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Semester 5: Basics of Remote Sensing and GIS
Remote Sensing: Definition, Type, Scope and Historical Development. Types of Satellites.
Remote Sensing
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Remote sensing is the process of acquiring information about an object or phenomenon without making physical contact. It utilizes various technologies to gather data from a distance, primarily using satellites or aerial platforms.
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Uses its own source of energy to illuminate the target. Examples include radar and LiDAR.
Relies on natural energy, usually sunlight, reflected off the Earth's surface. Examples include optical and infrared sensors.
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Remote sensing has a wide range of applications, including environmental monitoring, agriculture, urban planning, disaster management, and climate research. It plays a crucial role in gathering large-scale spatial data and analyzing Earth's systems.
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Remote sensing has evolved significantly since its inception in the late 19th century, initially with aerial photography. The launch of satellites in the mid-20th century marked a turning point, allowing for continuous and extensive observation of the Earth's surface.
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Designed specifically to monitor and collect data about the Earth's surface, these include LANDSAT and Sentinel satellites.
Facilitate data transmission and communication globally. Examples include Intelsat and Iridium satellites.
Monitor atmospheric conditions and provide data for weather forecasting. Examples include METEOSAT and GOES satellites.
Utilized for environmental, space, and astronomical research, like Hubble Space Telescope and JAXA's Hitomi.
Electro-magnetic radiation: Characteristics, spectral regions and bands. Stages or Process of Remote Sensing.
Electro-magnetic radiation: Characteristics, spectral regions and bands, Stages or Process of Remote Sensing
Characteristics of Electro-magnetic Radiation
Electro-magnetic radiation consists of electric and magnetic fields oscillating perpendicular to each other and the direction of propagation. It travels at the speed of light in a vacuum. Key characteristics include wavelength, frequency, amplitude, and energy. Wavelength is the distance between successive peaks. Frequency measures oscillations per second. The energy of radiation is inversely proportional to the wavelength.
Spectral Regions of Electro-magnetic Radiation
The electro-magnetic spectrum is divided into various spectral regions, including radio waves, microwaves, infrared radiation, visible light, ultraviolet radiation, X-rays, and gamma rays. Each region has distinct properties and applications. For example, visible light is critical for human vision, while infrared radiation is useful in thermal imaging.
Spectral Bands in Remote Sensing
In remote sensing, different spectral bands are used to capture specific wavelengths of electro-magnetic radiation reflected or emitted from the Earth's surface. Common bands include red, green, blue, near-infrared, and thermal infrared. Each band provides unique information about surface features such as vegetation, water bodies, and urban areas.
Stages of Remote Sensing Process
The process of remote sensing encompasses several stages: data acquisition, image processing, data analysis, and information extraction. Data acquisition involves capturing electro-magnetic radiation using sensors. Image processing enhances and corrects the data for clarity. Data analysis interprets the processed images to extract meaningful patterns. Information extraction involves converting analyzed data into actionable insights.
Remote sensing satellites: Platform and sensors. Resolution: Spatial, Spectral, Temporal, Radiometric Resolution.
Remote sensing satellites: Platform and sensors
Introduction to Remote Sensing Satellites
Remote sensing satellites are space-based platforms equipped with sensors to collect data about the Earth's surface. They are essential for various applications, including environmental monitoring, agriculture, urban planning, and disaster management.
Types of Remote Sensing Satellites
There are two main types of remote sensing satellites: active and passive. Active satellites emit their own signals and measure the reflected responses, while passive satellites detect natural energy reflected from the Earth's surface.
Satellite Platforms
Satellite platforms refer to the physical structure and equipment of the satellite. They must be designed to support the sensors, power sources, communication systems, and other vital components to ensure effective functioning in space.
Sensors Used in Remote Sensing
Different types of sensors include optical, infrared, microwave, and radar sensors. Each sensor type has distinct capabilities suited for specific applications, such as vegetation analysis, land cover classification, and surface temperature measurement.
Resolution in Remote Sensing
Resolution refers to the level of detail that can be captured by remote sensing systems. It is categorized into four main types: spatial, spectral, temporal, and radiometric resolution.
Spatial Resolution
Spatial resolution defines the smallest object that can be resolved within an image. Higher spatial resolution means more detail, allowing for better identification of features on the Earth's surface.
Spectral Resolution
Spectral resolution refers to the ability of the sensor to detect various wavelengths of electromagnetic radiation. Higher spectral resolution allows scientists to identify materials and monitor changes in vegetation and water bodies.
Temporal Resolution
Temporal resolution indicates how frequently a satellite revisits the same location to capture imagery. High temporal resolution is critical for monitoring dynamic changes, such as seasonal agricultural practices and disaster recovery efforts.
Radiometric Resolution
Radiometric resolution measures the sensor's ability to discriminate between slight differences in energy levels. Higher radiometric resolution allows for more detailed analysis of surface properties and conditions.
Remote Sensing data processing and applications: Visual and digital image processing techniques.
Remote Sensing Data Processing and Applications: Visual and Digital Image Processing Techniques
Introduction to Remote Sensing
Remote sensing refers to the acquisition of information about an object or phenomenon without making physical contact. It involves the use of satellite or aerial imagery to collect data regarding the Earth's surface.
Types of Remote Sensing Data
There are two primary types of remote sensing data: passive sensors, which detect natural energy (like sunlight) reflected or emitted from the Earth, and active sensors, which emit their own energy (like radar or LiDAR) and measure the reflection.
Image Processing Techniques
Image processing is essential for enhancing the quality of remote sensing data. Common techniques include geometric correction, radiometric correction, and image enhancement methods such as contrast stretching and histogram equalization.
Visual Interpretation of Remote Sensing Data
Visual interpretation involves analyzing images by human eyes, using knowledge of the subject matter. It helps in identifying features, patterns, and changes in the landscape over time.
Digital Image Processing Techniques
Digital image processing applies algorithms to manipulate images digitally. Techniques include filtering, segmentation, and classification, which help in extracting useful information from raw data.
Applications of Remote Sensing Data
Applications include land use and land cover mapping, environmental monitoring, disaster management, urban planning, and agricultural assessment, providing crucial insights for decision-making.
Future Trends in Remote Sensing
Emerging technologies, such as machine learning and artificial intelligence, are enhancing data processing capabilities, improving accuracy, and automating the classification and analysis of remote sensing data.
Remote Sensing applications in Urban Planning, Agriculture, Forestry, Land use/Land cover Mapping, Oceanic Studies and Disaster Management.
Remote Sensing Applications
Urban Planning
Remote sensing provides valuable data for urban planning by enabling accurate mapping of land use, infrastructure, and demographics. Satellite imagery can assist in monitoring urban sprawl, assessing environmental impacts, and planning transportation systems. Additionally, it helps in identifying green spaces and planning for sustainable urban growth.
Agriculture
In agriculture, remote sensing technology aids in precision farming by monitoring crop health, soil moisture, and land conditions. It allows farmers to optimize resource use, improve yield predictions, and manage pest control effectively. Remote sensing also supports crop mapping and can be instrumental in assessing agricultural land use changes.
Forestry
Remote sensing plays a crucial role in forestry management by facilitating the assessment of forest cover, biomass estimation, and deforestation monitoring. It helps in mapping forest types and conditions, guiding conservation efforts, and managing forest resources sustainably. Satellite data can also aid in fire detection and management.
Land use/Land cover Mapping
Land use and land cover mapping are fundamental applications of remote sensing. It provides insights into how land is utilized, facilitating effective land management and policy-making. High-resolution imagery allows for detailed categorization of land cover types, supporting urban development, agriculture, and conservation efforts.
Oceanic Studies
Remote sensing contributes significantly to oceanic studies by enabling the monitoring of ocean temperatures, currents, and chlorophyll levels. This data assists in marine resource management, predicting weather patterns, and studying the impacts of climate change on marine ecosystems.
Disaster Management
Remote sensing is vital in disaster management for hazard assessment, emergency response, and recovery planning. It provides timely data for monitoring natural disasters such as floods, hurricanes, and wildfires, facilitating effective response strategies. Satellite imagery can support damage assessment and impact analysis following disasters.
Introduction to GIS: Definition, concept and history of GIS.
Introduction to GIS: Definition, concept and history of GIS
Definition of GIS
GIS stands for Geographic Information System. It is a framework for gathering, managing, and analyzing spatial and geographic data. GIS enables users to visualize and interpret data to understand relationships, patterns, and trends in the form of maps.
Concept of GIS
The core concept of GIS involves the integration of hardware, software, data, people, and methods to capture, store, manage, analyze, and present spatial or geographic information. GIS utilizes various types of data including vector and raster data to create spatial relationships and perform spatial analyses.
History of GIS
The history of GIS can be traced back to the early 1960s with the development of the Canadian Geographic Information System by Roger Tomlinson. Over the years, GIS technology has evolved significantly, incorporating advances in computer technology, remote sensing, and software applications. The 1980s and 1990s saw the commercialization of GIS software, making it more accessible to a larger audience, and today, GIS is widely used across various fields such as urban planning, environmental management, and transportation.
Computer fundamentals for GIS, GIS Packages like ARC GIS, ERDAS, QGI etc.
Computer Fundamentals for GIS
Introduction to Computer Fundamentals
Understanding the basic components of computers including hardware, software, data storage, and processing. The importance of operating systems and file management.
GIS Overview
Geographic Information Systems (GIS) are designed to capture, store, analyze, and manage spatial and geographic data. Introduction to the concepts of layers, maps, and spatial analysis.
GIS Packages
Overview of popular GIS software such as ARC GIS, ERDAS, and QGIS. Discussion of their functionalities, applications, and user interfaces.
Remote Sensing Basics
Concept of remote sensing as a means to collect data from a distance using sensors. Differentiation between passive and active sensors and their applications in GIS.
Data Management in GIS
Importance of data management including data input, editing, storage, and output in GIS. Discussing different data formats such as vector and raster.
Applications of GIS
Exploration of various applications of GIS in urban planning, environmental management, disaster response, and resource allocation.
Future Trends in GIS
Discussion on emerging technologies in GIS including cloud computing, big data analytics, and the integration of AI and machine learning.
Coordinate system, Datum, Raster and vector data.
Coordinate system, Datum, Raster and Vector Data
Coordinate System
A coordinate system is a framework used to define the position of points in space. It consists of an origin, axes, and a scale. There are two primary types of coordinate systems: Cartesian (using X and Y axes) and Polar (using radius and angle). In GIS, geographic coordinate systems often utilize latitude and longitude, while projected coordinate systems use a planar representation to measure distances.
Datum
A datum is a reference point used for measuring geographic coordinates. It provides a frame of reference for the location of features on the Earth's surface. Datums can be global, like WGS84, used for GPS, or local, designed for specific regions. The choice of datum is crucial for accurate spatial analysis, as it relates directly to how coordinates are interpreted and transformed among different systems.
Raster Data
Raster data represents information in a grid format, where each cell (or pixel) has a value representing information, such as elevation or temperature. Raster data sets are often used for satellite imagery and aerial photography. They are advantageous for analyses involving continuous data but can result in loss of detail due to cell size.
Vector Data
Vector data represents geographic features using points, lines, and polygons to define shapes and boundaries. Each vector consists of coordinates that define its geometry. Vector data is preferred for representing discrete features like roads, lakes, or property boundaries, offering high precision and flexibility for spatial analysis.
