Skip to main content

Kriging in GIS and variogram

Kriging is an advanced spatial interpolation technique used in GIS (Geographic Information System) that estimates values for unknown locations based on the values observed at nearby known locations. It is a geostatistical method that takes into account not only the distances between points but also the spatial correlation or variability in the data.

Unlike simpler interpolation methods like IDW, which assume a constant variation across the study area, kriging incorporates the spatial autocorrelation of the data to produce more accurate and precise estimates. Kriging considers the spatial arrangement and patterns of the data points to generate a surface that honors the underlying spatial structure.

The key principle behind kriging is the variogram, which quantifies the spatial correlation between pairs of points at different distances. The variogram measures how the values of nearby points vary from each other as a function of distance. It provides information about the spatial dependence or variability in the dataset.

The kriging process involves three main steps:

1. Variogram modeling: The first step in kriging is to construct a variogram, which is a plot of the semivariance (a measure of dissimilarity or variability) against distance or lag between pairs of points. The variogram helps to understand the spatial structure of the data and determine the range, sill, and nugget effect. Based on the variogram, a mathematical model is fitted to describe the spatial correlation.

2. Interpolation: Once the variogram is modeled, kriging calculates the weights or coefficients for the known points based on their spatial relationship to the target location. The weights are determined through a process known as kriging equations, which consider the variogram and covariance between points. These equations generate the optimal weights that minimize the prediction error.

   - Ordinary Kriging (OK): Assumes a constant mean value across the study area.
   - Simple Kriging (SK): Accounts for an unknown mean value, estimating it from the data.
   - Universal Kriging (UK): Incorporates additional spatially correlated variables (covariates) in addition to the location coordinates.

3. Prediction: The final step is the estimation of values at the unknown locations using the calculated weights. Kriging provides not only the predicted values but also the estimation error or uncertainty associated with each prediction. This information can be valuable in decision-making processes.

Kriging is particularly useful when dealing with spatial datasets that exhibit spatial autocorrelation, anisotropy (directional dependence), or irregularly spaced points. It provides a more sophisticated approach to spatial interpolation by considering the inherent spatial relationships in the data.

GIS software typically provides various kriging algorithms and tools that allow users to model the variogram, perform the interpolation, and generate kriging predictions and associated error maps.

Comments

Post a Comment

Popular posts from this blog

geostationary and sun-synchronous

Orbital characteristics of Remote sensing satellite geostationary and sun-synchronous  Orbits in Remote Sensing Orbit = the path a satellite follows around the Earth. The orbit determines what part of Earth the satellite can see , how often it revisits , and what applications it is good for . Remote sensing satellites mainly use two standard orbits : Geostationary Orbit (GEO) Sun-Synchronous Orbit (SSO)  Geostationary Satellites (GEO) Characteristics Altitude : ~35,786 km above the equator. Period : 24 hours → same as Earth's rotation. Orbit type : Circular, directly above the equator . Appears "stationary" over one fixed point on Earth. Concepts & Terminologies Geosynchronous = orbit period matches Earth's rotation (24h). Geostationary = special type of geosynchronous orbit directly above equator → looks fixed. Continuous coverage : Can monitor the same area all the time. Applications Weather...
Post a Comment
Read more

Disaster Risk

Disaster Risk 
Post a Comment
Read more

Disaster Management

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...
Post a Comment
Read more

Evaluation and Characteristics of Himalayas

Time Period Event / Process Geological Evidence Key Terms & Concepts Late Precambrian – Palaeozoic (>541 Ma – ~250 Ma) India part of Gondwana , north bordered by Cimmerian Superterranes, separated from Eurasia by Paleo-Tethys Ocean . Pan-African granitic intrusions (~500 Ma), unconformity between Ordovician conglomerates & Cambrian sediments. Gondwana, Paleo-Tethys Ocean, Pan-African orogeny, unconformity, granitic intrusions, Cimmerian Superterranes. Early Carboniferous – Early Permian (~359 – 272 Ma) Rifting between India & Cimmerian Superterranes → Neotethys Ocean formation. Rift-related sediments, passive margin sequences. Rifting, Neotethys Ocean, passive continental margin. Norian (210 Ma) – Callovian (160–155 Ma) Gondwana split into East & West; India part of East Gondwana with Australia & Antarctica. Rift basins, oceanic crust formation. Continental breakup, East Gondwana, West Gondwana, oceanic crust. Early Cretaceous (130–125 Ma) India broke fr...
Post a Comment
Read more

Discrete Detectors and Scanning mirrors Across the track scanner Whisk broom scanner.

Multispectral Imaging Using Discrete Detectors and Scanning Mirrors (Across-Track Scanner or Whisk Broom Scanner) Multispectral Imaging:  This technique involves capturing images of the Earth's surface using multiple sensors that are sensitive to different wavelengths of electromagnetic radiation.  This allows for the identification of various features and materials based on their spectral signatures. Discrete Detectors:  These are individual sensors that are arranged in a linear or array configuration.  Each detector is responsible for measuring the radiation within a specific wavelength band. Scanning Mirrors:  These are optical components that are used to deflect the incoming radiation onto the discrete detectors.  By moving the mirrors,  the sensor can scan across the scene,  capturing data from different points. Across-Track Scanner or Whisk Broom Scanner:  This refers to the scanning mechanism where the mirror moves perpendicular to the direction of flight.  This allows for t...
Post a Comment
Read more