A landslide occurs when the natural stability of a slope is disrupted, causing the movement of rock, soil, and debris down the slope. This typically happens when gravity overcomes the forces that hold the materials in place. Several factors can contribute to this instability, such as heavy rainfall that saturates the soil, making it more prone to slipping, or seismic activity from earthquakes that shakes the ground and dislodges materials. Additionally, human activities like construction, deforestation, or mining can destabilize the slope by altering the land's natural balance. The movement of the materials can range from slow, gradual slumping to rapid, destructive rockfalls or debris flows, often resulting in significant damage to infrastructure, property, and even loss of life.
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| Figure 1: Landslide in Kathmandu, Nepal |
Kampung Padai which is part of hilly areas in Rompin, Pahang has been proposed to be an integrated housing area by Jasa Rumah Sdn Bhd. Based on the contour data provided by the developer, the elevation of the proposed area is from 1048 m up to 1475 m. Kampung Padai has been subjected to rapid development where forest has been converted to rubber plantation and also residential area has been increased rapidly. Consequently, the probability of landslides to occur had increased. In order to alleviate the problem, planning for future development is crucial. Your company had been assigned to produce landslide susceptible assessment map by the Town and Planning Development. Landslide susceptible assessment utilized many factors that affected landslides such as slope angle, lithology, structural geology and evidence of past instability.
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| Figure 2: Sample of landslide susceptibility assessment |
The objectives of the assessment are aimed at evaluating and understanding landslide susceptibility through geospatial and mathematical tools. First, utilizing contour data to generate a slope map is crucial, as it helps to visualize and analyze the steepness of the terrain, which is a significant factor in landslide occurrence. By identifying areas with steep slopes, we can better assess regions that are more prone to landslides. Second, employing mathematical equations in a raster environment allows for the precise calculation of various factors that influence slope stability, such as soil type, rainfall, and land use. These equations help in analyzing and quantifying the landslide risk in a more systematic manner. Third, producing a landslide susceptible map combines all the factors and layers of information, such as slope, geology, and hydrology, to create a visual representation of areas at high, moderate, or low risk for landslides. This map is a powerful tool for understanding where landslides are more likely to occur. Finally, the application of the landslide susceptible map for planning purposes is vital for land use management and disaster preparedness. By identifying high-risk zones, planners and decision-makers can implement strategies to mitigate landslide risks, such as regulating construction activities, improving drainage systems, and guiding land development in safer areas. This proactive approach ensures better management of natural hazards and helps protect both lives and property.
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| Figure 1: Adding the shapefile data for further analysis |
In this step, you are setting up your ArcMap workspace by loading various shapefiles as shown in Figure 1, such as Contour.shp, Border.shp, Fault.shp, Lithology.shp, and Fracture.shp, into the Table of Contents. By doing this, you are making these data layers available for analysis in the map view. However, at this stage, you only want to display the Contour.shp and Border.shp layers. This is typically done to focus on specific elements of your analysis, reducing visual clutter and ensuring that only the most relevant layers (such as the contours and border) are initially visible. This helps in managing map complexity and streamlining the workflow for further spatial analysis or visualization.
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| Figure 2: 3D Elevation Map (TIN) format |
In this step, you are using the 3D Analyst extension in ArcMap to generate a 3D elevation map from contour lines. The 3D Analyst extension allows you to create a Triangulated Irregular Network (TIN), which is a surface model that represents terrain elevation. By selecting the "Create TIN from features" option, you are converting the contour lines into a 3D surface that accurately reflects the elevation of the terrain. This process is crucial for visualizing topographic features in three dimensions, allowing for better analysis of the landscape's shape, slopes, and elevation changes. The resulting TIN will help in tasks like terrain analysis, hydrological modeling, and planning.
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| Figure 3: Slope Map of Study Area |
For this step a slope map is generated from the previously created elevation map (TIN). The Surface Slope tool from the 3D Analyst toolbox is used to calculate the rate of change in elevation (slope) across the surface, providing insight into the terrain's steepness. By adding a class break table, slope values are categorized into distinct ranges, enhancing the visual interpretation of the data as shown in Figure 3. The resulting slope map is valuable for applications such as land use planning, construction, and hydrology, as it helps identify areas with steep slopes or regions that may be more susceptible to erosion.
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| Figure 4: Ranking value for slope angle |
The table above outlines the ranking values for different slope angles based on their susceptibility to certain conditions. Slope angles between 0 to 15° are categorized as "Low" susceptibility, with a ranking value of 1. These areas typically represent gentler slopes, which are less prone to erosion or instability. Slope angles between 16° and 25° are considered to have "Moderate" susceptibility, assigned a ranking value of 3, indicating a moderate risk for issues like erosion or landslides. Finally, slopes greater than 26° are classified as "High" susceptibility, with a ranking value of 5. These areas represent steep terrain, which is more prone to instability and erosion. The ranking system helps in assessing the level of risk associated with different slope angles, providing valuable information for land use planning, environmental management, and identifying areas at higher risk of natural hazards.
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| Figure 5: Slope Rank |
Before reclassifying the slope map, it must first be converted into raster format. This conversion is necessary because the Reclassify tool works with raster data, allowing for a more efficient analysis of the slope values. Once the slope map is in raster format, the Reclassify tool is used to categorize the slope values into predefined classes, based on specified ranges. The classification process simplifies the interpretation of the slope data, making it easier to identify areas with specific slope characteristics, such as gentle or steep slopes. This reclassification is particularly useful for tasks like land zoning, erosion risk assessment, and determining suitable locations for development. The new value column in the Reclassify window reflects the ranking of the slope gradient within each class as shown in Figure 5. This classification simplifies the analysis, making it easier to identify areas with specific slope characteristics for applications like land use planning, risk assessment, and determining areas suitable for development or conservation.
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| Figure 6: Distance Ranking for fault buffer |
Next, the objective is to create a fault buffer layer that classifies the proximity of various areas to the fault. The closer a location is to the fault, the higher the likelihood of landslides occurring, as indicated in Figure 6, which assigns ranking values based on distance from the fault. The process involves using the Multiple Ring Buffer tool to generate buffers around the fault at different distances (10 m, 20 m, 30 m, and greater than 40 m). These distances are assigned ranking values according to the table, with the areas closest to the fault having the highest ranking and the highest risk of landslides. The output layer, called FaultBuf.shp, represents these buffer zones and is added to the map for further analysis. This buffer analysis helps in understanding the spatial relationship between the fault and surrounding areas, aiding in risk assessment and decision-making for land use and disaster management. Next, the objective is to examine and classify the buffer distances based on the proximity to the fault. After opening the attribute table for the Fault_Buf layer, the distance values are displayed, reflecting the different buffer zones generated earlier. To further categorize these distances, a new field called "Fault_Rank" is added to the attribute table. This new field will store ranking values based on the buffer distances, with higher rankings assigned to areas closer to the fault, indicating a higher risk of landslides. To input these ranking values, the attribute table is put into editing mode, allowing for manual updates. After the rankings are entered, the edits are saved, and the editing mode is stopped. This process ensures that each buffer zone is accurately classified according to its proximity to the fault, facilitating further analysis and decision-making for risk assessment.
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| Figure 7: Buffer Layer added |
The process is repeated for the Fracture and Lithology layers to assign ranking values based on their respective attributes. For the Fracture layer, a new field called "Frac_Rank" is created, and for the Lithology layer, a new field named "Litho_Rank" is added. The ranking values for these layers are based on specific criteria from the given tables: for Lithology, granite is assigned a ranking of 1, and metasediment a ranking of 5; for Fracture, areas with high fractures are ranked 5, moderate fractures are ranked 3, and areas with no fractures are ranked 1. These rankings are inputted into the newly created fields, with the attribute tables placed in editing mode for manual entry. This classification allows for easier analysis of the Fracture and Lithology layers based on their respective risk or susceptibility values, contributing to more accurate spatial analysis and decision-making in tasks like land suitability or hazard assessment. Next is to create a raster-based landslide susceptibility map by converting vector data (Fracture, Lithology, and Fault_Buf layers) into raster format for easier spatial analysis. First, the Spatial Analyst extension is used to convert the vector layers into raster data, which allows for continuous spatial analysis and better integration with other datasets. The Fault_Buf layer is then merged with the Border layer using the Union tool, combining both datasets into one. This step ensures that the buffer data aligns with the study area boundaries. Next, the merged data is clipped to the Border.shp layer to focus on the relevant area, creating a new Fault_Buf_SA layer. After correcting any attribute errors (like the "0" value in the ranking field, which represents the Border layer), the corrected Fault_Buf_SA layer is converted to raster format. These steps are crucial for integrating and standardizing the data in raster format, enabling effective analysis of landslide susceptibility based on various factors such as fault proximity, fracture presence, and lithology.
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| Figure 8: The output landslide susceptible assessment |
The landslide susceptibility assessment involves a systematic approach to analyzing how different environmental factors contribute to landslide risk. These factors such as slope, fault proximity, lithology, and fracture density are represented as raster layers, each encoding valuable spatial data. The Raster Calculator tool is employed to combine these factors into a single composite hazard map, using a weighted equation that integrates each layer’s values. This map provides a visual representation of the area's most at risk of landslides, with higher values indicating greater susceptibility. After generating this initial map, the Reclassify tool is used to categorize the results into distinct classes, such as low, moderate, or high susceptibility, based on predefined ranking values from Table 3. This step simplifies the data, making it easier to interpret and identify areas that require intervention or monitoring. The reclassified map, saved as "LanSuscp," now provides a more structured view of landslide risk. To further quantify the results, the area of each susceptibility class is calculated in hectares using the Field Calculator tool, and the percentage of each class within the study area is determined. This allows for a comprehensive understanding of the spatial distribution of landslide risk and offers insights for land-use planning, environmental protection, and disaster preparedness. The final output is crucial for prioritizing areas for mitigation efforts, such as reinforcing slopes, designing drainage systems, or restricting development in high-risk zones.
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| Figure 9: Ranking of the landslide susceptible map |
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Figure 10: Final Map of Landslide Susceptibility
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| Figure 11: Area and percentage for each class |
Conclusion
The landslide susceptibility assessment for Kampung Padai in Rompin, Pahang, has identified that the majority of the proposed integrated housing area is at low risk for landslides. According to the analysis, 82.92% of the area falls within the low susceptibility class, indicating that most of the terrain is relatively stable for development. However, 16.77% of the area is classified as moderately susceptible, suggesting that these regions may require further evaluation and mitigation measures to ensure safety during construction and future use. A small portion of the area, accounting for 0.31%, is categorized as highly susceptible to landslides. These high-risk zones are particularly critical and should either be avoided for development or require extensive stabilization efforts and monitoring.
Given the rapid development in Kampung Padai, including forest conversion and the expansion of residential areas, it is essential to incorporate the findings of this assessment into planning decisions. The susceptibility map provides a valuable tool for guiding sustainable development by identifying safe areas for housing projects and prioritizing mitigation strategies in more vulnerable zones. Proper slope management, drainage design, and adherence to geological recommendations will be critical to minimizing landslide risks and ensuring the safety and longevity of the integrated housing area.
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