Landslide Inventory Map Research Articles

Machine learning and deep learning-based landslide susceptibility mapping using geospatial techniques in Wayanad, Kerala state, India

Landslide susceptibility mapping is vital for disaster management and sustainable land-use planning. This research was conducted in Wayanad, Kerala, India, to identify landslide susceptible zones. The study used large geospatial datasets, such as elevation, slope, aspect, curvature, stream power index, topographic wetness index, land use and land cover, rainfall, flow accumulation, geology, and geomorphology. It is followed by the application of various machine learning and deep learning models such as the support vector machine, artificial neural networks, logistic regression, random forest, gradient boosting machine, recurrent neural networks long short-term memory, and deep neural network models to map the landslide susceptible zones. The model was trained and validated using the landslide inventory map, which contains 298 sites of landslides. The random forest model, with 97 % accuracy, performed best. It is possible to effectively mitigate landslides and plan long-term land use by identifying hazardous zones within the study region.

Landslide susceptibility analysis and hazard zonation of Koh-e-Suleman range using weighted overlay technique

A healthy environment improves the quality of life on Earth, while natural disasters affect the quality of the environment. An area’s Landslide Hazard Zonation (LHZ) provides key information about the susceptibility to natural or manmade disasters. Using the Weighted Overlay Technique, this research aims to develop the landslide susceptibility and zonation mapping of Koh-e-Suleman Range (a hill station around the heritage site). Six causative factors are evaluated: geology, elevation, slope inclination, distance from streams, type of soil, and annual rainfall intensity, considering equal risk influence for all the factors. All causative factor maps were generated to yield equal classes and provide uniformity to the results except for soil types found in the study area. The findings indicate that the study area may comprise four landslide hazard zones with high, medium, low, and very low-intensity susceptibility of landslides. The accuracy of the final LHZ map is verified by comparing the hazard zonation area percentages with the global landslide inventory map from a macro-study of NASA. It was found that the study areas largely fall within low- and very low-hazard zones.

Enhancing landslide inventory mapping through Multi-SAR image analysis: a comprehensive examination of current landslide zones

Enhancing landslide inventory mapping through Multi-SAR image analysis: a comprehensive examination of current landslide zones

Comparing Frequency Ratio and Analytical Hierarchy Models for Landslide Susceptibility in the Dharan Sub-Metropolitan Region of Eastern Nepal

This study compares the application of Frequency Ratio (FR) (statistical/quantitative) and Analytical Hierarchy Process (AHP) (heuristic/semi-quantitative) for landslide susceptibility analysis in Dharan Sub-Metropolitan Region of eastern Nepal. Nine different thematic layers including slope, aspect, curvature, geology, land use, distance to roads, distance to thrusts, topographic position index (TPI), and distance to streams were used for analysis. A landslide inventory map was prepared by identifying the landslides in recent Google Earth images, and it was then verified by field observations. The resulting susceptibility map with different susceptibility levels was validated using the Area Under the Curve (AUC) method, and the resulting AUC values were determined to be 78% for FR and 76.8% for AHP. Both methods offer a reliable strategy for landslide susceptibility mapping with a good prediction rate. The FR model enclosed 41%, 31%, 20%, 7% and 1% of the area as very low, low, moderate, high, and very high susceptibility, respectively, whereas AHP showed 21%, 24%, 27%, 21% and 7% of the area for the respective susceptibility levels. The specific zones identified within the range from very low to very high susceptibility provide valuable insights for local authorities, planners, and decision-makers, allowing them to identify areas susceptible to landslides and implement mitigation measures to aid in targeted risk management efforts.

GIS-based statistical analysis for landslide susceptibility evaluation and zonation mapping: A case from Blue Nile Gorge, Gohatsion-Dejen road corridor, Central Ethiopia

Globally, landslides in mountainous regions are considered to be major geo-hazards which are frequent, intensive and devastating that result in extensive destruction to people's lives, land, property and other infrastructure. To mitigate landslide-associated risks, identification, evaluation, and delineation of susceptible areas is essential for proper strategic planning and management. The present study was conducted in Blue Nile Gorge in the highlands of Central Ethiopia, about 185 km from Addis-Ababa, along the Gohatsion-Dejen road corridor. Landslides are frequently reported in the study area, particularly during the rainy season. The objective of the present study was to evaluate landslide susceptibility and to prepare a landslide susceptibility zoning (LSZ) map. For this purpose a statistical information value (IV) model was used that took into account various factors including slope material, elevation, slope, aspect, land-use and land-cover, proximity to the road, and proximity to streams. Each of these factors was classified based on different conditions like landslide concentration, topography, geology, and land-use and land-cover types. The landslide inventory mapping was conducted through field observations and Google Earth image interpretation. Later, the influence of each causative factor on past landslides was used to calculate their respective IVs. The statistical IVs were further used to evaluate the landslide susceptibility. The LSZ map produced from this study shows that 22.3 % of the study area falls into a very high susceptibility zone, 23.1 % into a high, 18.8 % in moderate, 23.1 % into a low, and the remaining 12.8 % into a low susceptibility zone. The LSZ map was validated using the Receiver Operating Characteristics Curve (ROC), landslide density index (LDI), and past landslide data. All validation methods showed acceptable results. The prepared LSZ map can be used for safe land-use planning of the area.

GIS-based MCDM approach for landslide hazard zonation mapping in east Gojjam zone, central Ethiopia

Landslides are prevalent in the Ethiopian highlands, particularly in the east Gojjam zone, which is highly affected by landslide problems. This research was carried out in the east Gojjam zone, northwestern Ethiopia. The study area is part of an economically important area in the country, and it is the main source of water for the Grand Ethiopian Renaissance Dam (GERD). The main objective of this work was to undertake a detailed inventory of past landslide locations and prediction of present and future landslide hazards, as well as the preparation of a landslide zonation map in the East Gojjam zone by using the Analytical Hierarchy Process (AHP) with the GIS technique. The parameters used for this study were slope degree, slope aspect, land use and land cover, road proximity, rainfall, lithology, altitude, and river proximity. The various causative parameters were collected from the field, and suitable modifications were made to the thematic maps. Finally, the ratings for various parameters were used as the basis to prepare the LHZ map in GIS windows. The landslide susceptibility and inventory mapping were produced in the GIS environment. The results of the study show that the main driving factors for the landslide hazards in the area were river proximity, rainfall, and manmade activities. Validation of this LHZ map revealed that more than 80% of past landslides match within the "high hazard zone" and reasonably accepted the rationality of the adopted methodology. The considered parameters, as well as their evaluation of the production of LHZ-Map, were confirmed. The produced landslide inventory map is very important for urban planners, agricultural studies, environmentalists, and future landslide hazardous prevention and mitigation strategies.

Landslide susceptibility assessment using different rainfall event-based landslide inventories: advantages and limitations

AbstractThe present work aims to evaluate potential sources of uncertainty associated with rainfall-triggered event-based landslide inventories within the framework of landslide susceptibility assessment. Therefore, this study addresses the following questions: (i) How representative is an event-based landslide inventory map of the total landslide activity and distribution in a study area?; (ii) How reliable is an event-based landslide susceptibility map?; (iii) How appropriate is an event-based landslide inventory map for independently validating a landslide susceptibility map? To address these questions, two independent and contrasting rainfall event-based landslide inventories were used, together with a historical landslide inventory, to assess landslide susceptibility for different types of landslides in a study area located north of Lisbon, Portugal. The results revealed the following findings: (i) contrasting rainfall critical conditions for failure can trigger similar landslide types, although they may vary in size and be spatially constrained by different predisposing conditions, particularly lithology and soil type; (ii) landslide susceptibility models using event-based landslide inventories are not reliable in the study area, regardless of the landslide inventory map used for training and validation; and (iii) complementary sources of uncertainty results from using incomplete historical landslide inventories to assess landslide susceptibility and non-totally independent landslide inventories for modeling validation. The present study enhances the understanding of regional landslide susceptibility patterns based on contrasting rainfall-trigger conditions, providing valuable information to minimize exposure; to design regional landslide early warning systems for specific rainfall-trigger landslide events; and to improve the response and preparedness of civil protection services.

Landslide susceptibility modeling in the Kulfo river catchment, rift valley, Ethiopia: An integrated geospatial and statistical analysis

Landslides occur when debris, rocks, or soil particles move downward. Examining the susceptibility of landslides is essential for safeguarding human well-being and assessing the consequences of landslides on the natural surroundings and ecosystems. This study utilized the frequency ratio technique to evaluate the probability of landslides happening in the Kulfo River watershed, situated in the Rift Valley area of Ethiopia. In order to ensure a comprehensive analysis, many data sources were employed, including satellite images, geological data, and historical records of landslides. This study developed a systematic approach to assess the probability of landslides by considering ten (10) influential factors: land use/land cover, slope, aspect, elevation, curvature, lithology, proximity to lineament, and normalized difference vegetation index (NDVI). The ten influential elements were prioritised based on literature review, expert knowledge, and preliminary study area analysis. The aforementioned causal elements are integrated with a comprehensive landslide inventory map. The sensitivity of the study's area to landslides was mapped using a frequency ratio (FR) model. Subsequently, it was classified as representing various degrees of vulnerability, spanning from extremely minimal to quite significant. The effectiveness of the suggested model was measured by evaluating the accuracy of the generated map of landslide susceptibility by the area under the curve (AUC) technique. Based on the most recent study results, the success rate curve has an area under the curve (AUC) of 79.6%, which indicates a highly satisfactory level of performance. Policymakers may utilize the findings of this study to make educated decisions on how to mitigate the risks of landslides in relation to land use and preparedness for disasters.

Remote Sensing and GIS-Based Accuracy Assessment of LULC Map and Landslide Susceptibility Prediction for Meghalaya, India

ABSTRACT Through this study, a Landslide Susceptibility Map (LSM) has been developed for the Meghalaya state, India using an Analytical Hierarchy Process (AHP). According to a 2012 Geological Survey of India report, the annual average number of landslides in Meghalaya is nearly 30, which is due to a combination of mountains, steep slopes, and excessive rainfall, leading the state to suffer a huge loss of life and property from landslides. For effective management of the current landslide situation, information about prior landslides is needed. Therefore, the landslide inventory map is prepared with 380 previously occurred events. The Landslide inventory records were separated into training samples (70%) and testing samples (30%) for the purpose of validation. In this regard, the present study has 15 conditioning factors, i.e., slope, rainfall, elevation, relative relief, aspect, distance from the road, curvature, distance from the stream, LULC, lineament density, geomorphology, geology, NDVI, MSAVI, NDWI, which are used to develop susceptibility map. Classification and accuracy assessment of LULC is carried out with segregation as 77% vegetation, 16.4% range land, 3.1% built area, 2.8% crops, 0.4% waterbodies, and 0.3% others (bare land, flooded vegetation, etc.). The Kappa for LULC categorization is 0.92, which is quite satisfactory and suggests that the LULC categorization is reliable. The developed susceptibility map is classified into four different classes, low susceptibility (35%), moderate susceptibility (41%), high susceptibility (20%), and very high susceptibility (4%), and has been verified using physical and Receiver Operating Characteristics (ROC) techniques. Results show that anticipated susceptibility classes are in good match with previous landslide events. The prepared map is reliable and can be used for land-use planning of the state in the future.

Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan

Natural disasters, notably landslides, pose significant threats to communities and infrastructure. Landslide susceptibility mapping (LSM) has been globally deemed as an effective tool to mitigate such threats. In this regard, this study considers the northern region of Pakistan, which is primarily susceptible to landslides amid rugged topography, frequent seismic events, and seasonal rainfall, to carry out LSM. To achieve this goal, this study pioneered the fusion of baseline models (logistic regression (LR), K-nearest neighbors (KNN), and support vector machine (SVM)) with ensembled algorithms (Cascade Generalization (CG), random forest (RF), Light Gradient-Boosting Machine (LightGBM), AdaBoost, Dagging, and XGBoost). With a dataset comprising 228 landslide inventory maps, this study employed a random forest classifier and a correlation-based feature selection (CFS) approach to identify the twelve most significant parameters instigating landslides. The evaluated parameters included slope angle, elevation, aspect, geological features, and proximity to faults, roads, and streams, and slope was revealed as the primary factor influencing landslide distribution, followed by aspect and rainfall with a minute margin. The models, validated with an AUC of 0.784, ACC of 0.912, and K of 0.394 for logistic regression (LR), as well as an AUC of 0.907, ACC of 0.927, and K of 0.620 for XGBoost, highlight the practical effectiveness and potency of LSM. The results revealed the superior performance of LR among the baseline models and XGBoost among the ensembles, which contributed to the development of precise LSM for the study area. LSM may serve as a valuable tool for guiding precise risk-mitigation strategies and policies in geohazard-prone regions at national and global scales.

Enhancing co-seismic landslide susceptibility, building exposure, and risk analysis through machine learning

Landslides are devastating natural disasters that generally occur on fragile slopes. Landslides are influenced by many factors, such as geology, topography, natural drainage, land cover, rainfall and earthquakes, although the underlying mechanism is too complex and very difficult to explain in detail. In this study, the susceptibility mapping of co-seismic landslides is carried out using a machine learning approach, considering six districts covering an area of 12,887 km2 in Nepal. Landslide inventory map is prepared by taking 23,164 post seismic landslide data points that occurred after the 7.8 MW 2015 Gorkha earthquake. Twelve causative factors, including distance from the rupture plane, peak ground acceleration and distance from the fault, are considered input parameters. The overall accuracy of the model is 87.2%, the area under the ROC curve is 0.94, the Kappa coefficient is 0.744 and the RMSE value is 0.358, which indicates that the performance of the model is excellent with the causative factors considered. The susceptibility thus developed shows that Sindhupalchowk district has the largest percentage of area under high and very high susceptibility classes, and the most susceptible local unit in Sindhupalchowk is the Barhabise municipality, with 19.98% and 20.34% of its area under high and very high susceptibility classes, respectively. For the analysis of building exposure to co-seismic landslide susceptibility, a building footprint map is developed and overlaid on the co-seismic landslide susceptibility map. The results show that the Sindhupalchowk and Dhading districts have the largest and smallest number of houses exposed to co-seismic landslide susceptibility. Additionally, when conducting a risk analysis based on susceptibility mapping, as well as considering socio-economic and structural vulnerability in Barhabise municipality, revealed that only 106 (1.1%) of the total 9591 households, were found to be at high risk. As this is the first study of co-seismic landslide risk study carried out in Nepal and covers a regional to the municipal level, this can be a reference for future studies in Nepal and other parts of the world and can be helpful in planning development activities for government bodies.

Assessing the Prediction Accuracy of Frequency Ratio, Weight of Evidence, Shannon Entropy, and Information Value Methods for Landslide Susceptibility in the Siwalik Hills of Nepal

The main objective of this study is to assess the prediction and success rate based on bivariate frequency ratio (FR), weight of evidence (WoE), Shannon entropy (SE), and information value (IV) models for landslide susceptibility in the sedimentary terrain of Nepal Himalaya, as the area is facing threat for sustainable development as well as sustainable resource management. This study also seeks to evaluate the causative factors for landslide susceptibility. Initially, a landslide inventory map was created, consisting of 1158 polygons. These polygons were randomly divided into two sets, with a ratio of 70% for training and 30% for testing data. The multicollinearity approach was evaluated to assess the relevance of selected conditioning variables and their inclusion in the model construction process. The area under the curve (AUC) and other arithmetic evaluation methods were employed to validate and compare the outcomes of the models. In comparison, the predictive accuracy of the FR model surpasses that of the IV and SE models. The success rates, ranked in descending order, are as follows: WoE (79.9%), FR (75.3%), IV (74.4%), and SE (73.2%). Similarly, the success rates of four distinct models, namely WoE, FR, IV, and SE, are 85%, 78.75%, 78.57%, and 77.2%, correspondingly. All models have an accuracy and prediction rate exceeding 70%, making them suitable for assessing landslide susceptibility in the Siwalik Hills of Nepal. Nevertheless, the weight of evidence model provides more precise outcomes than other models. This study is expected to provide important information for road and settlement sustainability in the study area.

Validation of four optimization evolutionary algorithms combined with artificial neural network (ANN) for landslide susceptibility mapping: A case study of Gilan, Iran

Landslides, the most significant geohazards in Iran, adversely affect the region's socioeconomic conditions and the environment. Landslide susceptibility mapping is crucial for proactive risk management, sustainable development, and protecting human lives, infrastructure, and the environment. It enables decision-makers to make informed choices, implement appropriate mitigation measures, and plan for potential landslide events, leading to safer and more resilient communities. Using a recommended subdivision methodology, this research has developed a landslide susceptibility map for a significant Gilan, Iran region. The production of the map involved utilizing an artificial neural network (ANN) model. This model incorporated sixteen causal components from several characteristics, including topographic and geomorphologic features, geological factors, land use patterns, hydrological aspects, and hydrogeological properties. Three hundred seventy instances were identified using multiple verified sources and analyzing aerial photographs to create the landslide inventory map. Examining and verifying the weights assigned to the causal elements were conducted per accepted mathematical standards, incorporating sensitivity analysis, earlier research, and empirical data on landslides. The area under the receiver operating characteristic curve (AUROC) was used to compare the various models. The study's findings indicated that the best swarm size value for COA-MLP11Cuckoo Optimization Algorithm (COA). is equal to 450, and the accuracy indices for this algorithm were 0.998 and 0.995 in training and testing datasets, respectively. Similarly, the AUC22Area under the curve (AUC). for the HS-MLP,33Harmony Search Algorithm (HS). SFS-MLP,44Stochastic Fractal Search (SFS). and TLBO-MLP55Teaching- Learning-Based Optiization (TLBO). were 0.997, 0.999, and 0.999 in training and 0.995, 0.996, and 0.995 in the testing dataset, respectively. Therefore, the use of optimization algorithms leads to an increase in the performance and accuracy of the neural network, and the high accuracy of the SFS-MLP model demonstrates the existence of a dependable criterion for delineating the susceptibility zones about forthcoming landslide occurrences. The model is a cost-effective and potentially indispensable tool for urban planners in growing cities and municipalities. Its effectiveness is demonstrated by comparisons with previous susceptibility analyses conducted in the region.

Multitemporal landslide inventory and susceptibility map for the Arun River Basin, Nepal

AbstractThe transboundary Arun River Basin (ARB) spreads across Nepal and Tibet. Nearly 95% of the basin lies in Tibet through which the Pumqu River flows, forming the Arun River once it enters Nepal. The ARB has five large hydropower projects undergoing construction or planned for the future. Rainfall and earthquake‐induced landslides, landslide‐dammed lakes and landslide‐induced glacial lake outburst floods pose major risks to smooth operation of these projects. To safeguard upcoming hydropower projects, areas susceptible to landslides in the ARB must be identified. We used high‐resolution satellite imagery and open‐source tools to generate a multitemporal landslide inventory for the basin. The rigorously quality‐controlled inventory represents a yearly record of landslides from 2011 to 2020. A data‐driven approach was used to map areas susceptible to landslides within the ARB. The multitemporal landslide inventory combined with other readily available Earth observation‐based variables was used to create a landslide susceptibility map. The susceptibility analysis provides a valuable initial estimate of where landslides are likely to initiate. These landslide products could form the basis of more comprehensive local studies to inform hydropower project development.

Research on the application of Breiman's algorithm integrated with the Random Forest in determining the importance of input factors to landslide formation in Son La province

Landslide susceptibility maps are effective and intuitive tools in natural disaster management, helping to minimize damage caused by natural disasters due to the specific spatial information they provide. However, the performance of these susceptibility maps depends mainly on the number and importance of input factors. Determining the importance and order of influencing factors often receives little attention in landslide prediction studies. Breiman's algorithm, integrated into the Random Forest method, can comprehensively determine the importance and order of input variables by considering the correlation relationship between the landslide inventory map and these input variables. Consequently, this study utilized Breiman's algorithm within the Random Forest technique to assess the importance of 16 input factors influencing the formation of landslide events in Son La province. The results obtained from this study serve as the foundation for selecting appropriate input factors to enhance the construction and accuracy of landslide susceptibility maps within the study area, especially in the context of climate change.

Remote Sensing based Early Warning Systems for Detection and Assessment of Landslides: A Case Study of Himachal Pradesh, India

Case studies within the framework of "Remote Sensing-Based Early Warning Systems for Slope Failures" present real-world instances of applying remote sensing technologies in monitoring slopes and detecting changes in elevation. The study focuses on Himachal Pradesh, India, known for its rugged terrain and varying climatic conditions. Himachal Pradesh, situated in North-West India, spans from 30°22'40" to 33°12'20" north latitudes and 75°45'55" to 79°04'20" east longitudes. With altitudes ranging from 271 meters to 6,751 meters, this mountain region exhibits diverse topography and climate. Geospatial data reveals its complexities, combining elevation, slope, hillside and roughness information to offer insights into the terrain's dynamics. The geological map highlights the State's Precambrian formations shaped by the collision of the Indian and Asian landmasses, resulting in the distinctive Himalayan landscape. The region has also experienced concentrated orographic precipitation, glacial activity and rapid erosion. Analyzing historical earthquakes and active faults reveals the seismic activity's correlation with landslides, highlighting their potential to trigger slope failures. The landslide inventory map records 6,289 landslides, outlining their distribution and movement patterns. Further insight is gained from the Landslide sustainability map, classifying regions into susceptibility levels. Integrated geospatial analyses provide a comprehensive understanding of Himachal Pradesh's terrain, offering practical applications in risk assessment, infrastructure planning and environmental conservation.

Standards for shallow landslide identification in Brazil: Spatial trends and inventory mapping

Mapping shallow landslide inventories represents the initial stage in conducting susceptibility, vulnerability, and risk analyses. The identification of areas with a history of landslides, both past and current events, enables the detection of spatio-temporal patterns and facilitates the evaluation of morphological and geological factors. Shallow landslides are frequent in Brazil, especially along the Brazilian south and southeastern coast. Despite their high frequency, the recognition and mapping of these landslides are inadequately developed due to the absence of established mapping guidelines. This resulted of limited investments by Brazilian authorities in initiatives aimed at preventing and mitigating mass movements. Thus, the aim of this study is to establish criteria for the recognition and mapping of shallow landslides in Brazil and to evaluate their applicability in susceptibility analysis. The study area is located in Itaóca and Apiaímunicipalities, São Paulo state, southeastern Brazil. Criteria for shallow landslide recognition were created based on the absence of vegetation, shape and size, drainage network distance, slope position, planar rupture surface, and altimetric variation. An extensive landslide interpretation was carried out using a set of free access Google Earth Pro images. Susceptibility maps were developed through a bivariate statistical approach, specifically the Informative Value method. The outcomes demonstrated that incorporating the deposition area in susceptibility studies for shallow landslides led to modifications in the final mapping, a slight reduction in validation rates, and, notably, an impact on the influence of the thematic variable curvature. Therefore, it is clear that the criteria used for landslide inventory maps have a significant effect on the accuracy of susceptibility maps and should be a critical consideration in studies related to this subject.

Utilizing a single-temporal full polarimetric Gaofen-3 SAR image to map coseismic landslide inventory following the 2017 Mw 7.0 Jiuzhaigou earthquake (China)

On August 8, 2017, a magnitude 7.0 earthquake struck Jiuzhaigou County in Sichuan Province, triggering numerous coseismic landslides. The prompt identification of these landslides is imperative for emergency rescue efforts and post-earthquake hazard assessments. Optical satellite and unmanned aerial vehicle (UAV) images are often obstructed by cloud cover and fog following earthquakes. In contrast, polarimetric synthetic aperture radar (PolSAR), unaffected by adverse weather conditions, emerges as an indispensable tool. However, the utilization of spaceborne single-temporal SAR for mapping the inventory of coseismic landslides is infrequent and encounters constraints due to several limitations. In this study, we analyzed the amplitude feature and polarimetric decomposition of multiple ground categories in a full PolSAR image, and proposed an automated method to accurately identify coseismic landslides using a single-temporal full PolSAR image. The coseismic landslide inventory following the Jiuzhaigou Earthquake was mapped and validated using high-resolution UAV images. Detailed analysis was conducted to identify error sources leading to omissions and false positives. Additionally, we evaluated various machine learning models to compare their performance with our proposed method. Finally, we conducted a comprehensive discussion on the strengths and weaknesses of different data types (PolSAR, optical satellite, and UAV) for coseismic landslide identification. Our results indicate that the proposed PolSAR-based method achieves high accuracy in coseismic landslide inventory mapping, offering an effective solution for timely post-earthquake emergency responses in complex environments and all weather conditions in the future.

Determination of alternative forest road routes using produced landslide susceptibility maps: A case study of Tonya (Trabzon), Türkiye

Firstly, Landslide Susceptibility Maps of the study area were produced using Frequency Ratio and Modified Information Value models. Nine factors were defined and the Landslide Inventory Map was used to produce these maps. In the Landslide Susceptibility Maps obtained from the Frequency Ratio and Modified Information Value models, the total percentages of high and very high-risk areas were calculated as 10% and 15%, respectively. To determine the accuracy of the produced Landslide Susceptibility Maps, the success and the prediction rates were calculated using the receiver operating curve. The success rates of the Frequency Ratio and Modified Information Value models were 82.1% and 83.4%, respectively, and the prediction rates were 79.7% and 80.9%. In the second part of the study, the risk situations of 125 km of forest roads were examined on the map obtained by combining the Landslide Susceptibility Maps. As a result of these investigations, it was found that 4.28% (5.4 km) of the forest roads are in very high areas and 4.27% (5.3 km) in areas with high landslide risk areas. In the last part of the study, as an alternative to forest roads with high and very high landslide risk, 9 new forest road routes with a total length of 5.77 km were produced by performing costpath analysis in with geographic information systems.

Spatial–Spectral Similarity Based on Adaptive Region for Landslide Inventory Mapping With Remote-Sensed Images

Spatial–Spectral Similarity Based on Adaptive Region for Landslide Inventory Mapping With Remote-Sensed Images