remote sensing Flashcards
In-situ Data Collection
- Weather Stations
- Soil Sampling
- Water Quality Monitoring
- Biological Surveys
Limitations of In-situ Data Collection
Time and Labor Intensive
* Limited Coverage
* Accessibility Issues
Remote Sensing?
information about objects,
areas, or phenomena from
a
distance, typically using
satellite or airborne sensors
to gather data.
launched the first weather
satellite,
On 1 April 1960 U.S. launched the first weather
satellite, TIROS-1
The first satellite dedicated to Earth
observation.
On 23 July 1972 U.S. launched Landsat-1
The first satellite dedicated to Earth
observation.
India’s first satellite was
Aryabhata, which was launched
on April 19, 1975
state-of-art remote sensing satellites, was
successfully launched into a polar sun-
synchronous orbit
IRS-1A, 1988
Atmospheric Window
atmospheric window refers
to specific ranges of
wavelengths in the
electromagnetic spectrum
In these windows,
energy can pass through the
atmosphere with minimal
absorption and scattering,
making it easier for remote
sensing instruments to capture
data from the Earth’s surface.
10
Captures clear water bodies and vegetation characteristics.
shallow water studies, as well as vegetation mapping.
Green Band
Detects vegetation health and chlorophyll content.
assessing forest density and agricultural health.
Red Band
plants absorb red light for photosynthesis.monitoring crop health and creating vegetation indices (like NDVI).
Near Infrared
NIR is highly reflective in healthy vegetation due to its cellular structure. It is used for vegetation mapping, distinguishing water bodies, soil analysis, and tracking plant health.
Shortwave Infrared
Differentiates between dry and moist vegetation and can distinguish among various rock types.
drought assessment, soil moisture mapping, and geological studies.
Thermal Infrared
Detects heat emitted by objects.wildfire detection, urban heat studies, and water temperature monitoring.
Microwave
Penetrates clouds, rain, and some vegetation; suitable for all-weather, day-night observations.
radar imaging for topography, agriculture (soil moisture), and disaster monitoring (floods,
landslides).
Passive Sensing:
Relies on natural radiation from the Sun.
Useful for daytime observations in the
visible, NIR, and thermal infrared bands.
Active Sensing:
Generates its own source of radiation (e.g., radar, LiDAR).
- Enables data collection in low-light and cloudy conditions, useful for nighttime and all-
weather imaging. - Example: RADARSAT (microwave radar) and LiDAR systems (laser).
interaction of energy with the Earth’s surface
how
different materials (water, vegetation, soil, etc.) reflect, absorb, and emit electromagnetic radiation
(EMR). These interactions create unique “spectral signatures”
Specular Reflection:
Occurs when energy reflects off smooth surfaces (like calm water
or polished metal) at an equal angle to the incoming radiation.
Diffuse (or Lambertian) Reflection:
Occurs on rough surfaces (like soil, vegetation),
scattering the energy uniformly in all directions.
Absorption
When certain materials absorb specific wavelengths of EM Radiation, they convert the
energy to heat or use it in chemical processes
water strongly absorbs infrared and microwave radiation, while
vegetation absorbs red and blue light for photosynthesis.
Vegetation:
High reflectance in the NIR, low in red (due to chlorophyll absorption), allowing identification of
healthy vegetation.
Water:
Low reflectance in NIR and SWIR, making it easy to identify water bodies.
Soil and Minerals:
Unique patterns in visible, NIR, and SWIR bands, which help in soil classification and mineral
exploration.
Platforms for Remote Sensing
Aerial Platforms
(Airborne)
manned aircraft
drones
Ground-Based
Platforms
stationary towers
manned towers
Satellite Platforms
geo-station orbits
polar(synchronous orbits)
Polar (Sun-Synchronous) Orbits
These satellites orbit at lower altitudes (around 700-800
km) and pass over the same area at the same solar time
daily, providing high-resolution, global coverage.
Landsat (polar orbit for environmental monitoring) (USA)
* MODIS (Moderate Resolution Imaging Spectroradiometer)
(USA)
* IRS-1A (1988),
Geostationary Orbits
Satellites orbit at about 36,000 km
above the equator, matching the Earth’s rotation, so they continuously monitor the same area.
The satellite follows the direction of the
Earth’s rotation, traveling from west to
east.
weather monitor
Geostationary Operational
Environmental Satellite (GOES)
- INSAT 3D
sensors
Sensors capture data by detecting and recording electromagnetic radiation (EMR) either reflected
or emitted from the Earth’s surface and atmosphere.
passive sensors
Multispectral and Hyperspectral Sensors:
Capture data across multiple spectral bands (visible, NIR, SWIR, etc.) and are widely used for land cover mapping, vegetation analysis, and
mineral exploration.
Thermal Infrared Sensors:
Detect radiation emitted by objects based on their temperature,
useful for applications like volcanic monitoring, soil moisture estimation, and urban heat
analysis.
Active sensors emit their own energy (usually microwave or laser pulses) and measure the reflected
signal from the Earth’s surface. This makes them independent of external light sources, enabling data
collection in low-light or all-weather conditions.
Synthetic Aperture Radar (SAR):
It uses microwave radiation and is effective for terrain mapping, monitoring
land subsidence, and assessing flood-prone areas.
LiDAR (Light Detection and Ranging):
Emits laser pulses to measure distances by calculating the time delay
of the reflected light, widely used in topographic mapping, forest canopy studies, and urban infrastructure
planning.
Spectral Resolution:
Higher Spectral Resolution: Sensors with more
bands (e.g., hyperspectral) can detect subtle
differences
in surface materials, making them useful for mineral exploration and vegetation
health assessment.
Lower Spectral Resolution: Multispectral sensors
with fewer bands are effective for broader
applications, like land cover classification.
Spatial Resolution:
High Spatial Resolution: Sensors like those on
Cartosat or SPOT capture finer details, ideal for
urban planning and infrastructure mapping.
Low Spatial Resolution: Sensors with larger pixel
sizes (e.g., MODIS) capture broader patterns
Radiometric Resolution:
sensitivity of a sensor to detect
slight differences in energy, often represented
by the number of bits
higher resolution :
enhances the detail and quality of images,
critical for detailed analyses like soil moisture
estimation or vegetation health.
Radiometric Corrections:
Adjusts data for sensor irregularities and atmospheric effects,
ensuring consistent brightness values across images.
- Atmospheric Correction: Removes distortions from atmospheric particles, haze, or gases,
providing accurate surface reflectance values. - Sensor Calibration: Ensures that the data aligns correctly with known reference values for consistent analysis over time.
Geometric Corrections:
Corrects positional errors caused by sensor angle, Earth curvature, and
topography, aligning images accurately with map coordinates.
Georeferencing: Aligns image data to a map projection, making it possible to integrate
remote sensing data with other geographic information systems (GIS).
Noise Removal:
Reduces random noise from the data, enhancing image quality and clarity.
change detection
Compares images over time to detect changes in
land use, vegetation, urban development, or
environmental conditions.
Common methods include image differencing,
post-classification comparison
Geographic Information Systems (GIS)
designed to capture, store,
manipulate, analyze, manage, and present
spatial or geographic data.
hardware GIS
computers, servers, mobile devices, GPS units, drones, and other data collection equipment.
software GIS
GIS software is essential for data input, storage, analysis, and visualization. Examples
include ArcGIS, QGIS, and Google Earth.
spatial data
Vector:
e.g. points, lines, and polygons
Vectors are composed of coordinates
Raster:
e.g. row and column matrix
Raster’s are composed of pixels
raster data
Raster data represents the world as a grid of
cells or pixels, where each cell contains a value
representing information about that area, such
as temperature, elevation, or land cover type.
Satellite imagery, digital elevation models
(DEMs), and aerial photography.
Ideal for continuous data that changes
gradually across an area.
Raster data have a backend database,
normally called an ‘attribute table’
feature class
Example: A “Roads” feature class might contain all the roads in a region, represented as line features. Each road has specific attributes, such as road name, width, type (highway, street), and speed limit.
Point: Individual points, such as fire hydrants, trees, or city locations.
- Line: Linear features, such as roads, rivers, or pipelines.
- Polygon: Closed shapes representing areas, such as land parcels, lakes, or city boundaries.
meta data
Metadata is descriptive information about the data itself, including the source, accuracy, date of
creation, and data format.
This information is essential for understanding the data’s reliability and context, allowing for
informed analysis and sharing.
geodatabase
Definition: A database structure for GIS to store and manage spatial and attribute data.
Purpose: Centralized, efficient, and scalable data management.
Features: Handles complex GIS relationships, ensures data integrity, and supports multi-user editing.
Benefits: Enables efficient querying, analysis, and sharing of geographic information.
Feature Dataset
container used to organize and group multiple related feature classes that share the same coordinate system.
In a city geodatabase, a “Transportation” feature
dataset could include multiple feature classes such as “Roads” (lines), “Railroads” (lines), and “Stations”(points).
- These different feature classes, stored together, allow spatial rules to be applied—such as ensuring road and railroads do not overlap incorrectly.
how do transform tabular data into spatial data
Joining
Use a shared unique identifier (GEOID, name, etc.) to match up
tabular data to the spatial data’s attribute table.
Geocoding
Use lat/lon coordinates in table to plot as points on map
Use addresses to plot locations
based on a street network
datum
A datum is a reference system that defines the size and shape of the Earth and provides a framework for measuring locations on the Earth’s surface.
geoid + epsilloid + datum
A datum is generated by aligning a geoid to an ellipsoid (sphere) representation of the earth and mapping the earth’s
surface features onto this ellipsoid/sphere.
Types of Datum:
- Local Datum: The choice of datum is largely driven by
the location of interest. (Eg: North American Datum of
1927 or NAD27) - Geocentric Datum: Eg: World Geodetic System 1984 or WGS84
coordinate system
specifying
locations using numerical coordinates. It can be either
geographic (spherical) or projected (flat) and is always
based on a datum.
types of co-ord
- Geographic Coordinate System (GCS):
Uses
latitude and longitude to locate points on the
Earth’s surface. It is based on an angular
measurement with units in degrees.
- Projected Coordinate System (PCS):
Projects the
Earth’s 3D surface onto a 2D plane. Units are
typically in meters or feet.
Projected Coordinate System
orange (earth)=> datum(spheroid) => peel out the orange and lay it flat (map of PCS)
PCS components
(MC’D UP)
Map Projection:
A mathematical method for converting the Earth’s
curved surface into a flat map, with different types like Mercator and
Albers, chosen to minimize distortion
- Coordinate Origin:
A designated starting point,
southwest corner of the map, ensuring all coordinates remain
positive.
Units of Measurement:
The linear units, typically meters or feet
Projection Parameters:
Key values like latitude of origin, central meridian, scale factor, false easting, and false northing.
Datum:
A reference model of Earth’s shape and position, such as
NAD83 or WGS84,
GIS SOFTWARE
Type- geobrowser
Processing power-weak(display only)
Eg-
google maps, g earth, applemaps,waze
Type-webbased
Processing power-
medium
upload info, minimal processing with display
Eg-
Carto, ArcGIS Online, Mapbox, Google MyMaps,
Type- desktop
Processing power-
strong(control over map created and adv.analyses)
Eg-ArcGIS Pro
QGIS
arcGIS vs QGIS
Spatial Analysis
process of
examining locations, attributes, and
relationships of features in spatial data to
uncover patterns, trends, and insights.
overlay
* Buffering
* Spatial Interpolation
* Viewshed Analysis
* Terrain Analysis
Spatial Analysis | Overlay Analysis
Combines multiple layers of data to
identify relationships and intersections
between features.
eg- combine feature data of homes +streets + rivers
to analyse
eg-land use and
flood risk layers to identify residential
areas at high risk of flooding
Spatial Analysis | Buffering
Creates a zone around a feature to analyze proximity and influence
Creating a 1-kilometer buffer around rivers to determine the areas
that might need protection from potential water pollution.
Spatial Analysis | Spatial Interpolation
Estimates unknown values at specific
locations based on values from known
points,
point data(random points)
=> raster data
Example: Estimating air pollution
levels across a city based on
measurements from specific monitoring
stations.
Spatial Analysis | Viewshed Analysis
Determines areas visible from aparticular point, useful in fields like
urban planning and landscape
assessment.
which areas would be
visible from a new observation tower to
optimize scenic views for visitors.
Spatial Analysis | Terrain Analysis
Analyzing slope
and elevation data to
assess landslide risk
zones in mountainous
regions.
Modern GIS
integrates with technologies such as artificial
intelligence (AI), machine learning, and big data analytics,
enhancing the ability to understand complex spatial patterns and dynamics.
Real-Time Data:
GIS can now process live
data from sources like satellites,
drones,
IoT sensors,
mobile devices,
enabling real-time tracking for applications like traffic management, environmental
monitoring, and emergency response.
3D and Augmented Reality (AR):
GIS
visualization has expanded into 3D modeling and AR, allowing users to interact with geographic data more
intuitively,
such as for city planning or
landscape visualization.
Real-Time Weather Mapping:
Color-coded layers, such as temperature
gradients or precipitation intensity, make it
easy to interpret complex data at a glance.
Why GIS Matters?
(cuz it’s imp for U DATE)
urban planning
disaster management
agriculture
transportation
environment monitoring
