A conditional GIS‐interpolation‐based model for mapping soil‐water erosion processes in Lebanon

Abstract

Soil erosion by water is a major cause of landscape degradation in Mediterranean environments, including Lebanon. This paper proposes a conditional decision-rule interpolation-based model to predict the distribution of multiple erosion processes (i.e. sheet, mass and linear) in a representative area of Lebanon from the measured erosion signs in the field (root exposure, earth pillars, soil etching and drift and linear channels). First, erosion proxies were derived from the structural OASIS classification of Landsat thematic mapper (TM) imageries combined with the addition of several thematic erosion maps (slope gradient, aspect and curvature, drainage density, vegetal cover, soil infiltration and erodibility and rock infiltration/movement) under a geographic information systems (GIS) environment. Second, erosion signs were measured in the field, and interpolated by the statistical moments (means and variance) in the defined erosion proxies, thus producing quantitative erosion maps (t ha À1 ) at a scale of 1:100 000. Seven decision rules were then generated and applied on these maps in order to produce the overall decisive erosion map reflecting all existing erosion processes, that is, equality (ER), dominance (DOR), bimodality (BR), masking (MR), aggravating (AR), dependence (DER) and independence (IR). The produced erosion maps are divided into seven classes ranging between 0 and more than 1Á8 t ha À1 for sheet erosion, and 0 and more than 10Á5 t ha À1 for mass and linear erosion. They are fairly matching with coincidences values equal to 43 per cent (sheet/linear), 48 per cent (sheet/mass) and 49 per cent (linear/mass). The overall accuracies of these maps were estimated to be 76 per cent (sheet erosion), 78 per cent (mass erosion) and 78Á5 per cent (linear erosion). The overall decisive erosion map with 15 classes corresponds well to land management needs. The model used is relatively simple, and may also be applied to other areas. It is particularly useful when GIS database on factors influencing erosion is limited.

Routing runoff and soil particles in a distributed model with GIS: implications for soil protection in mountain agricultural landscapes

“…Soil loss in productive croplands produced by rainfall splash and overland flow is a serious environmental and economic problem in many Mediterranean environments causing both on-and off-site effects (Bou Kheir, 2008). The loss of fertile soil in arable lands and the degradation in the quality of the soil resources are the main on-site consequences of soil erosion (Morgan, 2005).…”

Modeling soil erosion susceptibility and LULC dynamics for land degradation management using geoinformation technology in Debre Tabor district, Northwestern highlands of Ethiopia

“…The RUSLE integrates perfections in the influences founded on original and improved data but preserves the foundation of the USLE equation. RUSLE model stood established as a comparison representing key issues affecting soil erosion, specifically rainfall, soil types, slope, LULC characteristics (Bagwan, 2020;Balabathina et al, 2019;Boardman et al, 2009;Kheir, 2007). After the data of those parameters collected, investigation and meting out were complete by categorizing the compulsory evidence of respectively thematic layer utilizing Geospatial technology.…”

Integrative geospatial approaches for the comprehensive monitoring and assessment of land management sustainability: Rationale, Potentials, and Characteristics

“…Remote sensing can provide spatio‐temporally explicit information on various soil properties that are indicators of land degradation, including soil organic matter, surface roughness, texture, moisture, and salinity (Barnes et al ., 2003; Metternicht and Zinck, 2003; Anderson and Croft, 2009; Ben‐Dor et al ., 2009; Metternicht et al ., 2010). Geographic information systems approaches integrating various types of data (e.g., climate, topography, land cover) can help identify sediment source and sink areas (Jain et al ., 2009), evaluate physical, chemical and biological soil degradation (de Paz et al ., 2006; Odeh and Onus, 2008; Zhu et al ., 2009), and assess soil erosion risk (Erdogan et al ., 2007; Kheir, 2008; Beskow et al ., 2009; Setegn et al ., 2009; Nigel and Rughooputh, 2010). Models of nutrient or biogeochemical cycles, including fluxes of carbon, nitrogen, and water are often driven by remotely sensed data (e.g., vegetation type, leaf area index, fraction of absorbed photosynthetically active radiation, light‐use efficiency, and leaf nitrogen concentration; Asner and Ollinger, 2009).…”