Geographic Data Precision and Data Organization

Geographic Data Precision

Definition:
Precision in geographic data refers to the level of detail and exactness of spatial data, including coordinate measurements, attribute values, and scale representation.

Key Concepts and Terminologies:

Spatial Resolution: The smallest measurable unit in a dataset. For raster data, it refers to the pixel size (e.g., Sentinel-2 has a 10m resolution for some bands).
Positional Accuracy: The closeness of recorded spatial coordinates to their true location (e.g., GPS readings within ±3 meters).
Attribute Accuracy: The correctness of non-spatial information (e.g., land cover classification).
Temporal Accuracy: The precision of time-related aspects in data, such as timestamps in satellite imagery.
Scale Dependence: The relationship between data precision and map scale (e.g., a 1:10,000 scale map has more detailed features than a 1:100,000 map).
Error Propagation: The accumulation of inaccuracies when processing spatial data (e.g., errors in digital elevation models affecting watershed analysis).

Example of Geographic Data Precision:

A land use/land cover (LULC) map derived from high-resolution imagery (e.g., 5m resolution) will provide more precise details compared to a lower-resolution 30m Landsat image.
GPS tracking for wildlife monitoring may record locations with ±5m accuracy, affecting movement pattern analysis.

2. Geographic Data Organization

Definition:
Geographic data organization refers to the systematic structuring, storage, and management of spatial data to ensure efficient retrieval and analysis.

Types of Geographic Data Organization:

Spatial Data Models:
- Vector Data: Represents discrete features using points, lines, and polygons.
- Raster Data: Represents continuous surfaces through grid cells (e.g., elevation models).
Database Structures:
- Flat Files: Simple text or CSV files storing geographic coordinates and attributes.
- Relational Databases (RDBMS): Uses tables with spatial indexing (e.g., PostgreSQL/PostGIS).
- NoSQL Databases: For handling unstructured geographic data (e.g., MongoDB with geospatial indexing).
Data Hierarchies:
- Raw Data → Processed Data → Finalized Datasets
- Global → National → Regional → Local Datasets
Spatial Indexing & Metadata:
- Quadtrees & R-trees: Spatial indexing methods for efficient data retrieval.
- Metadata Standards: FGDC, ISO 19115 ensure proper documentation of spatial datasets.

Example of Geographic Data Organization:

In Google Earth Engine (GEE), Sentinel-2 imagery is stored as a raster dataset with bands representing different spectral wavelengths.
A city's road network stored in a GIS database may use a vector-based relational structure, where road segments have attributes like speed limits and road types.

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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...

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 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

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...

Geographic phenomena fields objects boundaries.

In geography, geographic phenomena refer to features or processes that can be observed and studied on Earth's surface. These phenomena can be classified into three main categories: fields , objects , and boundaries . Each category has distinct characteristics, representations, and applications in Geographic Information Systems (GIS). 1. Fields A field represents continuous, spatially varying data where a value is present at every location within the study area. It describes conditions that exist across a geographic area. Characteristics : Continuity : Fields have no discrete boundaries; the data is continuous. Gradual Variability : The values of a field change gradually across space. Representation : Typically modeled using raster data in GIS, where a grid structure assigns a value (e.g., temperature or elevation) to each cell. Examples : Temperature Map : Shows temperature variation across a region. Rainfall Distribution : Displays rainfall levels over a large g...

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