Skip to main content

The global dimensions of disaster

Disasters are not merely natural occurrences but complex interactions between natural hazards and human vulnerabilities. To effectively address disaster risk, we must consider several interconnected dimensions:

1. Vulnerability:

  • Definition: The susceptibility of individuals, communities, or assets to harm from a disaster.
  • Factors: Socioeconomic conditions, geographic location, and environmental factors influence vulnerability.
  • Example: Communities with high poverty rates and limited access to resources are more vulnerable to disaster impacts.

2. Exposure:

  • Definition: The degree to which people, property, and infrastructure are located in hazard-prone areas.
  • Factors: Population density, land use patterns, and infrastructure development influence exposure.
  • Example: Coastal cities with high population density are highly exposed to hurricane and tsunami risks.

3. Capacity:

  • Definition: A community's ability to prepare for, respond to, and recover from disasters.
  • Factors: Strong governance, early warning systems, resilient infrastructure, and community preparedness contribute to capacity.
  • Example: Countries with well-developed disaster management systems and resilient infrastructure can recover more quickly from disasters.

4. Hazard Characteristics:

  • Definition: The nature, intensity, frequency, and duration of a hazard.
  • Factors: Climate change, tectonic activity, and human activities can influence hazard characteristics.
  • Example: Increasing frequency and intensity of extreme weather events due to climate change pose significant risks to communities.

5. Data and Information Management:

  • Definition: The collection, analysis, and dissemination of data to inform decision-making and improve disaster response.
  • Factors: Advanced technologies, effective communication systems, and data-driven approaches are crucial.
  • Example: Early warning systems rely on real-time data to alert communities of impending hazards.

6. Governance:

  • Definition: The institutional framework that coordinates disaster risk reduction efforts.
  • Factors: Strong leadership, effective policies, and public-private partnerships are essential.
  • Example: Well-governed countries with transparent and accountable institutions are better equipped to manage disaster risks.

The Disaster Risk Equation

The interplay of these dimensions can be encapsulated in a simple equation:

Risk = Hazard x Vulnerability x Exposure / Capacity

By reducing vulnerability, exposure, and enhancing capacity, we can significantly mitigate disaster risk.

The Sendai Framework

The Sendai Framework for Disaster Risk Reduction 2015-2030 provides a global blueprint for building resilient societies. It emphasizes:

  • Reducing exposure and vulnerability through sustainable development.
  • Strengthening governance to improve coordination and decision-making.
  • Improving resilience and adaptive capacity to enhance community preparedness and response.




Fyugp note 
Disaster Management 

PG and Research Department of Geography,
Government College Chittur, Palakkad
https://g.page/vineeshvc

Comments

Popular posts from this blog

Accuracy Assessment

Accuracy assessment is the process of checking how correct your classified satellite image is . 👉 After supervised classification, the satellite image is divided into classes like: Water Forest Agriculture Built-up land Barren land But classification is done using computer algorithms, so some areas may be wrongly classified . 👉 Accuracy assessment helps to answer this question: ✔ "How much of my classified map is correct compared to real ground conditions?"  Goal The main goal is to: Measure reliability of classified maps Identify classification errors Improve classification results Provide scientific validity to research 👉 Without accuracy assessment, a classified map is not considered scientifically reliable . Reference Data (Ground Truth Data) Reference data is real-world information used to check classification accuracy. It can be collected from: ✔ Field survey using GPS ✔ High-resolution satellite images (Google Earth etc.) ✔ Existing maps or survey reports 🧭 Exampl...

Landsat 8 Band designation and Band Combination.

Landsat 8 Band designation and Band Combination.  Landsat 8-9 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) Bands Wavelength (micrometers) Resolution (meters) Band 1 - Coastal aerosol 0.43-0.45 30 Band 2 - Blue 0.45-0.51 30 Band 3 - Green 0.53-0.59 30 Band 4 - Red 0.64-0.67 30 Band 5 - Near Infrared (NIR) 0.85-0.88 30 Band 6 - SWIR 1 1.57-1.65 30 Band 7 - SWIR 2 2.11-2.29 30 Band 8 - Panchromatic 0.50-0.68 15 Band 9 - Cirrus 1.36-1.38 30 Band 10 - Thermal Infrared (TIRS) 1 10.6-11.19 100 Band 11 - Thermal Infrared (TIRS) 2 11.50-12.51 100 Vineesh V Assistant Professor of Geography, Directorate of Education, Government of Kerala. https://www.facebook.com/Applied.Geography http://geogisgeo.blogspot.com

Change Detection

Change detection is the process of finding differences on the Earth's surface over time by comparing satellite images of the same area taken on different dates . After supervised classification , two classified maps (e.g., Year-1 and Year-2) are compared to identify land use / land cover changes .  Goal To detect where , what , and how much change has occurred To monitor urban growth, deforestation, floods, agriculture, etc.  Basic Concept Forest → Forest = No change Forest → Urban = Change detected Key Terminologies Multi-temporal images : Images of the same area at different times Post-classification comparison : Comparing two classified maps Change matrix : Table showing class-to-class change Change / No-change : Whether land cover remains same or different Main Methods Post-classification comparison – Most common and easy Image differencing – Subtract pixel values Image ratioing – Divide pixel values Deep learning methods – Advanced AI-based detection Examples Agricult...

Landsat band composition

Short-Wave Infrared (7, 6 4) The short-wave infrared band combination uses SWIR-2 (7), SWIR-1 (6), and red (4). This composite displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation has lighter shades. Urban areas are blue and soils have various shades of brown. Agriculture (6, 5, 2) This band combination uses SWIR-1 (6), near-infrared (5), and blue (2). It's commonly used for crop monitoring because of the use of short-wave and near-infrared. Healthy vegetation appears dark green. But bare earth has a magenta hue. Geology (7, 6, 2) The geology band combination uses SWIR-2 (7), SWIR-1 (6), and blue (2). This band combination is particularly useful for identifying geological formations, lithology features, and faults. Bathymetric (4, 3, 1) The bathymetric band combination (4,3,1) uses the red (4), green (3), and coastal bands to peak into water. The coastal band is useful in coastal, bathymetric, and aerosol studies because...

Energy Interaction with Atmosphere and Earth Surface

In Remote Sensing , satellites record electromagnetic radiation (EMR) that is reflected or emitted from the Earth. Before reaching the sensor, radiation interacts with: The Atmosphere The Earth's Surface These interactions control how satellite images look and how we interpret them. I. Interaction of EMR with the Atmosphere When solar radiation travels from the Sun to the Earth, four main processes occur: 1. Absorption Definition: Absorption occurs when atmospheric gases absorb radiation at specific wavelengths and convert it into heat. Main absorbing gases: Ozone (O₃) → absorbs Ultraviolet (UV) Carbon dioxide (CO₂) → absorbs Thermal Infrared Water vapour (H₂O) → absorbs Infrared Concept: Atmospheric Windows These are wavelength regions where absorption is very low, allowing radiation to pass through the atmosphere. Remote sensing depends on these windows. For example, satellites like Landsat 8 use visible, near-infrared, and thermal bands located in atmospheric windows. 2. Trans...