Spatial data and their management in GIS.pdf

Spatial data management in a Geographic Information System (GIS) involves storing and organizing spatial data in a computer. Spatial data is information that has a locational attribute, such as the size, shape, or location of an object. GIS uses specialized software to access, visualize, and analyze spatial data.
Aspects of spatial data management in GIS:
Data capture: Storing spatial data in a computer
Database creation: Organizing spatial data in a computer
Data types: Spatial data can be represented as points, lines, polygons, or pixels. It can also include images as rasters, which are grids of pixels.
Data storage: Spatial data is often stored as coordinates and topology.
Data analysis: GIS tools can be used to analyze spatial data.
Data collection: Spatial data can be collected from a variety of sources, including ground surveys, census studies, land records, satellite images, aerial photographs, and GPS receivers.
Data joins: Joins can be used to combine feature information based on spatial location and association.
Relates: Relates can be used to temporarily associate two map layers or tables.

1. Spatial data and their management in GIS
Geoinformatics and Nanotechnology and Precision
Farming 2(1+1)
Course Teachers
Dr. M. KUMARESAN, Ph.D.
(Hort.)
School of Agriculture
Vels Institute of Science, Technology
and Advanced Studies (VISTAS)
Pallavaram, Chennai - 600 117

2. Spatial data is information that identifies the location and characteristics of
Earth surface. It can also be called geographic data or geospatial data.
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Spatial database management deals with the storage, indexing, and querying of
data with spatial features, such as location and geometric extent. Many
applications require the efficient management of spatial data, including
Geographic Information Systems, Computer Aided Design, and Location
Based Services.
Spatial data plays an important role in precision farming, enabling farmers to
optimize agricultural practices by providing detailed, location-based
information.
Spatial data

3. Spatial analysis in GIS helps to identify trends on the data, create new
relationships from the data, and view complex relationships between datasets
to make better decisions.
Vector and raster overlay operations are two different types of overlays in GIS
depending upon data structures. GIS provides different ways in which the
information can be presented once it is analyzed and processed by GIS.
Relational database management system (RDBMS) is the most effective and
efficient data storage and management model in spatial database generation
and management in GIS
Spatial data

4. Aspects of spatial data management in GIS
Data capture: Capturing spatial data involves storing it in a computer.
Spatial database creation: Organizing spatial data in a computer involves creating a
spatial database.
Data types: Spatial data can be stored in different formats, such as raster data and
vector data. Raster data is made up of grid cells, while vector data is made up of
points, polylines, and polygons.
Data tools: GIS tools can be used to access, update, manipulate, and analyze spatial
data in a spatial database.
Data quality: It's important to assess and achieve the quality of the data inputs and
information outputs of the system.
Social impacts: It's important to consider the positive and negative impacts that a GIS
can have on society.

5. 1. Types of Spatial Data in Precision Farming
A. Vector Data
Vector data represents discrete geographic features and is used to store information
about things that have specific locations, shapes, and boundaries.
In precision farming, vector data is often used for:
Field boundaries: Defining the boundaries of fields, plots, or zones where different
farming practices are applied.
Farm infrastructure: Locations of roads, irrigation systems, machinery storage areas,
and fencing.
Soil samples: Points representing soil sampling locations within the field.
Zones for Variable Rate Technology (VRT): Identifying specific zones in the field
based on soil properties or crop performance to apply variable fertilizer rates.

6. 1. Types of Spatial Data in Precision Farming
B. Raster Data
Raster data represents continuous geographic features and is composed of grids of
cells, each with a value (e.g., temperature, soil moisture, or elevation).
In precision farming, raster data is often used for:
Satellite or drone imagery: Multispectral and hyperspectral imagery to monitor crop
health, detect nutrient deficiencies, and observe field variability.
Soil maps: Elevation, pH, organic matter, and nutrient content across a field.
NDVI (Normalized Difference Vegetation Index) maps: Showing crop health based
on infrared and visible light reflectance, often used to detect stress or nutrient
deficiencies.
Weather data: Information on temperature, precipitation, and wind speed that
impacts crop growth.

7. 2. Sources of Spatial Data in Precision Farming
Several data sources are used in precision farming, contributing to the complete spatial
dataset needed for effective decision-making:
GPS (Global Positioning System): Provides precise location data for field mapping,
soil sampling, and real-time tracking of farm machinery.
Remote Sensing: Drones, UAVs, satellites, and aircraft capture multispectral and
thermal imagery to monitor crop health and environmental conditions.
IoT Sensors: In-field sensors that measure soil moisture, temperature, nutrient levels,
and pH.
Soil Test Data: Collected through manual sampling or automated systems, providing
soil nutrient levels, pH, and other key properties.
Weather Stations: Localized weather data (temperature, humidity, rainfall) to help
predict crop growth and optimize irrigation and fertilization schedules.

8. 3. Managing Spatial Data in GIS for Precision Farming
Efficient management of spatial data in GIS is vital for processing, analyzing, and
making decisions. The management process includes data collection, storage, analysis,
and visualization.
A. Data Collection and Input
Data is collected through various tools and technologies and is input into a GIS
system:
GPS-enabled devices: Allow farmers to collect field boundary data, soil sample
locations, and crop-specific data.
Remote sensing tools: Satellite and drone imagery are processed and fed into GIS to
create detailed maps of crop health, soil conditions, and environmental variables.
Manual and sensor data: Soil test results, field observations, and sensor data (e.g., soil
moisture and temperature) are input into GIS platforms for analysis.

9. 3. Managing Spatial Data in GIS for Precision Farming
B. Data Storage and Integration
GIS provides a central database to store all spatial and attribute data, which can be
accessed and manipulated for various agricultural tasks:
Database Management: Spatial data is stored in a database, with each dataset tagged
with metadata such as collection time, source, and precision level.
Data Layering: Different types of spatial data (e.g., soil nutrient levels, crop health, and
weather data) are stored in separate layers within the GIS. These layers are then
arranged and analyzed to identify patterns and make decisions. For instance, a field’s
soil pH data can be overlaid with yield maps to understand nutrient deficiencies.
Data Management: Real-time data from sensors, drones, and other sources can be
synchronized with the GIS platform to ensure up-to-date information is always
available.

10. 3. Managing Spatial Data in GIS for Precision Farming
C. Data Analysis and Processing
The core strength of GIS in precision farming lies in its ability to analyze spatial data
to extract meaningful insights. Various GIS analysis tools and techniques are used to
process spatial data:
Spatial Analysis: GIS tools allow for spatial analysis, such as determining areas of a
field with nutrient deficiencies, calculating crop yield variability, or identifying patterns
of disease or pest infestation.
Overlay Analysis: Combining layers of different data types (e.g., soil maps, crop health
data, yield maps) to identify relationships.
Trend Analysis: Identifying changes over time, such as soil nutrient depletion or
improvement in crop health due to specific farming practices.

11. 3. Managing Spatial Data in GIS for Precision Farming
D. Data Visualization
Visual representation of spatial data helps farmers interpret complex datasets and
make informed decisions:
Maps: GIS generates visual maps of the field that highlight various features such as soil
variability, crop health, and pest infestations. These maps make it easier for farmers to
understand which areas need attention. Example: Yield Maps: After harvest, GIS can
generate yield maps that display the spatial variability of crop yields across the field.
3D Visualization: Advanced GIS tools allow for 3D visualizations of the field, which
can help farmers better understand topography and field layout, aiding in irrigation
and drainage management.
Heat Maps: Heat maps show the intensity of specific features, such as soil nutrient
levels or crop stress, helping farmers quickly identify areas needing attention.

12. 3. Managing Spatial Data in GIS for Precision Farming
E. Decision Support and Prescription Maps
Once spatial data is analyzed, GIS can generate prescription maps that provide
recommendations for site-specific management.
These maps include:
Fertilizer Prescription Maps: Based on soil nutrient maps, these maps show where and
how much fertilizer to apply in different areas of a field. This is especially useful for
Variable Rate Technology (VRT) application.
Irrigation Maps: GIS can provide recommendations for irrigation based on soil
moisture data, topography, and weather patterns.
Pest and Disease Management: GIS tools can generate maps to help manage pest
outbreaks or diseases by identifying problem areas based on historical data and
current field conditions.

13. 4. Challenges in Managing Spatial Data for Precision Farming
While GIS is a powerful tool, there are challenges associated with the management of
spatial data in precision farming:
Data Accuracy: Precision farming relies on high-quality, accurate spatial data.
Inaccurate GPS locations, sensor malfunctions, or poor satellite imagery can lead to
imperfect decision-making.
Data Integration: Integrating data from multiple sources (e.g., satellite imagery,
sensors, weather data) can be complex, requiring specialized software and expertise.
Data Overload: The complete volume of data collected can be overwhelming.
Managing, processing, and analyzing large datasets requires robust storage systems and
advanced computational tools.
Cost of Technology: High-quality sensors, GPS devices, drones, and remote sensing
technology can be expensive, making it challenging for some farmers to access these
tools.

14. 5. Benefits of Spatial Data Management in Precision Farming
Proper spatial data management in GIS provides several benefits to precision farming:
Improved Efficiency: By making site-specific decisions based on spatial data, farmers
can optimize their resource use (e.g., water, fertilizer), leading to higher crop yields
with lower input costs.
Sustainability: GIS allows for more precise management of inputs, reducing waste and
environmental impact, such as nutrient runoff or overuse of water.
Increased Productivity: The ability to identify high- and low-performing areas within a
field allows farmers to take targeted actions, boosting overall farm productivity.
Better Decision Making: Access to detailed spatial data helps farmers make data-
driven decisions rather than relying on guesswork, leading to more effective farm
management.