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Service Description: We classified the landslide susceptibility results in a GIS (Geographic Information Systems) using standard deviations from a calculated probability that a landslide has occurred, is occurring, or will occur based upon a combination of landscape variables. The landslide susceptibility classes are:
Landslide Susceptibility (pixel values:
low 0.0 - 0.2
low-moderate 0.21 - 0.4
moderate 0.41 - 0.6
moderate-high 0.61 - 0.8
high 0.81 - 1.0
We developed an approach to landslide-susceptibility mapping combining machine-learning and geomorphic statistics (Crawford and others, 2021). The susceptibility model is based upon high-resolution airborne lidar-derived data sets from a 1.5-m regional digital elevation model and a detailed landslide inventory for Martin County, Kentucky. We used a logistic regression model to establish a connection between specific slope variables and landslide occurrence. We evaluated slope angle, aspect, elevation, terrain roughness, curvature, and plan curvature to determine what influences landslide occurrence. Next, we modeled the probability of occurrence and used those values to create a landslide-susceptibility map. Our model and resulting map estimate the probability of an event (a landslide) determined by the slope variables.
The probability and map classification are not a landslide prediction result from a scenario-based event (a rainfall event, for example), or a probability with a time component. This means the map classification will not predict how or when a landslide might occur, only the likelihood of a past or future occurrence. Data that occurs on less than a 3-degree slope were excluded because these areas are mostly flat. Although determining landslide susceptibility has inherent uncertainty, our map results, and distribution of higher probabilities (moderate, moderate-high, high) effectively reflect the geomorphic variables that are indicative of unstable ground conditions and potential landslide activity. The low-moderate and low susceptibility classes do not indicate that landslides have not or cannot occur in these areas.
Name: Hazards/MartinSusceptibility_Image
Description: We classified the landslide susceptibility results in a GIS (Geographic Information Systems) using standard deviations from a calculated probability that a landslide has occurred, is occurring, or will occur based upon a combination of landscape variables. The landslide susceptibility classes are:
Landslide Susceptibility (pixel values:
low 0.0 - 0.2
low-moderate 0.21 - 0.4
moderate 0.41 - 0.6
moderate-high 0.61 - 0.8
high 0.81 - 1.0
We developed an approach to landslide-susceptibility mapping combining machine-learning and geomorphic statistics (Crawford and others, 2021). The susceptibility model is based upon high-resolution airborne lidar-derived data sets from a 1.5-m regional digital elevation model and a detailed landslide inventory for Martin County, Kentucky. We used a logistic regression model to establish a connection between specific slope variables and landslide occurrence. We evaluated slope angle, aspect, elevation, terrain roughness, curvature, and plan curvature to determine what influences landslide occurrence. Next, we modeled the probability of occurrence and used those values to create a landslide-susceptibility map. Our model and resulting map estimate the probability of an event (a landslide) determined by the slope variables.
The probability and map classification are not a landslide prediction result from a scenario-based event (a rainfall event, for example), or a probability with a time component. This means the map classification will not predict how or when a landslide might occur, only the likelihood of a past or future occurrence. Data that occurs on less than a 3-degree slope were excluded because these areas are mostly flat. Although determining landslide susceptibility has inherent uncertainty, our map results, and distribution of higher probabilities (moderate, moderate-high, high) effectively reflect the geomorphic variables that are indicative of unstable ground conditions and potential landslide activity. The low-moderate and low susceptibility classes do not indicate that landslides have not or cannot occur in these areas.
Single Fused Map Cache: false
Extent:
XMin: 5809819.00206694
YMin: 3783875.0012307763
XMax: 5915179.00206694
YMax: 3890105.0012307763
Spatial Reference: 102763
(3089)
Initial Extent:
XMin: 5809819.00206694
YMin: 3783875.0012307763
XMax: 5915179.00206694
YMax: 3890105.0012307763
Spatial Reference: 102763
(3089)
Full Extent:
XMin: 5809819.00206694
YMin: 3783875.0012307763
XMax: 5915179.00206694
YMax: 3890105.0012307763
Spatial Reference: 102763
(3089)
Pixel Size X: 10.0
Pixel Size Y: 10.0
Band Count: 1
Pixel Type: F32
RasterFunction Infos: {"rasterFunctionInfos": [{
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Mensuration Capabilities: Basic
Inspection Capabilities:
Has Histograms: true
Has Colormap: false
Has Multi Dimensions : false
Rendering Rule:
Min Scale: 0
Max Scale: 0
Resampling: false
Copyright Text:
Service Data Type: esriImageServiceDataTypeGeneric
Min Values: 0.0020537381060421467
Max Values: 0.9984917640686035
Mean Values: 0.3243338578089716
Standard Deviation Values: 0.19743396709551989
Object ID Field:
Fields:
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Default Mosaic Method: Center
Allowed Mosaic Methods:
SortField:
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Mosaic Operator: First
Default Compression Quality: 75
Default Resampling Method: Bilinear
Max Record Count: null
Max Image Height: 4100
Max Image Width: 15000
Max Download Image Count: null
Max Mosaic Image Count: null
Allow Raster Function: true
Allow Copy: true
Allow Analysis: true
Allow Compute TiePoints: false
Supports Statistics: false
Supports Advanced Queries: false
Use StandardizedQueries: true
Raster Type Infos:
Name: Raster Dataset
Description: Supports all ArcGIS Raster Datasets
Help:
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Edit Fields Info: null
Ownership Based AccessControl For Rasters: null
Child Resources:
Info
Histograms
Statistics
Key Properties
Legend
Raster Function Infos
Supported Operations:
Export Image
Identify
Measure
Compute Histograms
Compute Statistics Histograms
Get Samples
Compute Class Statistics
Query Boundary
Compute Pixel Location
Compute Angles
Validate
Project