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Article

Influence of Digital Elevation Model Resolution on the Normalized Stream Length–Gradient Index in Intraplate Regions: A Case Study of the Yangsan Fault, Korea

by
Hyunjee Lim
1,
Sangmin Ha
2,
Sohee Kim
1,
Hee-Cheol Kang
1,3 and
Moon Son
1,*
1
Department of Geological Sciences, Pusan National University, Busan 46241, Republic of Korea
2
Department of Geography Education, Korea University, Seoul 02841, Republic of Korea
3
Institute of Geohazard Research, Pusan National University, Busan 46241, Republic of Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(9), 1638; https://doi.org/10.3390/rs17091638
Submission received: 10 December 2024 / Revised: 27 April 2025 / Accepted: 29 April 2025 / Published: 6 May 2025
(This article belongs to the Special Issue Remote Sensing Monitoring for Earthquakes, Tectonics and Seismic Hazards)
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Abstract

:
The spatial variability of input parameters plays a crucial role in the interpretation of geomorphic indices, with digital elevation models (DEMs) being the primary data source. However, the influence of DEM resolution on these indices has rarely been investigated. This study investigated the influence of DEM resolution on the assessment of tectonic activity using the normalized stream length–gradient (SLk) index, which reflects variations along river profiles. The SLk index is sensitive to changes in river gradients that may result from active faulting or differential uplift, making it a valuable tool for identifying zones of active tectonic deformation. Therefore, understanding the impact of DEM resolution on SLk analysis is critical for accurately detecting and interpreting subtle tectonic signals, particularly in intraplate regions where deformation is slow and geomorphic expressions are faint and discontinuous. By comparing high-resolution LiDAR-derived DEMs (L-DEMs) and low-resolution topographic map-derived DEMs (T-DEMs), we analyzed the SLk index distributions along the Yangsan Fault, Korean Peninsula, an intraplate setting with Quaternary activity. According to the results, SLk anomalies derived from L-DEMs had a continuous distribution along the fault, closely aligning with known surface ruptures and indicating active tectonic deformation. In contrast, SLk anomalies derived from T-DEMs were sporadic and less continuous, especially in low-relief landscapes such as alluvial fans and floodplains, highlighting the limitations of T-DEMs in detecting fault-related features. High-resolution DEMs were better able to capture finer-scale geomorphic features, such as fault scarps, deflected streams, and lineaments associated with active tectonics, providing a more comprehensive view of fault-related deformation. This discrepancy highlights the importance of resolution choice in tectonic assessments, as low-resolution DEMs may underestimate the tectonic activities of intraplate faults by missing subtle topographic variations. While the choice of DEM resolution may depend on study area, scope, and data availability, high-resolution DEMs are critical for identifying tectonic activity in intraplate regions where geomorphic features of faulting due to slow deformation are subtle and dispersed.

1. Introduction

Morphometrics, a branch of geomorphology, employs geomorphic indices to quantify the shapes and patterns of landforms and provides powerful tools for assessing tectonic activity [1,2]. Geomorphic indices such as the asymmetry factor, hypsometric curve and integral, ratio of valley floor width to height, mountain-front sinuosity, and stream length–gradient are commonly used for rapidly and quantitatively assessing landforms and tectonic activity [2,3,4,5,6,7,8,9]. In regions along plate boundaries, geomorphic indices have long been used to assess areas with high seismic activity and records of large earthquakes, supported by abundant geomorphic evidence of recent or ongoing tectonic activity [e.g., 10–16].
However, interpreting geomorphic features remains challenging in intraplate regions, such as the Korean Peninsula, where tectonic activity is low (slip rate of less than 1 mm/yr) [10,11,12,13,14]. The Korean Peninsula experiences strong seasonal climate variations, leading to high rates of weathering and denudation [15]. The combined effects of climate and long-term erosion have greatly affected the present-day topography and direct observations of structural landform changes [16,17]. Additionally, related human modifications, such as the construction of dams, tunnels, and roads, particularly since the 1960s due to government-led industrial development, have significantly altered landforms [18,19,20]. This further increases the complexity of geomorphic analysis. Despite such limitations, the recent increase in moderate earthquakes in intraplate regions such as the Korean Peninsula has highlighted the need for evaluating tectonic activity using geomorphic indices. Several researchers have utilized geomorphic indices to evaluate tectonic activity in major fault zones of the Korean Peninsula, including the Yangsan, Ulsan, Miryang (e.g., [21,22]), and Geumwang faults (e.g., [17]). Due to the extensive distribution of the study areas, the studies employed digital elevation models (DEMs) based on 1:5000 scale topographic maps to analyze relative tectonic activity centered on the fault zones [17,21,22]. DEMs are particularly useful tools in geomorphic index analysis, and DEM resolution may affect the characterization of tectonic environments. However, the accuracy and interpretative power of these indices can be influenced significantly by DEM resolution.
Although DEMs produced from aerial photographs, digital topographic maps, and LiDAR images vary greatly in resolution, research on the impact of DEM resolution on geomorphic indices remains scarce in low tectonic activity regions. Existing studies have focused primarily on active tectonic environments, e.g., [1,23,24,25,26,27,28], leaving gaps in our understanding of how resolution affects index calculations in slowly deforming regions. To address the gaps above, it is necessary to evaluate how DEM resolution influences normalized stream length-gradient (SLk) index results and the interpretation of tectonic activity. Previous studies have demonstrated that DEM resolution influences the accuracy of morphometric analyses significantly, as coarse-resolution data may fail to capture subtle terrain variations essential for tectonic and geomorphic interpretation [29]. Furthermore, whereas the geomorphic indices are widely utilized to infer tectonic activity, studies addressing their limitations—such as their sensitivity to DEM resolution and potential misinterpretation—remain limited.
This study performed a SLk analysis of the Byeokgye section [study area; [19,21]]—with the study area located in the southern part of the northern Yangsan Fault, Korea—using DEMs derived from LiDAR images and topographic maps. By comparing SLk index values obtained from different DEM resolutions, this study aims to provide new insights into the influence of resolution influences interpretations of tectonic activity in intraplate regions with low deformation rates. Additionally, this study highlights key methodological considerations for future research in similar settings.

2. Study Area

The Korean Peninsula experiences fewer earthquakes than neighboring countries such as Japan, Taiwan, China, and the Philippines, which lie on a plate boundary. However, stress may accumulate over extended periods in intraplate regions. The three recent earthquakes (Gyeongju: Mw 5.5, 12 September 2016 [30]; Pohang: Mw 5.4, 15 November 2017 [31]; and Buan: ML 4.82, 12 June 2024 [32]), as well as medium-scale instrumental and historical earthquakes, reflect the high potential for disastrous tectonic activity within Korea [31,33,34,35,36,37,38]. Most seismic activity is associated with major fault lines, among which the Yangsan Fault is one of the largest and most important [30,31,32,33,34,35,36,37,38].
Researchers have extensively studied the Yangsan Fault, revealing a complex history of Cenozoic evolution and Quaternary seismic activity (Figure 1). The Yangsan Fault extends over 200 km on land, with an N-S to NNE-strike, a several-hectometer-wide fault zone, and a 20–35 km dextral offset. It cuts through Cretaceous sedimentary rocks and Cretaceous to Eocene igneous rocks [39,40,41,42,43,44,45,46]. Since the Late Cretaceous, the fault has undergone multiple deformations, with each stage corresponding to different paleo-stress regimes. The Yangsan Fault exhibited a dextral strike-slip movement with a minor reverse component during the Quaternary, and certain segments remain active under a current stress regime in the E-W or ENE-WSW compression [47,48]. This study focuses on the Byeokgye section, the longest Quaternary surface rupture on the northern Yangsan Fault (Figure 1d) [13,14]. Previous paleoseismic investigations, including trenching and outcrop studies, indicated multiple faulting events after the deposition of Quaternary sediments [13,14,49,50,51,52,53]. The active fault in the study area has a slip rate of 0.12–0.57 mm/yr−1, and the most recent earthquake occurred 3000 years ago [13,14,53]. In topographic mapping, fault scarps and deflected streams formed by surface rupture have been recorded [52].

3. Data and Methods

3.1. Data

We used two different DEMs for the entire study area (Figure 2, approximately 4.2 × 9.1 km). As part of a research and development project on active faults in the Korean Peninsula, we acquired airborne LiDAR data over the southern part of the Northern Yangsan Fault. The CENNA 208 aircraft and Lite mapper-6800 laser pulse transceiver manufactured by IGO Co. (Trier, Germany) were used for data acquisition. Considering the regional characteristics, we selected the full-waveform method for its data-acquisition capability in mountainous areas. The period of data collection for LiDAR is from 8 December 2017 to January 2018, and the flight and system parameters are defined in Table 1. LiDAR images were captured in an east–west direction; however, to increase the point density and acquisition rate of ground data for a steep slope, the north–south and slope directions were taken together. This would enable the changes in each strip to represent the rapid changes in altitude in the study area. We designed the flight plan to cover the entire study area (69.6 km2). A total of 124 flight lines with a total flight distance of 875.4 km were flown over 7 days, achieving a 60% overlap. This represents a more than fourfold increase in the number of flight lines compared to previous studies of similar areas in Korea [52]. The point coordinate accuracy of the LiDAR system is shown in Table 1. A total of 1,856,096,462 laser return points were acquired, and 317,989,564 ground points remained after processing. The average point density was 21.28 points/m2 (maximum: 26.53 points/m2, minimum: 17.23 points/m2), and the average ground point density was 3.64 points/m2 (maximum: 4.35 points/m2, minimum: 3.07 points/m2). The vertical precision measured during the accuracy field verification was an average of 8.2 cm, a maximum of 15.7 cm, and a minimum of 1.2 cm. The 1:5000 topographic maps are freely available and can be downloaded from the National Geographic Information Institute (NGII), Korea (https://www.ngii.go.kr/; accessed 6 June 2022). We corrected elevation errors in the topographic map based on the 1-arc-second Shuttle Radar Topography Mission provided by the USGS. Consequently, two DEMs were generated—one derived from LiDAR (L-DEM) with a 0.5 m or finer resolution and another derived from topographic maps (T-DEM) with a 10 m resolution—for the SLk analysis. These specific resolutions were selected based on previous studies. We adopted a 0.5 m resolution for the L-DEM, following Ha et al. [52] and Oh and Kim [54], who used this fine resolution effectively in lineament and fault trace analysis in the study area. The 10 m resolution for T-DEM was selected based on the findings of Woo et al. [55], where 1:5000 topographic maps of the Korean Peninsula yielded optimal accuracy.

3.2. Normalized Stream Length–Gradient Index

Drainage basins are less affected by weathering and denudation than other landforms [15]. Indeed, they are preferred for studying tectonic movement because they preserve much of the original landform characteristics [15]. Thus, the SLk analysis focused on drainage basins—areas drained by a stream and its tributaries along a slope. In this instance, the stream transports water and sediment, attempting to maintain equilibrium with various environmental factors to minimize energy loss. Once equilibrium is reached, a concave-graded longitudinal profile with a gently decreasing slope is created [7,56]. However, external factors such as faults or lithological boundaries can generate a knickpoint or knickzone—an area of steep terrain with a convex longitudinal profile [2] characteristic of a fault scarp caused by faulting or tectonic uplift. The stream length-gradient (SL) index, which can quantitatively express the shape of a longitudinal profile, is a valuable tool for evaluating tectonic activity. The SL was defined by [57] as follows (Equation (1)):
S L = d H d L × L
where dH is the change in elevation of the stream segment, dL is the length of the segment, and L is the length upstream from the midpoint of the segment [57]. In landscape evolution, the SL index reflects the lithologic resistance of the underlying bedrock, with lower values typically observed in less resistant units and higher values in more resistant units [2]. Sedimentary rocks, characterized by relatively high erodibility, are commonly associated with lower SL values due to the development of gentler channel gradients [2]. In contrast, metamorphic and igneous rocks exhibit higher SL values, reflecting their greater resistance to erosional processes [2]. On particular lithologies, anomalously high SL values indicate areas of high tectonic activity or lithological contacts [2,57,58,59].
The erosion rate of a stream is greatly influenced by stream power, which also plays an important role in predicting the topographic profile, such as the SL index. Stream power is proportional to discharge and slope. Discharge is strongly correlated with the total channel length up to the segment L [57]. Consequently, this length can be utilized to adjust the gradient of each segment and increase discharge [2]. Because the length of each stream is different, the SL index of each stream can be normalized by directly comparing SL values [27,57,59], as illustrated in Equation (2) [27]:
k = d H t l n L t
where k is the stream gradient, dHt is the elevation between the headwaters and stream mouth, and Lt is the total stream length; k is a suitable variable for normalizing the SL index for comparison between streams [59]. The SLk was calculated using Equation (3) [27].
S L k = S L k
As with the anomalously high SL values, anomalously high SLk values (SLk anomalies) have also been related to stream channels, as evidenced by their strong associations with peaks in erosional dynamics [27,28,59]. Consequently, the SLk anomaly identifies landscapes with unusually high SLk values, and, as a result, active tectonic characteristics and lithological contacts (i.e., knickpoint and knickzone). It is common practice to classify SLk index values systematically when inferring tectonic activity. The SLk index was previously classified using variograms [16,60], standard deviations [27,61], and geometric intervals [62]. We selected the geometric interval method to ensure statistical reliability concerning the effect of DEM resolution on SLk [62], especially given the considerable number of SLk points analyzed for both DEMs. This classification enhances tectonic interpretation by SLk anomalies in this study area. We automatically extracted the drainage network from each DEM and calculated SLk values using ArcGIS Pro with the Spatial Analyst extension and Arc Hydro Tools (https://www.esri.com/en-us/industries/water-resources/arc-hydro/downloads; accessed 17 August 2022).

3.3. Fieldwork

We performed a detailed ground-truthing investigation to validate the presence of tectonic landforms. SLk anomalies may not always indicate tectonic landforms and lithofacies discontinuities in the field since they may be influenced by man-made structures. To overcome the issues related to artificial landforms, it is essential to conduct fieldwork concurrent with data analysis. Ha et al. [14], Ha et al. [51], and Oh and Kim [54] reported high and low activity lineaments and surface ruptures with trenches (Figure 1c and Figure 2). We conducted fieldwork along the SLk anomalies identified in the results.

4. Results

The number and shape of the drainage basins varied depending on the resolution of the DEM. A total of 39 and 33 drainage basins were generated using the L-DEM and T-DEM, respectively. Drainage basins that were not properly formed along the study area boundary and those without surface ruptures were excluded from the analysis. The final analysis included 13 and 12 drainage basins–main streams derived from the L-DEM (total area: 19.88 km2) and T-DEM (total area: 19.07 km2), respectively (Figure 2). For the SLk analysis, 4200 and 1140 points were generated for the L-DEM and T-DEM, respectively. The SLk values ranged from 0.0 to 65.9 in the L-DEM and from 0.0 to 34.7 in the T-DEM (Figure 3). Classes 2 and 3 comprised > 60% of the SLk estimates in the L-DEM, while Class 1 comprised > 60% of the SLk estimates in the T-DEM (Figure 4). Although the SLk value distributions showed similar patterns, the extent of differences varied depending on the DEM resolution.
The distribution of the SLk index was associated with lithology in both DEMs (Figure 5). The SLk index is a good proxy for determining the influence of lithology (especially rock resistance) that considers the varying rates of erosion between rock types [2,63]. The two DEMs clearly illustrate these characteristics in the Miocene sedimentary and Cretaceous volcanic rocks. In the Miocene sedimentary rocks located to the east, relatively low SLk values (Class 1 and Class 2) are observed. Miocene sedimentary rocks are poorly consolidated compared to the older Cretaceous sedimentary formations [64]. The low cementation increases erodibility, resulting in reduced channel steepness and, consequently, lower SLk values. In contrast, the Cretaceous volcanic rocks showed relatively higher SLk values (Class 3), which can be attributed to their greater resistance to erosion in the study area.
A previous study reported several points of high-SLk (Classes 4 and 5) anomalies [2]. SLk anomalies accounted for 9% of the L-DEM and 5% of the T-DEM (Figure 4). The proportion of anomalies is approximately twice as high in the L-DEM as in the T-DEM. SLk anomalies tended to align with high-activity lineaments, including surface rupture and their extensions, in both DEMs (Figure 3). In the L-DEM, the longest high-activity lineament (Red-1, 7.6 km) and the branched NNE-striking high-activity lineament (Red-2, 5.5 km) exhibited strong relationships with SLk anomalies (Figure 3a). The SLk anomalies were also evident at the northern extension of the high-activity (Red-1) and two subsidiary high-activity lineaments (Red-3 and -4) located to the west. The SLk anomalies in the L-DEM aligned with the NNE (Black-1, -2, and -3) or the NW-striking (Black-4) low-activity lineaments. The SLk indexes in the T-DEM showed similar anomalies to those in the L-DEM only in the southern section of the main (Red-1) and northern section of the NNE-striking branched high-activity lineaments (Red-2) (Figure 3b). However, SLk anomalies and low-activity lineaments only showed strong relationships along the NNE-striking (Black-2, central part) and the NW-striking lineaments (Black-4, southeastern part).
The SLk index represents the detailed topographic variations of fault-related landforms (e.g., fault scarps, deflected streams) and effectively captures man-made features such as levees, roads, farm paths, and irrigation [54]. We excluded locations with continuous Class 5 values—specifically LD-07 and TD-07, LD-08 and TD-08, LD-11 and LD-10, and LD-13 and TD-12—which correspond to large man-made reservoirs. The field surveys identified SLk anomalies, including knickpoints associated with tectonic activity (Figure 6). For instance, artificial embankments occur frequently in the low-relief landform of the study area, along with significant human activity (Figure 6c,d). However, bedrock is exposed at the base of these artificial embankments, and differences in the relative elevation of the riverbed were identified using the exposed bedrock surface as a reference. The widespread presence of artificial concrete embankments within the low-relief landforms was likely part of an effort to prevent slope failure caused by natural processes, specifically tectonic landforms (Figure 6c,d). Aerial investigation of the SLk anomaly derived from the L-DEM in low-relief landforms through drone photography revealed a correspondence with the surface ruptures. Notably, in the northern area, SLk anomalies derived from both DEMs occurred along the NNE-striking branched surface rupture (Red-2) within 10 m of a trench reported by [14,52] (Figure 6b). In the central area, the SLk anomaly derived from the L-DEM was also either located directly on a surface rupture or within 2.0 m of fault scarps, in close proximity to trench and outcrop locations documented by [14,52] (Figure 6e).

5. Discussion

The resolution of the DEM influenced the geometry of the drainage basin and stream network delineation, particularly in low-relief and human-modified areas where subtle topographic variations play a key role in hydrological partitioning (Figure 2), consistent with previous findings [65,66]. The shapes and numbers of drainage basins in the southern region were consistent regardless of DEM resolution, while notable differences were observed in the northern and central regions. Specifically, while the drainage basin boundaries in the eastern mountainous area demonstrated minimal variation, the floodplains and alluvial fans in the western area differed depending on DEM resolution (Figure 2). The drainage basins are more linear in the L-DEM than those in the T-DEM. This suggests that, in the eastern and southern high-elevation mountainous areas, the impact of DEM resolution is minimal, while resolution is more important in the low-relief landforms of the western and northern areas (Figure 2 and Figure 7). The floodplains and alluvial fans in the low-relief landform have been altered with urbanization and agricultural development, including road and bridge construction, sewerage treatment, river straightening, and agricultural land expansion. Our findings suggest that the L-DEM effectively reflects these landforms, whereas the T-DEM does not, which was directly associated with the impact of DEM resolution based on landform, human disturbance, and elevation.
SLk values are strongly associated with lithology, regardless of DEM resolution, likely due to the increased exposure of bedrock at higher elevations (Figure 5 and Figure 7). In areas of hard rock substratum, the consistent SLk distribution patterns across DEM resolutions may be the result of varying structural landforms shaped by long-term differential weathering and erosion of minerals with various levels of resistance and the activity of fault zones in the bedrock. However, in the southernmost basins of the study area, where complex lithology and lineaments dominate (LD-13 in Figure 5a and TD-12 in Figure 5b), distinguishing SLk values based on lithological variations became particularly difficult. This may be associated with the longest flow being parallel to low-activity lineaments (Black-5 and Black-6, Figure 3), which likely have a greater influence than lithology. Collectively, our observations indicated that tectonic activity had a greater influence on the spatial distribution of SLk values than lithological factors, even in low-activity regions.
The SLk anomalies, previously documented in association with surface ruptures, outcrops, and trenches [14,51,52], are detected only in the L-DEM in the northern to central regions (Figure 3, Figure 6c,e, and Figure 8a,b,d,e). Anomalies are also detected in the south for both L-DEM and T-DEM (Figure 3, Figure 6b, and Figure 8c,f). In conclusion, the areas of high tectonic activity derived from the T-DEM are confined to the northern area of the branched surface rupture and the southern area. In contrast, the recent tectonic activity is evident in the presence of SLk anomalies derived from the L-DEM, which align with high- and low-activity lineaments, including surface ruptures (Figure 3, Figure 7, and Figure 8). These SLk anomalies suggested relatively high tectonic activity and are distributed continuously from the northern to the southern parts of the study area.
The L-DEM results are consistent with those of previous studies [14,52,53], which documented continuous surface ruptures extending from the northern to southern parts of the study area during the Quaternary. Paleoseismic evidence reported by [14] further supports this interpretation; their trench investigations at five sites identified at least six surface-faulting events, with the most recent event occurring approximately 3000 years ago, as determined from stratigraphic relationships and calibrated radiocarbon ages. These findings highlight the utility of high-resolution DEMs in capturing subtle geomorphic expressions of tectonic deformation, especially in low-activity intraplate regions.
The higher classes of SLk values are better represented by the L-DEM than by the T-DEM (Figure 3, Figure 4, Figure 7, and Figure 8), reflecting the good accuracy of elevation and slope information for high-resolution DEMs, whereas coarser-resolution DEMs tend to underestimate critical steep points [67]. Class 1 is particularly prominent in the floodplain and alluvial fan of the study area when using the T-DEM (Figure 3b, Figure 7, and Figure 8d,e). This reflects the high variation in the elevation change of the stream segment (dH) according to the segment length (dL) at adjacent points in the L-DEM, whereas dH is either 0 or almost absent between adjacent points in the T-DEM. Therefore, the L-DEM is more sensitive to detecting elevation changes within the microtopography, capturing details such as riverbeds, compared to the T-DEM. Future studies should consider the fact that low-resolution DEMs tend to underestimate geomorphic indices and tectonic activity.
Troiani and Della Seta [68] indicated that low-resolution DEMs are highly suitable for assessing tectonic activity across large areas. This is because high-resolution DEMs generate finer stream segmentation, which, in turn, increases the sensitivity of the SLk index to irregularities. However, the study by [68] focused on the Marche Apennines, Italy, an interplate region with significantly higher tectonic activity and a much broader area than the Korean Peninsula. Pedrera et al. [16] demonstrated that, despite very low tectonic activity, active folding was identified in the eastern Baetic Cordillera of Spain using the SLk index with a 10 m DEM resolution. However, the SLk index is only effective in locating active folding in regions with hard rock substratum, whereas areas with soft sediments (Neogene–Quaternary deposits) did not yield comparable results. These cases and this study’s results suggest that, in intraplate settings where surface ruptures are weak and low-relief landscapes are widespread, high-resolution DEMs are better suited for accurately capturing topographic features in geomorphic analysis.

6. Conclusions

This study evaluated the influence of DEM resolution on tectonic activity assessment using the SLk index by comparing high-resolution LiDAR-derived DEM (L-DEM) and low-resolution topographic map-derived DEM (T-DEM) for the Yangsan Fault, Korea. The SLk anomalies in the L-DEM, indicating high tectonic activity, extend continuously from the northern to the southern parts of the study area. This is consistent with the findings of previous studies documenting persistent surface ruptures along the Yangsan Fault throughout the Quaternary across the study area. In contrast, the T-DEM analysis revealed that the SLk anomalies are predominantly concentrated in the northern segmented surface ruptures and limited areas in the south. This suggests that the lower resolution of the T-DEM is unable to capture subtle topographic variations in the southern part of the study area. In regions characterized by low-relief landscapes, such as the floodplains and alluvial fans, the high-resolution DEM more accurately reflects landscape characteristics, emphasizing its importance in geomorphic and tectonic studies.
The necessity of high-resolution DEMs for assessing tectonic activity depends on various factors, including study objectives, spatial extent, and data availability. Our findings demonstrate that high-resolution DEMs enable more detailed analyses, particularly in intraplate regions where surface ruptures and landform variations are subtle. This is especially critical in intraplate settings such as the Korean Peninsula, where the slip rate is less than 1 mm/yr, as the low deformation rates and subtle geomorphic expressions require high-resolution topographic data for precise tectonic analysis. In such a context, the use of low-resolution DEMs may lead to underestimation of tectonic activity, potentially affecting geomorphic interpretations in intraplate regions. Therefore, careful consideration of DEM resolution is essential when using geomorphic indices for tectonic studies, as it directly influences the accuracy and reliability of tectonic activity assessments. These findings contribute to a better understanding of the implications of DEM resolution in tectonic geomorphology and provide a basis for selecting appropriate datasets in future studies.

Author Contributions

Conceptualization, H.L., S.H., S.K., H.-C.K. and M.S.; methodology, H.L. and S.K.; software, H.L; investigation, H.L., S.H. and H.-C.K.; data curation, H.L.; writing—original draft preparation, H.L.; writing—review and editing, S.H. and M.S.; visualization, H.L.; supervision, M.S.; project administration, M.S.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by a grant (2022-MOIS62-001(RS-2022-ND640011)) from the National Disaster Risk Analysis and Management Technology in Earthquake, funded by the Ministry of the Interior and Safety (MOIS, Republic of Korea).

Data Availability Statement

The original contributions presented in this study are included in the article. The DEM and shapefiles used for topographic analysis are available from the corresponding author upon reasonable request. Interested researchers may contact the corresponding author to discuss access and usage terms.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Bull, W.B.; McFadden, L.D. Tectonic Geomorphology North and South of the Garlock Fault, California. In Geomorphology in Arid Regions, Proceedings of the 8th Annual Geomorphology Symposium, Binghamton, NY, USA, 23–24 September 1977; Doehring, D.O., Ed.; State Univ. New York: Binghamton, NY, USA, 1977; pp. 115–138. [Google Scholar]
  2. Keller, E.A.; Pinter, N. Active Tectonics: Earthquakes, Uplift, and Landscape; Prentice Hall: New Jersey, NY, USA, 2002. [Google Scholar]
  3. Hare, P.W.; Gardner, T.W. Geomorphic Indicators of Vertical Neotectonism Along Converging Plate Margins, Nicoya Peninsula, Costa Rica. In Tectonic Geomorphology; Morisawa, M., Hack, J.T., Eds.; Allen & Unwin: Boston, MA, USA, 1985; pp. 75–104. [Google Scholar]
  4. Strahler, A.N. Hypsometric (Area-Altitude) Analysis of Erosional Topography. Geol. Soc. Am. Bull. 1952, 63, 1117–1142. [Google Scholar] [CrossRef]
  5. Pike, R.J.; Wilson, S.E. Elevation-Relief Ratio, Hypsometric Integral, and Geomorphic Area-Altitude Analysis. Geol. Soc. Am. Bull. 1971, 82, 1079–1084. [Google Scholar] [CrossRef]
  6. Mayer, L. Introduction to Quantitative Geomorphology: An Exercise Manual; Prentice Hall: Englewood Cliffs, NJ, USA, 1990. [Google Scholar]
  7. Bull, W.B. Tectonically Active Landscapes; John Wiley & Sons: Hoboken, NJ, USA, 2011. [Google Scholar]
  8. Keller, E.A. Investigation of Active Tectonics: Use of Surficial Earth Processes. In Active Tectonics; Wallace, R.E., Ed.; Natl. Acad. Press: Washington, DC, USA, 1986; pp. 136–147. [Google Scholar]
  9. Hack, J.T. Stream-Profile Analysis and Stream-Gradient Index. J. Res. U.S. Geol. Surv. 1973, 1, 421–429. [Google Scholar]
  10. Liu, M.; Stein, S. Mid-continental earthquakes: Spatiotemporal occurrences, causes, and hazards. Earth-Sci. Rev. 2016, 162, 364–386. [Google Scholar] [CrossRef]
  11. Williams, R.T.; Goodwin, L.B.; Sharp, W.D.; Mozley, P.S. Reading a 400,000-year record of earthquake frequency for an intraplate fault. Proc. Natl. Acad. Sci. USA 2017, 114, 4893–4898. [Google Scholar] [CrossRef]
  12. Kim, T.; Choi, J.H.; Cheon, Y.; Lee, T.-H.; Kim, N.; Lee, H.; Kim, C.-M.; Choi, Y.; Bae, H.; Kim, Y.S.; et al. Correlation of paleoearthquake records at multiple sites along the southern Yangsan Fault, Korea: Insights into rupture scenarios of intraplate strike-slip earthquakes. Tectonophisics 2023, 854, 229817. [Google Scholar] [CrossRef]
  13. Kim, T.; Lee, H.; Kim, D.E.; Choi, J.-H.; Choi, Y.; Han, M.; Kim, Y.S. Determination of the long-term slip rate of a fault in a slowly deforming region based on a reconstruction of the landform and provenance. Geomorphology 2024, 461, 109286. [Google Scholar] [CrossRef]
  14. Ha, S.; Kang, H.C.; Lee, S.; Seong, Y.B.; Choi, J.H.; Kim, S.J.; Son, M. Quaternary surface ruptures of the inherited mature Yangsan fault: Implications for intraplate earthquakes in Southeastern Korea. Solid Earth 2025, 16, 197–231. [Google Scholar] [CrossRef]
  15. Park, S.J. Crustal movement on the Korean Peninsula (I): Determination of the spatial distribution of crustal movement through DEM analysis. J. Korean Geogr. Soc. 2007, 42, 368–387, (In Korean with English Abstract). [Google Scholar]
  16. Pedrera, A.; Pérez-Peña, J.V.; Galindo-Zaldívar, J.; Azañón, J.M.; Azor, A. Testing the sensitivity of geomorphic indices in areas of low-rate active folding (eastern Betic Cordillera, Spain). Geomorphology 2009, 105, 218–231. [Google Scholar] [CrossRef]
  17. Kim, D.E.; Kim, C.M.; Cheon, Y.; Choi, J.-H.; Lee, T.-H.; Lee, H.; Choi, Y.; Bae, H.; Kim, T.; Ryoo, C.-R. A case study to find a tectonic landform using geomorphic indices on Keumwang Fault, Korea. In Proceedings of the 2022 Joint Fall Meeting of Geological Sciences and the 77th General Assembly of the Geological Society of Korea, CECO, Changwon, Republic of Korea, 26 October 2022; p. 180. [Google Scholar]
  18. NGII (National Geographic Information Institute). National Atlas of Korea I; NGII: Suwon, Republic of Korea, 2019. (In Korean)
  19. Guthrie, R. The catastrophic nature of humans. Nat. Geosci. 2015, 8, 421–422. [Google Scholar] [CrossRef]
  20. Jeong, A. The Impact of Humans on Desert Landforms in North America: A Case Study of Phoenix, Arizona. J. Korean Geomorphol. Assoc. 2019, 26, 69–85, (In Korean with English Abstract). [Google Scholar] [CrossRef]
  21. Kim, S.; Lim, H.; Ha, S.; Kim, K.; Son, M. Assessment of Tectonic Activity of Major Faults in Southeastern Korea Using Geomorphic Indices. J. Geol. Soc. Korea 2023, 59, 247–265, (In Korean with English Abstract). [Google Scholar] [CrossRef]
  22. Lee, C.H.; Seong, Y.B.; Weber, J.; Ha, S.; Kim, D.E.; Yu, B.Y. Topographic metrics for unveiling fault segmentation and tectono-geomorphic evolution with insights into the impact of inherited topography, Ulsan Fault Zone, South Korea. Earth Surf. Dyn. 2024, 12, 1091–1120. [Google Scholar] [CrossRef]
  23. Azor, A.; Keller, E.A.; Yeats, R.S. Geomorphic Indicators of Active Fold Growth: South Mountain–Oak Ridge Anticline, Ventura Basin, Southern California. Geol. Soc. Am. Bull. 2002, 114, 745–753. [Google Scholar] [CrossRef]
  24. Silva, P.G.; Goy, J.L.; Zazo, C.; Bardají, T. Fault-Generated Mountain Fronts in Southeast Spain: Geomorphologic Assessment of Tectonic and Seismic Activity. Geomorphology 2003, 50, 203–225. [Google Scholar] [CrossRef]
  25. El Hamdouni, R.; Irigaray, C.; Fernández, T.; Chacón, J.; Keller, E.A. Assessment of Relative Active Tectonics, Southwest Border of the Sierra Nevada (Southern Spain). Geomorphology 2008, 96, 150–173. [Google Scholar] [CrossRef]
  26. Khalifa, A.; Çakir, Z.; Owen, L.A.; Kaya, Ş. Evaluation of the Relative Tectonic Activity of the Adıyaman Fault within the Arabian-Anatolian Plate Boundary (Eastern Turkey). Geol. Acta 2019, 17, 1–17. [Google Scholar]
  27. Viveen, W.; Baby, P.; Hurtado-Enríquez, C. Assessing the Accuracy of Combined DEM-Based Lineament Mapping and the Normalised SL-Index as a Tool for Active Fault Mapping. Tectonophysics 2021, 813, 228942. [Google Scholar] [CrossRef]
  28. Negi, P.; Goswami, A.; Joshi, G.C. Geomorphic Indices Based Topographic Characterization of Alaknanda Catchment, Western Himalaya Using Spatial Data. Environ. Earth Sci. 2023, 82, 468. [Google Scholar] [CrossRef]
  29. Sharma, M.; Saraf, A.K. Effect of SRTM resolution on morphometric feature identification using neural network—Self organizing map. Geoinformatica 2010, 14, 241–258. [Google Scholar] [CrossRef]
  30. Woo, J.-U.; Rhie, J.; Kim, S.; Kang, T.-S.; Kim, K.-H.; Kim, Y. The 2016 Gyeongju Earthquake Sequence Revisited: Aftershock Interactions within a Complex Fault System. Geophys. J. Int. 2019, 217, 58–74. [Google Scholar] [CrossRef]
  31. Kim, K.-H.; Ree, J.-H.; Kim, Y.; Kim, S.; Kang, S.Y.; Seo, W. Assessing Whether the 2017 Mw 5.4 Pohang Earthquake in South Korea Was an Induced Event. Science 2018, 360, 1007–1009. [Google Scholar] [CrossRef] [PubMed]
  32. Korea Meteorological Administration (KMA). Earthquake Report of Buan Earthquake (ML 4.8, Jun 12, 2024); Korea Meteorological Administration: Seoul, Republic of Korea, 2024. (In Korean)
  33. Lee, K.; Lee, J.; Kyung, J.B. A Statistical Analysis of the Seismicity of the Yangsan Fault System. J. Eng. Geol. 1998, 8, 99–114. [Google Scholar]
  34. Lee, K.; Yang, W.S. Historical Seismicity of Korea. Bull. Seismol. Soc. Am. 2006, 96, 846–855. [Google Scholar] [CrossRef]
  35. Han, M.; Kim, K.H.; Son, M.; Kang, S.Y.; Park, J.H. Location of Recent Micro-Earthquakes in the Gyeongju Area. Geophys. Geophys. Explor. 2016, 19, 97–104, (In Korean with English Abstract). [Google Scholar] [CrossRef]
  36. Kim, K.H.; Kang, T.S.; Rhie, J.; Kim, Y.; Park, Y.; Kang, S.Y.; Kim, J. The 12 September 2016 Gyeongju Earthquakes: 2. Temporary Seismic Network for Monitoring Aftershocks. Geosci. J. 2016, 20, 753–757. [Google Scholar] [CrossRef]
  37. Kim, K.H.; Seo, W.; Han, J.; Kwon, J.; Kang, S.Y.; Ree, J.H.; Liu, K. The 2017 ML 5.4 Pohang Earthquake Sequence, Korea, Recorded by a Dense Seismic Network. Tectonophysics 2020, 774, 228306. [Google Scholar] [CrossRef]
  38. Lee, J.; Ryoo, Y.; Park, S.C.; Ham, Y.M.; Park, J.S.; Kim, M.S.; Bae, S. Seismicity of the 2016 ML 5.8 Gyeongju Earthquake and Aftershocks in South Korea. Geosci. J. 2018, 22, 433–444. [Google Scholar] [CrossRef]
  39. Reedman, A.J.; Um, S.H. The Geology of Korea; Geol. Min. Inst. Korea: Ga Jeong, Republic of Korea, 1975; 139p. (In Korean) [Google Scholar]
  40. Chang, K.H.; Woo, B.G.; Lee, J.H.; Park, S.O.; Yao, A. Cretaceous and Early Cenozoic Stratigraphy and History of Eastern Kyongsang Basin, S. Korea. J. Geol. Soc. Korea 1990, 26, 471–487, (In Korean with English Abstract). [Google Scholar]
  41. Hwang, B.H.; Lee, J.D.; Yang, K. Petrological Study of the Granitic Rocks Around the Yangsan Fault: Lateral Displacement of the Yangsan Fault. J. Geol. Soc. Korea 2004, 40, 161–178, (In Korean with English Abstract). [Google Scholar]
  42. Chang, C.J.; Chang, T.W. Structural Movement History of the Yangsan Fault Through High-Stress Analysis. J. Eng. Geol. 1998, 8, 35–49, (In Korean with English Abstract). [Google Scholar]
  43. Hwang, B.H.; Lee, J.D.; Yang, K.; McWilliams, M. Cenozoic Strike-Slip Displacement Along the Yangsan Fault, Southeast Korean Peninsula. Int. Geol. Rev. 2007, 49, 768–775. [Google Scholar] [CrossRef]
  44. Hwang, B.H.; McWilliams, M.; Son, M.; Yang, K. Tectonic Implication of A-Type Granites Across the Yangsan Fault, Gigye and Gyeongju Areas, Southeast Korean Peninsula. Int. Geol. Rev. 2007, 49, 1094–1102. [Google Scholar] [CrossRef]
  45. Cheon, Y.; Ha, S.; Lee, S.; Cho, H.; Son, M. Deformation Features and History of the Yangsan Fault Zone in the Eonyang-Gyeongju Area, SE Korea. J. Geol. Soc. Korea 2017, 53, 95–114, (In Korean with English Abstract). [Google Scholar] [CrossRef]
  46. Cheon, Y.; Cho, H.; Ha, S.; Kang, H.-C.; Kim, J.-S.; Son, M. Tectonically Controlled Multiple Stages of Deformation Along the Yangsan Fault Zone, SE Korea, Since Late Cretaceous. J. Asian Sci. 2019, 170, 188–207. [Google Scholar] [CrossRef]
  47. Kim, H.J.; Moon, S.; Jou, H.T.; Lee, G.H.; Yoo, D.G.; Lee, S.H.; Kim, K.H. The Offshore Yangsan Fault Activity in the Quaternary, SE Korea: Analysis of High-Resolution Seismic Profiles. Tectonophysics 2016, 693, 85–95. [Google Scholar] [CrossRef]
  48. Cheon, Y.; Choi, J.-H.; Choi, Y.; Bae, H.; Han, K.-H.; Son, M.; Choi, S.-J.; Ryoo, C.-R. Understanding the Distribution and Internal Structure of the Main Core of the Yangsan Fault Zone: Current Trends and Future Work. J. Geol. Soc. Korea 2020, 56, 619–640, (In Korean with English Abstract). [Google Scholar] [CrossRef]
  49. Ryoo, C.-R.; Lee, B.-J.; Cho, D.-L.; Chwae, U.-C.; Choi, S.-J.; Kim, J.-Y. Quaternary Fault of Dangu-ri in Gyeongju Gangdong-myeon: Byeokgye Fault. In Proceedings of the Korean Society of Economic and Environmental Geology/The Korean Society of Mineral and Energy Resources Engineers/Korean Society of Earth and Exploration Geophysicists, Spring Joint Conference, Chungnam National Univ., Daejeon, Republic of Korea, 14 April 1999; p. 334. [Google Scholar]
  50. Lee, J.; Rezaei, S.; Hong, Y.; Choi, J.-H.; Choi, W.-H.; Rhee, K.-W.; Kim, Y.-S. Quaternary Fault Analysis Through a Trench Investigation on the Northern Extension of the Yangsan Fault at Dangu-ri, Gyungju-si, Gyeongsangbuk-do. J. Geol. Soc. Korea 2015, 51, 471–485, (In Korean with English Abstract). [Google Scholar] [CrossRef]
  51. Song, Y.; Ha, S.; Lee, S.; Kang, H.-C.; Choi, J.-H.; Son, M. Quaternary Structural Characteristics and Paleoseismic Interpretation of the Yangsan Fault at Dangu-ri, Gyeongju-si, SE Korea, Through Trench Survey. J. Geol. Soc. Korea 2020, 56, 155–173, (In Korean with English Abstract). [Google Scholar] [CrossRef]
  52. Ha, S.; Son, M.; Seong, Y.B. Active Fault Trace Identification Using a LiDAR High-Resolution DEM: A Case Study of the Central Yangsan Fault, Korea. Remote Sens. 2022, 14, 4838. [Google Scholar] [CrossRef]
  53. Naik, S.P.; Rockwell, T.K.; Jeong, S.H.; Kim, Y.S.; Shin, H.C.; Choi, J.H.; Son, M. Evidence for Large Holocene Earthquakes Along the Yangsan Fault in the SE Korean Peninsula Revealed in Three-Dimensional Paleoseismic Trenches. Geol. Soc. Am. Bull. 2024, 137, 427–446. [Google Scholar] [CrossRef]
  54. Oh, J.-S.; Kim, D.E. Lineament Extraction and Its Comparison Using DEMs based on LiDAR, Digital Topographic Map, and Aerial Photo in the Central Segment of Yangsan Fault. J. Korean Geomorphol. Soc. 2019, 54, 507–525, (In Korean with English Abstract). [Google Scholar]
  55. Woo, J.Y.; Koo, J.H.; Hong, C.H.; Kim, T.H. A Study on Interpolation Methods and Size of Grid to the Topographical Characteristics for the Construction of DEM (Digital Elevation Model). J. Korea Spat. Inf. Syst. Soc. 2001, 3, 5–19, (In Korean with English Abstract). [Google Scholar]
  56. Bull, W.B. Tectonic Geomorphology of Mountains: A New Approach to Paleoseismology; John Wiley & Sons: Hoboken, NJ, USA, 2008. [Google Scholar]
  57. Hack, J.T. Studies of Longitudinal Stream Profiles in Virginia and Maryland; U.S. Government Printing Office: Washington, DC, USA, 1957; Volume 294. [Google Scholar]
  58. Viveen, W.; Van Balen, R.T.; Schoorl, J.M.; Veldkamp, A.; Temme, A.J.A.M.; Vidal-Romani, J.R. Assessment of Recent Tectonic Activity on the NW Iberian Atlantic Margin by Means of Geomorphic Indices and Field Studies of the Lower Miño River Terraces. Tectonophysics 2012, 544, 13–30. [Google Scholar] [CrossRef]
  59. Pérez-Peña, J.V.; Azañón, J.M.; Azor, A.; Delgado, J.; González-Lodeiro, F. Spatial Analysis of Stream Power Using GIS: SLk Anomaly Maps. Earth Surf. Process. Landforms 2009, 34, 16–25. [Google Scholar] [CrossRef]
  60. Wu, L.; Xiao, A.; Yang, S. Impact of Wind Erosion on Detecting Active Tectonics from Geomorphic Indexes in Extremely Arid Areas: A Case Study from the Hero Range, Qaidam Basin, NW China. Geomorphology 2014, 224, 39–54. [Google Scholar] [CrossRef]
  61. Font, M.; Amorese, D.; Lagarde, J.L. DEM and GIS Analysis of the Stream Gradient Index to Evaluate Effects of Tectonics: The Normandy Intraplate Area (NW France). Geomorphology 2010, 119, 172–180. [Google Scholar] [CrossRef]
  62. Ferrater, M.; Booth-Rea, G.; Pérez-Peña, J.V.; Azañón, J.M.; Giaconia, F.; Masana, E. From Extension to Transpression: Quaternary Reorganization of an Extensional-Related Drainage Network by the Alhama de Murcia Strike-Slip Fault (Eastern Betics). Tectonophysics 2015, 663, 33–47. [Google Scholar] [CrossRef]
  63. Burbank, D.W.; Anderson, R.S. Geomorphic Markers. In Tectonic Geomorphology; Blackwell Publishing: Malden, MA, USA, 2001; pp. 13–32. [Google Scholar]
  64. Kim, S.-W.; Choi, E.-K.; Lee, Y.-H. Rock Mass Classification of Tertiary Unconsolidated Sedimentary Rocks In Pohang Area. In Proceedings of the Korean Geotechnical Society Spring National Conference, Incheon Univ., Gyeonggi, Republic of Korea, 27 March 2009; p. 999, (In Korean with English Abstract). [Google Scholar]
  65. Vaze, J.; Teng, J.; Spencer, G. Impact of DEM Accuracy and Resolution on Topographic Indices. Environ. Model. Softw. 2010, 25, 1086–1098. [Google Scholar] [CrossRef]
  66. Dávila-Hernández, S.; González-Trinidad, J.; Júnez-Ferreira, H.E.; Bautista-Capetillo, C.F.; Morales de Ávila, H.; Cázares Escareño, J.; López-Baltazar, E.A. Effects of the Digital Elevation Model and Hydrological Processing Algorithms on the Geomorphological Parameterization. Water 2022, 14, 2363. [Google Scholar] [CrossRef]
  67. Chang, K.T.; Tsai, B.W. The Effect of DEM Resolution on Slope and Aspect Mapping. Cartogr. Geogr. Inf. Syst. 1991, 18, 69–77. [Google Scholar] [CrossRef]
  68. Troiani, F.; Della Seta, M. The Use of the Stream Length–Gradient Index in Morphotectonic Analysis of Small Catchments: A Case Study from Central Italy. Geomorphology 2008, 102, 159–168. [Google Scholar] [CrossRef]
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Figure 1. Maps of region and study area. (a) Location of Yangsan Fault in the Korean Peninsula. (b) Digital elevation map showing Yangsan Fault System and Quaternary faults of SE Korea [13,14,52]. Blue stars mark recent medium-scale earthquakes in SE Korea [30,31]. (c) Regional geological map of SE Korea (modified from [13,14,40,41,42,43,44,52]). (d) Detailed geological map of the Byeokgye section. The study area has surface rupture recognized as fault scarps, deflected streams, and nine Quaternary fault sites (the lineaments are modified from [43,44,51,52]).
Figure 1. Maps of region and study area. (a) Location of Yangsan Fault in the Korean Peninsula. (b) Digital elevation map showing Yangsan Fault System and Quaternary faults of SE Korea [13,14,52]. Blue stars mark recent medium-scale earthquakes in SE Korea [30,31]. (c) Regional geological map of SE Korea (modified from [13,14,40,41,42,43,44,52]). (d) Detailed geological map of the Byeokgye section. The study area has surface rupture recognized as fault scarps, deflected streams, and nine Quaternary fault sites (the lineaments are modified from [43,44,51,52]).
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Figure 2. Drainage basins for (a) DEM derived from LiDAR (L-DEM) and (b) topographic maps (T-DEM), and the drainage basin number. Thirteen drainage basins were analyzed in the L-DEM and twelve in the T-DEM.
Figure 2. Drainage basins for (a) DEM derived from LiDAR (L-DEM) and (b) topographic maps (T-DEM), and the drainage basin number. Thirteen drainage basins were analyzed in the L-DEM and twelve in the T-DEM.
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Figure 3. Distribution of normalized stream length–gradient (SLk) index derived from (a) L-DEM and (b) T-DEM mapped onto lineaments. Numbers in circles indicate lineaments, and colors determine lineament activity: red circles indicate high activity (Red-X) and black circles indicate low activity (Black-X).
Figure 3. Distribution of normalized stream length–gradient (SLk) index derived from (a) L-DEM and (b) T-DEM mapped onto lineaments. Numbers in circles indicate lineaments, and colors determine lineament activity: red circles indicate high activity (Red-X) and black circles indicate low activity (Black-X).
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Figure 4. Comparison of SLk index percentage derived from L-DEM and T-DEM. The proportion of Class 4 and 5 is higher in L-DEM than in T-DEM.
Figure 4. Comparison of SLk index percentage derived from L-DEM and T-DEM. The proportion of Class 4 and 5 is higher in L-DEM than in T-DEM.
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Figure 5. SLk index distribution derived from (a) L-DEM and (b) T-DEM mapped onto regional lithological features.
Figure 5. SLk index distribution derived from (a) L-DEM and (b) T-DEM mapped onto regional lithological features.
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Figure 6. Fault-induced knickpoint outcrops detected by SLk analysis. (a) LiDAR hillshade map with location of outcrops (modified from [14,52]). (b) Drone image of 2.1 m high knickpoint with surface rupture and trench. (c) Photograph of 2.3 m knickpoint observed with artificial embankment for preventing collapse. (d) Image of knickpoint with a maximum height of 2.0 m. (e) Drone photo of area corresponding to the 3.0 m knickpoint and 2.0 m fault scarp with fault rupture and trench.
Figure 6. Fault-induced knickpoint outcrops detected by SLk analysis. (a) LiDAR hillshade map with location of outcrops (modified from [14,52]). (b) Drone image of 2.1 m high knickpoint with surface rupture and trench. (c) Photograph of 2.3 m knickpoint observed with artificial embankment for preventing collapse. (d) Image of knickpoint with a maximum height of 2.0 m. (e) Drone photo of area corresponding to the 3.0 m knickpoint and 2.0 m fault scarp with fault rupture and trench.
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Figure 7. River profiles (blue line) and their associated SLk values (black line). The lineaments that the rivers cross are given (see Figure 3 and Figure 5 for descriptions of the lineaments), and lithologies that the rivers cross are given (see Figure 5 for descriptions of the lithologies). Dashed colored lines mark boundaries between SLk class divisions.
Figure 7. River profiles (blue line) and their associated SLk values (black line). The lineaments that the rivers cross are given (see Figure 3 and Figure 5 for descriptions of the lineaments), and lithologies that the rivers cross are given (see Figure 5 for descriptions of the lithologies). Dashed colored lines mark boundaries between SLk class divisions.
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Figure 8. SLk anomalies in the (a,d) northern, (b,e) central, and (c,f) southern parts of the study area, analyzed from the L-DEM and T-DEM. The blue dashed circle indicates the SLk anomaly that matches the lineaments.
Figure 8. SLk anomalies in the (a,d) northern, (b,e) central, and (c,f) southern parts of the study area, analyzed from the L-DEM and T-DEM. The blue dashed circle indicates the SLk anomaly that matches the lineaments.
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Table 1. Range of flight and system parameters.
Table 1. Range of flight and system parameters.
Observation date8 December 2017 (20 strips)
12 December 2017 (2 strips)
14 December 2017 (26 strips)
22 December 2017 (14 strips)
27 December 2017 (22 strips)
1 January 2018 (27 strips)
2 January 2018 (19 strips)
# stripsr (ea)124
Flying speed (km/h)325
Flying altitude (m)830–1193
Scan frequency (kHz)200
Swath width (m)1300
Laser return points (ea)1,856,096,462
Ground point remained (ea)317,989,564
Point density (points/m2)17.23–26.53 (avg. 21.28)
Ground point density (points/m2)3.07–4.36 (avg. 3.64)
Vertical precision (cm)1.2–15.7 (avg. 8.2)
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Lim, H.; Ha, S.; Kim, S.; Kang, H.-C.; Son, M. Influence of Digital Elevation Model Resolution on the Normalized Stream Length–Gradient Index in Intraplate Regions: A Case Study of the Yangsan Fault, Korea. Remote Sens. 2025, 17, 1638. https://doi.org/10.3390/rs17091638

AMA Style

Lim H, Ha S, Kim S, Kang H-C, Son M. Influence of Digital Elevation Model Resolution on the Normalized Stream Length–Gradient Index in Intraplate Regions: A Case Study of the Yangsan Fault, Korea. Remote Sensing. 2025; 17(9):1638. https://doi.org/10.3390/rs17091638

Chicago/Turabian Style

Lim, Hyunjee, Sangmin Ha, Sohee Kim, Hee-Cheol Kang, and Moon Son. 2025. "Influence of Digital Elevation Model Resolution on the Normalized Stream Length–Gradient Index in Intraplate Regions: A Case Study of the Yangsan Fault, Korea" Remote Sensing 17, no. 9: 1638. https://doi.org/10.3390/rs17091638

APA Style

Lim, H., Ha, S., Kim, S., Kang, H.-C., & Son, M. (2025). Influence of Digital Elevation Model Resolution on the Normalized Stream Length–Gradient Index in Intraplate Regions: A Case Study of the Yangsan Fault, Korea. Remote Sensing, 17(9), 1638. https://doi.org/10.3390/rs17091638

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