Integration of Remotely Sensed Soil Sealing Data in Landslide Susceptibility Mapping

Abstract

Soil sealing is the destruction or covering of natural soils by totally or partially impermeable artificial material. ISPRA (Italian Institute for Environmental Protection Research) uses different remote sensing techniques to monitor this process and updates yearly a national-scale soil sealing map of Italy. In this work, for the first time, we tried to combine soil sealing indicators as additional parameters within a landslide susceptibility assessment. Four new parameters were derived from the raw soil sealing map: Soil sealing aggregation (percentage of sealed soil within each mapping unit), soil sealing (categorical variable expressing if a mapping unit is mainly natural or sealed), urbanization (categorical variable subdividing each unit into natural, semi-urbanized, or urbanized), and roads (expressing the road network disturbance). These parameters were integrated with a set of well-established explanatory variables in a random forest landslide susceptibility model and different configurations were tested: Without the proposed soil-sealing-derived variables, with all of them contemporarily, and with each of them separately. Results were compared in terms of AUC ((area under receiver operating characteristics curve, expressing the overall effectiveness of each configuration) and out-of-bag-error (estimating the relative importance of each variable). We found that the parameter “soil sealing aggregation” significantly enhanced the model performances. The results highlight the potential relevance of using soil sealing maps on landslide hazard assessment procedures.

Definition of Environmental Indicators for a Fast Estimation of Landslide Risk at National Scale

“…Soil sealing is the most intense form of artificial land take and it can be defined as the removal or covering of soil by buildings, constructions or other totally or partly impermeable artificial material [52]. Since then, every year, a national cartography of soil sealing is produced by remote sensing techniques and it is released as a raster map (pixel size 10*10 m), in which the whole Italian territory is classified into two classes: sealed soil/not sealed soil [53,54]. Sealed soil includes built-up areas, paved areas, railways, airports, ports and even reversible land consumption such as dirt roads [54].…”

“…Since then, every year, a national cartography of soil sealing is produced by remote sensing techniques and it is released as a raster map (pixel size 10*10 m), in which the whole Italian territory is classified into two classes: sealed soil/not sealed soil [53,54]. Sealed soil includes built-up areas, paved areas, railways, airports, ports and even reversible land consumption such as dirt roads [54]. Although all those elements are not distinguished from each other, the information conveyed by the soil sealing maps is very useful for the aim of this study because it provides useful information (updated on a yearly basis) about all anthropic elements exposed to risk, with a relatively very high spatial resolution.…”

“…To this regard, it should be stressed that, typically, urban areas in Italy are not clustered and are characterized by a peculiar diffuse pattern (referred to as "sprawl" and "sprinkling") [19]. As a consequence, other land cover/land use monitoring products (such as Corine Land Cover) are not able to adequately capture the spatial and temporal evolution of this phenomenon [54]. Moreover, the remote sensing techniques developed by ISPRA are specifically conceived and calibrated to detect the diffuse and scattered patterns of Italian urban fabric [19].…”

The use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in Jinsha River

“…Figure 2 shows the structure. The convolutional kernel moves regularly on the image, grabs the features with the same structure in the image to make a feature map, which is the basis of image classification [30,31].…”

Application of Bayesian Hyperparameter Optimized Random Forest and XGBoost Model for Landslide Susceptibility Mapping

“…The impact of human engineering activities on landslides is complex and involves many forms, such as the construction of roads and buildings, unreasonable impermeabilization and drainage outlets (Luti et al, 2020). When building roads, large-scale side slope excavation is required to change the natural shape and stress of the original slope toe.…”