National scale soil sealing monitoring data as a new explanatory variable for landslide susceptibility models

2021 ◽  
Author(s):  
Tania Luti ◽  
Samuele Segoni ◽  
Michele Munafò ◽  
Nicola Casagli
Keyword(s):  
Land Cover ◽  
Landslide Susceptibility ◽  
State Of The Art ◽  
Continuous Variable ◽  
Categorical Variable ◽  
Operating Characteristics ◽  
Mapping Unit ◽  
Explanatory Variables ◽  
Soil Sealing ◽  
Remote Sensing Techniques

<div> <p>It is widely known that human activities can negatively affect the equilibrium of slope systems, triggering or predisposing to landslides. In Italy, ISPRA (Italian Institute for Environmental Protection Research) uses remote sensing techniques to monitor the expansion of artificialization of the territory and releases every year an updated map of soil sealing, which is defined as the destruction or covering of natural soils by totally or partially impermeable artificial material. The soil sealing map covers the entire national territory and has a fine spatial resolution (10 m).</p> <p>In this work, for the first time, soil sealing indicators are used as explanatory variables in a landslide susceptibility assessment. Three new parameters were derived from the raw soil sealing map: “soil sealing aggregation” (continuous variable expressing the 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).</p> <p>These parameters were added to a set of state-of-the-art explanatory variables in a random forest landslide susceptibility model. In particular, the parameters derived from soil sealing were compared with two state-of-the-art parameters widely used to account for human disturbance: land cover/land use (as derived from a CORINE land cover map) and road network.  </p> <p>Results were compared in terms of AUC (area under receiver operating characteristics curve, expressing the overall effectiveness of the configurations tested) and out-of-bag-error (used to quantify the relative importance of each variable). We found that the parameter “soil sealing aggregation” significantly enhanced the model performances. The results open new perspectives for the use of data derived from soil sealing monitoring programs to improve landslide hazard studies.  </p> </div>

scholarly journals Integration of Remotely Sensed Soil Sealing Data in Landslide Susceptibility Mapping

2020 ◽  
Vol 12 (9) ◽  
pp. 1486
Author(s):  
Tania Luti ◽  
Samuele Segoni ◽  
Filippo Catani ◽  
Michele Munafò ◽  
Nicola Casagli
Keyword(s):  
Landslide Susceptibility ◽  
Landslide Susceptibility Mapping ◽  
Categorical Variable ◽  
Operating Characteristics ◽  
Mapping Unit ◽  
Explanatory Variables ◽  
The Road ◽  
Soil Sealing ◽  
Susceptibility Model ◽  
Remote Sensing Techniques

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.

Constructing and model-fitting receiver operator characteristics using continuous data.

2018 ◽  
Author(s):  
Josephine Ann Urquhart ◽  
Akira O'Connor
Keyword(s):  
Signal Detection ◽  
Least Squares Method ◽  
Model Fitting ◽  
Simulated Data ◽  
Continuous Variable ◽  
Continuous Data ◽  
Model Parameters ◽  
Operating Characteristics ◽  
Unequal Variance ◽  
Discrete Discrimination

Receiver operating characteristics (ROCs) are plots which provide a visual summary of a classifier’s decision response accuracy at varying discrimination thresholds. Typical practice, particularly within psychological studies, involves plotting an ROC from a limited number of discrete thresholds before fitting signal detection parameters to the plot. We propose that additional insight into decision-making could be gained through increasing ROC resolution, using trial-by-trial measurements derived from a continuous variable, in place of discrete discrimination thresholds. Such continuous ROCs are not yet routinely used in behavioural research, which we attribute to issues of practicality (i.e. the difficulty of applying standard ROC model-fitting methodologies to continuous data). Consequently, the purpose of the current article is to provide a documented method of fitting signal detection parameters to continuous ROCs. This method reliably produces model fits equivalent to the unequal variance least squares method of model-fitting (Yonelinas et al., 1998), irrespective of the number of data points used in ROC construction. We present the suggested method in three main stages: I) building continuous ROCs, II) model-fitting to continuous ROCs and III) extracting model parameters from continuous ROCs. Throughout the article, procedures are demonstrated in Microsoft Excel, using an example continuous variable: reaction time, taken from a single-item recognition memory. Supplementary MATLAB code used for automating our procedures is also presented in Appendix B, with a validation of the procedure using simulated data shown in Appendix C.

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

2021 ◽  
Vol 13 (9) ◽  
pp. 1623
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

scholarly journals How far from the road should land cover be assessed? A case study on mesopredator mortality on roads

2021 ◽  
Vol 67 (2) ◽  
Author(s):  
Angelika Nieszała ◽  
Daniel Klich
Keyword(s):  
Land Cover ◽  
Red Fox ◽  
Home Range Size ◽  
Buffer Size ◽  
Local Conditions ◽  
Explanatory Variables ◽  
The Road ◽  
Spatially Correlated ◽  
European Badger ◽  
The Impact

AbstractThe methods used to assess the significance of land cover in the vicinity of a road for the mortality of mesopredators are diverse. In assessing the effect of land cover along the road on road causalities, scientists use various buffer sizes, or even no buffer along the road. The aim of this study was to verify how results of land cover effects on the mortality of mesopredators on roads may differ when analyzing various buffer sizes from the road. We assessed road causalities in the Warmian-Masurian voivodeship (Poland) from 3 consecutive years: 2015, 2016, and 2017. The roads were divided into equal sections of 2000 m each with buffer size of radius: 10, 250, 500, and 1000 m. We analyzed the number of road kills of red fox and European badger separately in a generalized linear model, whereas explanatory variables we used land cover types (based on the Corine Land Cover inventory) and traffic volume. Mean annual mortality from road collisions amounts to 2.36% of the red fox population and 3.82% of the European badger population. We found that the buffer size determines the results of the impact of land cover on mesocarnivore mortality on roads. The red fox differed from the European badger in response to land cover depending on the buffer size. The differences we have shown relate in particular to built-up areas. Our results indicate a 500-m buffer as best reflecting the land cover effects in road kills of both species. This was confirmed by model evaluation and a tendency to use or avoid the vicinity of human settlements of the analyzed species. We concluded that buffer size will probably affect mostly the significance of cover types that are spatially correlated with roads, positively or negatively. We suggest that the home range size of given species in local conditions should be assessed before determining the size of the buffer for analysis.

scholarly journals Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 884 ◽  
Author(s):  
Tingyu Zhang ◽  
Ling Han ◽  
Wei Chen ◽  
Himan Shahabi
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Shaanxi Province ◽  
Slope Aspect ◽  
Support Vector ◽  
Operating Characteristics ◽  
Study Region ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

The main purpose of the present study is to apply three classification models, namely, the index of entropy (IOE) model, the logistic regression (LR) model, and the support vector machine (SVM) model by radial basis function (RBF), to produce landslide susceptibility maps for the Fugu County of Shaanxi Province, China. Firstly, landslide locations were extracted from field investigation and aerial photographs, and a total of 194 landslide polygons were transformed into points to produce a landslide inventory map. Secondly, the landslide points were randomly split into two groups (70/30) for training and validation purposes, respectively. Then, 10 landslide explanatory variables, such as slope aspect, slope angle, altitude, lithology, mean annual precipitation, distance to roads, distance to rivers, distance to faults, land use, and normalized difference vegetation index (NDVI), were selected and the potential multicollinearity problems between these factors were detected by the Pearson Correlation Coefficient (PCC), the variance inflation factor (VIF), and tolerance (TOL). Subsequently, the landslide susceptibility maps for the study region were obtained using the IOE model, the LR–IOE, and the SVM–IOE model. Finally, the performance of these three models was verified and compared using the receiver operating characteristics (ROC) curve. The success rate results showed that the LR–IOE model has the highest accuracy (90.11%), followed by the IOE model (87.43%) and the SVM–IOE model (86.53%). Similarly, the AUC values also showed that the prediction accuracy expresses a similar result, with the LR–IOE model having the highest accuracy (81.84%), followed by the IOE model (76.86%) and the SVM–IOE model (76.61%). Thus, the landslide susceptibility map (LSM) for the study region can provide an effective reference for the Fugu County government to properly address land planning and mitigate landslide risk.

scholarly journals Land use and land cover mapping and identification of misuse in the permanent preservation areas in the Tailândia Municipality - PA

2017 ◽  
Vol 38 (3) ◽  
pp. 1145 ◽  
Author(s):  
Rosana Sumiya Gurgel ◽  
Paulo Roberto Silva Farias ◽  
Sandro Nunes de Oliveira
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Palm Oil ◽  
Regulatory Agencies ◽  
Visual Interpretation ◽  
Livestock Farming ◽  
Land Cover Data ◽  
The Government ◽  
Remote Sensing Techniques ◽  
Bodies Of Water

The objective of this study is to expand the mapping of land use and land cover, as well as of the permanent preservation areas (PPAs), and identify land misuse areas in the PPAs in the Tailândia municipality in the state of Pará, which is part of the Amazon biome. Remote sensing techniques and geographic information systems (GIS) were used to achieve these goals. Mapping and classification for the year 2012 were made by visual interpretation of images obtained from the RapidEye satellite, which has a 5 m spatial resolution. In this work, we identified nine classes of land use and land cover. From the hydrography vectors it was possible to determinate the Permanent Preservation Areas of the bodies of water according to the environmental legislation. Analysis of misuse in the PPAs was made by crossing-checking the land use and land cover data with that of the PPAs. The results show that 53 % of the municipality (2,347.64 km²) is occupied by human activities. Livestock farming is the activity that has most increased the use of area (30 %), followed by altered vegetation (14.6 %) and palm oil (7.2 %). The PPAs have a high percentage of misuse (47.12 %), with livestock being the largest contributor, occupying 26.65 % of the PPAs, followed by altered vegetation (12.64 %) and palm oil (4.29 %). Therefore, the main objective in Tailândia is to reconcile economic activity with sustainable development. It is important to emphasize the partnerships between the government, research institutions, regulatory agencies, states departments and local communities, else it would be impossible to monitor or control an area as vast as the Amazon.

scholarly journals A pharmacogenomic approach to the treatment of children with GH deficiency or Turner syndrome

2013 ◽  
Vol 169 (3) ◽  
pp. 277-289 ◽  
Author(s):  
P Clayton ◽  
P Chatelain ◽  
L Tatò ◽  
H W Yoo ◽  
G R Ambler ◽  
...  
Keyword(s):  
Gene Expression ◽  
Turner Syndrome ◽  
Genetic Markers ◽  
Growth Response ◽  
Gh Deficiency ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Continuous Variable ◽  
High Response ◽  
Categorical Variable

ObjectiveIndividual sensitivity to recombinant human GH (r-hGH) is variable. Identification of genetic factors contributing to this variability has potential use for individualization of treatment. The objective of this study was to identify genetic markers and gene expression profiles associated with growth response on r-hGH therapy in treatment-naïve, prepubertal children with GH deficiency (GHD) or Turner syndrome (TS).DesignA prospective, multicenter, international, open-label pharmacogenomic study.MethodsThe associations of genotypes in 103 growth- and metabolism-related genes and baseline gene expression profiles with growth response to r-hGH (cm/year) over the first year were evaluated. Genotype associations were assessed with growth response as a continuous variable and as a categorical variable divided into quartiles.ResultsEleven genes in GHD and ten in TS, with two overlapping between conditions, were significantly associated with growth response either as a continuous variable (seven in GHD, two in TS) or as a categorical variable (four more in GHD, eight more in TS). For example, in GHD, GRB10 was associated with high response (≥Q3; P=0.0012), while SOS2 was associated with low response (≤Q1; P=0.006), while in TS, LHX4 was associated with high response (P=0.0003) and PTPN1 with low response (P=0.0037). Differences in expression were identified for one of the growth response-associated genes in GHD (AKT1) and for two in TS (KRAS and MYOD1).ConclusionsCarriage of specific growth-related genetic markers is associated with growth response in GHD and TS. These findings indicate that pharmacogenomics could have a role in individualized management of childhood growth disorders.

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