Slope Units Research Articles

Influence of soil mechanical and hydraulic parameters on the definition of rainfall intensity and duration thresholds based on Transient rainfall infiltration and grid-based regional slope-stability model (TRIGRS)

As a supplement to empirical-statistical methods, physically based methods can be employed to define rainfall thresholds for triggering landslides in areas lacking records of landslides. The transient rainfall infiltration and grid-based regional slope-stability model (TRIGRS), as a physically based model, has been applied to define rainfall thresholds at the basin, slope unit, and grid cell scales. However, as far as we know to date, the influence of soil mechanical and hydraulic parameters on defining rainfall thresholds at various scales has not been comprehensively evaluated. In this study, TRIGRS was used to define rainfall intensity (I) and duration (D) thresholds at various scales for Buzhe village, Pu’an county, Guizhou province, China, under the conditions of different soil physical parameters. The results show that the number of rainfall thresholds decreased with cohesion (c) and internal friction angle (φ) and increased with soil unit weight (γs), excluding the basin scale. Threshold position varied positively with c and φ and negatively with γs. Soil mechanical parameters have a greater influence on the definition of rainfall thresholds based on TRIGRS than hydraulic parameters.

Landslide Susceptibility Prediction Considering Neighborhood Characteristics of Landslide Spatial Datasets and Hydrological Slope Units Using Remote Sensing and GIS Technologies

Landslides are affected not only by their own environmental factors, but also by the neighborhood environmental factors and the landslide clustering effect, which are represented as the neighborhood characteristics of modelling spatial datasets in landslide susceptibility prediction (LSP). This study aims to innovatively explore the neighborhood characteristics of landslide spatial datasets for reducing the LSP uncertainty. Neighborhood environmental factors were acquired and managed by remote sensing (RS) and the geographic information system (GIS), then used to represent the influence of landslide neighborhood environmental factors. The landslide aggregation index (LAI) was proposed to represent the landslide clustering effect in GIS. Taking Chongyi County, China, as example, and using the hydrological slope unit as the mapping unit, 12 environmental factors including elevation, slope, aspect, profile curvature, plan curvature, topographic relief, lithology, gully density, annual average rainfall, NDVI, NDBI, and road density were selected. Next, the support vector machine (SVM) and random forest (RF) were selected to perform LSP considering the neighborhood characteristics of landslide spatial datasets based on hydrologic slope units. Meanwhile, a grid-based model was also established for comparison. Finally, the LSP uncertainties were analyzed from the prediction accuracy and the distribution patterns of landslide susceptibility indexes (LSIs). Results showed that the improved frequency ratio method using LAI and neighborhood environmental factors can effectively ensure the LSP accuracy, and it was significantly higher than the LSP results without considering the neighborhood conditions. Furthermore, the Wilcoxon rank test in nonparametric test indicates that the neighborhood characteristics of spatial datasets had a great positive influence on the LSP performance.

High-resolution Bayesian mapping of landslide hazard with unobserved trigger event

Statistical models for landslide hazard enable mapping of risk factors and landslide occurrence intensity by using geomorphological covariates available at high spatial resolution. However, the spatial distribution of the triggering event (e.g., precipitation or earthquakes) is often not directly observed. In this paper we develop Bayesian spatial hierarchical models for point patterns of landslide occurrences using different types of log-Gaussian Cox processes. Starting from a competitive baseline model that captures the unobserved precipitation trigger through a spatial random effect at slope unit resolution, we explore novel complex model structures that take clusters of events arising at small spatial scales into account as well as nonlinear or spatially-varying covariate effects. For a 2009 event of around 5000 precipitation-triggered landslides in Sicily, Italy, we show how to fit our proposed models efficiently, using the integrated nested Laplace approximation (INLA), and rigorously compare the performance of our models both from a statistical and applied perspective. In this context we argue that model comparison should not be based on a single criterion and that different models of various complexity may provide insights into complementary aspects of the same applied problem. In our application our models are found to have mostly the same spatial predictive performance, implying that key to successful prediction is the inclusion of a slope-unit resolved random effect capturing the precipitation trigger. Interestingly, a parsimonious formulation of space-varying slope effects reflects a physical interpretation of the precipitation trigger: in subareas with weak trigger, the slope steepness is shown to be mostly irrelevant.

Slope-Unit Scale Landslide Susceptibility Mapping Based on the Random Forest Model in Deep Valley Areas

Landslide susceptibility evaluation is critical for landslide prevention and risk management. Based on the slope unit, this study uses the information value method- random forest (IV-RF) model to evaluate the landslide susceptibility in the deep valley area. First, based on the historical landslide data, a landslide inventory was developed by using remote sensing technology (InSAR and optical remote sensing) and field investigation methods. Twelve factors were then selected as the input data for a landslide susceptibility model. Second, slope units with different scales were obtained by the r.slopeunits method and the information value method- random forest (IV-RF) model is used to evaluate the landslide susceptibility. Finally, the spatial distribution characteristics of landslide susceptibility grade under the optimal scale are analyzed. The results showed that under the slope unit obtained when c = 0.1 and a = 3 × 105 m2, the internal homogeneity/external heterogeneity of 8425 slope units extracted by the r.slopeunits method is the best, with an AUC of 0.905 and an F1 of 0.908. In this case, the accuracy of landslide susceptibility evaluation is the highest as well; it is shown that the finer slope units would not always lead to the higher accuracy of landslide susceptibility evaluation results; it is necessary to comprehensively consider the internal homogeneity and external heterogeneity of the slope units. Under the optimal slope unit scale, the number of landslides in the highly and extremely highly susceptible areas in the landslide susceptibility map accounted for 82.60% of the total number of landslides, which was consistent with the actual distribution of landslides; this study shows that the method, combining the slope unit and the information value method- random forest (IV-RF) model, for landslide susceptibility evaluation can obtain high accuracy.

A Quick QGIS-Based Procedure to Preliminarily Define Time-Independent Rockfall Risk: The Case Study of Sorba Valley, Italy

Rockfalls are widespread, rapid, and high-energy landslide phenomena that could potentially affect large portions of populated lands. The preliminary identification of the most rockfall-prone zones is a challenging task, especially in times of extreme and unpredictable climate change. Even slight environmental modifications can produce significant consequences in terms of exposure, hazard, and risk. Therefore, a timely risk assessment is paramount for territorial administrators to plan and prioritize adequate countermeasures. Risk assessment is crucial to guaranteeing the safety of human lives, the integrity of structures and infrastructures, the preservation of historic and environmental heritage, and the safeguard of economic activities. Hence, new and rapid evaluation methods for rockfall hazard, vulnerability, and risk are needed to identify the most critical areas where more indepth analyses aimed at the design of protective works should be carried out. This study proposes a quick, innovative, and completely GIS-based procedure to preliminarily assess rockfall time-independent hazard and risk in large areas. Propagation analysis is performed by integrating powerful QGIS plugin QPROTO, which can estimate rockfall energy within the invasion area in a simplified way, with the slope units polygons of the Italian territory for the definition of the input parameters. The quantification of risk was obtained by the application of the multidisciplinary IMIRILAND methodology, again within a free and open QGIS environment. Lastly, to test the capabilities of the method, the procedure was applied to a case study of the Sorba Valley (Piemonte, Italy), a tourist region in the northwestern Italian Alps. The findings offer an important contribution to the field of land-planning activities and risk-management strategies.

Assessment of landslide susceptibility along mountain highways based on different machine learning algorithms and mapping units by hybrid factors screening and sample optimization

To develop a better spatial prediction model of landslide susceptibility along mountain highways, this study compared assessment models of landslide susceptibility along mountain highways based on different machine-learning-algorithms (random forest (RF), support vector machine (SVM)), and mapping units (grid and slope units) using factor screening and sample optimization. Using a typical mountainous county, Chengkou, as a case study, a landslide inventory was prepared based on field investigations, satellite images, and historical records. In total, 334 landslides were identified. Twenty landslide conditioning factors influenced by topography, geology, environmental conditions, and human activities were initially identified. Less important conditioning factors were identified and eliminated using a geographical detector. Two common non-linear machine-learning models (RF and SVM) were used under the grid and slope units, and on all cells (including landslides and non-landslides); 10-fold cross-validation was utilized to select the training and validation datasets for these models. Subsequently, the trained models were utilized for landslide susceptibility mapping in the entire study area, divided into five classes. Subsequently, the performance of the models under different units was evaluated using a confusion matrix and receiver operating characteristic curve. Finally, partial dependency plots (PDP) and local interpretable model-agnostic explanations (LIME) were used to study the interpretability of landslide susceptibility model. The research results show that after removing the noise-generating factors, the model trained by the remaining 11 landslide-conditioning factors can deliver ideal forecast accuracy. In this study, the evaluation results of the slope unit for the RF or SVM model were more accurate and reasonable than those of the grid unit. In this case, the slope unit was found to be ideal. Under both the slope and grid units, the performance of the RF model was better than that of the SVM model. This study provides a reference for the spatial prediction of landslide susceptibility along mountain highways.

Dynamic Evolution and Quantitative Attribution of Soil Erosion Based on Slope Units: A Case Study of a Karst Plateau-Gorge Area in SW China

Exploring the dynamics of soil erosion and identifying its driving mechanisms is key to understanding soil erosion processes, particularly in karst areas. In this study, the RUSLE model, optimized on the basis of rocky desertification factors, was used to estimate soil erosion in a karst plateau gorge area in SW China. The spatial and temporal dynamics of soil erosion in the region over the past 20 years were analyzed on the basis of slope units, while the relationship between soil erosion and elevation, slope, fractional vegetation cover (FVC), karst rocky desertification (KRD), rainfall, and land use cover/change (LUCC) was identified quantitatively by the geographical detector on the basis of spatial heterogeneity. The results showed that: (1) The no erosion area decreased from 2000 to 2020, with the highest proportion of light to medium erosion and an increasing trend of soil erosion. (2) Soil erosion conversion mainly occurred between no erosion, slight erosion, and light erosion. (3) The hotspots of erosion occurred in high slope–low elevation and high slope–high elevation units, while the coldspots of erosion occurred in low slope–low elevation units. (4) Soil erosion was positively correlated with FVC and slope, and negatively correlated with KRD. (5) The dominant factor of soil erosion changed from KRD-slope to LUCC-slope and finally to elevation-slope, while the q value of rainfall-elevation had the most significant increase throughout the study period. This study will help to advance the goal of sustainable development of soil and water conservation in karst areas.

An Improved Method for the Evaluation and Local Multi-Scale Optimization of the Automatic Extraction of Slope Units in Complex Terrains

Slope units (SUs) are sub-watersheds bounded by ridge and valley lines. A slope unit reflects the physical relationship between landslides and geomorphological features and is especially useful for landslide sensitivity modeling. There have been significant algorithmic advances in the automatic delineation of SUs. But the intrinsic difficulties of determining input parameters and correcting for unreasonable SUs have hindered their wide application. An improved method of the evaluation and local multi-scale optimization for the automatic extraction of SUs is proposed. The Sus’ groups more consistent with the topographic features were achieved through a stepwise approach from a global optimum to a local refining. First, the preliminary subdivisions of multiple SUs were obtained based on the r.slopeunit software. The optimal subdivision scale was obtained by a collaborative evaluation approach capable of simultaneously measuring objective minimum discrepancies and seeking a global optimum. Second, under the selected optimal scale, unreasonable SUs such as over-subdivided slope units (OSSUs) and under-subdivided slope units (USSUs) were further distinguished. The local average similarity (LS) metric for each SU was designed based on calculating the SU’s area, common boundary and neighborhood variability. The inflection points of the cumulative frequency curve of LS were calculated as the distinguishing intervals for those unrealistic SUs by maximum interclass variance threshold. Third, a new effective optimization mechanism containing the re-subdivision of USSUs and merging of OSSUs was put into effect. We thus obtained SUs composed of terrain subdivisions with multiple scales, which is currently one of the few available methods for non-single scales. The statistical distributions of density, size and shapes demonstrate the excellent performance of the refined SUs in capturing the variability of complex terrains. Benefiting from the sufficient integrating approach of diverse features for each object, it is a significant advantage that the processing object can be transferred from general entirety to each precise individual.

Rapid landslide risk zoning toward multi-slope units of the Neikuihui tribe for preliminary disaster management

Abstract. Taiwan features steep terrain and a fragile geological environment accompanied by frequent earthquakes and typhoons annually. Meanwhile, with the booming economy and rapid population growth, activities pivot from metropolises to Taiwan's suburban and mountain areas. However, for example, the Neikuihui tribe in northern Taiwan experiences landslide disasters during extreme rainfall events. To rapidly examine landslide risk in the tribe area for preliminary disaster management, the well-known principle of risk, which comprises hazard, exposure, and vulnerability, was carefully adapted to scrutinize 14 slope units around the Neikuihui tribe region. The framework of risk zoning is improved based on the previous quantified findings regarding the inventory of the deep-seated landslides in southern Taiwan. Moreover, the proposed procedures comprehensively assess susceptibility, activity, exposure, and vulnerability of each slope unit. The rapid risk zoning analysis of multi-slope units delivers a sloping unit with a high level of landslide risk, and this slope unit did suffer from landslide disasters in the 2016 typhoon event. This study preliminarily proves that the proposed framework and details of rapid risk zoning can help identify a relatively high-risk slope unit around a tribal region and address pre-countermeasures for disaster management.

Early landslide mapping with slope units division and multi-scale object-based image analysis — A case study in the Xianshui River basin of Sichuan, China

Early landslide mapping with slope units division and multi-scale object-based image analysis — A case study in the Xianshui River basin of Sichuan, China

INTEGRATING INSAR INFORMATION AND SPATIAL-TEMPORAL FACTORS IN MACHINE LEARNING ANALYSIS FOR LANDSLIDE PREDICTION – A CASE STUDY FOR PROVINCIAL HIGHWAY 18 AREA IN TAIWAN

Abstract. Taiwan is located in subtropical monsoon area and Pacific Ring of Fire. Both the rate of crustal uplift and annual rainfall are among the highest in the world. Earthquakes and heavy rainfall have led to massive landslides and debris flow. Frequent disasters and the high rate of surface erosion have caused drastic changes in river topography and catchment areas, and, consequently, have impacted the safety of human lives. To mitigate the losses, better simulation and prediction of landslides are critical. Existing landslide prediction research works employed terrain, geology, rainfall, earthquakes and human activities as landslide triggering factors in the predicting model. In addition to aforementioned environmental conditions, this study would like to explore the use of SAR differential interferometry (InSAR) information to help observe characteristics of the slope movement behavior, which is also an important factor. Factors are analyzed and quantified on the basis of slope units. To confirm the applicability of selected factors to landslide, factors are firstly analyzed with Spearman correlation, and then those with higher correlations are incorporated into the prediction model. Machine learning based techniques are then employed to establish the prediction model. The experiment result demonstrates that InSAR information can improve the accuracy by more than 5% in landslide prediction.

Landslide risk evaluation based on slope unit:a case on the Western Hubei area, China

Landslide risk evaluation based on slope unit:a case on the Western Hubei area, China

Landslide Susceptibility Evaluation Using Different Slope Units Based on BP Neural Network.

Landslides are one of the most widespread natural hazards that cause damage to both property and life every year. Therefore, the landslide susceptibility evaluation is necessary for land hazard assessment and mitigation of landslide-related losses. Selecting an appropriate mapping unit is an essential step for landslide susceptibility evaluation. This study tested the back propagation (BP) neural network technique to develop a landslide susceptibility map in Qingchuan County, Sichuan Province, China. It compared the results of applying six different slope unit scales for landslide susceptibility maps obtained using hydrological analysis. We prepared a dataset comprising 973 historical landslide locations and six conditioning factors (elevation, slope degree, aspect, lithology, distance to fault lines, and distance to drainage network) to construct a geospatial database and divided the data into the training and testing datasets. We based on the BP learning algorithm to generate landslide susceptibility maps using the training dataset. We divided Qingchuan County into six different scales of slope unit: 4,401, 13,146, 39,251, 46,504, 56,570, and 69,013, then calculated the receiver operating characteristic (ROC) curve, and used the area under the curve (AUC) for the quantitative evaluation of 6 different slope unit scales of landslide susceptibility maps using the testing dataset. The verification results indicated that the evaluation generated by 56,570 slope units had the highest accuracy with a ROC curve of 0.9424. Overelaborate and rough division of slope units may not get the best evaluation results, and it is necessary to obtain the slope units most consistent with the actual situation through debugging. The results of this study will be useful for the development of landslide hazard mitigation strategies.

Geomorphological slope units of the Himalayas

ABSTRACT Slope units represent surface slopes by means of polygons delimited by drainage and divide lines obtained on a digital topography. Objective slope unit delineation for a given digital elevation model is still an open issue and, often, a limitation that may dictate the use of a more traditional pixel-based approach for spatial analysis. Availability of slope unit maps facilitates many kinds of studies and allows scholars to focus on specific scientific issues rather than on preparing sound mapping units from scratch for their research. Here, we present a slope unit map of a large portion of the Himalayas. The map is prepared following a widely tested, parameter-free optimization algorithm. The area encompassed by the map is relevant to studies of the well-known 2015 Gorkha earthquake and monsoons, which makes it relevant to a vast portion of the scientific community working in natural hazards including, but not limited to, landslide scientists and practitioners. The map contains 112,674 polygons with average area of 0.38 km and is published in vector form. The map is accompanied by a selection of data including morphometric and thematic quantities. In addition to describing the rationale behind the delineation of the polygonal map and selected data, we describe an application devoted to unsupervised terrain classification. We applied a k-means clustering procedure with two strategies: one at (coarser) basin scale and one at (finer) slope unit scale. We show similarities and differences between the two classification strategies, highlighting the role of the slope unit subdivision in the two cases.

Landslide Risk Assessment Using a Combined Approach Based on InSAR and Random Forest

Landslide risk assessment is important for risk management and loss–damage reduction. Herein, we assessed landslide susceptibility, hazard, and risk in the urban area of Yan’an City, which is located on the Loess Plateau of China and affected by many loess landslides. Based on 1841 slope units mapped in the study area, a random forest machine learning classifier and eight environmental factors influencing landslides were used for a landslide susceptibility assessment. In addition, differential synthetic aperture radar interferometry (DInSAR) technology was used for a hazard assessment. The accuracy of the random forest is 0.903 and the area under the receiver operating characteristics (ROC) curve is 0.96. The results show that 16% and 22% of the slope units were classified as being at very high and high-susceptibility levels for landslides, respectively, whereas 16% and 24% of the slope units were at very high and high-hazard levels for landslides, respectively. The landslide risk was obtained based on the susceptibility map and hazard map of landslides. The results show that only 26% of the slope units were located at very high and high-risk levels for landslides and these are mainly concentrated in urban centers. Such risk zones should be taken seriously and their dynamics must be monitored. Our landslide risk map is expected to provide information for planners to help them choose appropriate locations for development schemes and improve integrated geohazard mitigation in Yan’an City.

Landslide Susceptibility Mapping along a Rapidly Uplifting River Valley of the Upper Jinsha River, Southeastern Tibetan Plateau, China

As a result of the influence of plate movement, the upper reaches of Jinsha River have strong geological tectonic activities, large topographic fluctuations, and complex climate characteristics, which result in the frequent occurrence of landslide disasters. Hence, there is the need to carry out landslide susceptibility mapping in the upper reaches of Jinsha River to ensure the safety of local people’s property and the safe exploitation of hydraulic resources. In this study, InSAR technology and a field geological survey were used to map the landslides. Then, the curvature watershed method was used to divide the slope units. A conditioning factor system was established, which can reflect the characteristics of the rapid uplift and vertical distribution of rainfall in the special geological environment of the study area. Finally, logistic regression, random forest, and artificial neural network models were used to establish the landslide susceptibility model. The results show that the random forest model is optimal for the landslide susceptibility mapping in this area. Additionally, the area percentages of the very low, low, moderate, high, and very high susceptibility classes were 40.13%, 20.06%, 13.39%, 12.55%, and 13.87%, respectively. Based on the analysis of the landslide susceptibility map, we suggest that the landslide geological hazards resulting from the rapid uplift of the Tibetan Plateau and the significant decrease in sea level during a glacial period in the upper reaches of Jinsha River are controlled by the double disaster effect of the geodynamic system. Consequently, this study can guide local prevention and mitigation.

Multi-Temporal Landslide Inventory-Based Statistical Susceptibility Modeling Associated With the 2017 Mw 6.5 Jiuzhaigou Earthquake, Sichuan, China

The 2017 Mw 6.5 Jiuzhaigou earthquake (Sichuan, China) is the first strong ground motion that struck the famous world heritage site, causing widespread landslides and severe rock mass damage effects and landscapes undergoing rapid evolution in the Jiuzhaigou National Geopark. However, the understanding of the variability of pre- and post-earthquake landslide susceptibility and landslide conditioning factor effects over time remains limited. This study aims to carry out multi-temporal statistical landslide susceptibility modeling at the slope-unit level related to this event. To achieve this, we initially used a set of remote sensing imageries in GIS to obtain systematic landslide inventories across the pre-, co-, and post-seismic periods. Based on three landslide inventory datasets, we developed three statistical models by incorporating 14 landslide conditioning (seismic, topographic, and geologic) factors into a binary logistic regression (BLR) model. Finally, we utilized the area under the receiver operating characteristic (AUC) (QA) curve to assess each model’s calibration and validation performance. The results show that the BLR model has good prediction applicability for both normal and seismic landslides in the study area with outstanding to excellent predictive accuracy for Mod1 (pre-seismic, AUC = 0.801), Mod2 (co-seismic, AUC = 0.942), and Mod3 (post-seismic, AUC = 0.880) periods. There are variations in both the importance of landslide conditioning factors and susceptibility maps through time, and the number of slope units with a mean probability over 0.8 from only one (pre-seismic) increased to 21 (post-seismic). The dynamic susceptibility maps are of great significance for identifying potentially unstable slopes and providing references for hazard and risk assessment, which could provide new insights into geo-environmental protection and regional landslide evaluation in scenery spots, even for those world heritage sites in the tectonic active mountainous region. Moreover, more frequent or extended observation periods could contribute a further understanding of the post-seismic landslide developments in the Jiuzhaigou area.

Seismic hazard prediction using multispectral amplification maps in a complex topographic area: A case study of Qiaozhuang town, Sichuan Province, Southwest China

Earthquakes can cause widely distributed slope failures and damage in mountainous areas. The accurate prediction of ground motions in mountainous areas is essential for managing the seismic risk of urban cities near mountains but is restricted primarily by complex seismic site amplification effects in areas of uneven terrain. This study selected Qiaozhuang town located in the Qingchuan—Pingwu fault zone, Southwest China, as a case study. A simulator for mapped seismic responses using a hybrid model (SiSeRHMap) was applied to compute the multispectral seismic topographic amplification maps at the three slope units surrounding Qiaozhuang town (Weigan hill, Mt. Dong, and Mt. Shizi). Post-earthquake damage survey maps, 1D seismic site response spectral ratios, and H/V spectral ratios of earthquake data were used to validate the computed seismic site amplification factors and resonance frequencies. The results suggest that strong topographic amplification effects usually occur at distinct slope locations, such as hilltops, convex slope positions, upslope, and narrow ridges. The computed topographic amplification factors in the study area reached up to 2.4 at upslope or hilltops, and the resonance frequencies were between 3 and 10 Hz. Topographic effects can be as important as stratigraphic effects when assessing seismic amplification effects in the study area. We conclude that both topographic and stratigraphic effects should be considered in the comprehensive seismic hazard assessment of the study area or other similar mountain towns.

Improved Method of Defining Rainfall Intensity and Duration Thresholds for Shallow Landslides Based on TRIGRS

The Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model has been widely used to define rainfall thresholds for triggering shallow landslides. In this study, the rainfall intensity(I)-duration(D) thresholds for multiple slope units of an area in Pu’an County, Guizhou Province, China were defined based on TRIGRS. Given that TRIGRS is used to simulate the slope stability under the conditions of a given increasing sequence of I-D data, if the slope reaches instability at I = a, D = b, it will also become unstable in the case of I = a, D > b or I > a, D = b. To explore the effect of these I-D data with the same I or D values on the definition of I-D thresholds and the best method to exclude these data, two screening methods were used to exclude the I-D data that caused instability in the TRIGTS simulation. First, I-D data with the same I values when D values are greater than a certain limit value were excluded. Second, several D values were selected to exclude I-D data with the same I values for a slope unit. Then, an I value was selected to exclude I-D data with the same D values. After screening, two different I-D thresholds were defined. The comparison with the thresholds defined without screening shows that the I-D data with the same I or D values will reduce the accuracy of thresholds. Moreover, the second screening method can entirely exclude these data.

Geomorphological Mapping of The Prambanan Hills Yogyakarta

Understanding geomorphological characteristics must provide morphographic, morphometric,morphogenetic, and morphochronological information. This study aims to identify geomorphological characteristics in the Prambanan hills area compiled in the form of a geomorphological map. The research is based on maps, previous research reports, and field observations in the form of field checks on theelements studied. The geomorphological mapping system used is the ITC system. The main analysis in this research is spatial analysis in the form of overlapping spatial data. The relationship between landform units,lithology, and slopes is information that is analyzed descriptively. The results showed that on a map with a scale of 1: 30,000, 9 geomorphological units could be identified. The 9 geomorphological units are a combination of morphometry, genetics, and lithology. The combination consists of 2 landforms, namelytraditional and structural hills, and slopes; The lithology consists of 3 layers, namely the Lapili Formation of Semilir, Sand Formation of Kebo Butak, and Tuff Formation of Semilir. While the morphometry is in theform of slope units, namely 8-15%, 15-25%, 25-45%, and >45%. While the age based on lithology is Late Oligocene – Early Miocene in the Kebo Butak Formation and Early Miocene – Middle Miocene in theSemilir Formation.