Integration of Remotely Sensed Soil Sealing Data in Landslide Susceptibility Mapping

被引:32
作者
Luti, Tania [1 ]
Segoni, Samuele [1 ]
Catani, Filippo [1 ]
Munafo, Michele [2 ]
Casagli, Nicola [1 ]
机构
[1] Univ Firenze, Dipartimento Sci Terra, Via La Pira 4, I-50121 Florence, Italy
[2] ISPRA Italian Inst Environm Protect & Res, Via Brancati 48, I-00144 Rome, Italy
关键词
landslide susceptibility; soil sealing; land take; land consumption; land cover; imperviousness; landslide hazard; landslide risk; random forest; urban density; SPATIAL PREDICTION MODELS; SENSING DATA; LOGISTIC-REGRESSION; HAZARD ASSESSMENT; VALIDATION; RAINFALL; GIS; CLASSIFICATION; OPTIMIZATION; MULTIVARIATE;
D O I
10.3390/rs12091486
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:19
相关论文
共 72 条
[1]   Increased flash flooding in Genoa Metropolitan Area: a combination of climate changes and soil consumption? [J].
Acquaotta, Fiorella ;
Faccini, Francesco ;
Fratianni, Simona ;
Paliaga, Guido ;
Sacchini, Alessandro ;
Vilimek, Vit .
METEOROLOGY AND ATMOSPHERIC PHYSICS, 2019, 131 (04) :1099-1110
[2]  
Aleotti P, 1999, B ENG GEOL ENVIRON, V58, P21, DOI [DOI 10.1007/S100640050066, 10.1007/s100640050066]
[3]   Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling [J].
Alvioli, Massimiliano ;
Marchesini, Ivan ;
Reichenbach, Paola ;
Rossi, Mauro ;
Ardizzone, Francesca ;
Fiorucci, Federica ;
Guzzetti, Fausto .
GEOSCIENTIFIC MODEL DEVELOPMENT, 2016, 9 (11) :3975-3991
[4]   A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran [J].
Arabameri, Alireza ;
Rezaei, Khalil ;
Cerda, Artemi ;
Conoscenti, Christian ;
Kalantari, Zahra .
SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 660 :443-458
[5]   The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan [J].
Ayalew, L ;
Yamagishi, H .
GEOMORPHOLOGY, 2005, 65 (1-2) :15-31
[6]   A country-wide spatial assessment of landslide susceptibility in Romania [J].
Balteanu, Dan ;
Chendes, Viorel ;
Sima, Mihaela ;
Enciu, Petru .
GEOMORPHOLOGY, 2010, 124 (3-4) :102-112
[7]   Validation of landslide hazard models using a semantic engine on online news [J].
Battistini, Alessandro ;
Rosi, Ascanio ;
Segoni, Samuele ;
Lagomarsino, Daniela ;
Catani, Filippo ;
Casagli, Nicola .
APPLIED GEOGRAPHY, 2017, 82 :59-65
[8]   From Picture to Movie: Twenty Years of Ground Deformation Recording Over Tuscany Region (Italy) With Satellite InSAR [J].
Bianchini, Silvia ;
Raspini, Federico ;
Solari, Lorenzo ;
Del Soldato, Matteo ;
Ciampalini, Andrea ;
Rosi, Ascanio ;
Casagli, Nicola .
FRONTIERS IN EARTH SCIENCE, 2018, 6
[9]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[10]   Spatial prediction models for landslide hazards: review, comparison and evaluation [J].
Brenning, A .
NATURAL HAZARDS AND EARTH SYSTEM SCIENCES, 2005, 5 (06) :853-862