Skip to main content

Landslide Upslope Downslope Factors

Landslides occur when the driving forces (gravity, water saturation, seismic activity) exceed the resisting forces (cohesion, friction, vegetation cover) on a slope. To understand landslide dynamics, we classify contributing factors into:

  1. Upslope Factors – Conditions on the upper part of the slope that contribute to instability.
  2. Downslope Factors – Conditions at the base that further exacerbate landslides, often by removing support or altering water flow.

1. Upslope Factors

(a) Steep Slope Angle (Gradient Effect)

  • A higher slope gradient increases shear stress, making the slope more susceptible to failure.
  • Angle of Repose: The maximum angle at which a material remains stable; exceeding this angle leads to landslides.
  • Example: The Himalayan region has high landslide risks due to steep slopes and tectonic activity.

(b) Weak Soil or Rock Type

  • Lithology (rock type) determines slope strength.
  • Clay-rich soils (e.g., montmorillonite) expand when wet, reducing stability.
  • Weathered rocks (e.g., shale, phyllite) lose cohesion over time.
  • Example: Western Ghats experience landslides in lateritic soils after monsoon rains.

(c) Vegetation Cover

  • Roots reinforce soil and absorb excess water.
  • Deforestation increases erosion and runoff.
  • Example: Amazon Basin has stable slopes due to dense tree cover, while Haiti suffers from deforestation-induced landslides.

(d) Water Saturation (Pore Water Pressure Effect)

  • Hydrostatic pressure increases soil weight and reduces internal friction.
  • Infiltration capacity varies; sandy soils drain better than clayey soils.
  • Example: Uttarakhand floods (2013) triggered landslides due to excessive rainfall.

(e) Joints and Fissures

  • Geological discontinuities (faults, bedding planes, joints) act as failure planes.
  • Example: The San Andreas Fault in California increases landslide risks due to active tectonics.

(f) Human Disturbances

  • Construction (roads, dams) alters load distribution.
  • Mining induces vibrations and weakens slopes.
  • Example: The Malin landslide (Maharashtra, India, 2014) was worsened by hill cutting for agriculture.

2. Downslope Factors

(a) Erosion at the Base

  • Undercutting by rivers, waves, or glaciers removes slope support.
  • Example: Konkan coast, India experiences coastal landslides due to wave erosion.

(b) Changes in Water Table

  • High groundwater levels increase pore pressure and reduce cohesion.
  • Example: Oso landslide (Washington, USA, 2014) was linked to high water table changes.

(c) Steep Channel Gradients

  • Accelerated water flow scours the base, destabilizing the slope.
  • Example: Teesta River valley, Sikkim has landslide-prone zones due to steep stream gradients.

(d) Presence of Debris

  • Accumulated sediments create barriers, leading to sudden failures.
  • Example: Landslide dams in Nepal Himalayas cause flash floods when breached.

(e) Previous Landslide Activity

  • Remobilization of old debris increases future risks.
  • Example: Darjeeling region experiences recurring landslides due to historical instability.
  • Interplay Between Factors: Upslope and downslope factors often act together.
  • Mitigation Strategies:
    • Upslope: Reforestation, slope drainage, terracing.
    • Downslope: Retaining walls, river training, landslide barriers.
  • GIS-Based Risk Mapping: Combining remote sensing and Digital Elevation Models (DEM) helps in hazard prediction.

Comments

Popular posts from this blog

Remote Sensing Technology

Remote sensing is a rapidly evolving geospatial technology used to collect information about the Earth's surface and atmosphere without direct physical contact . It involves detecting and measuring electromagnetic radiation (EMR) reflected or emitted from objects using sensors mounted on satellites, aircraft, or drones. Remote sensing systems are fundamentally classified based on (1) the energy source used for illumination and (2) the region of the electromagnetic spectrum utilized for sensing . 1. Types of Remote Sensing Based on Energy Source Remote sensing systems are commonly categorized according to whether the sensor generates its own energy or relies on naturally available radiation . Passive Remote Sensing Principle: Passive remote sensing relies on natural sources of electromagnetic energy , primarily solar radiation reflected from the Earth's surface or thermal radiation emitted by objects. Operation: Most passive sensors operate during daylight when sunlight is av...

Spectral Signature vs. Spectral Reflectance Curve

Spectral Signature  A spectral signature is the unique pattern in which an object: absorbs energy reflects energy emits energy across different wavelengths of the electromagnetic spectrum. ✔ Key Points Every natural and man-made object on Earth interacts with sunlight differently. These interactions produce a distinct pattern , just like a "fingerprint". Sensors on satellites record these patterns as digital numbers (DN values) . These patterns help to identify and differentiate objects such as vegetation, soil, water, snow, buildings, minerals, etc. ✔ Examples of Spectral Signatures Healthy vegetation → High reflectance in NIR , strong absorption in red Water → Strong absorption in NIR and SWIR , low reflectance Dry soil → Gradual increase in reflectance from visible to NIR Snow → High reflectance in visible , low in SWIR ✔ Why Spectral Signature Matters It allows: Land cover classification Chan...

REMOTE SENSING INDICES

Remote sensing indices are band ratios designed to highlight specific surface features (vegetation, soil, water, urban areas, snow, burned areas, etc.) using the spectral reflectance properties of the Earth's surface. They improve classification accuracy and environmental monitoring. 1. Vegetation Indices NDVI – Normalized Difference Vegetation Index Formula: (NIR – RED) / (NIR + RED) Concept: Vegetation reflects strongly in NIR and absorbs in RED due to chlorophyll. Measures: Vegetation greenness & health Uses: Agriculture, drought monitoring, biomass estimation EVI – Enhanced Vegetation Index Formula: G × (NIR – RED) / (NIR + C1×RED – C2×BLUE + L) Concept: Corrects for soil and atmospheric noise. Measures: Vegetation vigor in dense canopies Uses: Tropical rainforest mapping, high biomass regions GNDVI – Green Normalized Difference Vegetation Index Formula: (NIR – GREEN) / (NIR + GREEN) Concept: Uses Green instead of Red ...

Spatial Entity and Spatial Object

Concepts Spatial Entity : Refers to any real-world feature or phenomenon that exists in a specific location and can be identified in space. This emphasizes the actual physical or conceptual presence of the feature. Spatial Object : Represents the digital or computational representation of a spatial entity within a Geographic Information System (GIS). This includes its geometry (e.g., points, lines, polygons) and associated attributes. Key Distinction : While the terms are often interchangeable, spatial entity tends to focus on the real-world phenomenon, whereas spatial object highlights its representation in GIS. Key Terminologies Geographic Coordinates : Define the location of spatial entities using a coordinate system (e.g., latitude and longitude). Example: A building at 40.748817° N, 73.985428° W . Geometry Types : Point : Represents a single location (e.g., a well or a bus stop). Line : Represents linear features (e.g., roads, rivers). Polyg...

Atmospheric Window

The atmospheric window in remote sensing refers to specific wavelength ranges within the electromagnetic spectrum that can pass through the Earth's atmosphere relatively unimpeded. These windows are crucial for remote sensing applications because they allow us to observe the Earth's surface and atmosphere without significant interference from the atmosphere's constituents. Key facts and concepts about atmospheric windows: Visible and Near-Infrared (VNIR) window: This window encompasses wavelengths from approximately 0. 4 to 1. 0 micrometers. It is ideal for observing vegetation, water bodies, and land cover types. Shortwave Infrared (SWIR) window: This window covers wavelengths from approximately 1. 0 to 3. 0 micrometers. It is particularly useful for detecting minerals, water content, and vegetation health. Mid-Infrared (MIR) window: This window spans wavelengths from approximately 3. 0 to 8. 0 micrometers. It is valuable for identifying various materials, incl...