Wednesday, February 7, 2024

Miscellaneous topics of further interest to the GIS professional

First GIS in Canada in 1960s.
MIDAS was used in the USA for processing data on natural resources
Nystuen fundamental spatial concepts - distance, orientation, connectivity
Tobler computer algorithms for map projections, computer cartography
Bunge-theoretical geography-basis for geographic points, lines and areas
Berry's geographical matrix of places by characteristics-regional studies by overlaying maps of different themes
Boost to GISdevelopment by 1960 by CGIS
Second burst of activity by US census in late 1960s = DIME led to ODESSEY GIS in late 1970s
WWW was developed in CERN (European Organization for Nuclear Research) in Switzerland in 1990
Virtual reality and GIS have many features in common and are becoming more and more integrated.
Human cognition of the spatial world
Cognitive maps
Spatial learning and development
Navigation
Spatial language
GIS and spatial cognition
Cartographic abstraction
Types of maps - cartographic, thematic
Mapping software - CAD, GDS(Graphic Design System), DIPS(Digital Image Processing System), GIS
Mapping concepts, features, properties
-Map features
-Scale
-Map resolution
-Map accuracy (absolute, relative, attribute, currency, completeness)
Types of information in a digital map
Shape of the Earth - Datum
Datum types - Horizontal, Vertical, Complete
Reference ellipsoids
Geodetic datums
-Topographic surface
-Sea level
-Gravity models
-Geoids
-Spheroid
-Ellipsoid
Global Systems & Regional Systems
General coordinate system (Plane & Global)
Earth coordinate geometry
-Rotation
-Equator
-Geographic grid
-Latitude
-Meridian
-Degrees, minutes, seconds
Great & Small circles
Latitude & distance
Longitude & distance
Map projection & GIS
-Tissot's indicatrix
-Planar projections
-Conic projections
-Cylindrical projections
-Non-geometric projections
Variations of mercator projection shown as secant
-Equatorial
-Transverse
-Oblique
Projections preserving shape (CONFORMAL)
-Lambert conformal conic
-Mercator
Projections preserving area (EQUIVALENT)
-Albers equal area
-Sinusoidal
Projections preserving neither area norshape (COMPROMISE)
-Goodes Homolosine
-Robinson
Geometric analogy
-Tangent
-Secant
Conformal (Orthomorphic) projection
Equal area projection
Equidistant projection
Universal Transverse Mercator (UTM)
COMMON MAP PROJECTIONS: Properties & Applicarion areas
-Albers equal area
-Azimuth equidistant
-Lambert conformal conic
-Mercator
-Equidistant conical
-Polyconic-conical
-Sinusoidal-cylindrical
-Stereographic-planar
-Transverse mercator-cylindrical
Coordinates & Distortions
WGS - World Geographic Reference System
Regional Systens
-British National Grid (BNG)
-Indian Grid System
-State Plane Coordinates (SPC)
Georeferencing
-Place name
-Postal address
-Postal Code
-Telephone code
-Latitude / Longitude
-UTM
-State Plane Coordinates
Discrete georeferencing
-Street address
-Postal code system
-US Public Land Survey System
Affine & Curvilinear transformations
-Affine transformation primitives (four primitives)
--Translation
--Scaling
--Rotation
--Reflection
-Curvilinear transformations
Complex affine transformations
Affine transformations in GIS
INFORMATION ORGANIZATION & DATA STRUCTURE
Data & Information
-Linguistic expression
-Symbolic expression
-Mathematical expression
-Signals
INFORMATION
-Relevant
-Reliable, accurate, verifiable
-Up-to-date & timely
-Complete
-Intelligible
-Consistent
-Convenient
-Easy to handle
-Adequately protected
INFORMATION SYSTEM
-Conversion
-Organization
-Structuring
-Modeling
Geographic data & Geographic Information
-Temporal
-Thematic
-Spatial
Information Organization
-Data perspective
-Relationship perspective
-Operating System perspective
-Application architecture perspective
One dimensional array is called a VECTOR
Two dimensional array is called a MATRIX
DATA FILES
-Tree
--Binary tree
--Heap
DATABASE
-Permanent
-Transient
Database is a CHANGE in the perception of data, mode of data processing and purposes of using the data RATHER THAN physical storage of data.
Characteristics of a data file
Characteristics of a database
THE DIFFERENT WAYS IN WHICH DATABASES ORGANIZE DATA ARE KNOWN AS DATABASE MODELS.
-Relational
-Hierarchical
-Network
-Object oriented
Information organization of graphical data
-point
-line or arc
-polygon or area
Levels of data abstraction
-data models & database models
Data structure is a higher level of data abstraction than information organization.
-It represents the human implementation-oriented view of data and expressed in terms of database models
-Data structure is software-dependent
-It forms the next level of data abstraction in information system: FILE STRUCTURE OR FILE FORMAT
-File structure is hardware-dependent
Descriptive data structures
-Relational data structure
-Object-oriented data structure
Graphical data structure
-Raster data structure
--Picture Element (PixEl)
--Triangulated Irregular Network (TIN)
--Hierarchical tessellation (Ex: Quad trees)
--Scan-line
-Vector data structure
--Spaghetti
--Hierarchical
--Topological
-Georelational data structure
Relationship perspective of information organization
-Categorical
-Spatial
Scales of measurement
-Nominal
-Ordinal
-Interval
-Ratio
Spatial relationships
-Topological
--Adjacency
--Connectivity
--Containment
-Proximal
Data
Spatial & Non-spatial data
-Spatial data are generally multi-dimensional & auto-correlated
-Non-dimensional data are one-dimensional & independent
Databases for spatial data
-Total DBMS solution
-Mixed solution
Repository is a structure that stores and protects data.Functionality provided by repositories
-Add/insert data
-Retrieve data (find, select)
-Delete data
Repositories are like a bank vault. They exist to protect their contents from theft and accidental destruction
Advantages of a database approach
-Reduction in data redudancy
-Shared
-Maintenance, quality & data integrity
-Data is self-documented and descriptive
-Avoidance of inconsistencies. Imposition of PRESCRIBED MODELS, RULES & STANDARDS
-Reduced cost of software development
-Security restrictions
Database Management Systems (DBMS)
Queries
-Data Definition Language (DDL)
-Data Manipulation Language (DML)
Query Language implements DDL or DML or BOTH. (Ex: SQL = Structured Qurey Language, QUEL, ISBL, Query-by-Example)
Data models
-A mathematical formalism consisting of:
--Notation for describing data
--Set of operations to manipulate data
Data model is a way of organizing a collection of facts pertaining to a system under investigation
Data models provide a way of thinking about the world and a method of organizing the phenomena that interest us.
A data model is an abstract language
The theoretical foundation of the model helps to:
-Perform analysis
-Enables extraction of inferences &
-Create deductions that emerge from raw data
The THREE levels of abstraction in DBMS are:
-Physical
-Conceptual
-View
Steps in Data modelling:
-Conceptual data modeling
-Logical data modelling &
-Physical data modelling
LEVELS OF DATA ABATRACTION IN DATABASE DESIGN:
Conceptual data modelling -> Data model
Logical data modelling -> Data Structure
Physical data modelling -> File structure
CONCEPTUAL DATA MODELLING
-Identifying entities
-Identifying attributes
-Determining relationships
-Drawing Entity-Relatioinship (ER) diagrams
LOGICAL DATA MODELLING
-Comprehensive process to consolidate & refine conceptual data model.
-Proposed database is reviewed to identify potential problems like:
--irrelevent data
--ommitted or missing data
--inappropriate representation of entities
--lack of integration between various parts of database
--unsupported applications
--potential additional cost to revise database
END PRODUCT of logical data modelling is a LOGICAL SCHEMA developed by mapping the ER diagram to SOFTWARE DEPENDENT DESIGN DOCUMENT
PHYSICAL DATA MODELLING involves:
-Data format
-Storage requirements
-Physical location of data
END PRODUCT OF PHYSICAL DATA MODELLING IS A PHYSICAL SCHEMA also called:
-Data dictionary
-Item definition table
-Data specific table or
-Physical database definition
-IT IS BOTH SOFTWARE & HARDWARE SPECIFIC
PROCESS MODELLING
-It is a process-oriented approach
-It identifies processes that the information system will perform
-It also identifies how information is transformed from one process to another
-END PRODUCT IS  a Data Flow Diagram (DFD)
-Process modelling is concerned with processes, information organization and data structure.
-Data Flow Diagram is the principal modelling tool constructed with:
-PROCESS
-ENTITY
-DATA STORE &
-DATA FLOW in a business function
SPATIAL DATA FORMATS
-Raster files are used for:
--Digital representations of aerial photographs, satellite images, scanned paper maps and other applications with very detailed images
--To reduce costs
--When map does not require analysis of individual map features
--When 'backdrop' maps are required
--The method of representing geographic eatures by pixels is called raster data model and data is described as raster data
--The raster method is also called 'tessellation method'
--Raster method is used for resource and environmental oriented applications
Relationship between "cell size" and "number of cells" is known as RESOLUTION of the raster
FINE RESOLUTION gives more accurate and better quality image
Vector files are used for:
-Highly precise applications
-When file size is important
-WHEN INDIVIDUAL MAP FEATURE REQUIRES ANALYSIS
-When descriptive information must be stored
-The method pf representing geographic features by basic graphic elements of points, lines and polygons is called vector data model
-Vector data is always organized by themes referred to as layers or coverages
-Themes covering very large geographic area are always divided into tiles
-A tile is the digital equivalent of an individual map in a map series
-A collection of themes of vector data covering the same geographic area and serving the common needs of a many users constitutes the "spatial component of a geographic database"
-Vector method of representing geographic features is based on identifying features as discrete entities
-It is the 'object view' of information organization in conventional mapping and cartography.
-The vector method is based on the 'object view of the real world'. It is the method of information organization in conventional mapping and cartography
CHOICE BETWEEN RASTER AND VECTOR DATA
-DATA COLLECTION = Rapid for raster and slow for vector
-DATA VOLUME = Large for raster and small for vector
-DATA STRUCTURE = Simple for raster and complex for vector
-GEOMETRICAL ACCURACY = Low for raster and high for vector
-GRAPHIC TREATMENT = Average for raster and high for vector
-AREA ANALYSIS = Good for raster and average for vector
-NETWORK ANALYSIS = Poor for raster and Good for vector
-GENERALIZATION = Simple for raster and complex for vector
GIS DATA STREAM
-Data (in the form of maps, satellite data, digital data, tabular data and soft ideas) are input into a GIS by
--Digitizing
--Scanning
--Data transfer &
--Key coding
-Data capture
-Editing/Cleaning
-Reprojection
-Generalization
-Edge matching & Rubber sheeting
-Layering
GEOGRAPHIC DATA FORMATS
-Vector
--Automated Mapping System (AMS)
--ESRI coverage
--Computer Graphics Metafile (CGM)
--Digital Feature Analysis Data (DFAD)
--Encapsulated Postscript (EPS)
--Microstation Drawing file format (DGN)
--Dual Independent Map Encoding (DIME)
--Digital Line Graph (DLG)
--AutoCAD Drawing Exchange FFormat (DXF)
--AutoCAD drawing format (DWG)
--MapBase file (ETAK)
--ESRI geodatabase
--Land Use Land Cover Data (GIRAS)
--Interactive Graphic Design Software (IGDS)
--Initial Graphics Exchange Standard (IGES)
--Map Information Assembly Display System (MIDAS)
--MOSS Export File (MOSS)
--Topologically Integrated Geographic Encoding and Referencing (TIGER/line file)
--Spatial Data Transfer Standard/Topological
--Vector profile (SDTS/TVP)
-Raster (Image)
--Arc Digitized Raster Graphics (ARDG)
--Band Interleaved by Line (BIL)
--Band Interleaved by Pixel (BIP)
--Band Sequential (BS)
--Windows Bitmap (BMP)
--Device Independent Bitmap (DIP)
--Compressed Arc Digitized Raster Graphics (CADRG)
--Compressed Image Base (CIB)
--Digital Terrain Elevation Data (DTED)
--ER mapper
--Graphics Interchange Format (GIF)
--ERDAS IMAGINE (IMG)
--ERDAS 7.5 (GIS)
--ESRI GRID file (GRID)
--JPEG File Interchange Format (JFIF)
--Multi-resolution Seamless Image Database (MrSID)
--Tag Image File Format (TIFF; GeoTIFF)
--Portable Network Graphics (PNG)
DATA ENTRY
Attributes can be directly entered into GIS by:
-Direct data loggers
-Manual Keyboard entry
-Optical Character Recognition (OCR)
-Voice regnition
DATA EDITING
-Errors in data may be due to original source data or during encoding process
-Errors in coordinate data or inaccuracies and uncertainities in attribute data
-Data errors should be intercepted before they contaminate the GIS database and propogate to higher levels of information.
-This process is called "Data editing" or "CLEANING". This involves:
--Detection and correction of errors
--Re-projection
--Transformation and generalization
--Edge matching and rubber-sheeting
Detecting and correcting errors
-Main sources are:
--Errors in source data
--Errors introduced during encoding
--Errors propogated during data transfer and conversion
Common spatial errors:
-Missing entities
-Duplicate entities
-Mislocated entities
-Missing labels
-Duplicate labels
-Artifacts of digitizing (Ex: Undershoots, Overshoots, Loops, Spikes)
-Noise
Reprojection, Transformation and generalization
--After encoding and editing spatial and attribute data, it is necessary to process the data geometrically in order to provide a common framework of reference.
--Scale and resolution of source data should be taken into account when combining data from a range of sources to form an integrated database.
--Data derived from different sources should be transformed and projected into a common coordinate system.
--It is of absolute importance to transform the coordinates of each of the input data sets into a common coordinate system.
--The above can be done using linear mathematical transformations.
-In case of translation and scaling:
--In this case, the coordinates are multiplied by a dataset factor depending on the scale
-In case a common origin is to be created:
--The origin of one of the data set is shifted in line with the other by adding the difference between the two origins
-In case of rotation:
--Map coordinates may be rotated using simple trigonometry to more datasets onto a grid of common orientation
-The accuracy of output from a GIS analysis is ONLY AS GOOD AS the WORST INPUT DATA.
-Accuracy of large scale maps is good due to low level of generalization and abstraction while the exact opposite is true for small scale maps
-Routines exist in GIS packages for WEEDING OUT unnecessary points from digitized lines so that the basic shape of the line is preserved.Ex: Douglas-Peucker algorithm
-The disadvantage is that the shape of features may not be preserved.
EDGE MATCHING & RUBBER SHEETING:
-If a study area exists across two or more map sheets, differences or misamtches between adjacent sheets should be resolved.
-Each sheet is digitized separately and joined to adjacent sheets after editing, re-projection, transformation and generalization. This process is known as edge matching and involves the following stages:
--Resolving mismatches at sheet boundaries to ensure complete features and topologically correct data
--In order to use data as a vector layer, the topology must be rebuilt as new lines and polygons are created from joining segments across sheets
--redundant map sheet boundary lines are deleted or dissolved
-Geocoding is the process of converting an address into a point location
Data conversion
-Manipulation and analysis of data is possible only when all data is in the SAME FORMAT
-When different layers are used simultaneously, they should ALL be in either VECTOR or RASTER format
-Normally, conversion is from vector to raster since biggest part of analysis is done in the raster domain
-Vector data are transformed to raster data by overlaying the grid with a user-defined cell size.
-Since raster data require huge storage space, data reduction can be achieved by converting raster data to vector data
-Remote sensing images are digital datasets recorded by satellite operating agencies and stored in their image database. These images should be converted into spatial database format before they can be downloaded
GEOGRAPHIC DATA - LINKAGES & MATCHING
-Linkages
-Exact matching
-Hierarchical matching
-Fuzzy matching (Pg 141)

Wednesday, November 20, 2019

Important Terms in GIS and Remote Sensing

Absorptance is a measure of the ability of a material to absorb electromagnetic energy at a specific wavelength

An absorption Band is a range of wavelengths, frequencies or energies in the electromagnetic spectrum which are characteristic of a particular transition from initial to final state in a substance.It is also defined as the wavelength interval within which electromagnetic radiation is absorbed by the atmosphere or by other substances

Achromatic vision is the perception by the human eye of changes in brightness, often used to describe the perception of monochrome or black and white scenes

Active remote sensing
Active remote sensing involves sending out a signal or pulse of energy and measuring the reflection or return of that signal from the target object or surface. This method contrasts with passive remote sensing, where sensors detect natural radiation emitted or reflected by the target. Active remote sensing systems generate their own electromagnetic radiation, typically in the form of microwaves or lasers, and record the energy that is reflected back to the sensor. This technique allows for precise control over the timing and characteristics of the transmitted signal, enabling more detailed analysis of the target.

One example of active remote sensing is LiDAR (Light Detection and Ranging). LiDAR systems emit laser pulses towards the Earth's surface and measure the time it takes for the pulses to return after bouncing off objects and terrain features. By precisely measuring the time delay and intensity of the returning pulses, LiDAR can generate highly detailed three-dimensional maps of the Earth's surface, including terrain elevation, vegetation structure, and building heights.

Uses of active remote sensing in the present context are manifold:
  1. Topographic Mapping: LiDAR is extensively used for high-resolution topographic mapping, providing detailed elevation data crucial for various applications such as urban planning, flood risk assessment, and infrastructure development.
  2. Forestry Management: Active remote sensing techniques like LiDAR are invaluable for assessing forest structure, biomass estimation, and monitoring forest health. This aids in forest management practices, including timber inventory, conservation efforts, and wildfire risk assessment.
  3. Urban Planning and Development: LiDAR-derived data supports urban planning initiatives by providing accurate information on building heights, land use patterns, and infrastructure planning. It aids in designing efficient transportation networks, identifying suitable locations for development, and assessing urban sprawl.
  4. Natural Resource Management: Active remote sensing assists in monitoring natural resources such as water bodies, wetlands, and agricultural lands. LiDAR can accurately measure water depths, track changes in wetland ecosystems, and optimize agricultural practices through precision agriculture techniques.
  5. Disaster Management: Active remote sensing plays a vital role in disaster response and mitigation efforts. LiDAR data can rapidly assess damage after natural disasters such as earthquakes, landslides, or hurricanes, aiding in search and rescue operations, infrastructure restoration, and risk assessment for future events.
  6. Environmental Monitoring: Active remote sensing helps monitor environmental changes over time, including deforestation, land degradation, and habitat loss. It supports environmental impact assessments for various projects and aids in enforcing regulations related to land use and conservation.
 
Acuity
In the context of remote sensing or GIS (Geographic Information Systems), acuity refers to the ability of the system to discern fine details or features within an image or dataset. It is essentially a measure of the level of detail that can be captured, represented, or analyzed by the remote sensing or GIS technology.

There are two main types of acuity relevant to remote sensing and GIS:

  1. Spatial Acuity: Spatial acuity relates to the ability to distinguish between objects or features based on their spatial dimensions, such as size, shape, and arrangement. It is typically measured in terms of spatial resolution, which refers to the smallest discernible feature or object in an image or dataset. Higher spatial resolution corresponds to finer detail and greater ability to distinguish between objects. Spatial resolution is often specified in terms of meters per pixel, with smaller pixel sizes indicating higher spatial acuity. For example, satellite imagery with a spatial resolution of 0.5 meters can discern objects as small as half a meter on the ground, while imagery with a resolution of 10 meters would not be able to distinguish objects smaller than 10 meters in size.
  2. Radiometric Acuity: Radiometric acuity pertains to the ability to distinguish between different levels of brightness or intensity within an image or dataset. It is influenced by factors such as sensor sensitivity, dynamic range, and calibration accuracy. Higher radiometric acuity enables the detection of subtle variations in reflectance or emission properties of surfaces, which can be useful for differentiating between materials, identifying features, and detecting changes over time.
In remote sensing and GIS applications, acuity is a critical consideration for various tasks, including:

  1. Land cover classification: Higher spatial acuity allows for more accurate classification of land cover types based on their spectral and spatial characteristics.
  2. Feature extraction: Acuity enables the identification and extraction of specific features or objects of interest from imagery or datasets, such as buildings, roads, vegetation, and water bodies.
  3. Change detection: Acuity facilitates the detection of changes in the Earth's surface over time, including urban expansion, deforestation, land use changes, and natural disasters.
  4. Environmental monitoring: Acuity supports the monitoring of environmental parameters such as vegetation health, water quality, and land degradation by capturing fine-scale spatial and radiometric variations.
  5. Infrastructure planning and management: Acuity aids in assessing and analyzing infrastructure assets, urban growth patterns, transportation networks, and other spatially distributed phenomena for effective planning and management.
Additive primary color
In remote sensing, additive primary colors refer to the three primary colors of light—red, green, and blue—that are combined in various proportions to produce a wide range of colors in images. This concept is based on the additive color model, where different colors are created by adding different intensities of light together.

The significance of additive primary colors in remote sensing lies in their role in capturing and representing the spectral characteristics of objects and surfaces on the Earth's surface. Remote sensing instruments, such as digital cameras or satellite sensors, often capture electromagnetic radiation across different wavelengths within the visible spectrum, ranging from approximately 400 to 700 nanometers. By recording the intensity of light within the red, green, and blue spectral bands, these sensors create images where each pixel is represented by a combination of these additive primary colors.

An example to illustrate the significance of additive primary colors in remote sensing:is given below:

Suppose we have a satellite image of a forested area captured using a multispectral sensor that records data in the red, green, and blue spectral bands. In this image, each pixel is represented by a combination of intensity values for red, green, and blue light. The spectral response of vegetation in these bands varies due to differences in chlorophyll absorption and other biochemical properties.

Red Band: Vegetation typically reflects a high amount of near-infrared light and absorbs red light due to chlorophyll absorption. In the red band of the image, healthy vegetation appears relatively dark because it absorbs most of the red light, while non-vegetated surfaces such as soil or water may appear brighter due to their higher reflectance in this band.

Green Band: In the green band, healthy vegetation reflects a moderate amount of light, resulting in relatively bright pixel values. This is because chlorophyll absorbs less green light compared to red light. Non-vegetated surfaces may still appear darker compared to vegetation due to their lower reflectance in the green band.

Blue Band: The blue band is less sensitive to vegetation properties and is more influenced by atmospheric scattering and water content. Vegetation may still exhibit some reflectance in the blue band, but it generally appears darker compared to the red and green bands.

By combining the intensity values from the red, green, and blue bands, we can create a color composite image where different features and land cover types are visually distinguishable based on their spectral responses. For example, healthy vegetation may appear as shades of green, while water bodies may appear blue, and urban areas may appear as mixtures of different colors depending on the materials present.

The use of additive primary colors in remote sensing enables the visualization and interpretation of spectral information captured by sensors, allowing analysts to identify and classify different features on the Earth's surface for various applications such as land cover mapping, environmental monitoring, and resource management.
 
ADF
An Automatic Direction Finder (ADF) is a device used in remote sensing for precisely determining the azimuth or direction to a target or point of interest from an observer's location. ADFs are often used in conjunction with aerial or satellite imagery interpretation, ground surveys, and geospatial data collection to gather accurate directional information for mapping, navigation, and analysis purposes.

Here's how an ADF works and its significance with the help of an example:

A team of researchers conducting a field survey to map the distribution of wildlife habitats in a remote forested area using aerial imagery and ground-based observations. To accurately record the location and orientation of different habitat types, they use an ADF to determine the direction to specific habitat boundaries, landmarks, or survey points relative to their current position.

Operation of the ADF: The ADF consists of a directional antenna or sensor mounted on a tripod or handheld device. The observer points the antenna towards the target of interest, and the ADF automatically measures the azimuth or angle between the observer's location and the target direction. This information is typically displayed on a digital readout or integrated into a GIS software interface for further analysis.

Example Scenario: If researchers are interested in mapping the boundary between a forested area and a wetland ecosystem within the study area. Using aerial imagery as a reference, they identify key visual features such as tree lines, water bodies, and vegetation transitions indicative of the forest-wetland interface.

Using the ADF: With the ADF, the researchers can precisely determine the direction to specific points along the forest-wetland boundary from their current location. For example, they may use the ADF to measure the azimuth to a prominent tree or vegetation patch marking the transition zone between the forest and wetland habitats.

Data Integration: The directional information collected using the ADF is integrated with other spatial data, such as GPS coordinates and imagery, within a GIS environment. This allows the researchers to accurately geo-reference the habitat boundaries and generate detailed maps showing the distribution of different habitat types within the study area.

Significance of ADF: The use of an ADF enhances the accuracy and efficiency of field data collection by providing precise directional information for mapping and spatial analysis. It enables researchers to systematically document habitat boundaries, navigate challenging terrain, and verify ground-truth observations with respect to remote sensing data. Additionally, ADF-derived azimuths can be used to orient aerial or satellite imagery, aiding in the interpretation and integration of multi-temporal datasets for monitoring habitat changes over time.

ADFs play a significant role in remote sensing and GIS applications by providing automated and accurate directional information for spatial data collection, mapping, and analysis, thereby enhancing the efficiency and reliability of field surveys and research activities.
 
Advanced Very High Resolution Radiometer

An Automatic Direction Finder (ADF) is a device used in remote sensing for precisely determining the azimuth or direction to a target or point of interest from an observer's location. ADFs are often used in conjunction with aerial or satellite imagery interpretation, ground surveys, and geospatial data collection to gather accurate directional information for mapping, navigation, and analysis purposes.

Here's how an ADF works and its significance with the help of an example:

Imagine a team of researchers conducting a field survey to map the distribution of wildlife habitats in a remote forested area using aerial imagery and ground-based observations. To accurately record the location and orientation of different habitat types, they use an ADF to determine the direction to specific habitat boundaries, landmarks, or survey points relative to their current position.

  1. Operation of the ADF: The ADF consists of a directional antenna or sensor mounted on a tripod or handheld device. The observer points the antenna towards the target of interest, and the ADF automatically measures the azimuth or angle between the observer's location and the target direction. This information is typically displayed on a digital readout or integrated into a GIS software interface for further analysis.

  2. Example Scenario: Suppose the researchers are interested in mapping the boundary between a forested area and a wetland ecosystem within the study area. Using aerial imagery as a reference, they identify key visual features such as tree lines, water bodies, and vegetation transitions indicative of the forest-wetland interface.

  3. Using the ADF: With the ADF, the researchers can precisely determine the direction to specific points along the forest-wetland boundary from their current location. For example, they may use the ADF to measure the azimuth to a prominent tree or vegetation patch marking the transition zone between the forest and wetland habitats.

  4. Data Integration: The directional information collected using the ADF is integrated with other spatial data, such as GPS coordinates and imagery, within a GIS environment. This allows the researchers to accurately geo-reference the habitat boundaries and generate detailed maps showing the distribution of different habitat types within the study area.

  5. Significance of ADF: The use of an ADF enhances the accuracy and efficiency of field data collection by providing precise directional information for mapping and spatial analysis. It enables researchers to systematically document habitat boundaries, navigate challenging terrain, and verify ground-truth observations with respect to remote sensing data. Additionally, ADF-derived azimuths can be used to orient aerial or satellite imagery, aiding in the interpretation and integration of multi-temporal datasets for monitoring habitat changes over time.

In summary, ADFs play a significant role in remote sensing and GIS applications by providing automated and accurate directional information for spatial data collection, mapping, and analysis, thereby enhancing the efficiency and reliability of field surveys and research activities.

An Automatic Direction Finder (ADF) is a device used in remote sensing for precisely determining the azimuth or direction to a target or point of interest from an observer's location. ADFs are often used in conjunction with aerial or satellite imagery interpretation, ground surveys, and geospatial data collection to gather accurate directional information for mapping, navigation, and analysis purposes.

Here's how an ADF works and its significance with the help of an example:

Imagine a team of researchers conducting a field survey to map the distribution of wildlife habitats in a remote forested area using aerial imagery and ground-based observations. To accurately record the location and orientation of different habitat types, they use an ADF to determine the direction to specific habitat boundaries, landmarks, or survey points relative to their current position.

  1. Operation of the ADF: The ADF consists of a directional antenna or sensor mounted on a tripod or handheld device. The observer points the antenna towards the target of interest, and the ADF automatically measures the azimuth or angle between the observer's location and the target direction. This information is typically displayed on a digital readout or integrated into a GIS software interface for further analysis.

  2. Example Scenario: Suppose the researchers are interested in mapping the boundary between a forested area and a wetland ecosystem within the study area. Using aerial imagery as a reference, they identify key visual features such as tree lines, water bodies, and vegetation transitions indicative of the forest-wetland interface.

  3. Using the ADF: With the ADF, the researchers can precisely determine the direction to specific points along the forest-wetland boundary from their current location. For example, they may use the ADF to measure the azimuth to a prominent tree or vegetation patch marking the transition zone between the forest and wetland habitats.

  4. Data Integration: The directional information collected using the ADF is integrated with other spatial data, such as GPS coordinates and imagery, within a GIS environment. This allows the researchers to accurately geo-reference the habitat boundaries and generate detailed maps showing the distribution of different habitat types within the study area.

  5. Significance of ADF: The use of an ADF enhances the accuracy and efficiency of field data collection by providing precise directional information for mapping and spatial analysis. It enables researchers to systematically document habitat boundaries, navigate challenging terrain, and verify ground-truth observations with respect to remote sensing data. Additionally, ADF-derived azimuths can be used to orient aerial or satellite imagery, aiding in the interpretation and integration of multi-temporal datasets for monitoring habitat changes over time.

In summary, ADFs play a significant role in remote sensing and GIS applications by providing automated and accurate directional information for spatial data collection, mapping, and analysis, thereby enhancing the efficiency and reliability of field surveys and research activities.

Aerial magnetic survey
An aerial magnetic survey is a technique used in remote sensing to measure variations in the Earth's magnetic field from an airborne platform. This method involves flying a magnetometer-equipped aircraft over a target area while recording magnetic field intensity data. Aerial magnetic surveys are commonly employed in geological exploration, mineral prospecting, and mapping of subsurface geological structures.
 
The working of an aerial magnetic survey works and its use in remote sensing is described below with the help of an example:

Operation of Aerial Magnetic Survey: In an aerial magnetic survey, a specialized aircraft equipped with magnetometer sensors is flown at a relatively low altitude over the target area. The magnetometer measures the total magnetic field intensity, which is influenced by variations in the Earth's magnetic field caused by underlying geological structures and mineral deposits.

Example Scenario: A mining company is interested in prospecting for iron ore deposits in a remote region. They decide to conduct an aerial magnetic survey to identify potential areas with elevated magnetic signatures indicative of subsurface iron mineralization.

Data Acquisition: The magnetometer-equipped aircraft flies systematic flight lines over the target area, covering the entire survey area with overlapping passes. As the aircraft traverses the survey area, the magnetometer records magnetic field intensity measurements at regular intervals, typically at a rate of several measurements per second.

Data Processing: After completing the aerial survey, the recorded magnetic field intensity data is processed and analyzed to create a magnetic anomaly map of the survey area. This map highlights areas where the measured magnetic field deviates from the expected background levels, indicating the presence of subsurface geological features or mineral deposits with contrasting magnetic properties.

Interpretation and Targeting: Geoscientists and exploration geologists interpret the magnetic anomaly map to identify potential targets for further investigation. Areas with high magnetic anomalies may indicate the presence of magnetically susceptible minerals such as iron ore, magnetite, or other metallic deposits. These targets are prioritized for ground-based exploration methods, such as geological mapping, drilling, and geophysical surveys, to confirm the presence and extent of mineralization.

Use in Remote Sensing: Aerial magnetic surveys complement other remote sensing techniques, such as satellite imagery, LiDAR, and aerial photography, by providing valuable subsurface geological information not visible to the naked eye. By mapping variations in the Earth's magnetic field, aerial magnetic surveys help geoscientists delineate geological structures, map mineral deposits, and guide exploration activities in challenging terrains and inaccessible areas.

Aerial magnetic surveys are a valuable remote sensing technique for mapping subsurface geological features and mineral deposits. By measuring variations in the Earth's magnetic field from an airborne platform, these surveys provide essential information for geological exploration, mineral resource assessment, and land-use planning in diverse environments.
 
Airborne Imaging Spectrometer
An Airborne Imaging Spectrometer, also known as an airborne hyperspectral sensor or imaging spectroradiometer, is a remote sensing device used to collect high-resolution spectral data across a wide range of wavelengths. This instrument measures the intensity of electromagnetic radiation reflected or emitted from the Earth's surface in numerous narrow contiguous spectral bands, enabling detailed spectral analysis of objects and materials.

Operation of an Airborne Imaging Spectrometer and its applications with the help of an example is given below:

Operation of Airborne Imaging Spectrometer: The Airborne Imaging Spectrometer is typically mounted on an aircraft or unmanned aerial vehicle (UAV) and flown over the target area. As the sensor scans the Earth's surface, it collects reflected sunlight or emitted radiation in hundreds or even thousands of spectral bands covering the visible, near-infrared, and sometimes the shortwave infrared regions of the electromagnetic spectrum.

Consider a forestry company interested in assessing the health and composition of a large forested area for sustainable management practices. An airborne imaging spectrometer survey is conducted to obtain detailed spectral information about the vegetation types, forest structure, and health indicators within the study area.

Data Acquisition: The airborne platform equipped with the imaging spectrometer flies systematic flight lines over the forested region, capturing high-resolution spectral data over the entire area of interest. The sensor records the spectral signature of each pixel on the ground, providing information about the unique reflectance properties of different land cover types, vegetation species, and environmental conditions.

Data Processing: After completing the airborne survey, the collected spectral data is processed and analyzed to generate hyperspectral imagery of the study area. This imagery consists of numerous spectral bands, each representing a specific wavelength range, allowing for detailed characterization and classification of surface features and materials.

Applications of Airborne Imaging Spectrometer:

  1. Vegetation Analysis: Airborne imaging spectrometers are widely used for vegetation mapping, species discrimination, and assessment of vegetation health. By analyzing the spectral reflectance patterns of vegetation, researchers can identify different plant species, detect stress indicators such as disease or nutrient deficiencies, and monitor changes in vegetation over time.
  2. Mineral Exploration: The unique spectral signatures of minerals in the visible and infrared regions of the electromagnetic spectrum enable airborne imaging spectrometers to map mineral deposits and geological formations. By analyzing the spectral reflectance patterns of rocks and soil, geologists can identify potential mineralization targets and guide exploration activities.
  3. Environmental Monitoring: Airborne imaging spectrometers are employed for various environmental monitoring applications, including mapping of water quality parameters, detection of pollutants, and assessment of ecosystem health. These sensors can detect subtle variations in spectral signatures associated with environmental factors such as water clarity, algal blooms, and soil contamination.
  4. Precision Agriculture: In agriculture, airborne imaging spectrometers are used for precision farming applications, including crop monitoring, yield prediction, and nutrient management. By analyzing the spectral reflectance of crops and soil, farmers can optimize irrigation, fertilizer application, and pest management practices to improve crop productivity and resource efficiency.

In this scenario, the forestry company analyzes the airborne imaging spectrometer data to generate hyperspectral imagery of the forested area. By mapping vegetation types, assessing forest health indicators, and monitoring changes in canopy structure, they gain valuable insights for sustainable forestry management practices, such as habitat conservation, timber harvesting, and wildfire risk assessment.

Airborne imaging spectrometers are powerful remote sensing devices that provide detailed spectral information for a wide range of applications, including vegetation analysis, mineral exploration, environmental monitoring, and precision agriculture. By capturing high-resolution spectral data across numerous bands, these sensors enable precise characterization and classification of surface features and materials, supporting informed decision-making and resource management efforts in diverse industries and sectors.
 
 Airborne sensing
Airborne sensing refers to the use of sensors and instruments mounted on aircraft or drones to collect data about the Earth's surface and atmosphere. These sensors capture various types of information, such as images, spectral data, and environmental parameters, from an elevated perspective.
 
The various components of an airborne sensing system are listed below:
  1. Platform: Aircraft or drones equipped with specialized sensors and instruments are flown over the area of interest. 
  2. Sensors: These platforms carry different types of sensors depending on the application. For example, cameras capture high-resolution images, while multispectral or hyperspectral sensors measure the intensity of reflected or emitted light across different wavelengths. 
  3. Data Collection: As the aircraft or drone travels along predefined flight paths, the sensors continuously collect data about the Earth's surface and atmosphere.
  4. Data Processing: After the flight, the collected data is processed and analyzed using remote sensing and GIS software. This involves correcting for distortions, aligning images, and extracting useful information from the raw data.
  5. Interpretation: Remote sensing experts interpret the processed data to extract valuable insights about the environment. This may include mapping land cover types, detecting changes over time, or assessing environmental conditions.
Applications of Airborne Sensing:

  1. Environmental Monitoring: Airborne sensing is used to monitor environmental factors such as land cover changes, deforestation, and pollution. For example, multispectral sensors can detect changes in vegetation health, while thermal sensors can identify hotspots indicating wildfires.
  2. Natural Resource Management: Airborne sensing helps in the management of natural resources such as forests, water bodies, and agricultural lands. It enables the assessment of crop health, water quality, and mineral resources.
  3. Disaster Management: During natural disasters like floods, hurricanes, or earthquakes, airborne sensing provides critical information for disaster response and recovery efforts. It helps in assessing damage, identifying areas in need of assistance, and planning evacuation routes.
  4. Urban Planning: Airborne sensing supports urban planning by providing detailed information about infrastructure, land use, and population distribution. This data helps city planners make informed decisions about zoning, transportation, and development projects.
Example:

Consider a scenario where a city wants to monitor its green spaces to improve urban planning and environmental conservation efforts. An airborne sensing campaign is conducted using drones equipped with multispectral cameras.
  1. The drones fly over the city's parks and forests, capturing high-resolution images and spectral data. After processing the data, remote sensing analysts identify different types of vegetation, assess their health, and measure the extent of green cover in each area.
  2. Based on the findings, city planners can make decisions about park maintenance, tree planting initiatives, and land use zoning to enhance the city's green spaces and promote biodiversity.
Airborne sensing is a powerful tool in remote sensing and GIS that provides valuable information about the Earth's surface and atmosphere. It finds applications in various fields, from environmental monitoring to disaster management and urban planning, contributing to informed decision-making and sustainable resource management.
 
 Airborne Visible and Infrared Spectrometer (AVIRIS)
 The Airborne Visible and Infrared Spectrometer (AVIRIS) is a specialized remote sensing instrument mounted on aircraft to capture high-resolution spectral data across visible and infrared wavelengths. AVIRIS collects detailed spectral information by measuring the intensity of reflected sunlight or emitted radiation from the Earth's surface in hundreds of contiguous spectral bands. This comprehensive spectral coverage allows for the precise characterization of surface materials, vegetation types, and environmental conditions.

The working of AVIRIS is discussed below:
  1. Spectral Bands: AVIRIS measures the intensity of electromagnetic radiation across the visible (VIS), near-infrared (NIR), and sometimes shortwave infrared (SWIR) regions of the spectrum. It typically covers wavelengths ranging from approximately 0.4 to 2.5 micrometers.
  2. Flight Operation: AVIRIS is mounted on an aircraft and flown over the target area in a systematic pattern, capturing spectral data as it scans the Earth's surface below. The aircraft's altitude, speed, and flight path are carefully controlled to ensure uniform coverage and high-quality data acquisition.
  3. Data Acquisition: As AVIRIS scans the Earth's surface, it collects spectral data for each pixel within its field of view. This data includes information about the unique spectral signature of surface materials, vegetation types, and atmospheric conditions.
  4. Data Processing: After the flight, the collected spectral data is processed and analyzed using specialized software. This involves calibrating the data, removing atmospheric effects, and generating spectral images or data cubes for further analysis.
Applications of AVIRIS in Remote Sensing:

  1. Geological Mapping: AVIRIS is used for geological mapping and mineral exploration by identifying mineral signatures and mapping geological formations. It can detect subtle variations in surface composition, helping geologists locate potential mineral deposits.
  2. Vegetation Analysis: AVIRIS provides detailed information about vegetation types, health, and structure. It can distinguish between different plant species, detect stress indicators, and monitor changes in vegetation cover over time.
  3. Environmental Monitoring: AVIRIS is employed for environmental monitoring applications, including land cover classification, mapping of wetlands and water bodies, and detection of environmental pollutants. It helps in assessing ecosystem health, biodiversity, and habitat quality.
  4. Precision Agriculture: AVIRIS supports precision agriculture by providing insights into crop health, nutrient levels, and water stress. Farmers can use AVIRIS data to optimize irrigation, fertilizer application, and pest management practices for improved crop yield and resource efficiency.
  5. Forest Management: AVIRIS aids in forest management by mapping forest types, assessing canopy structure, and monitoring changes in forest health. It helps in identifying areas at risk of wildfires, insect infestations, or disease outbreaks.
The example given below shows how AVIRIS data can be used to monitor changes in our environment:

  1. AVIRIS data is used in this case to study coral reef ecosystems in a tropical marine environment. A series of airborne surveys using AVIRIS-equipped aircraft helps to collect spectral data over coral reef areas.
  2. Using AVIRIS data, the researchers analyze the spectral signatures of different coral species, algae, and substrate types. They map the distribution of coral reefs, assess their health status, and identify areas with high biodiversity.
  3. Marine biologists can develop conservation strategies to protect vulnerable coral reefs, monitor changes in reef health over time, and assess the impacts of environmental stressors such as ocean warming and pollution.
Thus, AVIRIS is a valuable remote sensing instrument for capturing high-resolution spectral data across visible and infrared wavelengths. Its applications range from geological mapping and vegetation analysis to environmental monitoring and precision agriculture, providing valuable insights into Earth's surface and ecosystems for scientific research and resource management.

ALS

The Airborne Visible and Infrared Spectrometer (AVIRIS) is a specialized remote sensing instrument mounted on aircraft to capture high-resolution spectral data across visible and infrared wavelengths. AVIRIS collects detailed spectral information by measuring the intensity of reflected sunlight or emitted radiation from the Earth's surface in hundreds of contiguous spectral bands. This comprehensive spectral coverage allows for the precise characterization of surface materials, vegetation types, and environmental conditions.

How AVIRIS Works:

  1. Spectral Bands: AVIRIS measures the intensity of electromagnetic radiation across the visible (VIS), near-infrared (NIR), and sometimes shortwave infrared (SWIR) regions of the spectrum. It typically covers wavelengths ranging from approximately 0.4 to 2.5 micrometers.

  2. Flight Operation: AVIRIS is mounted on an aircraft and flown over the target area in a systematic pattern, capturing spectral data as it scans the Earth's surface below. The aircraft's altitude, speed, and flight path are carefully controlled to ensure uniform coverage and high-quality data acquisition.

  3. Data Acquisition: As AVIRIS scans the Earth's surface, it collects spectral data for each pixel within its field of view. This data includes information about the unique spectral signature of surface materials, vegetation types, and atmospheric conditions.

  4. Data Processing: After the flight, the collected spectral data is processed and analyzed using specialized software. This involves calibrating the data, removing atmospheric effects, and generating spectral images or data cubes for further analysis.

Applications of AVIRIS in Remote Sensing:

  1. Geological Mapping: AVIRIS is used for geological mapping and mineral exploration by identifying mineral signatures and mapping geological formations. It can detect subtle variations in surface composition, helping geologists locate potential mineral deposits.

  2. Vegetation Analysis: AVIRIS provides detailed information about vegetation types, health, and structure. It can distinguish between different plant species, detect stress indicators, and monitor changes in vegetation cover over time.

  3. Environmental Monitoring: AVIRIS is employed for environmental monitoring applications, including land cover classification, mapping of wetlands and water bodies, and detection of environmental pollutants. It helps in assessing ecosystem health, biodiversity, and habitat quality.

  4. Precision Agriculture: AVIRIS supports precision agriculture by providing insights into crop health, nutrient levels, and water stress. Farmers can use AVIRIS data to optimize irrigation, fertilizer application, and pest management practices for improved crop yield and resource efficiency.

  5. Forest Management: AVIRIS aids in forest management by mapping forest types, assessing canopy structure, and monitoring changes in forest health. It helps in identifying areas at risk of wildfires, insect infestations, or disease outbreaks.

Example:

Let's consider a scenario where a research team uses AVIRIS data to study coral reef ecosystems in a tropical marine environment. The team conducts a series of airborne surveys using AVIRIS-equipped aircraft to collect spectral data over coral reef areas.

Using AVIRIS data, the researchers analyze the spectral signatures of different coral species, algae, and substrate types. They map the distribution of coral reefs, assess their health status, and identify areas with high biodiversity.

Based on the findings, marine biologists can develop conservation strategies to protect vulnerable coral reefs, monitor changes in reef health over time, and assess the impacts of environmental stressors such as ocean warming and pollution.

In summary, AVIRIS is a valuable remote sensing instrument for capturing high-resolution spectral data across visible and infrared wavelengths. Its applications range from geological mapping and vegetation analysis to environmental monitoring and precision agriculture, providing valuable insights into Earth's surface and ecosystems for scientific research and resource management.

Albedo
Albedo is a fundamental concept in remote sensing and Earth science that refers to the fraction of solar radiation reflected by a surface. It is defined as the ratio of reflected solar radiation to incident solar radiation, expressed as a value between 0 and 1 or as a percentage.

  1. Surface Characterization: Albedo provides important information about the reflective properties of different surface types, such as land cover, vegetation, water bodies, and urban areas. Remote sensing instruments measure albedo across various spectral bands to characterize surface materials and distinguish between different land cover types.
  2. Climate Studies: Albedo plays a crucial role in Earth's energy balance and climate system. Surfaces with high albedo, such as snow-covered areas and ice sheets, reflect more solar radiation back to space, contributing to cooling effects and influencing regional and global climate patterns. Conversely, surfaces with low albedo, such as forests and oceans, absorb more solar radiation, leading to warming effects.
  3. Surface Temperature Estimation: Albedo affects the amount of solar radiation absorbed by a surface, which in turn influences surface temperatures. Remote sensing techniques use albedo measurements in combination with thermal infrared data to estimate surface temperatures and monitor changes in thermal properties over time. This is particularly useful for studying urban heat islands, land-atmosphere interactions, and climate-related phenomena.
  4. Snow and Ice Monitoring: Albedo is a critical parameter for monitoring snow and ice cover dynamics in polar regions and mountainous areas. Changes in snow and ice albedo affect surface energy balance, snowmelt rates, and freshwater availability. Remote sensing of albedo helps researchers track changes in snow cover extent, albedo values, and melt patterns, providing insights into climate change impacts on cryospheric regions.
  5. Vegetation Dynamics: Albedo variations across different vegetation types and conditions provide valuable information about ecosystem dynamics, vegetation phenology, and biomass productivity. Remote sensing of vegetation albedo helps monitor seasonal changes in vegetation cover, assess ecosystem health, and quantify carbon uptake and release through photosynthesis and respiration processes.
  6. Land Surface Modeling: Albedo is a key input parameter in land surface models used for climate simulations, weather forecasting, and hydrological modeling. Remote sensing-derived albedo data are integrated into these models to improve simulations of surface energy fluxes, land-atmosphere interactions, and feedback mechanisms, enhancing our understanding of Earth's complex environmental systems.

Tuesday, December 19, 2017

Overview of GIS

GIS - GIS is an integrated collection of computer software and data used to view and manage information about geographic places, analyse spatial relationships and model spatial processes

Model - A model is a simplification to describe, predict or analyse reality. It is usually done to answer a question or solve a problem.
Issues with models are that they represent a real world problem with several assumptions and simplifications involving compromise, sub-division,  reclassification, generalization and imposition of temporal limits resulting in applying subjective constraints.
GIS was developed in 1963 by Dr. Roger Tomlison who is regarded as the "Father of GIS".
Location data is information that describes the location and properties (attributes) of features. It may be stored as raster or vector data.
A map project references data files but does not contain them. Ex: ArcMap, QGIS
Map document has an extension .mxd in ArcMap
Vector data model defines objects with definite boundaries.
Vector geometries are represented using (x,y,z) coordinate pairs: Point, (poly)line, Polygon
Vector data uses geographic coordinates and attribute information to locate and determine features.
Attribute types are: Nominal, Ordinal, Interval & Ratio.
Nominal : Refers to QUALITY of a feature, NOT QUANTITY
Ordinal : Refers to rank
Interval: Refers to quality measurement that is linear (Ex: Temperature)
Ratio : Quantity measurement that is linear, but has a fixed zero point.
Raster data model: It is a mixture of cells (PIXEL-Picture Element) organized into rows and colmns where each cell contains a value representing information. Size increases exponentially with increasing cell size.
Uses of raster:
  • Base map - As a background for vector layers
  • Surface map - Representing changing data in a landscape
  • Thematic map - Grouping values into classes or categories

Methods to capture data
  • Primary (mesuring data directly using instruments like GPS or techniques like surveying and remote sensing)
  • Secondary (Digitizing maps from physical maps, photographs, using photogrammetry)

Remote sensing refers to images recorded from sensors without direct contact like UAVs (Unmanned Aerial Vehicles), Planes, Satellites. It is useful and economical for large areas.
Photogrammetry is the technology used to make measurements in the real world using photographs
The following are used to consider data quality assessment

  • -Resolution
  • -Scale
  • -Age
  • -Author
  • -Source
  • -Position & attribute
  • -Accuracy
  • -Completeness
  • -Metadata

WFS- Web map service: It is a web mapping data format that represents map images (*.png, *.gif, *.jpg). It shares data online. It is the open geospatial consortium standard protocol for requesting georeferenced map images from a spatial database.
ESRI - Environmental Systems Research Institute is a private organization that created the popular GIS software ArcGIS for desktop and online
ArcMap - It is a software application created by ESRI to display, explore and edit GIS datasets
ArcCatalog- It is a software application created by ESRI to organise and manage geographic information for ArcGIS for desktop.

Vector file formats are those that can be stored as geodatabase classes in geodatabase and shapefile, CAD Ex: shp, shx, dbf, prj, xml.
File geodatabase- Geodatabase is a collection of geographic datasets that is easily managed and scalable depending on the intended use. - IT IS EASIER TO STORE.
It is a collection of files in a folder that can store, query and manage spatial and non-spatial data. It is composed of:
  • -Feature classes
  • -Feature dataset
  • -Raster dataset
  • -Non-spatial tables and
  • -Toolboxes


Geodatabase is the native data model for ESRI software. It has the ability to handle different data models and datatypes all within one file folder.
Feature class - It is found in geodatabase files and is a collection of vectors with set attributes, but can also refer to annotations, multipoints or multipatches.
Raster file formats- They are saved in geodatabase as mosaic model, and tiff, jpeg, GeoTIFF, jpeg2000, DEM formats. The format determines:

  1. -How colours are handled and
  2. -How geographic data is stored.

Metadata - Metadata refers to data about data. It provides additional information about a feature and its attribute. For example:
  1. - Item description
  2. -Who created the data
  3. -Usage constraints

Geographic Coordinate system or GCS is a three dimensional surface used to determine locations on the Earth. A point can be referenced by longitude and latitude values measured from Earth's center to a point on the surface.
Spehroid is also known as an ellipsoid. It is a three dimensinal shape created from a two dimensional ellipse. It is a model of the Earth.
NORTH AMERICAN SPHHEROID = NAD83 - is the North American Datum of 1983
It is recommended to use a common datum in a dataset and convert the datum as required.
(x,y) coordinates are used to measure distance North or South of the equator and East or West of the prime meridian
MAP PROJECTIONS:
  1. -Conformal projection category preserves local angles. Ex: Mercator projection                       -A PROJECTION CANNOT BE BOTH EQUAL AREA AND CONFORMAL
  2. -Equal area projection category preserves area
  3. -Equidistant projection category preserves scale in agiven direction
  4. -Compromise projection category involves moderate distortion of SHAPE, AREA. DISTANCE, DIRECTION & SCALE.

MAP PROJECTION CLASS:
  1. -CONIC
  2. -CYLINDRICAL
  3. -PLANAR

MAP PROJECTION CASE is a form of intersection that can be:

  1. -TANGENT or
  2. -SECANT

MAP PROJECTION is a mathematically described technique of representing the Earth's surface on a flat map. Projection can be described by:
  1. -Class
  2. -Projection case
  3. -Aspect and
  4. -Category

A UTM projection is composed of

  1. -60 zones that are divided by North or South
  2. -Each zone is a secant cylindrical mercator projection
  3. -Standard lines are approximately 180 km to eah side of the centrall meridian

GCS is used to:
  1. -store data in a central database where users can project them as needed.
  2. -make a quick map
  3. -when distortion of shape, area and distance are irrelevant
  4. -when spatial queies based on location and distance will not be performed.

Map projections are used to preserve a property
Ex:
  1.  -Distance queries
  2. -To measure areas
  3. -GIS analysis
  4. -Editing GIS features
  5. -Correct visualisation

Transormation function can update the display without changing the dataset resulting in inaccurate measurement and data calculation
ArcScene and ArcGlobe  are 3D visualization applications
Georeferencing is a method of assigning real world spatial coordinates. It integrates new data into a GIS or assigns control points to reconizable features. It is used only for raster and CAD.
Measuring dimensions in GIS refers to quantifying characteristics of a feature by length, perimeter and area.
Measuring distance involves capturing the distance between two or more spatial entities (point, line, polygon)
Distance relationships can be drawn in a straight line on a map in the form of euclidean or great circle with consideration of time, perception and barrier distance.
Measuring density involves consideration of feature distribution in a landscape.
Standarzitaion refers to attribute data. It should be divided by a dimension of the spatial entity it relates to.
Summary statistics are statistical measurements of attribute data. Ex;
  1. Mean, 
  2. Median, 
  3. Variance and 
  4. Count

3D measurements refer to measurement of spatial data that uses (x,y,z) coordinates including surface area and volume.
Surface area refers to 3D measurement that measures along slopes and it is always bigger than 2D surface area.
Categorical measurement refers to comparing categories of different factors using a common criteria.
Ex:

  1. Suitability analysis, 
  2. Weighted site selection. 

Metaphorically speaking, it allows analysts to compare "apples to oranges"
Following are the issues with data quality:
  1. -Accuracy
  2. -Source &
  3. -Metadata


SQL - Structured Query Language is a set of operators strung together to form a request. It is based on the input layer and a query is defined that searches for and selects records that satisfy the query.
Comparison operators used are:
  1. -Equal to (=)
  2. -Greater than (>)
  3. -Less than (<)
  4. -Greater than or equal to (>=)
  5. -Less than or equal to (<=)
  6. -Not equal to (<>)

Logical operators used are:
  1. -AND
  2. -OR
  3. -NOT
  4. -XOR

Wildcard search symbols
  1. -LIKE
  2. -'-'
  3. -'%'
  4. Null values
  5. -IS
  6. -IS NOT operators are used to identify NULL values

Spatial selection
  1. -Accessing spatial data to select records that meet a set of spatial criteria Ex: Test relationship of different datasets
  2. -Intersection
  3. -Adjacency
  4. -Containment
  5. -Distance
  6. Joining data
  7. -Combining data from multiple input tables into a single output table using a common key in the table

Types of relationships:

  1. ONE-TO-MANY
  2. MANY-TO-ONE


  • Attribute join involves appending the fields of one table to those of another through a field common to both tables
  • Benefit of attribute join is that all the data does not have to be stored in one table. NON-SPATIAL DATA can be mappable

SPATIAL JOIN
This operation joins the attributes of two layers based on the location of the features in the layers. This is possible ONLY if BOTH the layers have the SAME COORDINATE SYSTEM
The purpose of spatial join is:

  • -To find the nearest feature
  • -Contents of a polygon &
  • -Use as a measurement tool

-THIS IS DONE USING SPATIAL JOIN TOOL (in the OVERLAY toolkit) or Join data by location
Web map Vs Digital map
Advantages of spatial join:
  1. -Cheaper and less time-intensive to produce
  2. -Wide audience (accessible by anyone with internet access)
  3. -Easier to update
  4. -Interactive
  5. -Can be used to link to related information

Vector classification

  • -Feature level classification to explore and display existing trends in data

Thematic classification

  • -It conveys information about a single topic or theme

Chloropleth

  • -It is a thematic map in which vector areas are distinctly colored or shaded to represent classed values of a particular phenomenon.

Classification techniques:
  1. -Equal interval, Defined interval
  2. -Quantile
  3. -Natural breaks (in ArcMap)
  4. -Petty breaks (in QGIS)
  5. -Standard deviation (How much a feature's attribute value varies from the mean)
  6. -Subjective (Manual)
  7. -Unclassified (Unique values)

Geographical standardization refers to standardization across different areas where the absolute data is divided by a dimension of the spatial entity. (For example: Using density instead of population)
Raster classification involves:

  • -Reclassifying cells to genaralize existing trends
  • -Creating themes in raster models. Ex: Chloropleth

Classification problems:
  • -Confirmation bias
  • -Ecological fallacy
  • -Modifiable Areal Unit Problem (MAUP)

Components of geodatabase
  • -VECTOR -Feature classes
  • -RASTER -Raster datasets (A gridded spatial data model)
  • -NON-SPATIAL -Tables made-up of rows and columns

Feature subtype : Geodatabase behaviour that represents a subset of features as a method to categorise data with same characteristics

TOPOLOGY
  • -Topology is defined as a set of geographic relationships of one or more feature classes with common geometries in a geodatabase.
  • -Topology describes how features are spatially related
  • -Shared features between feature classes can be managed using topology, nodes, edges and faces. Their relationship to one another and their features can be effectively discovered and assembled.
  • -Topology provides a mechanism to perform integrity checks on associated data thereby validating and maintaining better feature representations.

For example:

  1. -Navigating along features
  2. -Finding adjacent features

Features share geometry in a topology in the following ways:
  1. -Adjacent
  2. -Polygon topology
  3. Edge node topology

Geoprocessing is a GIS operation used to manipulate GIS data and derive new information
Useful geoprocessing tools are:
  1. -Clip
  2. -Merge
  3. -Append
  4. -Dissolve
  5. -Buffer

Buffers may be:
  1. - Fixed
  2. -Concentric or
  3. -Data derived

Geodesic buffer is an alternateive for large scale buffers
Types of Overlay analysis:
  1. -intersect
  2. -union
  3. -difference

The following are the problems with physical overlays:
  1. -Poor precision
  2. -Time consuming
  3. -Manual rescaling
  4. -Hard to make changes to analysis
  5. Plenty of error propogation
  6. Problems with overlays:
  7. -Error propogation
  8. -Computationally intensive
  9. -Sliver creation
  10. -Scale

A Model builder consists of:
  1. -Tools
  2. -Input variables and
  3. -Connectors

  • Accuracy is defined as the closeness to true or known value
  • Precision is defined as closeness of two or more values to  each other

REMOTE SENSING
Types of sensors:

  1. -ACTIVE Ex: LiDAR, RaDAR
  2. -PASSIVE Ex: Visible panchromatic, Visible multispectral, InfraRed, Thermal

Reflectance is defined as the radiation that is given off by objects. Different classes of features reflect a different band of radiation at different rates.
Image band is also known as raster band that is represented by a single matrix of cell values. It can also be a raster with multiple bands stored as a Digital Number(DN).
Landsat8 spectral bands:
-11 bands in total. Each band is good in reading specific features.
  1. -BAND-1 useful for mapping coastal & aerosol studies
  2. -BLUE Bathymetric mapping to distinguish soil from vegetation
  3. -GREEN Emphasizes peak vegetation to assess plant vigour
  4. -RED Discrimitaes vegetatin slopes
  5. -NIR Emphasizes biomass content and shorelines
  6. -SWIR-1 Emphasizes moisture content of soil and vegetation; penetrates thin clouds
  7. -SWIR-2 Improved SWIR-1
  8. -BAND-8 Panchromatic - 15 m resolution, sharper image definition
  9. -BAND-9 Improved detection of cirrus cloud contamination
  10. -BAND-10 TIRS-1 100m resolution, thermal imaging and estimated soil moisture
  11. -BAND-11 TIRS-2 Improved TIRS-1
TIRS - Thermal Infra Red Sensor
NVDI - Normalized Difference Varience Index

  1. - It uses NIR and Red bands ratio to read vegetative density more clearly (NIR-Red)/(NIR+Red)

Aerial photography - Data is colleted (multi-spectral, elevation) by a plane flying over the study area.

  1. - The study area is usually small
  2. - It has high resolution and is expensive

Satellite imagery - Data (multispectral, panchromatic, elevation) is collected by a satellite in orbit with 0.5 to 1 km resolution
Unmanned Aerial Vehicle (UAV) - Data (photographic, LiDAR, InfraRed, Thermal) is collected by unmanned aircraft controlled fro ground for a small area
Spatial resolution - Raster resolution that covers areas in pixel. Smaaller areas give higher resolution which implies larger file and more expensive data.
Spectral resolution - It is the ability of a sensor to define fine wavelength ranges to separate them
Radiometric resolution - In terms of raster resolution, it is the ability of an imaging system to discriminate slight differences in energy using reflectance values
Temporal resolution - It is defined as the frequency with which a sensor can collect imagery of the same area (revisit period)
Surface analysis - This involves capturing and analysing the physical structure of the Earth in 3D. In raster, it forms DEM.
Examples of surface analysis are:
  1. -Surface interpretation
  2. -Hydrological analysis
  3. -Statisticl analysis
  4. -Image classification

Suitability analysis-Raster layers can be combined (overlay analysis) to model suitable area.
The steps involved in Suitability analysis are:
  1. -Using an established criteria
  2. -Reclassification into common values
  3. -Assigning weights to criteria
  4. -Overlay and
  5. -Evaluation

Components of raster resolution:
  1. -Spatial resolution
  2. -Spectral resolution
  3. -Radiometric resolution
  4. -Temporal resolution

History of GIS
- I generation (1993-99)
  1. - Zoom-in, Zoom-out
  2. - Not continuous surface
  3. - Layers cannot be toggled

- II generation (1999-2004)
  1. - GIS vendors developed server based softwares
  2. - Users could publish interactive maps on the web using GIS sofwares with interactivity and performance
  3. - In 1996 mapquest launched web service, users got directions but it was slow to load

- III generation (2005-present)

  1. - In 2005 google maps developed tiles
  2. - Tiles load faster than one big map.
  3. -Maps are prepared at multiple scales. More data presented at each scale and web map loads only tiles needed by user.

Monday, December 4, 2017

An overview of GPS

Global Positioning System (GPS)

  • The official name for GPS is NAVigation Satellite Timing And Ranging Global Positioning System or NAVSTAR GPS
  • It was developed in 1973 to overcome the limitations of previous navigation Systems. It was created by the United States department of Defence (USDOD) and was originally run with 24 satellites. It became fully operational in 1995. Brad Parkinson, Roger L. Easton and Ivan A Getting invented it. It is maintained by the United States Government and is freely accessible by anyone with a GPS receiver.
  • It consists of more than 30 satellites in medium Earth Orbit (2000 - 35000 km). Two dozen satellites working in harmony are known as a satellite constellation. It is basically used for 


  1. -Navigation
  2. -Map making and
  3. -Surveying


  • It consists of the following three segments:


  1. -Space segment
  2. -Control segment
  3. -User segment

Space segment consists of:

  1. -GPS satellites that fly in circular orbits at an altitude of 20,200 km and with a period of 12 hours
  2. -They are powered by solar cells
  3. -The satellites continuously orient themselves to point their solar panels toward the sun and their antenna toward the Earth
  4. -The orbital planes of the satellites are centered toward the Earth
  5. -Orbits are designed so that, atleast, six satellites are always within line of sight from any location on the plane.

Control segment consists of three units:

  1. -Master Control System
  2. -Moitoring stations and
  3. -Ground Antennas


  • -The master control station is located in Falcon Air Base in colorado springs
  • -It is responsible for overall management of the remote monitoring and transmission sites
  • -Here, a check-up is performed twice a day by each of the six stations as the satellites complete their journey around the Earth.
  • -It can reposition satellites to maintain optimal GPS constellation

Monitor stations:

  • -Check the exact altitude, position, speed and overall health of orbiting saellites.
  • -The control segment ensures that the GPS satellite orbits and clocks remain within acceptable limits
  • -A station can monitor upto 11 satellites at a time
  • -This "check-up" is performed twice a day, by each station

Monitor stations are located at:

  1. -Falco air base in colorado
  2. -Cape canaveral
  3. -Florida
  4. -Hawaii
  5. -Ascension island in Atlantic ocean
  6. -Diego Garcia Atoll in the Indian Ocean and
  7. -Kwajalein Island in the south Pacific ocean

Ground antennas:

  1. -Ground antennas are used to monitor and track the satellites from horizon to horizon 
  2. -They also transmit correct information to individual satellites
  3. -They also communicate with GPS satellites for command and control

User segment consists of the GPS receiverwhich in-turn consists of:

  1. -An antenna tuned to the frequencies transmitted by the satellite
  2. -Receiver processors and
  3. -A crystal oscillator as a highly stable clock


  • -The GPS receiver may also include a display for showing location and speed information to the user
  • -A receiver is often described by the number of channels signigying the number of satellites that it can monitor simultaneously
  • -Receivers usually have anythin between twelve to twenty channels

WORKING PRINCIPLE

  1. -GPS works on the principle of determination of any location if its distance from any two already known locations is available
  2. -GPS satellites orbit the Earth at an altitude of 11000 miles.
  3. -The orbits and locations of satellites are known in advance
  4. -GPS receivers store the orbit information or all satellites in an ALMANAC which is a file containing positional information for ALL the GPS satellites
  5. -A GPS receiver can tell its position by using its position data and comparing it with three or more GPS satellites
  6. -The distance of each satellite is measured by the time taken by the radio signal to travel from the satellite to the receiver
  7. -All electromagnetic radiation (Ex: Radio waves) travel at the speed of light
  8. -The distance from satellite to receiver is computed using the satndard formula and the receiver's position is determined using trilateration.

-The position calculated by a GPS receiver depends on three accurate measurements:

  1. -Current time
  2. -Position of the satellite and
  3. -Time delay for signal

Worst-case accuracy of a GPS signal is 7.8m at 95% confidence level

  • Sources of GPS signal errors are due to:


  1. -Satellite clock
  2. -Receiver clock
  3. -GPS jamming
  4. -Atmospheric errors
  5. -Multi-path error
  6. Accuracy can be improved using
  7. -Precision monitoring and
  8. -Augmentation
  9. Limitations


  • -GPS can provide worldwide, 3 dimensional positions, 24 hours a day in any kind of weather. However, ther must be a clear line-of-sight between the GPS antenna and four or more satellites
  • -The above condition may be a major problem in urban areas
  • -GPS signal may bounce-off nearby objects causing a problem called "multi-path interference"


APPLICATIONS

  1. -SURVEYING:
  2. -TELEMATICS:
  3. -Vehicle tracking
  4. -Military applications
  5. --GPS integrated into fighters, tanks, helicopters, ships, submarines, tanks, jeeps and soldier's equipment
  6. --Target tracking
  7. --Search and rescue operations


An overview of the concepts involved in spatial sciences

An overview of concepts in GIS and Remote Sensing
GIS -
GIS is an integrated collection of computer software and data used to view and manage information about geographic places, analyse spatial relationships and model spatial processes

Model-
A model is a simplification to describe, predict or analyse reality. It is usually done to answer a question or solve a problem.
Issues with models are that they represent a real world problem with several assumptions and simplifications involving compromise, sub-division,  reclassification, generalization and imposition of temporal limits resulting in applying subjective constraints.

GIS was developed in 1963 by Dr. Roger Tomlison who is regarded as the "Father of GIS".


  • Location data is information that describes the location and properties (attributes) of features. It may be stored as raster or vector data.
  • A map project references data files but does not contain them. Ex: ArcMap, QGIS
  • Map document has an extension .mxd in ArcMap
  • Vector data model defines objects with definite boundaries. Vector geometries are represented using (x,y,z) coordinate pairs: Point, (poly)line, Polygon
  • Vector data uses geographic coordinates and attribute information to locate and determine features.
  • Attribute types are: Nominal, Ordinal, Interval & Ratio.


  1. Nominal : Refers to QUALITY of a feature, NOT QUANTITY
  2. Ordinal : Refers to rank
  3. Interval: Refers to quality measurement that is linear (Ex: Temperature)
  4. Ratio : Quantity measurement that is linear, but has a fixed zero point.

Raster data model: It is a mixture of cells (PIXEL-Picture Element) organized into rows and colmns where each cell contains a value representing information. Size increases exponentially with increasing cell size.
Uses of raster:

  1. Base map - As a background for vector layers
  2. Surface map - Representing changing data in a landscape
  3. Thematic map - Grouping values into classes or categories

Methods to capture data: 

  1. Primary (mesuring data directly using instruments like GPS or techniques like surveying and remote sensing)
  2. Secondary (Digitizing maps from physical maps, photographs, using photogrammetry)


  • Remote sensing refers to images recorded from sensors without direct contact like UAVs (Unmanned Aerial Vehicles), Planes, Satellites. It is useful and economical for large areas.
  • Photogrammetry is the technology used to make measurements in the real world using photographs

The following are used to consider data quality assessment

  1. -Resolution
  2. -Scale
  3. -Age
  4. -Author
  5. -Source
  6. -Position & attribute
  7. -Accuracy
  8. -Completeness
  9. -Metadata


  • WFS- Web map service: It is a web mapping data format that represents map images (*.png, *.gif, *.jpg). It shares data online. It is the open geospatial consortium standard protocol for requesting georeferenced map images from a spatial database.
  • ESRI - Environmental Systems Research Institute is a private organization that created the popular GIS software ArcGIS for desktop and online
  • ArcMap - It is a software application created by ESRI to display, explore and edit GIS datasets
  • ArcCatalog- It is a software application created by ESRI to organise and manage geographic information for ArcGIS for desktop.
  • Vector file formats are those that can be stored as geodatabase classes in geodatabase and shapefile, CAD; shp, shx, dbf, prj, xml.
  • File geodatabase- Geodatabase is a collection of geographic datasets that is easily managed and scalable depending on the intended use. - IT IS EASIER TO STORE. It is a collection of files in a folder that can store, query and manage spatial and non-spatial data. It is composed of:


  1. -Feature classes
  2. -Feature dataset
  3. -Raster dataset
  4. -Non-spatial tables and
  5. -Toolboxes

Geodatabase is the native data model for ESRI software. It has the ability to handle different data models and datatypes all within one file folder.

  • Feature class - It is found in geodatabase files and is a collection of vectors with set attributes, but can also refer to annotations, multipoints or multipatches.

Raster file formats- They are saved in geodatabase as mosaic model, and tiff, jpeg, GeoTIFF, jpeg2000, DEM formats. The format determines:

  1. -How colours are handled and
  2. -How geographic data is stored.

Metadata - Metadata refers to data about data. It provides additional information about a feature and its attribute. For example:

  1. - Item description
  2. -Who created the data
  3. -Usage constraints


  • Geographic Coordinate system or GCS is a three dimensional surface used to determine locations on the Earth. A point can be referenced by longitude and latitude values measured from Earth's center to a point on the surface.
  • Spehroid is also known as an ellipsoid. It is a three dimensinal shape created from a two dimensional ellipse. It is a model of the Earth.
  • NORTH AMERICAN SPHHEROID = NAD83 - is the North American Datum of 1983
  • It is recommended to use a common datum in a dataset and convert the datum as required.
  • (x,y) coordinates are used to measure distance North or South of the equator and East or West of the prime meridian 

MAP PROJECTIONS:

  • -Conformal projection category preserves local angles. Ex: Mercator projection
  • -A PROJECTION CANNOT BE BOTH EQUAL AREA AND CONFORMAL
  • -Equal area projection category preserves area
  • -Equidistant projection category preserves scale in agiven direction
  • -Compromise projection category involves moderate distortion of SHAPE, AREA. DISTANCE, DIRECTION & SCALE.

MAP PROJECTION CLASS:

  • -CONIC
  • -CYLINDRICAL
  • -PLANAR

MAP PROJECTION CASE is a form of intersection that can be:

  • -TANGENT or
  • -SECANT

MAP PROJECTION is a mathematically described technique of representing the Earth's surface on a flat map. Projection can be described by:

  • -Class
  • -Projection case
  • -Aspect and
  • -Category

A UTM projection is composed of

  • -60 zones that are divided by North or South
  • -Each zone is a secant cylindrical mercator projection
  • -Standard lines are approximately 180 km to eah side of the centrall meridian

GCS is used to: 

  • -store data in a central database where users can project them as needed.
  • -make a quick map
  • -when distortion of shape, area and distance are irrelevant
  • -when spatial queies based on location and distance will not be performed.

Map projections are used to preserve a property
Ex: -Distance queries
  • -To measure areas
  • -GIS analysis
  • -Editing GIS features
  • -Correct visualisation
Transormation function can update the display without changing the dataset resulting in inaccurate measurement and data calculation

  • ArcScene and ArcGlobe  are 3D visualization applications
  • Georeferencing is a method of assigning real world spatial coordinates. It integrates new data into a GIS or assigns control points to reconizable features. It is used only for raster and CAD.
  • Measuring dimensions in GIS refers to quantifying characteristics of a feature by length, perimeter and area.
  • Measuring distance involves capturing the distance between two or more spatial entities (point, line, polygon)
  • Distance relationships can be drawn in a straight line on a map in the form of euclidean or great circle with consideration of time, perception and barrier distance.
  • Measuring density involves consideration of feature distribution in a landscape.
  • Standarzitaion refers to attribute data. It should be divided by a dimension of the spatial entity it relates to.
  • Summary statistics are statistical measurements of attribute data. Ex; Mean, Median, Variane and count
  • 3D measurements refer to measurement of spatial data that uses (x,y,z) coordinates including surface area and volume.
  • Surface area refers to 3D measurement that measures along slopes and it is always bigger than 2D surface area.
  • Categorical measurement refers to comparing categories of different factors using a common criteria. Ex: Suitability analysis, Weighted site selection. Metaphorically speaking, it allows analysts to compare "apples to oranges"

Following are the issues with data quality:

  1. -Accuracy
  2. -Source &
  3. -Metadata


SQL - Structured Query Language is a set of operators strung together to form a request. It is based on the input layer and a query is defined that searches for and selects records that satisfy the query.
Comparison operators used are:

  1. -Equal to (=)
  2. -Greater than (>)
  3. -Less than (<)
  4. -Greater than or equal to (>=)
  5. -Less than or equal to (<=)
  6. -Not equal to (<>)

Logical operators used are:

  1. -AND
  2. -OR
  3. -NOT
  4. -XOR

Wildcard search symbols

  1. -LIKE
  2. -'-'
  3. -'%'
  4. Null values

The operators are used to identify NULL values are:

  1. -IS
  2. -IS NOT

Spatial selection

  • -Accessing spatial data to select records that meet a set of spatial criteria Ex: Test relationship of different datasets


  1. -Intersection
  2. -Adjacency
  3. -Containment
  4. -Distance
  5. Joining data


  • -Combining data from multiple input tables into a single output table using a common key in the table
  • Types of relationships:


  1. ONE-TO-MANY
  2. MANY-TO-ONE


  • Attribute join involves appending the fields of one table to those of another through a field common to both tables
  • Benefit of attribute join is that all the data does not have to be stored in one table. NON-SPATIAL DATA can be mappable

SPATIAL JOIN

  • This operation joins the attributes of two layers based on the location of the features in the layers. This is possible ONLY if BOTH the layers have the SAME COORDINATE SYSTEM

The purpose of spatial join is:

  1. -To find the nearest feature
  2. -Contents of a polygon &
  3. -Use as a measurement tool


  • -THIS IS DONE USING SPATIAL JOIN TOOL (in the OVERLAY toolkit) or Join data by location

Web map Vs Digital map

  1. -Cheaper and less time-intensive to produce
  2. -Wide audience (accessible by anyone with internet access)
  3. -Easier to update
  4. -Interactive
  5. -Can be used to link to related information

Vector classification

  1. -Feature level classification to explore and display existing trends in data
  2. Thematic classification
  3. -It conveys information about a single topic or theme

Chloropleth
-It is a thematic map in which vector areas are distinctly colored or shaded to represent classed values of a particular phenomenon.

Classification techniques:

  1. -Equal interval, Defined interval
  2. -Quantile
  3. -Natural breaks (in ArcMap)
  4. -Petty breaks (in QGIS)
  5. -Standard deviation (How much a feature's attribute value varies from the mean)
  6. -Subjective (Manual)
  7. -Unclassified (Unique values)

Geographical standardization refers to standardization across different areas where the absolute data is divided by a dimension of the spatial entity. (For example: Using density instead of population)
Raster classification involves:

  1. -Reclassifying cells to genaralize existing trends
  2. -Creating themes in raster models. Ex: Chloropleth

Classification problems:

  1. -Confirmation bias
  2. -Ecological fallacy
  3. -Modifiable Areal Unit Problem (MAUP)

Components of geodatabase

  1. -VECTOR -Feature classes
  2. -RASTER -Raster datasets (A gridded spatial data model)
  3. -NON-SPATIAL -Tables made-up of rows and columns

Feature subtype: Geodatabase behaviour that represents a subset of features as a method to categorise data with same characteristics

TOPOLOGY

  • -Topology is defined as a set of geographic relationships of one or more feature classes with common geometries in a geodatabase.
  • -Topology describes how features are spatially related
  • -Shared features between feature classes can be managed using topology, nodes, edges and faces. Their relationship to one another and their features can be effectively discovered and assembled.
  • -Topology provides a mechanism to perform integrity checks on associated data thereby validating and maintaining better feature representations.

For example:

  1. -Navigating along features
  2. -Finding adjacent features

Features share geometry in a topology in the following ways:

  1. -Adjacent
  2. -Polygon topology
  3. Edge node topology

Geoprocessing is a GIS operation used to manipulate GIS data and derive new information
Useful geoprocessing tools are:

  1. -Clip 
  2. -Merge 
  3. -Append
  4. -Dissolve
  5. -Buffer

Buffers may be:

  1. - Fixed
  2. -Concentric or
  3. -Data derived

Geodesic buffer is an alternateive for large scale buffers

Types of Overlay analysis:

  1. -intersect
  2. -union
  3. -difference

The following are the problems with physical overlays:

  1. -Poor precision
  2. -Time consuming
  3. -Manual rescaling
  4. -Hard to make changes to analysis
  5. Plenty of error propogation

Problems with overlays:

  1. -Error propogation
  2. -Computationally intensive
  3. -Sliver creation
  4. -Scale

A Model builder consists of:

  1. -Tools
  2. -Input variables and
  3. -Connectors


  • Accuracy is defined as the closeness to true or known value
  • Precision is defined as closeness of two or more values to  each other

REMOTE SENSING
Types of sensors:

  1. -ACTIVE Ex: LiDAR, RaDAR
  2. -PASSIVE Ex: Visible panchromatic, Visible multispectral, InfraRed, Thermal


  • Reflectance is defined as the radiation that is given off by objects. Different classes of features reflect a different band of radiation atdifferent rates.
  • Image band is also known as raster band that is represented by a single matrix of cell values. It can also be a raster with multiple bands stored as a Digital Number(DN).

Landsat8 spectral bands:
-11 bands in total. Each band is good in reading specific features.

  1. -BAND-1 useful for mapping coastal & aerosol studies
  2. -BLUE Bathymetric mapping to distinguish soil from vegetation
  3. -GREEN Emphasizes peak vegetation to assess plant vigour
  4. -RED Discrimitaes vegetatin slopes
  5. -NIR Emphasizes biomass content and shorelines
  6. -SWIR-1 Emphasizes moisture content of soil and vegetation; penetrates thin clouds
  7. -SWIR-2 Improved SWIR-1
  8. -BAND-8 Panchromatic - 15 m resolution, sharper image definition
  9. -BAND-9 Improved detection of cirrus cloud contamination
  10. -BAND-10 TIRS-1 100m resolution, thermal imaging and estimated soil moisture
  11. -BAND-11 TIRS-2 Improved TIRS-1

TIRS - Thermal Infra Red Sensor
NVDI - Normalized Difference Varience Index

  • It uses NIR and Red bands ratio to read vegetative density more clearly (NIR-Red)/(NIR+Red)
  • Aerial photography - Data is colleted (multi-spectral, elevation) by a plane flying over the study area.


  1. - The study area is usually small
  2. - It has high resolution and is expensive

Satellite imagery

  1. - Data (multispectral, panchromatic, elevation) is collected by a satellite in orbit with 0.5 to 1 km resolution 
  2. Unmanned Aerial Vehicle (UAV) - Data (photographic, LiDAR, InfraRed, Thermal) is collected by unmanned aircraft controlled fro ground for a small area


  • Spatial resolution - Raster resolution that covers areas in pixel. Smaaller areas give higher resolution which implies largwer file and more expensive data.
  • Spectral resolution - It is the ability of a sensor to define fine wavelength ranges to separate them
  • Radiometric resolution - In terms of raster resolution, it is the ability of an imaging system to discriminate slight differences in energy using reflectance values
  • Temporal resolution - It is defined as the frequency with which a sensor can collect imagery of the same area (revisit period)
  • Surface analysis - This involves capturing and analysing the physical structure of the Earth in 3D. In raster, it forms DEM.

Examples of surface analysis are:

  1. -Surface interpretation
  2. -Hydrological analysis
  3. -Statisticl analysis
  4. -Image classification

Suitability analysis-Raster layers can be combined (overlay analysis) to model suitable area.
The steps involved in Suitability analysis are:

  1. -Using an established criteria
  2. -Reclassification into common values
  3. -Assigning weights to criteria
  4. -Overlay and
  5. -Evaluation

Components of raster resolution:

  1. -Spatial resolution
  2. -Spectral resolution
  3. -Radiometric resolution
  4. -Temporal resolution

History of GIS
- I generation (1993-99)
  1. - Zoom-in, Zoom-out
  2. - Not continuous surface
  3. - Layers cannot be toggled

- II generation (1999-2004)
  1. - GIS vendors developed server based softwares
  2. - Users could publish interactive maps on the web using GIS sofwares with interactivity and performance
  3. - In 1996 mapquest launched web service, users got directions but it was slow to load

- III generation (2005-present)
  1. - In 2005 google maps developed tiles
  2. - Tiles load faster than one big map. Maps are prepared at multiple scales. More data presented at each scale and web map loads only tiles needed by user.