Scaling land-surface variables for landslide detection

Sîrbu, Flavius; Drăguţ, Lucian; Oguchi, Takashi; Hayakawa, Yuichi S.; Micu, Mihai

doi:10.1186/s40645-019-0290-1

Cited by 11 publications

(8 citation statements)

References 48 publications

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“…Additional points of view state that different land-surface factors also have different optimal scales, and that the applied modelling technique may also influence this parameterization. Therefore, multiscale approaches are recommended for better performance in complex terrain settings (Catani et al 2013aSîrbu et al 2019. This has also been corroborated by Dragićević et al (2014), who examined these types of complex and multi-scalar contexts from regional to municipal and local scales.…”

Section: Theoretical Background Key Definitionsmentioning

confidence: 73%

Landslide Susceptibility Mapping of Urban Areas: Logistic Regression and Sensitivity Analysis applied to Quito, Ecuador

Puente-Sotomayor

Mustafà

Teller

2021

Geoenviron Disasters

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Although the Andean region is one of the most landslide-susceptible areas in the world, limited attention has been devoted to the topic in this context in terms of research, risk reduction practice, and urban policy. Based on the collection of landslides data of the Andean city of Quito, Ecuador, this article aims to explore the predictive power of a binary logistic regression model (LOGIT) to test secondary data and an official multicriteria evaluation model for landslide susceptibility in this urban area. Cell size resampling scenarios were explored as a parameter, as the inclusion of new “urban” factors. Furthermore, two types of sensitivity analysis (SA), univariate and Monte Carlo methods, were applied to improve the calibration of the LOGIT model. A Kolmogorov–Smirnov (K-S) test was included to measure the classification power of the models. Charts of the three SA methods helped to visualize the sensitivity of factors in the models. The Area Under the Curve (AUC) was a common metric for validation in this research. Among the ten factors included in the model to help explain landslide susceptibility in the context of Quito, results showed that population and street/road density, as novel “urban factors”, have relevant predicting power for landslide susceptibility in urban areas when adopting data standardization based on weights assigned by experts. The LOGIT was validated with an AUC of 0.79. Sensitivity analyses suggested that calibrations of the best-performance reference model would improve its AUC by up to 0.53%. Further experimentation regarding other methods of data pre-processing and a finer level of disaggregation of input data are suggested. In terms of policy design, the LOGIT model coefficient values suggest the need for a deep analysis of the impacts of urban features, such as population, road density, building footprint, and floor area, at a household scale, on the generation of landslide susceptibility in Andean cities such as Quito. This would help improve the zoning for landslide risk reduction, considering the safety, social and economic impacts that this practice may produce.

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Section: Theoretical Background Key Definitionsmentioning

confidence: 73%

Landslide Susceptibility Mapping of Urban Areas: Logistic Regression and Sensitivity Analysis applied to Quito, Ecuador

Puente-Sotomayor

Mustafà

Teller

2021

Geoenviron Disasters

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“…In the Buzău study area , there is an available database containing 577 landslide scarps, acquired in the last 40 years and compiled from different sources, such as archive data, detailed geomorphological field mapping, local authority databases, digital stereographic photo interpretation using color aerial ortho-photographs 33 , 34 . For the Shizuoka study area, we used an inventory of 371 landslide scarps, provided by the National Research Institute for Earth Science and Disaster Resilience, Japan (NIED) 35 – 37 .…”

Section: Methodsmentioning

confidence: 99%

“…For the Shizuoka study area, we used an inventory of 371 landslide scarps, provided by the National Research Institute for Earth Science and Disaster Resilience, Japan (NIED) 35 – 37 . The inventory was derived by visual interpretation of topographic discontinuities using stereo-paired aerial photographs at a 1:40,000 scale, acquired in the 1970s 33 . Both inventories lack attributes regarding landslide types.…”

Section: Methodsmentioning

confidence: 99%

Influence of sampling design on landslide susceptibility modeling in lithologically heterogeneous areas

Dornik

Drăguţ

Oguchi

et al. 2022

Sci Rep

Self Cite

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This work aims at evaluating the sensitivity of landslide susceptibility mapping (LSM) to sampling design in lithologically-heterogeneous areas. We hypothesize that random sampling of the landslide absence data in such areas can be biased by statistical aggregation of the explanatory variables, which impact the model outputs. To test this hypothesis, we train a Random Forest (RF) model in two different domains, as follows: (1) in lithologically heterogeneous areas, and (2) in lithologically homogeneous domains of the respective areas. Two heterogeneous areas are selected in Japan (125 km2) and Romania (497 km2), based on existing landslide inventories that include 371 and 577 scarps, respectively. These areas are divided into two, respectively three domains, defined by lithological units that reflect relatively homogeneous topographies. Fourteen terrain attributes are derived from a 30 m SRTM digital elevation model and employed as explanatory variables. Results show that LSM is sensitive to a random sampling of the absence data in lithologically heterogeneous areas. Accuracy measures improve significantly when sampling and LSM are conducted in lithologically homogeneous domains, as compared to heterogeneous areas, reaching an increase of 9% in AUC and 17% in the Kappa index.

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“…This allowed us to preserve all landslide surface features while significantly decreasing the data volume and removing the artifacts present in the data at the original resolution. The issues connected with the suitable DEM resolution have been investigated by various scientists, many of whom reported that the finest DEM resolution is not the best choice [12,14,15,26,87]. Then, DEM derivatives (also called topographic variables or land-surface variables and DEM variables) were calculated from the DEM.…”

Section: Dem Generation and Feature Extractionmentioning

confidence: 99%

“…In certain conditions, such as densely vegetated terrain, field-based investigation is ineffective or even impossible [9]. Benefiting from an abundant collection of remote sensing (RS) data, automatic approaches have been introduced to landslide studies by various scientists [1,[3][4][5][6][7][8][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Among the automatic methods, pixel-based (PBA) [4,5,14,16,19] and object-based (OBIA) [7,8,[10][11][12][13]15,20] classification methods can be distinguished.…”

Section: Introductionmentioning

confidence: 99%

On the Importance of Train–Test Split Ratio of Datasets in Automatic Landslide Detection by Supervised Classification

Pawłuszek-Filipiak

Borkowski

2020

Remote Sensing

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Many automatic landslide detection algorithms are based on supervised classification of various remote sensing (RS) data, particularly satellite images and digital elevation models (DEMs) delivered by Light Detection and Ranging (LiDAR). Machine learning methods require the collection of both training and testing data to produce and evaluate the classification results. The collection of good quality landslide ground truths to train classifiers and detect landslides in other regions is a challenge, with a significant impact on classification accuracy. Taking this into account, the following research question arises: What is the appropriate training–testing dataset split ratio in supervised classification to effectively detect landslides in a testing area based on DEMs? We investigated this issue for both the pixel-based approach (PBA) and object-based image analysis (OBIA). In both approaches, the random forest (RF) classification was implemented. The experiments were performed in the most landslide-affected area in Poland in the Outer Carpathians-Rożnów Lake vicinity. Based on the accuracy assessment, we found that the training area should be of a similar size to the testing area. We also found that the OBIA approach performs slightly better than PBA when the quantity of training samples is significantly lower than the testing samples. To increase detection performance, the intersection of the OBIA and PBA results together with median filtering and the removal of small elongated objects were performed. This allowed an overall accuracy (OA) = 80% and F1 Score = 0.50 to be achieved. The achieved results are compared and discussed with other landslide detection-related studies.

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Scaling land-surface variables for landslide detection

Cited by 11 publications

References 48 publications

Landslide Susceptibility Mapping of Urban Areas: Logistic Regression and Sensitivity Analysis applied to Quito, Ecuador

Landslide Susceptibility Mapping of Urban Areas: Logistic Regression and Sensitivity Analysis applied to Quito, Ecuador

Influence of sampling design on landslide susceptibility modeling in lithologically heterogeneous areas

On the Importance of Train–Test Split Ratio of Datasets in Automatic Landslide Detection by Supervised Classification

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