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Article

Establishing a Landslide Traces Inventory for the Baota District, Yan’an City, China, Using High-Resolution Satellite Images

by
Sen Zhang
1,2,
Chong Xu
2,3,*,
Zhenjiang Meng
1,4,*,
Tao Li
2,3,
Chao Li
1,
Yuandong Huang
2,3,
Xiaoyi Shao
2,3,
Liye Feng
2,3,
Penghan Luo
1 and
Changyou Luo
1
1
College of Geology Engineering and Geomatics, Chang’an University, Xi’an 710064, China
2
National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China
3
Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, China
4
Scientific Research Institute of Chang’an University, Chang’an University, Xi’an 710064, China
*
Authors to whom correspondence should be addressed.
Land 2024, 13(10), 1580; https://doi.org/10.3390/land13101580
Submission received: 1 August 2024 / Revised: 18 September 2024 / Accepted: 26 September 2024 / Published: 28 September 2024
Browse Figures
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Abstract

:
The Baota District of Yan’an City, located in the Loess Plateau, is an important patriotic education base in China. The region’s fragile geological environment and frequent geological disasters pose significant threats to the production and livelihood of residents. Establishing a landslide traces inventory can provide crucial assistance for studying regional land disaster distribution patterns and implementing disaster prevention and mitigation measures. However, the Baota District has not yet established a comprehensive and detailed landslide traces inventory, resulting in a lack of clear understanding and comprehensive knowledge regarding the threats and impacts of landslide disasters in the area. Therefore, this study employed high-resolution satellite images, applying a human–computer interactive visual interpretation method in conjunction with field survey verifications, to develop the most detailed and comprehensive landslide traces inventory for the Baota District to date. The results indicate that within the 3556 km2 area of the Baota District, there are 73,324 landslide traces, with an average landslide density of 20.62 km-2 and a total landslide area of 769.12 km2, accounting for 21.63% of the total land area. These landslides are relatively evenly distributed throughout the district, with a higher concentration in the east compared to the west. Most of the landslides are small in size. This study can support disaster prevention and mitigation efforts in the Baota District and serve as a reference for establishing landslide inventories in other regions of the Loess Plateau.

1. Introduction

Natural hazards pose a significant challenge to the sustainable development of humanity [1,2,3,4,5], especially in the context of global climate change, where the frequency and intensity of such events are increasing. Among these frequent natural hazards, geological hazards are one of the most predominant types. Landslide geological hazards can be triggered by factors such as earthquakes, rainfall, and human engineering activities, causing significant damage to human life, property, and the local ecological environment [6,7,8,9,10]. Landslides, as one of the most widespread geological hazards, are a typical slope instability phenomenon, and they are commonly found in many geologically active and topographically rugged countries and regions around the world [11,12]. Such disasters are mainly caused by the displacement of soil and rock masses along potential sliding surfaces under the influence of gravity. Their instability mechanisms are complex and are affected by various geological and environmental factors [13,14]. Landslides often pose a serious threat to the lives and property of local people and can also cause damage to the local ecological environment [15,16].
Therefore, the prevention and control of landslide disasters are extremely important for social development and the livelihood of people. A complete and detailed inventory of landslide traces can provide data support for the study of landslide distribution patterns and development characteristics within the region [17,18,19,20]. Additionally, it can play a crucial role in geological hazard risk assessment and zoning [21,22,23,24]. Therefore, establishing a comprehensive and well-documented landslide relic database is of paramount importance. Researchers in many countries worldwide have established landslide inventories for different regions or events. Pennington et al. have developed the most extensive landslide database in the UK, encompassing over 17,000 records of landslide events. This database is extremely important for the local government in formulating disaster prevention and mitigation policies [25]. Rabby Yasin Wahid and Li Yingkui compiled a dataset of 730 landslides in the Chittagong region of Bangladesh, covering the period from January 2001 to March 2017 [26]. Miet Van Den Eeckhaut and Javier Hervás conducted a detailed analysis of existing national or regional databases in Europe, which included 633,700 landslides [27]. Bentivenga et al. have conducted interdisciplinary studies on historical debris-flow disasters in Italy by combining historical data records and field surveys, emphasizing the importance of using comprehensive methods in natural hazard investigations [28]. Owen et al. established a list of landslides triggered by the Kashmir earthquake on 8 October 2005, and analyzed the main factors influencing the occurrence of these landslides [29]. In China, Lin et al. established a list of significant landslide events in China from 1950 to 2016 based on various data sources and conducted an analysis. The study found that the spatial distribution of landslide disasters is influenced by multiple factors [30]. Lan et al. used GIS technology and field surveys to establish a spatial landslide database for the Xiaojiang River basin in the southwestern region, including landslide inventory data and influencing factors, and studied the relationship between landslides and influencing factors in this area [31]. Shao et al. created a database of 3126 ancient and large landslides in the Wudongde Reservoir area and conducted studies on the spatial distribution patterns based on this database [32]. Li et al. compiled an inventory of landslide traces in the Jianzha County area at the junction of the Qinghai Plateau and the Loess Plateau, where 713 landslides are distributed, mainly consisting of large loess landslides [33]. Wang et al. identified the distribution of landslides in the northwestern margin of the Qinghai–Tibet Plateau, recognizing 13,003 landslides and analyzing their distribution patterns [34]. Feng et al. conducted a study on the distribution of landslides in the Qinling region, identifying a total of 169,888 large landslides covering an area of 1575 km2 in the Qinling Mountains [35].
The Loess Plateau is located in the northwest region of China, spanning the provinces of Gansu, Qinghai, Shaanxi, and Shanxi, covering a total land area of approximately 6.3 × 105 km2, which represents the majority of the bulk accumulation of loess worldwide [36]. This region has a fragile geological environment and is prone to frequent geological disasters. According to incomplete statistics, nearly one-third of the geological disasters in China over the decade since 2002 have occurred in the Loess Plateau region [37,38,39]. Owing to the distinctive engineering characteristics of loess and factors such as rainfall, the Loess Plateau has become a high-incidence area for landslide disasters [40,41,42]. The factors inducing loess landslides can be divided into natural factors and human factors, with rainfall and human engineering activities being the most significant [43,44,45]. With climate change and increased human engineering activities, the frequency of loess landslides is increasing year by year. These disasters present a severe threat to the safety of people’s lives and property and cause significant damage to the local ecological environment. In the Loess Plateau region, many scholars have also compiled landslide inventories for different areas. Peng et al. identified the distribution patterns of loess landslides in the Heifangtai area of the Loess Plateau through field surveys and studied the conditions for landslide formation [46]. Xu et al. used satellite technology and field surveys to establish the most comprehensive existing landslide database for the Loess Plateau in China, with approximately 80,000 landslides [47]. Zhu et al. studied the distribution patterns and development characteristics of landslides based on detailed geological hazard survey results from 13 counties in Yan’an City [48]. Li et al. assembled an extensive catalog documenting 3440 significant landslides in Baoji City, Shaanxi Province, and developed a comprehensive landslide distribution map for the region. Utilizing this map, they performed an in-depth analysis of the spatial distribution patterns of these landslides [49]. Chen et al. utilized satellite images to compile an inventory of landslide traces, documenting 2924 landslides in Xianyang City, Shaanxi Province [50]. Zhao et al. compiled a landslide traces inventory for Hancheng City, Shaanxi Province, containing 6785 landslides, and analyzed the number and scale of the landslides [51]. Wang et al. used high-resolution Quickbird imagery to map the landslide inventory in the Changshou valley of Baoji city and analyze the landslide characteristics [52]. Liu et al. used satellite images in conjunction with historical landslide records and field surveys to establish a landslide database for the Beiluo River basin, and on this basis analyzed the geometric characteristics and spatial distribution features of the landslides [53]. However, within the Baota District a complete and detailed landslide traces inventory has not yet been established. Due to the terrain and landforms of the Baota District, residential buildings and cultural landscapes in the area are mostly built along the mountains, making building safety significantly affected by landslide disasters. The occurrence of landslide disasters poses a great threat to the safety of residents’ lives and property. Therefore, establishing a complete and detailed landslide traces inventory in the Baota District is crucial for in-depth research on the distribution patterns and development characteristics of landslides in the area, and is also a necessary condition for the construction of disaster prevention and mitigation projects.
In light of this, this study utilized the Google Earth platform to visually interpret high-resolution satellite images covering the area. Based on this, field surveys and on-site verifications were conducted within the study area. Combined with previous research findings and disaster investigation records, a landslide traces inventory for the Baota District was compiled, and the distribution, quantity, and scale of landslides were analyzed to a certain extent. This study can provide data support for subsequent research on the spatial distribution of landslides in the Baota District and can also assist in land disaster prevention and mitigation efforts in the area. Furthermore, this study can serve as a reference for compiling landslide trace inventories in other areas of the Loess Plateau.

2. Study Area

The Baota District of Yan’an City is located in the central part of the northern Loess Plateau, in the middle reaches of the Yellow River. It covers a land area of 3556 km2, with a spatial range of 109°14′10″~110°30′43″ E, 36°10′33″~37°2′5″ N. The region features a fragmented land surface and significant topographical variation, with an average elevation of 1200 m, as shown in Figure 1.
The terrain exhibits a general gradient, with higher elevations in the northwest and lower elevations in the southeast, predominantly featuring hilly and gully landforms. The river channels within the district all belong to the Yellow River basin, with a total river length of 2413.1 km and a river network density of 0.68 km/km2. The primary tributaries are the Yan River and Yunyan River, which generally flow southeast or east along the surface slope into the Yellow River.
As shown in Figure 2, the exposed strata in the Baota District are composed of Triassic and Jurassic formations, as well as Neogene and Quaternary deposits, with the Quaternary strata being the most predominantly exposed. The Quaternary loess covers most of the Baota District. Due to the properties of the loess and external factors such as rainfall, loess slopes are prone to landslides, which is one of the main causes of frequent landslides in the Yan’an region. Geologically, the Baota District is located in the mid-eastern part of the Ordos block of the North China Craton, far from the Western block and the Inner Mongolia fold belt, making it one of the most stable regions in the North China Craton [54]. Within the district, there are only two sets of NE-trending and one set of NW-trending concealed faults, making the structure relatively simple [55]. The crustal deformation rate in this area is between 1 and 2 mm/a, indicating a relatively stable crust with a low frequency of earthquakes. Based on information from the China Earthquake Networks Center, there have been no historical earthquakes with an Ms greater than 6.0 in the Baota District and surrounding areas (https://news.ceic.ac.cn, accessed on 15 June 2024). In short, tectonic activity in this region is not intense, and seismic activity is low, so geological structures have no significant impact on the occurrence of landslides.
In terms of meteorology and hydrology, the Baota District has an average annual rainfall of around 500 mm, with precipitation mainly concentrated between July and October, accounting for more than 70% of the annual total. July and August primarily experience short-duration heavy rainfall, while September and October see prolonged continuous rain [56]. Notably, in July 2013 Yan’an City experienced the most intense and longest-lasting heavy rainfall since meteorological records began in 1945, with the shortest intervals between rainfalls. This event triggered 7594 landslide sites, causing significant casualties and property damage [57].
Besides surface water systems and rainfall-triggered landslides, the region also experiences diverse and frequent human engineering activities. Excavation during urban construction alters the stress state within slopes, thereby inducing landslide disasters. Additionally, constructing reservoirs and silt dams in larger gullies can saturate and soften the land slopes, altering the water pressure state within the rock and soil, leading to slope instability and causing collapses and landslide disasters [58,59].

3. Data and Methods

Establishing a regional landslide traces inventory is a fundamental task for disaster prevention and risk assessment. Currently, the methods for compiling landslide inventories are mainly divided into field surveys, automatic extraction, and human–machine interactive visual interpretation using satellite images. Field surveys, as a traditional method, rely on professionals conducting on-site investigations in landslide-prone areas to determine the location and size of landslides. This method is highly accurate but labor-intensive, requiring significant time and financial investment and being greatly restricted by terrain and climatic conditions [46,60]. Automatic extraction, as an emerging method for compiling landslide inventories, determines the location and size of landslides through automatic interpretation and analysis of remote sensing images. This method is cost-effective, efficient, and not limited by terrain or climate conditions [61,62]. However, its accuracy is relatively low, with frequent false positives and negatives, resulting in less accurate inventories [63,64,65,66]. Human–machine interactive visual interpretation involves manually identifying landslides from satellite images to determine their location and scale. This method is efficient and cost-effective and is not limited by terrain or climatic conditions. However, its accuracy may be lower than field surveys, and it requires specialized knowledge and a thorough understanding of the interpretation targets [67].
Due to the large area of the Baota District and its location on the Loess Plateau, the undulating and rugged terrain makes on-site surveys challenging, rendering the feasibility of compiling a landslide inventory through field surveys low. The method of interactive visual interpretation for compiling a landslide traces inventory, while sacrificing some accuracy, offers a higher quality and efficiency. Consequently, this study employs interactive visual interpretation of high-resolution satellite images within the research area, combined with field survey verification, to compile a landslide traces inventory. This approach not only avoids the limitations of solely relying on field surveys or automated extraction methods but also compensates for the shortcomings of the interactive method. The images used in this study are all high-resolution, multi-temporal optical images from the Google Earth platform. In recent years, many scholars have utilized the Google Earth platform for regional landslide investigations [68,69,70,71,72]. The Google Earth platform offers high-resolution satellite images from various periods and sources worldwide, greatly facilitating landslide interpretation in the study area [73,74,75,76].
On the Google Earth platform, the coverage of high-resolution satellite images within the Baota District of Yan’an City is 100%. The high-resolution satellite images used in this study were provided by the Google Earth platform, including images from CNES Airbus and Maxar Technologies, with a resolution of 0.3–1.5 m. The images used for visual interpretation in this study range from 17 January 2013 to 8 December 2022. Our most used satellite images are centered on February 2017, March 2019, January 2021, and November 2022. The use of multi-temporal optical images greatly assists in determining landslide boundaries and helps avoid missing landslides due to cloud or snow cover. To avoid human oversight, this study added latitude and longitude grids to the satellite image for grid-by-grid interpretation. Additionally, the Google Earth platform can overlay regional topographic data, allowing researchers to utilize the three-dimensional features of the image to zoom in on the terrain within the study area. This helps minimize misinterpretations and improve the accuracy of landslide identification.
In this study, we referred to the principles used by previous researchers in the Loess Plateau when establishing landslide trace inventories through human–computer interactive visual interpretation, combined with the morphological characteristics of landslides [49,50,77,78]. Taking into account the actual geomorphological characteristics of the Baota District and other factors, we followed the following principles when identifying mountain landslides using satellite images:
  • Morphological characteristics: The landslide’s rear wall appears chair-shaped, the source area is concave, the accumulation area is convex, and cracks develop at the front of the accumulation area. The landslide body appears terraced and may be altered into farmland or residential areas.
  • Vegetation cover: Landslides cause changes in vegetation cover, which can be accurately identified by comparing historical images. Additionally, densely vegetated areas may show features like “knife-shaped trees” and “drunken forests”.
  • Fresh exposure: Landslide-affected slopes expose relatively fresh soil or rock at the landslide’s rear wall, displaying colors different from the surrounding undisturbed areas.
  • Surface water diversion: Landslide debris near river systems may block channels, causing river diversions or forming dammed lakes.

4. Results and Analysis

4.1. Landslide Traces Inventory

An inventory of landslide traces is essential for comprehending the distribution patterns of landslides within the study area and for performing regional hazard assessments. It serves as an important foundation for landslide disaster management and disaster mitigation projects in the region. This study completed the compilation of a landslide traces inventory for the Baota District of Yan’an City using an interactive visual interpretation method combined with field survey verification. There are a total of 73,324 landslide sites in this region, with an average landslide density of 20.62 km−2, as shown in Figure 3. Figure 3 shows that landslides are relatively evenly distributed throughout the Baota District, occurring across the entire land area (except for the Yan’an New Area, where terrain has been altered due to engineering activities). The area of landslides ranges from 574 m2 to 332,420 m2, with an average landslide area of 9264.1 m2. There are 11 landslides with an area exceeding 200,000 m2, with a total landslide area of 769.12 km2, accounting for 21.63% of the total land area.
Figure 4 shows the cumulative frequency curve of the landslide area versus the number of landslides in the Baota District. It can be seen that the vast majority of landslides in the study area have an area smaller than 50,000 m2 (a total of 72,396 landslides, accounting for approximately 98.74% of the total number of landslides in the area). The main graph shows that the cumulative frequency rapidly approaches 100% as the landslide area increases, indicating that small landslides account for a very high proportion. The shape of the curve suggests that most landslides are relatively small in area, and as the area increases the cumulative frequency tends to saturate. This indicates that small landslides dominate the landslide trace. The steep rise in the cumulative frequency curve shows that the number of small landslides is far higher than that of large ones. When the landslide area exceeds a certain threshold (50,000 m2), the curve flattens, indicating that the increase in cumulative frequency slows down, implying that large landslides are rare. The zoomed-in inset shows the cumulative frequency distribution for landslide areas ranging from 0 to 50,000 m2. This further confirms that most landslides are small in area, consistent with the trend shown in the main graph, indicating that most landslides in the Baota District are small.
Figure 5 shows the proportion of landslide numbers in different area ranges for landslides smaller than 50,000 m2. Among these landslides, 66.32% have an area smaller than 10,000 m2, and 25.48% have an area between 10,000 m2 and 20,000 m2. This indicates that small-scale landslides predominate in the Baota District. Through the interpretation process and field survey verification, it can be determined that shallow and superficial landslides account for the majority of small-scale landslides in the Baota District.

4.2. Display of Typical Landslides and Field Survey Verification

By utilizing multi-temporal high-resolution satellite images from Google Earth, it is possible to clearly delineate the boundaries of landslides, identify landslide debris, and accurately determine the direction of landslide movement. As shown in Figure 6, twelve large loess landslides with typical characteristics (arranged from south to north) were selected from satellite images. The boundaries and movement directions of these landslides can be identified from the satellite image. The landslide debris is also clearly visible. Although some of the debris has been altered by human engineering activities, the size and extent of the landslides can still be determined.
Besides these large landslides, small-scale landslides are the overwhelming majority in the Baota District. As shown in Figure 7, Figure 8 and Figure 9, these small landslides are mostly distributed continuously along the foothills, moving towards the free face of the slope. The landslide backwalls often overlap, and the landslide debris is not clearly defined.
Field survey verification allows for more accurate checking of the quality of the landslide traces inventory and reduces interpretation errors. We used drones to capture aerial images of landslides in the Baota District and then compared these images with those at the same locations in Google Earth satellite images. As shown in Figure 10, we selected landslides from three different locations in the Baota District for image comparison. Through this work, we can more accurately determine the extent of landslides in the satellite images, improving the accuracy of landslide interpretation.

4.3. Landslide Density Statistics

Figure 11 and Figure 12 show the point density map and the area density map of landslides in the Baota District. As seen in Figure 11, the landslide density in the eastern part of the Baota District is higher than in the western part. The maximum point density of landslides in the entire area is 43.61 km−2.
However, in the area density map (Figure 12), the landslide area in the western part of the Baota District is larger than in the eastern part, with a maximum area density of 41%. Comprehensive point density analysis indicates that although the number of landslide traces in the Baota District is large, their overall area is relatively small.

5. Discussion

Landslide disasters are the most common and frequent geological hazards worldwide, causing significant impacts on the local ecological environment, land and human production, and life [15,79,80]. Currently, research on landslide disasters is abundant, mainly focusing on the mechanisms of individual landslide occurrences and the spatial distribution characteristics of regional landslides [81,82]. Landslide databases serve as a vital foundation for examining the spatial distribution characteristics of regional landslides. An extensive and detailed inventory of landslide traces is indispensable for subsequent investigations into spatial distribution patterns and susceptibility assessments of landslides.

5.1. Characteristics of the Landslide Inventory in the Baota District and Analysis of the Importance of Establishing a Landslide Inventory in the Loess Plateau Region

Unlike the landslide inventories in the Tibetan Plateau region [33,34], the landslide traces inventory in the Loess Plateau is distinguished by small-scale, numerous, and densely distributed landslides (as shown in Figure 7 and Figure 8). The main factors contributing to this phenomenon are the physical properties of loess. Loess has well-developed vertical joints and is prone to damage under the influence of external factors such as plant root disturbance and water erosion, combined with its gravity and other external forces [83]. Additionally, the Baota District has a dense river network, and rivers easily erode the foot of slopes, creating free faces at the slope base, which can trigger landslides. Furthermore, the region primarily experiences short-duration heavy rainfall, mainly concentrated in the summer. Most of the water from these heavy rains forms runoff on the slope surface, creating gullies on loess slopes, leading to soil erosion and the formation of small-scale free faces, causing slope failure and landslides under gravity and other factors. A small portion of the water infiltrates the soil, creating potential slip zones within the soil [84].
Influenced by factors such as rainfall and gravity, shallow slides frequently occur in the Baota District. After a shallow slide occurs on the surface of a slope, the stress–strain state of the soil in the affected area changes, making it more prone to deep-seated landslides compared to areas where slides have not occurred. Areas that have experienced slides have relatively less vegetation cover, leaving the loess exposed to the air without vegetation protection. In this situation, the exposed loess becomes a preferential point for surface runoff or water infiltration. Water infiltration leads to the deterioration of loess, making it more likely to form a slip zone. Once the slip zone is formed, loess slopes are more prone to deep-seated landslides, causing greater environmental damage and property loss. Under similar topographical conditions (slope direction, slope angle, etc.), and the influence of external factors such as rainfall, river erosion, and human engineering activities, there is a high likelihood of continuous deep-seated landslides occurring on slopes, which could potentially result in extensive damage. Establishing a landslide traces inventory in the Baota District is therefore crucial for the prevention and control of potential large deep-seated landslides in the future, providing valuable assistance for regional disaster prevention and mitigation.

5.2. Comparison with Landslide Inventories from Other Regions of the Loess Plateau

Landslide disasters are frequent in the Loess Plateau region, and many scholars have established regional landslide inventories in this area, aiding the study of regional landslide distribution patterns. Compared to the landslide inventories established in other areas of the Loess Plateau, the Baota District has a significantly higher number of landslide traces (73,324) and an average landslide density of 20.62 km−2, which is much higher than Baoji (3442 landslides, average density 0.190 km−2) [49], Xianyang (2924 landslides, average density 0.283 km−2) [50], and Hancheng (6785 landslides, average density 4.19 km−2) [51]. The specific reasons for this phenomenon require further research. However, based on the number and scale of landslides obtained from the inventory, landslide disasters pose a serious threat to the Baota District, necessitating increased awareness and attention to landslide hazards in this area.

5.3. Prospects

Currently, this study has only established a comprehensive landslide traces inventory for the Baota District. In the landslide traces inventory we have established, we did not specifically categorize the types of landslides. In future research, we will classify them based on previous studies [85,86,87]. Unlike previous studies that conducted in-depth research using landslide inventories in other regions, this study has not specifically analyzed the distribution patterns and developmental characteristics of the landslides in the inventory [88,89,90,91]. In the future, the spatial distribution patterns of landslides in the Baota District could be analyzed by integrating factors such as geological structure and lithology. This approach will facilitate susceptibility assessments, offering crucial support for disaster prevention and mitigation efforts in the Baota District. Additionally, it will serve as a valuable reference for landslide distribution studies in other areas of the Loess Plateau.

6. Conclusions

This study utilized a human–computer interactive visual interpretation method combined with field survey verification to establish the most comprehensive and detailed landslide traces inventory for the Baota District, Yan’an City, to date. It also conducted a preliminary analysis of the landslide quantity, area, and density within the inventory. The inventory contains 73,324 landslide traces, primarily consisting of small landslides, with a lower proportion of large landslides. The total coverage area of the landslides is 769.12 km2, with an average landslide area of 9264.10 m2 and an average landslide density of 20.62 km−2, with a maximum point density of 43.61 km−2. These landslides are distributed relatively evenly throughout the Baota District, with a slightly higher density in the east compared to the west. The inventory obtained from this study can lay the foundation for future research on the spatial distribution patterns, developmental characteristics, and regional susceptibility assessments of landslides. Moreover, it provides important data support for disaster prevention and mitigation efforts in the Baota District, Yan’an City. Additionally, it can serve as a reference for establishing landslide inventories in other regions of the Loess Plateau.

Author Contributions

Conceptualization, C.X.; Investigation, S.Z., T.L., C.L. (Changyou Luo), Y.H., L.F., P.L. and C.L. (Chao Li); Resources, C.X.; Writing—original draft, Sen Zhang; Writing—review & editing, C.X., Z.M., C.L. (Chao Li) and X.S.; Visualization, S.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Institute of Natural Hazards, Ministry of Emergency Management of China (2023-JBKY-57), the National Natural Science Foundation of China (42077259), the Fundamental Research Funds for the Central Universities, CHD (300102264908), and Open Subject Project of Observation and Research Station of Ground Fissure and Land Subsidence, Ministry of Natural Resources, Xi’an, Shaanxi (GKF2024-04).

Data Availability Statement

The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Land 13 01580 g001
Figure 1. Geographic location map of the Baota District. (Note: The base map is 12.5 m ALOS PALSAR DEM, from https://search.asf.alaska.edu/ (accessed on 25 June 2024)).
Figure 1. Geographic location map of the Baota District. (Note: The base map is 12.5 m ALOS PALSAR DEM, from https://search.asf.alaska.edu/ (accessed on 25 June 2024)).
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Figure 2. Geological map of the Baota District. (Note: The data source: http://www.ngac.org.cn/ (accessed on 25 June 2024)).
Figure 2. Geological map of the Baota District. (Note: The data source: http://www.ngac.org.cn/ (accessed on 25 June 2024)).
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Figure 3. Spatial distribution map of landslides in the Baota District. (Note: The base map is 12.5 m ALOS PALSAR DEM, from https://search.asf.alaska.edu/ (accessed on 25 June 2024)).
Figure 3. Spatial distribution map of landslides in the Baota District. (Note: The base map is 12.5 m ALOS PALSAR DEM, from https://search.asf.alaska.edu/ (accessed on 25 June 2024)).
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Figure 4. Cumulative frequency curve of landslide area and number.
Figure 4. Cumulative frequency curve of landslide area and number.
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Figure 5. Proportion of landslides with an area less than 50,000 m2.
Figure 5. Proportion of landslides with an area less than 50,000 m2.
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Figure 6. Display of typical large landslides in the Baota District (dashed lines represent the landslide perimeter, and arrows indicate the direction of sliding). (Note: The satellite images were derived from Google Earth).
Figure 6. Display of typical large landslides in the Baota District (dashed lines represent the landslide perimeter, and arrows indicate the direction of sliding). (Note: The satellite images were derived from Google Earth).
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Figure 7. Display of typical small landslides in the Baota District (dashed lines represent the landslide perimeter, and arrows indicate the direction of sliding). (Note: The satellite image was derived from Google Earth).
Figure 7. Display of typical small landslides in the Baota District (dashed lines represent the landslide perimeter, and arrows indicate the direction of sliding). (Note: The satellite image was derived from Google Earth).
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Figure 8. Display of typical small landslides in the Baota District (dashed lines represent the landslide perimeter, and arrows indicate the direction of sliding). (Note: The satellite image was derived from Google Earth).
Figure 8. Display of typical small landslides in the Baota District (dashed lines represent the landslide perimeter, and arrows indicate the direction of sliding). (Note: The satellite image was derived from Google Earth).
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Figure 9. Display of typical small landslides in the Baota District (dashed lines represent the landslide perimeter, and arrows indicate the direction of sliding). (Note: The satellite image was derived from Google Earth).
Figure 9. Display of typical small landslides in the Baota District (dashed lines represent the landslide perimeter, and arrows indicate the direction of sliding). (Note: The satellite image was derived from Google Earth).
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Figure 10. Comparison of landslide field survey verification images with satellite images. (a–c) are Google Earth satellite images. (d–f) are landslide photos taken during field surveys. (a–c) correspond to (d–f), respectively. (Note: Landslide images were taken by a drone during the field survey).
Figure 10. Comparison of landslide field survey verification images with satellite images. (a–c) are Google Earth satellite images. (d–f) are landslide photos taken during field surveys. (a–c) correspond to (d–f), respectively. (Note: Landslide images were taken by a drone during the field survey).
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Figure 11. Landslide point density map. (Note: The map was generated using GeoScene 2.1 software, URL: https://www.geoscene.cn/ (accessed on 25 May 2024)).
Figure 11. Landslide point density map. (Note: The map was generated using GeoScene 2.1 software, URL: https://www.geoscene.cn/ (accessed on 25 May 2024)).
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Figure 12. Landslide area density map. (Note: The map was generated using GeoScene 2.1 software, URL: https://www.geoscene.cn/ (accessed on 25 May 2024)).
Figure 12. Landslide area density map. (Note: The map was generated using GeoScene 2.1 software, URL: https://www.geoscene.cn/ (accessed on 25 May 2024)).
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Zhang, S.; Xu, C.; Meng, Z.; Li, T.; Li, C.; Huang, Y.; Shao, X.; Feng, L.; Luo, P.; Luo, C. Establishing a Landslide Traces Inventory for the Baota District, Yan’an City, China, Using High-Resolution Satellite Images. Land 2024, 13, 1580. https://doi.org/10.3390/land13101580

AMA Style

Zhang S, Xu C, Meng Z, Li T, Li C, Huang Y, Shao X, Feng L, Luo P, Luo C. Establishing a Landslide Traces Inventory for the Baota District, Yan’an City, China, Using High-Resolution Satellite Images. Land. 2024; 13(10):1580. https://doi.org/10.3390/land13101580

Chicago/Turabian Style

Zhang, Sen, Chong Xu, Zhenjiang Meng, Tao Li, Chao Li, Yuandong Huang, Xiaoyi Shao, Liye Feng, Penghan Luo, and Changyou Luo. 2024. "Establishing a Landslide Traces Inventory for the Baota District, Yan’an City, China, Using High-Resolution Satellite Images" Land 13, no. 10: 1580. https://doi.org/10.3390/land13101580

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