GIS. Raster Data Analysis

Raster data analysis involves applying mathematical and statistical functions to the pixel values within a raster dataset. This process enables various tasks such as image classification and segmentation. Here's an overview of how these techniques are commonly used:

1. Image Classification: Image classification is the process of assigning predefined categories or classes to individual pixels in an image based on their spectral characteristics. This technique allows you to classify land cover, vegetation types, or any other features of interest in a raster dataset. Common classification algorithms include Maximum Likelihood, Support Vector Machines (SVM), and Random Forest. These algorithms use mathematical and statistical techniques to differentiate and categorize pixels based on their spectral signatures.

2. Image Segmentation: Image segmentation involves dividing an image into meaningful and homogeneous regions based on pixel values. It aims to group pixels with similar characteristics and is often used as a preprocessing step for further analysis. Segmentation algorithms such as K-means clustering, region-growing, or watershed transform utilize mathematical calculations and statistical measures to partition the image into distinct regions.

3. Mathematical Operations: Raster data analysis allows you to perform mathematical operations on pixel values within a raster dataset. These operations include addition, subtraction, multiplication, division, exponentiation, logarithmic transformations, and more. Mathematical functions can be used to enhance or normalize data, calculate indices (e.g., vegetation indices like NDVI), or combine multiple raster datasets for further analysis.

4. Statistical Analysis: Statistical functions can be applied to raster data to derive valuable insights and explore spatial patterns. Common statistical measures include mean, median, mode, standard deviation, variance, range, skewness, and kurtosis. These measures help characterize the distribution of pixel values within a raster dataset, identify outliers, or analyze patterns of variation.

5. Change Detection: Raster data analysis enables the comparison of pixel values between different time periods or datasets to detect and quantify changes in the landscape. By applying statistical techniques like image differencing, t-tests, or chi-square tests, you can identify areas where significant changes have occurred, such as land cover change, urban expansion, or vegetation growth.

6. Hyperspectral Analysis: Hyperspectral analysis involves working with raster datasets with numerous spectral bands, providing detailed information about the Earth's surface. Advanced mathematical and statistical techniques, such as spectral unmixing, endmember extraction, or feature selection algorithms, are used to analyze and interpret hyperspectral data for applications like mineral mapping, environmental monitoring, or precision agriculture.

These techniques demonstrate how mathematical and statistical functions play a crucial role in raster data analysis, allowing for image classification, segmentation, and gaining insights into spatial patterns and changes. GIS software provides a range of tools and algorithms to perform these analyses efficiently and effectively.

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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...

Development and scope of Environmental Geography and Recent concepts in environmental Geography

Environmental Geography studies the relationship between humans and nature in a spatial (place-based) way. It combines Physical Geography (natural processes) and Human Geography (human activities). A. Early Stage 🔹 Environmental Determinism Concept: Nature controls human life. Meaning: Climate, landforms, and soil decide how people live. Example: People in deserts (like Sahara Desert) live differently from people in fertile river valleys. 🔹 Possibilism Concept: Humans can modify nature. Meaning: Environment gives options, but humans make choices. Example: In dry areas like Rajasthan, people use irrigation to grow crops. 👉 In this stage, geography was mostly descriptive (explaining what exists). B. Evolution Stage (Mid-20th Century) Environmental problems increased due to: Industrialization Urbanization Deforestation Pollution Geographers started studying: Environmental degradation Resource management Human impact on ecosystems The field became analytical and problem-solving...

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