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Technical Note

Analysis of Roadside Land Use Changes and Landscape Ecological Risk Assessment Based on GF-1: A Case Study of the Linghua Expressway

by
Mengdi Wen
1,2,†,
Liangliang Zhang
3,4,†,
Huawei Wan
1,*,
Peirong Shi
1,
Longhui Lu
1,
Zixin Zhao
3,
Zhiru Zhang
1 and
Jinhui Wu
3
1
Satellite Environmental Application Center of Ministry of Ecology and Environment, Beijing 100094, China
2
Information Center of Ministry of Natural Resources, Beijing 100812, China
3
China Institute of Geo-Environment Monitoring, China Geological Survey, Beijing 100081, China
4
School of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Remote Sens. 2025, 17(2), 211; https://doi.org/10.3390/rs17020211
Submission received: 2 October 2024 / Revised: 17 December 2024 / Accepted: 23 December 2024 / Published: 8 January 2025
(This article belongs to the Topic Recent Progress and Applications in Quantitative Remote Sensing)
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Abstract

:
The rapid construction of expressways in China has brought significant economic and social benefits, but it has also imposed substantial ecological pressures, particularly in sensitive regions. Landscape ecological risk assessment, as an important means to predict and measure the adverse effects of human activities on the ecological environment, is being paid more and more attention. However, most studies focus on the static landscape mosaic pattern and lack dynamic analysis. Moreover, they mainly focus on the ecological effect of the road operation stage, ignoring the monitoring and analysis of the whole construction process. Based on this, the current study examines the landscape ecological risk and land use changes along the Linghua Expressway in Gansu Province using high-resolution GF-1 remote sensing imagery. A landscape ecological risk assessment (LERA) model was employed to quantify the land use changes and assess the ecological risks before and after the expressway construction between 2018 and 2022. The results revealed a decrease in cropland and forest land, accompanied by an increase in the grassland and road areas. The landscape ecological risk index decreased from 0.318 in 2018 to 0.174 in 2022, indicating an improvement in ecological resilience. However, high-risk zones remain near the expressway, emphasizing the need for continuous monitoring and proactive ecological management strategies. These findings contribute to sustainable infrastructure planning, particularly in ecologically sensitive regions.

1. Introduction

The construction of expressways in China is experiencing rapid development, becoming a critical component of the country’s infrastructure expansion [1,2]. While the establishment of transportation networks brings significant economic and social benefits, it simultaneously imposes substantial pressure on the surrounding natural ecosystems [3,4]. These impacts are particularly pronounced in ecologically sensitive regions where expressway construction disrupts habitat integrity and alters landscape structure, leading to biodiversity loss and ecosystem degradation [5,6]. The expansion of transportation networks often requires the clearance of large areas of natural habitats to accommodate road construction, creating a new boundary line that fragments habitat and disrupts ecological connectivity [7,8]. This, in turn, leads to habitat loss, increased road mortality, migration barriers, and changes in wildlife behavior and mobility patterns [9,10]. As human activities extend into previously undisturbed areas, the pressure on ecosystems intensifies, resulting in increased urbanization, deforestation, and land use changes that further degrade the environment [11,12]. Therefore, monitoring the ecological impacts of road construction, especially in regions of high biodiversity, is essential for maintaining ecosystem balance [13,14].
In recent years, the ecological impacts of transportation networks have become a key research focus globally [15,16]. Numerous countries are now prioritizing the integration of ecological considerations into road development, shifting from traditional small-scale assessments to broader landscape-scale studies [17,18,19]. Since the 1990s, research has increasingly emphasized the landscape level, assessing how road networks affect larger ecological processes, such as habitat fragmentation, species distribution, and landscape connectivity [20,21]. Despite these advances, significant challenges remain, particularly regarding data availability [22,23]. Many studies suffer from a lack of comprehensive datasets, which hinders the ability to evaluate the full spectrum of ecological impacts [24]. Furthermore, most of the existing research predominantly focuses on the ecological effects during the operational phase of highways, neglecting the critical insights that can be gained from monitoring the entire construction process [25]. This gap underscores the necessity of continuous assessment throughout all phases of highway development [26,27]. Remote sensing (RS) and geographic information systems (GIS) have emerged as powerful tools in addressing this gap by enabling the large-scale, continuous monitoring of landscape patterns and changes in land use [28,29]. The application of RS data, such as high-resolution GF-1 imagery, provides valuable insights into landscape dynamics, making it indispensable for evaluating the environmental impacts of infrastructure projects [30,31].
This study focuses on assessing the landscape ecological risk of the Linghua Expressway (Lingtai to Huating Expressway) in Pingliang City, Gansu Province, using GF-1 remote sensing imagery [32,33]. The region is known for its rich biodiversity and ecological resources [34,35], making it particularly sensitive to infrastructure development [36]. By employing a landscape ecological risk assessment (LERA) model, this study aims to quantify changes in land use and assess the spatial distribution of ecological risks along the expressway corridor [37,38]. Through the integration of RS data with the LERA framework, we aim to identify high-risk areas that require targeted conservation measures [39,40]. The results of this study will provide valuable insights for balancing infrastructure development with ecological conservation, contributing to the sustainable development of the region [41,42]. Additionally, the findings may serve as a reference for other infrastructure projects in ecologically sensitive areas, underscoring the importance of incorporating advanced monitoring techniques into environmental impact assessments [43,44].

2. Materials and Methods

Based on supervised classification and the Landscape Ecological Risk Model, the land use changes and landscape ecological risks before and after the construction of Linghua Expressway were evaluated, and the potential impacts of road construction on the ecological environment were revealed. Figure 1 presents a schematic of the entire process.

2.1. Research Area

The Linghua Expressway, spanning 68 km, is situated in the Loess Plateau of eastern Gansu Province [45,46]. This area mainly belongs to the geological structural unit of North China, and the stratigraphy mainly includes Mesozoic and Cenozoic sedimentary rocks. This expressway predominantly follows the river valley, with approximately 5 km of tunnels located in the ecologically sensitive Yulongshan Provincial Forest Park at the border of Huating and Chongxin counties [47,48] (Figure 2). Construction on the first phase of the expressway commenced in November 2019, and it officially opened to traffic in December 2023 [49]. The region is characterized by a semi-arid to semi-humid continental climate, classified as a cold temperate semi-humid zone according to provincial standards [50]. The southern part of the area experiences wetter conditions, while the northern section is drier, with distinct temperature variations influenced by the complex topography and elevation changes across the landscape [51].

2.2. Data Sources and Preprocessing

In this study, GF-1 remote sensing images from May to September of 2018 and 2022 were selected, as these months represent optimal vegetation growth periods with minimal cloud interference. Compared to Landsat and Sentinel-2 data, GF-1 images offer higher spatial resolution, better detail-capturing capabilities, and shorter revisit cycles, making them well-suited for accurate real-time dynamic monitoring [52,53]. The images underwent a rigorous preprocessing workflow, which included absolute radiometric calibration using coefficients from domestic land-based observation satellites [54,55]. Following this, atmospheric correction was applied to mitigate the effects of atmospheric scattering on image quality [56,57]. The preprocessed panchromatic and multispectral images were then fused, stitched, and cropped to generate the final remote sensing datasets for 2018 and 2022 [58,59], as illustrated in Figure 3.
The boundary of the Linghua Expressway was provided by the construction company, representing the primary route [60]. Additionally, 30 m spatial resolution digital elevation model (DEM) data were obtained from the Geospatial Data Cloud (https://www.gscloud.cn (accessed on 1 March 2022)), which were subsequently utilized to derive slope and aspect data through geospatial calculations [61,62].

2.3. Land Use Classification and Landscape Ecological Risk Model

Land use data were acquired through supervised classification of the GF-1 remote sensing images [63,64]. The classification system categorized land use into seven types: cropland, forest land, grassland, house construction, roads, unutilized land, and water bodies [65,66]. In ArcGIS Pro, over 100 sample points were selected for each land use type to ensure comprehensive representation of various forms and periods [67,68]. The resulting classification was manually interpreted and modified to produce the final raster data for land use interpretation [69,70].
Furthermore, a landscape ecological risk model was constructed based on the disturbance index (Ei) and the fragility index (Fi) [71,72]. The Ei reflects the landscape’s resistance to external disturbances, informed by landscape fragmentation, separation, and dominance [73,74] to which we assigned specific weights of 0.5, 0.3, and 0.2, respectively [75,76]. The Fi indicates the vulnerability of different ecosystems, where a higher value denotes greater susceptibility to disturbances [77,78]. Normalization was applied following value assignment [79]. The landscape ecological risk index (ERI) was calculated as follows:
E R I = 1 S k ∑ i = 1 N S k i · E i · F i
In this formula, N represents the total number of landscape types within the sample area, Ski denotes the area of landscape type i, Sk is the total area of the sample area, Ei is the disturbance index for landscape type i, and Fi represents the vulnerability index for landscape type i [80,81].
To facilitate landscape ecological risk calculations, the study area was divided into a 1 km by 1 km grid, with each grid cell serving as an independent research unit [82,83]. The research of landscape ecology shows that when the area of landscape samples is 2–5 times of the average patch area, it can better reflect the landscape information around the sampling area, and detailed spatial analysis of the ecological risk index within the buffer zone can be carried out [84,85]. Utilizing ArcGIS 10.8, a total of 246 sample areas were generated for comprehensive risk assessment [86,87] (Figure 4). According to the established landscape ecological risk model, the landscape ecological risk index was calculated for each sample area and assigned to the central point of the sample area. In order to display the spatial distribution characteristics of ecological risk in the study area more intuitively, the spatial distribution of landscape ecological risk index in the study area in 2018 and 2022 was obtained by using the common Kriging interpolation method.

3. Results

3.1. Evaluation of the Accuracy in Land Use Classification

To assess the accuracy of the land use classification results, over 400 precision points were randomly selected, utilizing visual interpretation as the reference for true surface conditions. In the 2018 accuracy assessment, the overall classification accuracy for the road area reached 82.73%, with a kappa coefficient of 0.80. These results indicate a satisfactory classification performance consistent with local conditions. Among the various land use types, cropland, forest land, and water bodies exhibited the highest accuracies of 97.30%, 94.59%, and 97.22%, respectively. Conversely, the grassland and road classifications yielded accuracies of 76.92% and 70.97%. Notably, housing construction showed the lowest accuracy at 53.85%, suggesting challenges in distinguishing this land use type. In the accuracy evaluation for 2022, the overall classification accuracy slightly declined to 82.04%, accompanied by a kappa coefficient of 0.79. Despite the decrease, the results remained adequate for subsequent remote sensing analyses. Once again, cropland, forest land, and water bodies maintained high classification accuracies of 97.18%, 94.37%, and 97.18%, respectively. Grassland and road accuracies improved slightly to 78% and 70%, while unutilized land recorded an accuracy of 64.86%. The persistent low accuracy for housing construction underscores the need for enhanced classification methodologies in future studies. These accuracy evaluations confirm the reliability of the land use classification results, which are crucial for understanding the dynamics of land use in the context of the Linghua Expressway construction. The high accuracies for cropland and forest land highlight their significance in the regional landscape, while the lower accuracies for housing construction and roads indicate areas for methodological improvement.

3.2. Analysis of Land Use Changes

Land use interpretation was conducted based on seven categories: cropland, forest land, grassland, housing construction, road, unutilized land, and water bodies, as shown in Figure 5a,b. By 2022, significant changes in land use distribution occurred within the 1 km buffer zone surrounding the Linghua Expressway compared to 2018. The construction of the expressway resulted in noticeable fragmentation of the landscape, particularly evident in the bisected areas of forest land and grassland.
In the western region, characterized by cropland and residential areas, the landscape appeared more fragmented, although the road construction did not drastically alter the overall land use patterns. Throughout both 2018 and 2022, cropland and forest land remained the predominant land use types, suggesting that the construction of the Linghua Expressway did not significantly disrupt established land use dynamics (Table 1). In 2018, cropland and forest land were the primary land use types, with cropland covering 52.87 km2 (36.6% of the total area) and forest land encompassing 48.77 km2 (33.8%). Together, these types represented over 70% of the total area. Water bodies were minimal, comprising only 0.21 km2 (0.1% of the total area). By 2022, the cropland area decreased to 44.36 km2 (30.7%), and forest land reduced to 40.14 km2 (27.8%), indicating slight declines of 8.51 km2 and 8.63 km2, respectively. In contrast, the grassland area increased from 22.01 km2 in 2018 to 30.17 km2 in 2022, marking an increase of 8.16 km². The housing construction area expanded to 18.08 km2, a rise of 2.87 km2 from 2018, while road areas increased by 5.07 km2, from 4.84 km2 in 2018 to 9.91 km2 in 2022. Water bodies exhibited minimal change, with an increase of only 0.13 km2.

3.3. Landscape Ecological Risk Distribution

Using the established landscape ecological risk model, we calculated the landscape ecological risk index for each sample area, assigning it to the central point of the sample area. The Kriging interpolation method was employed to visually represent the spatial distribution of ecological risk, as illustrated in Figure 6. In 2018, the average landscape ecological risk value was 0.32, with high-risk areas concentrated in the central-western region. Partially high-risk zones were identified in the central and eastern regions, including segments of Chongxin County and Lingtai County. By 2022, the average risk value decreased to 0.17, with previously high-risk areas in the west downgraded to relatively high-risk status. Conversely, the ecological risk in the central and western regions increased, while the overall risk in the eastern region showed a decline. The spatial distribution in 2022 revealed a trend of decreasing risk levels moving outward from the main road corridor, indicating that road construction exerts considerable risks on surrounding landscape patches, particularly those closer to the roadway.

3.4. Landscape Ecological Risk Dynamics

The calculated proportions of landscape ecological risks before and during the construction of the Linghua Expressway (Figure 7) demonstrate that prior to construction, the majority of the area fell within the medium-risk category at 29.45%, followed by 28.35% in the relatively low-risk category, 21.59% in the relatively high-risk category, and 17.92% in the high-risk category. The low-risk category accounted for only 2.69%. This distribution indicates that the overall landscape ecological risk level in the study area was predominantly medium to high. With the construction of the Linghua Expressway, the proportion of low-risk areas increased to 9.85%, while relatively high-risk areas rose by 3.59% to 25.18%. In contrast, the proportions of relatively low-risk and medium-risk areas decreased by 2.12% and 1.4%, respectively, while high-risk areas decreased by 7.23%. Although the overall landscape ecological risk has decreased, the region remains predominantly characterized by relatively low-, medium-, and relatively high-risk levels. The reduction in high-risk areas and the increase in low-risk areas can be partially attributed to the expansion of construction land. However, the Linghua Expressway’s construction has also elevated the landscape ecological risks in surrounding areas, with minimal changes in the proportions of other risk categories.

4. Discussions

4.1. Evaluation of the Accuracy in Land Use Classification

The evaluation of land use classification accuracy revealed overall classification accuracies of 82.73% in 2018 and 82.04% in 2022 [88,89], indicating that the supervised classification approach employed is both reliable and efficient for large-scale remote sensing applications [90,91]. The high accuracies for cropland, forest land, and water bodies demonstrate their distinctive spectral characteristics, which facilitate accurate differentiation [92,93]. However, the comparatively lower accuracies for house construction, particularly at 53.85% in both years, underscore the complexities involved in classifying urban land types [94]. The building forms are diverse, with varying sizes and colors. The distance between them is relatively close, and artificial gardens and grass are intermingled in the middle, which is prone to cause misjudgment. It is necessary to adopt higher-resolution remote sensing data and combine manual visual interpretation with computer automatic classification to further enhance the classification accuracy [95,96]. The consistency of classification results across the study years also reflects the stability of land use patterns, thereby confirming the effectiveness of the chosen classification strategy [97,98].

4.2. Analysis of Land Use Changes

The analysis of land use changes highlights significant ecological transformations along the Linghua Expressway, driven primarily by infrastructure development [99,100]. The decline in cropland and forest land, alongside the increase in grassland, raises concerns about potential biodiversity loss and ecosystem degradation [101,102]. The conversion of forest land to grassland, in particular, indicates a shift towards less stable ecosystems [103,104], which can have cascading effects on local hydrology and soil health [105]. This alteration not only diminishes the area’s ecological integrity but also exacerbates issues such as reduction in water retention capacity and soil erosion, critical factors in maintaining environmental sustainability [106,107]. Furthermore, the fragmentation observed in land distribution underscores the impact of the expressway on ecological connectivity, leading to isolated habitats that can hinder wildlife movement and reduce genetic diversity [108,109].

4.3. Landscape Ecological Risk Distribution

The calculated landscape ecological risk index decreased from 0.318 in 2018 to 0.174 in 2022 [110,111], suggesting a relative improvement in the ecological resilience of the area. However, the persistence of high-risk zones, particularly near the expressway, indicates that while some areas may have benefited from construction, significant ecological vulnerabilities remain [112]. High-risk areas were identified, especially in regions such as Hengqu Village to Lingtai County, underscoring the need for continuous ecological monitoring [113,114]. These findings align with previous studies, which emphasize that infrastructure projects often increase ecological vulnerability in sensitive areas due to habitat fragmentation and land use conversion [115,116]. However, most previous studies focused on the ecological effects in the road operation stage and paid little attention to the impact of pre-construction work on the ecological environment. Damage to vegetation and the environment in the early stages of construction is often the key reason for the intensification of ecological risks. This study assessed the ecological risks before and after road construction and provides a more comprehensive view of the impact of road construction on the environment. The spatial distribution of risk, as indicated by the Kriging interpolation method, reveals the significant ecological effects of road construction, with the most impacted areas being those adjacent to the expressway [117]. In 2018, high-risk areas were mainly concentrated around house construction, where human activities were intense. The risk gradually decreased as the distance from the house construction Increased, and the landscape ecological risk of forest land and grassland was the lowest. In 2022, the distribution of high-risk areas changed significantly, mainly in areas where forest land degradation occurred due to road construction. Although the forest land has not completely vanished and only part of it has been degraded to grassland, its landscape ecological risk is much higher than that of housing construction areas. It can be observed that forest land is of great significance for maintaining the stability of the ecosystem, and the damage caused to the ecosystem by road construction is severe.

4.4. Landscape Ecological Risk Dynamics

The dynamics of landscape ecological risk reveal a complex interplay between infrastructure development and ecological health [118,119]. While the overall landscape ecological risk index shows a decrease in high-risk areas, the region remains predominantly characterized by medium- to relatively high-risk levels [120]. This suggests that although the construction of the Linghua Expressway contributed to some reduction in ecological risks, it also elevated risks in the surrounding areas, particularly those closer to the road [121,122]. The shift from high-risk to medium- and low-risk categories in some areas reflects the stabilization of the construction land [123,124], which typically has a stronger ability to resist ecological disturbances [125]. However, the ecological risks associated with the ongoing human activities, including urbanization and increased land use, cannot be overlooked [126,127]. Proactive measures, such as habitat restoration and continuous ecological assessments, are essential during and after the construction phases to mitigate long-term impacts on the landscape [128]. In addition, the study also found that species richness as a driving factor has a certain degree of impact on landscape ecological risk. Therefore, restoring high-risk areas and ensuring the maintenance of biodiversity and ecosystem functions will be critical to the sustainable development of the region [129,130]. The research results can provide a theoretical basis for the government to realize the sustainability of road construction and the maintenance of the regional ecological environment and reduce the ecological risk brought by road construction.

4.5. Limitations and Recommendations for Future Research

Through comparative analysis of land use and landscape ecological risk changes before and after the construction of the Linghua Expressway, this study effectively reflects the impact of road construction on the ecological environment, but there are still two limitations. First, the research focuses on the landscape ecological risk at the ecosystem level, and the ecological sensitivity is not adequately reflected. Second, the study reflects the impact of Linghua Expressway construction on the ecological environment. Whether the construction of other roads in different ecological environments has the same impact remains to be verified. In future studies, the research scope can be extended to other ecosystem functions and structures, such as ecological sensitivity, watershed connectivity, habitat connectivity, and the number of typical species, so as to increase the number of studies on the impact of road construction under different environmental backgrounds and further reflect the impact of road construction on ecosystems.

5. Conclusions

This study assesses the ecological impacts of the Linghua Expressway construction using remote sensing data, with land use classification accuracies of 82.73% in 2018 and 82.04% in 2022. Cropland, forest land, and water bodies were classified with the highest accuracy, providing reliable insights for subsequent ecological analyses. Between 2018 and 2022, cropland and forest land remained the dominant land use type, but there was a major shift in land use, with large areas of forest land and cropland converted into grasslands and roads due to the expressway’s construction. The landscape ecological risk index decreased from 0.318 in 2018 to 0.174 in 2022, reflecting an overall improvement in the area’s ecological resilience. However, the increase in ecological risk in the central and western regions and the gradually decreasing risk level from the main road corridor outward indicate that road construction has caused a greater risk to the surrounding landscape patches; high-risk areas near the expressway remain, highlighting the need for ongoing ecological monitoring and management. While the risk in high-risk areas declined, the increase in relatively high-risk areas close to the road underscores the importance of proactive measures such as habitat restoration. These findings emphasize the critical need to integrate ecological considerations into infrastructure projects to balance development with environmental protection and ensure the region’s long-term sustainability.

Author Contributions

Conceptualization, M.W. and H.W.; methodology, H.W.; formal analysis, M.W.; investigation, L.Z.; data curation, P.S., L.L., Z.Z. (Zixin Zha), and Z.Z. (Zhiru Zhang); writing—original draft preparation, M.W. and L.Z.; writing—review and editing, J.W.; supervision, J.W. and H.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China, grant number 2021YFB2600101.

Data Availability Statement

Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The flow chart of this study.
Figure 1. The flow chart of this study.
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Figure 2. Linghua Expressway main line and research area.
Figure 2. Linghua Expressway main line and research area.
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Figure 3. Remote sensing images of the study area in 2018 (a) and 2022 (b).
Figure 3. Remote sensing images of the study area in 2018 (a) and 2022 (b).
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Figure 4. Landscape ecological risk sample grid of the study area.
Figure 4. Landscape ecological risk sample grid of the study area.
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Figure 5. Land use classification maps of the study area in 2018 (a) and 2022 (b).
Figure 5. Land use classification maps of the study area in 2018 (a) and 2022 (b).
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Figure 6. Spatial distribution of landscape ecological risk of the study area in 2018 (a) and 2022 (b).
Figure 6. Spatial distribution of landscape ecological risk of the study area in 2018 (a) and 2022 (b).
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Figure 7. Landscape ecological risk classification area proportion.
Figure 7. Landscape ecological risk classification area proportion.
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Table 1. Area and percentage of each land use type in the study area, 2018 and 2022.
Table 1. Area and percentage of each land use type in the study area, 2018 and 2022.
Land Use Type20182022
Area/km2Percentage/%Area/km2Percentage/%
Cropland52.8736.644.3630.7
Forest land48.7733.840.1427.8
Grassland22.0115.330.1720.9
House Construction15.2110.518.0812.5
Roads4.843.49.916.9
Unutilized Land0.380.31.290.9
Water Bodies0.210.10.340.2
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Wen, M.; Zhang, L.; Wan, H.; Shi, P.; Lu, L.; Zhao, Z.; Zhang, Z.; Wu, J. Analysis of Roadside Land Use Changes and Landscape Ecological Risk Assessment Based on GF-1: A Case Study of the Linghua Expressway. Remote Sens. 2025, 17, 211. https://doi.org/10.3390/rs17020211

AMA Style

Wen M, Zhang L, Wan H, Shi P, Lu L, Zhao Z, Zhang Z, Wu J. Analysis of Roadside Land Use Changes and Landscape Ecological Risk Assessment Based on GF-1: A Case Study of the Linghua Expressway. Remote Sensing. 2025; 17(2):211. https://doi.org/10.3390/rs17020211

Chicago/Turabian Style

Wen, Mengdi, Liangliang Zhang, Huawei Wan, Peirong Shi, Longhui Lu, Zixin Zhao, Zhiru Zhang, and Jinhui Wu. 2025. "Analysis of Roadside Land Use Changes and Landscape Ecological Risk Assessment Based on GF-1: A Case Study of the Linghua Expressway" Remote Sensing 17, no. 2: 211. https://doi.org/10.3390/rs17020211

APA Style

Wen, M., Zhang, L., Wan, H., Shi, P., Lu, L., Zhao, Z., Zhang, Z., & Wu, J. (2025). Analysis of Roadside Land Use Changes and Landscape Ecological Risk Assessment Based on GF-1: A Case Study of the Linghua Expressway. Remote Sensing, 17(2), 211. https://doi.org/10.3390/rs17020211

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