**1. Introduction**

Landslides are common natural phenomena on mountains and slopes that can change the geomorphology of the landscape. Thus, the massive destruction caused by landslides is of great concern [1,2]. With global climate change and increasingly intense human engineering, landslides tend to occur more frequently, resulting in huge economic losses and many casualties [3,4]. Therefore, risk assessment is often the focus of research [5–8], especially in populated areas that are prone to landslides. This should help provide the necessary information to governments and decision makers [6,9]. Risk assessment is the basis for risk management. It refers to the possibility and severity of landslides impacting life, health, property, and the environment. In practice, the risk of landslides is computed as the product of landslide hazard and the vulnerability to potential value loss [5]. Quantitative and accurate risk assessment can be effective information for government departments in land and resources planning, engineering construction, the prevention and early warning of landslides, and sustainable development.

**Citation:** Liu, W.; Zhang, Y.; Liang, Y.; Sun, P.; Li, Y.; Su, X.; Wang, A.; Meng, X. Landslide Risk Assessment Using a Combined Approach Based on InSAR and Random Forest. *Remote Sens.* **2022**, *14*, 2131. https:// doi.org/10.3390/rs14092131

Academic Editors: Paolo Mazzanti and Saverio Romeo

Received: 16 February 2022 Accepted: 26 April 2022 Published: 29 April 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

It is crucial to select an optimal model and methodology for landslide risk assessment because different assessments may have different results and accuracies for the same areas. In recent decades, numerous landslide susceptibility, hazard, and risk assessment methods have been applied. However, there has been no study showing that a certain model has the optimal solution for all risk assessments [10,11]. Models for landslide susceptibility assessment can be classified as physically deterministic, heuristic based on experts' knowledge, and data-driven quantitative [12]. Physically deterministic models are commonly based on hydrological characterizations combined with infinite-slope stability analyses to estimate the relative stability of slopes [13]. Some research has pointed out that these models are used only for particular hydrological conditions and high model preconditions [14], such as detailed and homogeneous soil mechanical parameters, hydro environmental factors, and simple landslide types. For this reason, they could be effective only for mapping small areas in detail [15,16]. Heuristic models based on experts' knowledge, including the analytical hierarchy process (AHP) [17], expert knowledge systems [18], and gray relational modes [18], mainly rely on constructing a relatively simple ranking method determined by experts' knowledge [16]. Although heuristic models have the advantages of easy application, the assessment results have low accuracy with a certain level of subjectivity [19]. Previous studies show that data-driven quantitative models are preferred and applied more frequently than qualitative evaluation models, such as heuristic or geomorphological mapping [20,21]. Logistic regression (LR) [22,23], frequency ratio (FR) [24,25] and weights of evidence [26] are the most frequently used statistical models. They are based on considered classical statistics; index-based, machine learning; neural networks; and multi-criteria decision analysis. In particular, the use of machine learning for landslide assessment is rapidly increasing [20]. It is a modeling methodology that builds complex relationships between data and target variables through iterative training and learning without assuming additional structural constraints [27,28]. Machine learning is often used to solve nonlinear geological environment problems, such as landslide susceptibility assessment and prediction. For example, Chen et al. [29] introduced a new bivariate statistical-based kernel logistic regression to obtain landslide susceptibility maps by optimizing different kernel functions and two-component statistical correlation analyses. Behnia et al. [30] produced susceptibility maps for debris flows and other geohazards along the Yukon Alaska Highway Corridor, in Canada. Hong et al. [31] built a higher-precision susceptibility map of the Guangchang area in China based on a decision tree model. Furthermore, many studies have compared the accuracy of machine learning with classical statistical models in landslide susceptibility assessment [32–34]. They showed that machine learning models provide more accurate assessments and predictions [35].

Apart from models and methods, selecting appropriate mapping units associated with the research purpose is a key issue for reasonable and accurate assessment maps. Generally, the mapping units fall into several groups: grid cells, terrain units, unique conditional units, topographic units, slope units, complementary geohydrological units and political or administrative units [20,36]. Each type of unit has certain analytical advantages and disadvantages. For this reason, the type of unit needs to be determined at the beginning of a study according to the purpose and scale of the research [36,37]. Landslides tend to show a clear shape and boundary soon after their occurrence so the slope unit is often preferred for representing the form of landslides or unstable slopes. In some studies, the slope unit also performed better than the pixel unit in landslide assessment [38–40].

Yan'an, which is located in the north of Shaanxi Province, on the Loess Plateau, is a typical valley city. Its particular geography and geological environment background, as well as increasing human engineering activities, appear to be the causes of more frequent landslides, collapses, and other geohazards [41]. Several studies have evaluated the susceptibility and stability of landslides in Yan'an City and Baota District based on qualitative methods and physical models [42–44]. However, the evaluation factors in those studies are limited to geological or topographic conditions, and few studies have focused on the deformations which can reflect the activity of slopes through SAR data in the risk assessment of the study area. Interferometric synthetic aperture radar (InSAR) technology can be used to optimize the landslide susceptibility assessment and reduce landslide classification errors [45]. Additionally, a smaller range and larger scale of quantitative assessments are necessary for future urban development in Yan'an City if we want to mitigate the geohazards occurring in current urban constructions. Therefore, this study aims at constructing a detailed landslide risk assessment in Yan'an City using high-resolution aerial images and a digital elevation model (DEM). A detailed investigation and understanding of the characteristics of the geological hazards in the urban area of Yan'an City is considered a critical part of risk assessment. In the process, it becomes necessary to combine ground deformation using InSAR technology with conventional topographic and geomorphic factors for risk analysis. Advanced random forest machine learning classifiers and InSAR technology are used in our study to assess landslide susceptibility, landslide hazards, and the identification of areas exposed to a higher landslide risk in the urban parts of Yan'an City. It is expected that the assessments of urban hazards and risks in urban areas based on the slope units can provide more accurate information for government departments and decision makers in urban planning, construction, and disaster prevention as well as control.

#### **2. Study Area**

The present study area is the central urban area of Yan'an City, which is located in the northern part of Shaanxi Province, China, on the Loess Plateau between the latitudes of 36◦27 N and 36◦41 N and the longitudes of 109◦22 E and 109◦33 E, covering an area of 185 km2 (Figure 1). Its landform features typical and complex loess beams, mounds, and gullies. The highest elevation in the study area is 1300 m and the lowest elevation is 927 m, which is in the river valley, so the elevation difference is about 370 m. The climate in the area is semi-humid and semi-arid, with a continental monsoon climate. In the past, the average annual precipitation in Baota District was 537 mm, which occurred mainly from June to September [46].

**Figure 1.** The location, boundary, and geomorphology of the present study area in Yan'an City and Shaanxi Province. YAND—Yan'an New District; HZP—Hezhuangping Town; QG—Qiaogou Street; CK—Chuankou Town; BTS—Baotashan Street; FHS—Fenghuangshan Street; ZY—Zaoyuan Town; NS—Nanshi Street; WHS—Wanhuashan Town; LL—Liulin Town.

From the perspective of regional geology and geotectonics, the study area is located in the middle-eastern part of the Ordos Block in the North China Block. The tectonic movement is slight without strong structural deformation and maintains the characteristics of a stable sedimentary basin. The strata are mainly Mesozoic and Cenozoic, including Triassic, Jurassic, and Quaternary; however, the Quaternary loess is the most widely distributed [47]. Triassic and Jurassic strata are mostly seen along both sides of the valley. Although there is no strong tectonic movement and fault, many landslides have occurred and developed in the area due to the unique physical and mechanical properties of loess. Loess is characterized by high porosity, low bulk density, weak cementation, water sensitivity, collapsibility, structural joints, vertical joints, unloading cracks, and a soft layer structural plane. Under the area's special landform conditions, landslide hazards could be induced by summer rainstorms and human engineering activities, which seriously affect the sustainable development of the local economy and society.
