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EESS 212 > remote sensing > Flashcards

remote sensing Flashcards

1
Q

In-situ Data Collection

A
  • Weather Stations
  • Soil Sampling
  • Water Quality Monitoring
  • Biological Surveys
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2
Q

Limitations of In-situ Data Collection

A

Time and Labor Intensive
* Limited Coverage
* Accessibility Issues

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3
Q

Remote Sensing?

A

information about objects,
areas, or phenomena from
a
distance, typically using
satellite or airborne sensors
to gather data.

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4
Q

launched the first weather

satellite,

A

On 1 April 1960 U.S. launched the first weather

satellite, TIROS-1

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5
Q

The first satellite dedicated to Earth

observation.

A

On 23 July 1972 U.S. launched Landsat-1
The first satellite dedicated to Earth

observation.

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6
Q

India’s first satellite was
Aryabhata, which was launched

on April 19, 1975

A
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7
Q

state-of-art remote sensing satellites, was

successfully launched into a polar sun-
synchronous orbit

A

IRS-1A, 1988

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8
Q
A
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9
Q

Atmospheric Window

A

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

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10
Q

Captures clear water bodies and vegetation characteristics.

A

shallow water studies, as well as vegetation mapping.

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11
Q

Green Band

A

Detects vegetation health and chlorophyll content.
assessing forest density and agricultural health.

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12
Q

Red Band

A

plants absorb red light for photosynthesis.monitoring crop health and creating vegetation indices (like NDVI).

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13
Q

Near Infrared

A

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.

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14
Q

Shortwave Infrared

A

Differentiates between dry and moist vegetation and can distinguish among various rock types.

drought assessment, soil moisture mapping, and geological studies.

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15
Q

Thermal Infrared

A

Detects heat emitted by objects.wildfire detection, urban heat studies, and water temperature monitoring.

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16
Q

Microwave

A

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).

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17
Q

Passive Sensing:

A

Relies on natural radiation from the Sun.

Useful for daytime observations in the
visible, NIR, and thermal infrared bands.

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18
Q

Active Sensing:

A

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).
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19
Q

interaction of energy with the Earth’s surface

A

how
different materials (water, vegetation, soil, etc.) reflect, absorb, and emit electromagnetic radiation
(EMR). These interactions create unique “spectral signatures”

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20
Q

Specular Reflection:

A

Occurs when energy reflects off smooth surfaces (like calm water
or polished metal) at an equal angle to the incoming radiation.

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21
Q

Diffuse (or Lambertian) Reflection:

A

Occurs on rough surfaces (like soil, vegetation),
scattering the energy uniformly in all directions.

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22
Q

Absorption

A

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.

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23
Q

Vegetation:

A

High reflectance in the NIR, low in red (due to chlorophyll absorption), allowing identification of
healthy vegetation.

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24
Q

Water:

A

Low reflectance in NIR and SWIR, making it easy to identify water bodies.

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25
Soil and Minerals:
Unique patterns in visible, NIR, and SWIR bands, which help in soil classification and mineral exploration.
26
Platforms for Remote Sensing Aerial Platforms (Airborne)
manned aircraft drones
27
Ground-Based Platforms
stationary towers manned towers
28
Satellite Platforms
geo-station orbits polar(synchronous orbits)
29
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),
30
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
31
sensors
Sensors capture data by detecting and recording electromagnetic radiation (EMR) either reflected or emitted from the Earth’s surface and atmosphere.
32
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.
33
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.
34
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.
35
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
36
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.
37
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.
38
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).
39
Noise Removal:
Reduces random noise from the data, enhancing image quality and clarity.
40
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
41
Geographic Information Systems (GIS)
designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data.
42
hardware GIS
computers, servers, mobile devices, GPS units, drones, and other data collection equipment.
43
software GIS
GIS software is essential for data input, storage, analysis, and visualization. Examples include ArcGIS, QGIS, and Google Earth.
44
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
45
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.
46
Raster data have a backend database, normally called an ‘attribute table’
47
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.
48
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.
49
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.
50
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.
51
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
52
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.
53
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
54
coordinate system
specifying locations using numerical coordinates. It can be either geographic (spherical) or projected (flat) and is always based on a datum.
55
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.
56
Projected Coordinate System
orange (earth)=> datum(spheroid) => peel out the orange and lay it flat (map of PCS)
57
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,
58
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
59
arcGIS vs QGIS
60
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
61
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
62
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.
63
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.
64
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.
65
Spatial Analysis | Terrain Analysis
Analyzing slope and elevation data to assess landslide risk zones in mountainous regions.
66
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.
67
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.
68
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.
69
Real-Time Weather Mapping:
Color-coded layers, such as temperature gradients or precipitation intensity, make it easy to interpret complex data at a glance.
70
Why GIS Matters?
(cuz it's imp for U DATE) urban planning disaster management agriculture transportation environment monitoring

EESS 212 (8 decks)

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