Remote sensing technique and ICONA based-GIS mapping for assessing the risk of soil erosion: A case of the Rudbar Basin, Iran

Abstract

Soil erosion is a major environmental concern because of its devastating effects on agriculture and food production worldwide. In our research, we developed a sophisticated and effective model to predict soil erosion risk and developed a risk map to aid decision making to address earth surface degradation as a means to preserve soil. We describe the modeling process adopting the ICONA (Instituto Nacional para la Conservacion de la Naturaleza) model, in conjunction with an Analytic Hierarchy Process referred to as the AHP, together with the application of a geodatabase and remote sensing data for the Rudbar basin, Guilan Province, Iran. The basin is highly sensitive to soil erosion. With advances in remote sensing and GIS, challenges of conventional methods can be overcome such as the time-consuming and data-intensive identification of soil erosion-prone areas. The land slope, the lithofacies, land-use/cover, vegetation cover, and a digital elevation model (DEM) were our key layer inputs for the model. The ASTER DEM data were used to extract the land slope layers while the land-use/cover layers were prepared using NDVI (Normalized Difference Vegetation Index) data from a Landsat 7 ETM + image of the year 2017. Land slope and lithofacies data were used to create soil erodibility map, whereas land-use/-cover, vegetation cover and the DEM were used to create a soil map for soil protection. The erodibility and the soil protection map were then overlapped to create a risk map for soil erosion. Zones of high to very high risk of erosion were identified in a quarter of our study area. We discussed the practical application of the results as a guideline to planning and implementation of effective regional land and water management strategies.