Automatic landslide detection using Dempster–Shafer theory from LiDAR-derived data and orthophotos

Abstract

A good landslide inventory map is a prerequisite for landslide hazard and risk analysis. In tropical countries, such as Malaysia, preparation of the landslide inventory is a challenging task because of the rapid growth of vegetation. Thus, it is crucial to use rapid and accurate technique and effective parameters. For this purpose, Dempster Shafer theory (DST) was applied in fusing high resolution LiDAR derived data products and Greenness index derived from orthophoto imagery. Two sites were selected, for the implementation and evaluation of the DST model; site "A" for DST implementation and site "B" for the comparison. For model implementation, vegetation index, slope and height were used as effective parameters for identifying automatic landslide detection. Two type of DST based fusions were evaluated; (greenness and height) and (greenness and slope). Furthermore, validation techniques were used to validate the accuracy are confusion matrix and area under the curve. The overall accuracy of the first and second evaluated fusions were (73.4% and 84.33%), and area under the curve were (0.76 and 0.81) respectively. Additionally, the result was compared with Random Forest (RF) based detection approach. The results showed that DST does not require a priori knowledge.

Improving Landslide Detection from Airborne Laser Scanning Data Using Optimized Dempster–Shafer

“…Moreover, the quality of the landslide inventory map may be significantly improved by using texture features [2,6]. These data provide extensive information on landslide detection [28]. The accuracy and ability of DEM to represent the surface are affected by terrain morphology, sampling density, and the interpolation algorithm [41].…”

“…Various methods exist for taking a final decision using the DST decision technique as found in the literature: the maximum mass, plausibility, or belief [28]. From the probabilistic SVM, RF, and KNN classifiers, the posterior probabilities are converted in the form of mass function (m).…”

“…Also, the model has been used in remote sensing applications such as mapping landslide susceptibility [22][23][24][25], groundwater assessment and potentials [26], and risk assessment of groundwater pollution [27]. In 2017, Mezaal et al [28] applied the DST method for automatic detection of landslide locations using very high LiDAR-derived data and orthophoto imagery.…”

Improving the Accuracy of Landslide Detection in “Off-site” Area by Machine Learning Model Portability Comparison: A Case Study of Jiuzhaigou Earthquake, China

“…Conventional techniques for producing reliable landslide inventories mainly relies on field surveys [10][11][12] and the visual interpretation of aerial photos [1,13], but these ways are time-costly and labor-intensive [1]. Also, it is difficult to make a complete observation of landslides in densely vegetated areas [14]. With the development of earth observation data, various satellite images are widely considered as the most accessible data providing critical information necessary for supporting landslide detection [15], and the emergence of satellite image processing techniques means that landslides can be detected more accurately, completely and rapidly than ever before [1,[16][17][18].…”

“…The information contained within and among these units (e.g., spectral, textural, etc.) can be subjected to a variety of classification algorithms [33], especially the emerging machine learning (ML) methods [15,34], which are considered effective approaches for remote sensing applications with emphasis on image classification and object recognition [35], such as SVM [36,37], ANN [38,39], RF [21,23,40], logistic regression [39,41,42], Bayesian theory [43], Dempster-Shafer theory [14] and neuro-fuzzy classifier [27]. All of these approaches above aim to increase the quality of the landslide detection and limit classification errors which are typical of the landslide maps obtained from the classification of satellite images [1], and the above literature review shows that these models have achieved convincing results in different areas.…”