Abstract
Landslides represent a severe hazard in many areas of the world. Accurate landslide maps are needed to document the occurrence and extent of landslides and to investigate their distribution, types, and the pattern of slope failures. Landslide maps are also crucial for determining landslide susceptibility and risk. Satellite data have been widely used for such investigations—next to data from airborne or unmanned aerial vehicle (UAV)-borne campaigns and Digital Elevation Models (DEMs). We have developed a methodology that incorporates object-based image analysis (OBIA) with three machine learning (ML) methods, namely, the multilayer perceptron neural network (MLP-NN) and random forest (RF), for landslide detection. We identified the optimal scale parameters (SP) and used them for multi-scale segmentation and further analysis. We evaluated the resulting objects using the object pureness index (OPI), object matching index (OMI), and object fitness index (OFI) measures. We then applied two different methods to optimize the landslide detection task: (a) an ensemble method of stacking that combines the different ML methods for improving the performance, and (b) Dempster–Shafer theory (DST), to combine the multi-scale segmentation and classification results. Through the combination of three ML methods and the multi-scale approach, the framework enhanced landslide detection when it was tested for detecting earthquake-triggered landslides in Rasuwa district, Nepal. PlanetScope optical satellite images and a DEM were used, along with the derived landslide conditioning factors. Different accuracy assessment measures were used to compare the results against a field-based landslide inventory. All ML methods yielded the highest overall accuracies ranging from 83.3% to 87.2% when using objects with the optimal SP compared to other SPs. However, applying DST to combine the multi-scale results of each ML method significantly increased the overall accuracies to almost 90%. Overall, the integration of OBIA with ML methods resulted in appropriate landslide detections, but using the optimal SP and ML method is crucial for success.
Highlights
Landslides represent a significant threat to human life, natural resources, infrastructure, and properties in mountainous areas [1]
In the present study, we integrate the widely used machine learning (ML) methods of logistic regression (LR), the multilayer perceptron neural network (MLP-NN), and random forest (RF) with object-based image analysis (OBIA) for landslide detection, based on optical data and topographic factors resulting from PlanetScope satellite images and Digital Elevation Model (DEM) data, respectively
The concept of Dempster–Shafer theory (DST) is made based on a frame of discernment and known as a belief function (Bel) that is derived from Bayesian probability theory (BPT)
Summary
Landslides represent a significant threat to human life, natural resources, infrastructure, and properties in mountainous areas [1]. Defining the optimal parameters for object definition plays an essential role in detecting landslides through the image segmentation process. The DST method performed well in landslide detection in their tropical study area These pieces of evidence from previously published papers motivated us to apply the DST probability concept to improve the ML classification accuracy through integration with different classifiers. In the present study, we integrate the widely used ML methods of logistic regression (LR), the multilayer perceptron neural network (MLP-NN), and RF with OBIA for landslide detection, based on optical data and topographic factors resulting from PlanetScope satellite images and Digital Elevation Model (DEM) data, respectively. All resulting landslide detection maps are validated using standard RS accuracy metrics and the validation method of receiver operating characteristics (ROC)
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