Vineesh V, Geography

 The model highlights the interaction between the aeolian and fluvial environments and the dominance of the upper flow regime and high sediment load structures within the fluvial environment. (Priddy and Clarke, 2020).

🍀 The Normalized Difference Vegetation Index (NDVI) is used to quantify vegetation greenness and is useful in understanding vegetation density and assessing changes in plant health. 🍀 The Enhanced Vegetation Index (EVI) corrects for some atmospheric conditions and canopy background noise and is more sensitive in areas with dense vegetation. 🍀 The Soil Adjusted Vegetation Index (SAVI) is used to correct NDVI for the influence of soil brightness in areas with low vegetation cover.  🍀 The Modified Soil Adjusted Vegetation Index (MSAVI) further minimizes the effects of bare soil in areas with low vegetation cover.

Graduated Symbol with Quantile Classification Geographical data visualization plays a crucial role in GIS-based research, helping to reveal spatial patterns and distributions. One such method is the Graduated Symbol Map with Quantile Classification , which combines statistical categorization with symbolic representation for effective data interpretation. 1. The Concept of Graduated Symbols Graduated symbols in GIS are proportional representations of numerical data assigned to geographical features. The size of each symbol changes according to the magnitude of the associated data attribute. This technique is commonly used for: Visualizing variation in spatial datasets (e.g., crime rates, GDP, population density). Highlighting relative differences rather than absolute values. Avoiding misinterpretation often caused by color-based representations in choropleth maps. For instance, in a crime rate map , cities with higher crime rates would be represented with larger circles,...

Schematic longitudinal cross-section of a Gilbert-type delta, showing characteristic architectural features and stratal stacking patterns (Winsemann et al., 2018).

When a river flows into a lake or ocean, its speed slows down, and it starts dropping the sediment it was carrying. Over time, these sediments pile up and form a delta—a landform that grows outward into the water. 🔹 Why does this happen? As the water slows, heavier particles like sand and gravel settle first, while lighter ones like silt and clay drift further out. This creates layers of sediment, with sloping layers building outward into the lake. 🔹 What does this look like over time? With more and more sediment, the delta keeps expanding. You can often see sandy, sloping deposits near the river mouth and finer mud deposits spreading further into the lake. Deltas are fascinating because they shape landscapes, provide habitats, and even preserve records of past climates!

GIS 

1. Disaster Risk Analysis → Disaster Risk Reduction → Disaster Management Cycle Disaster Risk Analysis is the first step in managing disasters. It involves assessing potential hazards, identifying vulnerable populations, and estimating possible impacts. Once risks are identified, Disaster Risk Reduction (DRR) strategies come into play. DRR aims to reduce risk and enhance resilience through planning, infrastructure development, and policy enforcement. The Disaster Management Cycle then ensures a structured approach by dividing actions into pre-disaster, during-disaster, and post-disaster phases . Example Connection: Imagine a coastal city prone to cyclones: Risk Analysis identifies low-lying areas and weak infrastructure. Risk Reduction includes building seawalls, enforcing strict building codes, and training residents for emergency situations. The Disaster Management Cycle ensures ongoing preparedness, immediate response during a cyclone, and long-term recovery afterw...

1. Divergent Basins (Extensional Basins) Form due to crustal extension and thinning, commonly associated with rifting and continental breakup. Examples: Rift basins, passive margin basins a) Rift Basins Develop along extensional fault zones where the lithosphere is stretched. Characterized by normal faults, grabens, and half-grabens. Common in early-stage continental breakup (e.g., East African Rift System, North Sea Rift). Petroleum Significance: Excellent source rocks (lacustrine shales) and structural traps (fault-bounded reservoirs). b) Passive Margin Basins Found along continental margins after rifting stops and seafloor spreading begins. Thick sequences of sediments accumulate due to thermal subsidence. Examples: Gulf of Mexico, West African Margin. Petroleum Significance: Rich in organic-rich marine shales (source rocks) and large sandstone reservoirs. 2. Convergent Basins (Compressional Basins) Form due to plate collision and crustal shortening. Examples: Foreland basins, forea...

This diagram illustrates the factors influencing coastal cliff erosion, including sea level rise, wave energy, coastal slope, beach width, beach height, and rock strength. These factors interact to control cliff stability, erosion rates, and long-term coastal retreat. (Credit: USGS)

The mapping process involves several systematic steps to transform real-world spatial information into a readable, accurate, and useful representation. Below is a structured explanation of each step in the mapping process, with key concepts, terminologies, and examples. 1. Defining the Purpose of the Map Before creating a map, it is essential to determine its purpose and audience . Different maps serve different objectives, such as navigation, analysis, or communication. Types of Maps Based on Purpose: Thematic Maps: Focus on specific subjects (e.g., climate maps, population density maps). Topographic Maps: Show natural and human-made features (e.g., contour maps, landform maps). Tourist Maps: Highlight attractions, roads, and landmarks for travelers. Cadastral Maps: Used in land ownership and property boundaries. Navigational Maps: Used in GPS systems for wayfinding. Example: A disaster risk map for floods will highlight flood-prone areas, emergency shelters, and ...

S patial Data Components Location or Position This defines where a spatial object exists on the Earth's surface. It is represented using coordinate systems , such as: Geographic Coordinate System (GCS) – Uses latitude and longitude (e.g., WGS84). Projected Coordinate System (PCS) – Converts Earth's curved surface into a flat map using projections (e.g., UTM, Mercator). Example: The Eiffel Tower is located at 48.8584° N, 2.2945° E in the WGS84 coordinate system. Attribute Data (Descriptive Information About Location) Describes characteristics of spatial features and is stored in attribute tables . Types of attribute data: Nominal Data – Categories without a numerical value (e.g., land use type: residential, commercial). Ordinal Data – Ranked categories (e.g., soil quality: poor, moderate, good). Interval Data – Numeric values without a true zero (e.g., temperature in °C). Ratio Data – Numeric values with a true zero (e.g., population count, rainfall amoun...

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GIS vector data analysis involves processing and interpreting geographic features represented as points, lines, and polygons to identify spatial relationships, patterns, and trends. This analysis supports decision-making in urban planning, environmental management, transportation networks, and other spatial applications. Vector Data Types Vector data represents discrete spatial features and is ideal for precise location analysis. The three main types are: Point Data Represents individual locations. Example : Store locations, crime incidents, weather stations. Line Data Represents linear features with length but no width. Example : Roads, rivers, power lines. Polygon Data Represents enclosed areas with boundaries. Example : Administrative zones, lakes, land use areas. Vector Data Attributes Each vector feature has an associated attribute table , containing descriptive information such as: Population Density (for city polygons) Road Type (for road l...

Raster analysis is a powerful spatial analysis technique used in GIS to process and interpret grid-based datasets. It is widely applied in fields such as land cover classification, terrain modeling, hydrological studies, environmental monitoring, and spatial decision-making. How Raster Analysis Works Raster data is stored in a grid format where each cell (or pixel) represents a specific geographic location and contains a single value. The value can represent elevation, temperature, land cover type, or any other spatially continuous variable. 1. Spatial Resolution The size of each cell in a raster dataset determines the level of detail. Example : A 30m resolution DEM (Digital Elevation Model) means each cell represents a 30m × 30m area. 2. Extent The geographic area covered by a raster dataset. Example : A raster covering an entire country will have a larger extent than one covering a single city. 3. Cell Values and Data Types Continuous Data : Represents smoothly vary...

Summary of common stacking patterns and their associated siliciclastic settings. 

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...

A cartogram  (also called a value-area map or an anamorphic map, the latter common among German-speakers) is a type of thematic map where the geographic size of areas (like countries or provinces) is distorted to reflect a specific variable, such as population, GDP, or travel time. This makes it easier to visualize data patterns. Types Contiguous Cartograms – Shapes are distorted, but areas remain connected. Non-contiguous Cartograms – Shapes maintain their form but are resized and may be separated. Diagrammatic Cartograms – Replace areas with simple geometric shapes (circles, squares). Mosaic Cartograms – Divide areas into small equal-sized units, like hexagons or squares. Linear Cartograms – Adjust the length of lines to represent variables like travel time. History The first cartogram was made in 1876 by Pierre Émile Levasseur , using squares to represent European countries by population, economy, and religion. Hermann Haack and Hugo Weichel (1898) created the first ...

A dasymetric map is a type of thematic map that improves upon choropleth maps by refining the way data is distributed over geographic areas. Instead of using administrative boundaries (such as counties or districts) to show data, it uses ancillary data (like land use or satellite imagery) to more accurately represent where people or other mapped features are actually located. The term "dasymetric" was coined in 1911 by Benjamin Semyonov-Tian-Shansky, who first fully developed and documented the technique, defining them as maps "on which population density, irrespective of any administrative boundaries, is shown as it is distributed in reality, i.e. by natural spots of concentration and rarefaction. Key Features of a Dasymetric Map Uses Additional Data – Unlike choropleth maps, it integrates extra data sources like land cover, population density, or satellite imagery. More Accurate Representation – It removes uninhabited areas (e.g., water bodies, forests) and ...

A spatial query in Geographic Information Systems (GIS) is a type of database query that retrieves geographic data based on spatial relationships such as location, proximity, or overlap. Unlike attribute-based queries, which retrieve data based on non-spatial characteristics (e.g., "find all schools with more than 500 students"), spatial queries leverage geometric data (points, lines, polygons) to analyze relationships between spatial features. 1. Spatial Relationships Spatial queries analyze how geographic features relate to each other in space. The key spatial relationships include: Distance (Proximity) : How far apart features are. Direction (Orientation) : The relative position of one feature concerning another. Containment : Whether one feature is completely inside another. Intersection : Whether two or more features share common space. Adjacency (Touching) : Whether features share a boundary. Overlay : Combining multiple layers to derive new information. 2. G...