Giuseppe; Scorza, Francesco; Murgante, Beniamino/congresso_nome:21st International Conference on Computational Science and Its Applications, ICCSA 2021/congresso_luogo:Virtual, Online/congresso_data:13 September 2021 through 16 September 2021/anno:2021/pagina_da:704/pagina_a:716/intervallo_pagine:704–716
Publication Year :
2021
Publisher :
Springer International Publishing, 2021.

Abstract

Soil degradation is a phenomenon that describes the degradation of soil quality due to which agricultural land in particular is unproductive as a consequence of the loss of ability to produce crops and biomass. The causes are many but, especially in the inland areas of the Mediterranean regions, some dynamics related to agriculture have particularly influenced the grading process. Specifically, agricultural over exploitation with unsustainable practices and land abandonment are causing ecological alterations that require contextual analysis to assess the medium and long-term effects. The aim of this work is to investigate the role of some factors that make up the RUSLE index have in the detection and monitoring of potentially degraded areas. In particular, the areas cultivated with arable crops were chosen as the area to be analyzed, because the average annual rate of soil erosion (A factor in RUSLE equation) is high despite the presence of vegetation cover and shown evident problems due to the phenomenon of degradation. In order to identify the potential degraded areas, two factor of RUSLE index have been correlated: C factor that describes the vegetation cover of the soil and A factor which represent the amount of potential soil erosion. All methodologies have been applied in a rural area in the northern part of Basilicata Region (Italy) using GIS and remote sensing approaches, as allows the possibility to perform a series of a complex studies and can be efficiently implemented in environmental monitoring plans.

Details

ISBN :
978-3-030-86978-6
ISBNs :
9783030869786
Database :
OpenAIRE
Journal :
Computational Science and Its Applications – ICCSA 2021 ISBN: 9783030869786, ICCSA (6), 21st International Conference on Computational Science and Its Applications, ICCSA 2021, pp. 704–716, Virtual, Online, 13 September 2021 through 16 September 2021, info:cnr-pdr/source/autori:Tucci, Biagio; Nole, Gabriele; Lanorte, Antonio; Santarsiero, Valentina; Cillis, Giuseppe; Scorza, Francesco; Murgante, Beniamino/congresso_nome:21st International Conference on Computational Science and Its Applications, ICCSA 2021/congresso_luogo:Virtual, Online/congresso_data:13 September 2021 through 16 September 2021/anno:2021/pagina_da:704/pagina_a:716/intervallo_pagine:704–716
Accession number :
edsair.doi.dedup.....82c8da35d154d52652e90d3c40109e3c
Full Text :
https://doi.org/10.1007/978-3-030-86979-3_50