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Get Jensen's Remote Sensing of the Environment PDF for Free: A Review and Summary of the Book



Remote Sensing of the Environment: An Earth Resource Perspective




Remote sensing is the science and art of obtaining information about an object or area without being in direct contact with it. It is a powerful tool for studying and managing the Earth's resources and environment. Remote Sensing of the Environment by John R. Jensen is one of the most comprehensive and authoritative books on this topic. It covers the fundamentals of remote sensing from an earth resource perspective and provides a wealth of examples and applications from various fields. In this article, we will give you an overview of what remote sensing is, how it works, what are its benefits and challenges, how you can access and use remote sensing data, and how you can learn more about it.




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What is remote sensing and why is it important?




Remote sensing is the process of acquiring information about an object or area by measuring the electromagnetic radiation that is reflected or emitted by it. The electromagnetic spectrum ranges from gamma rays to radio waves and includes visible light. Different objects have different spectral signatures that can be detected by sensors mounted on platforms such as satellites, aircrafts, drones, or ground stations. The sensors record the intensity and wavelength of the radiation at each pixel or location in an image. The images can then be processed and analyzed to extract useful information about the object or area.


Remote sensing has many applications in various fields such as geology, geography, ecology, agriculture, forestry, hydrology, meteorology, climatology, oceanography, archaeology, urban planning, disaster management, and national security. Some examples of remote sensing applications are:


  • Mapping the land cover and land use of the Earth



  • Monitoring the changes and trends in the environment and climate



  • Assessing the availability and quality of natural resources such as water, soil, vegetation, and minerals



  • Detecting and measuring the impacts of natural hazards such as floods, droughts, landslides, earthquakes, volcanoes, and wildfires



  • Identifying and locating the features and patterns of human activities such as settlements, roads, agriculture, and industry



  • Supporting the decision-making and planning processes for sustainable development and management of the Earth's resources and environment



How does remote sensing work?




Remote sensing involves four main components: electromagnetic radiation, sensors, platforms, and image analysis. The following diagram illustrates the basic steps of remote sensing:


![Remote sensing diagram](https://www.semanticscholar.org/author/John-R.-Jensen/144913992/figure/0) Electromagnetic radiation is the energy that travels in waves from a source to a target. The source can be natural (such as the sun) or artificial (such as a radar). The target can be any object or area on the Earth's surface or atmosphere. The radiation interacts with the target in different ways depending on its physical and chemical properties. Some of the radiation is absorbed, some is transmitted, and some is reflected or scattered. The reflected or scattered radiation is then captured by sensors.


Sensors are devices that measure the intensity and wavelength of the electromagnetic radiation at each pixel or location in an image. There are two main types of sensors: passive and active. Passive sensors rely on natural sources of radiation such as the sun or the Earth's thermal emission. Active sensors emit their own radiation such as microwaves or lasers and measure the echo or backscatter from the target. Sensors can also be classified based on their spectral range (such as visible, infrared, or microwave) or their spatial resolution (such as coarse, medium, or fine).


Platforms are vehicles that carry the sensors and provide them with a vantage point to observe the target. Platforms can be stationary (such as ground stations or towers) or mobile (such as satellites, aircrafts, drones, or balloons). Platforms can also be classified based on their altitude (such as low, medium, or high) or their orbit (such as polar, equatorial, or geostationary).


Image analysis is the process of processing and interpreting the images acquired by the sensors to extract useful information about the target. Image analysis involves several steps such as preprocessing, enhancement, classification, segmentation, feature extraction, change detection, and accuracy assessment. Image analysis can also use various techniques such as visual interpretation, statistical analysis, machine learning, and artificial intelligence.


Types of remote sensing data




Remote sensing data can be categorized based on four main characteristics: spatial resolution, spectral resolution, temporal resolution, and radiometric resolution. These characteristics determine the quality and quantity of information that can be derived from remote sensing data.


  • Spatial resolution refers to the size of each pixel or location in an image. It indicates how much detail can be seen in an image. Spatial resolution is usually measured in meters or kilometers. For example, a spatial resolution of 30 meters means that each pixel represents an area of 30 by 30 meters on the ground. Higher spatial resolution means more detail but less coverage.



  • Spectral resolution refers to the number and width of spectral bands or wavelengths that a sensor can measure. It indicates how much spectral information can be obtained from an image. Spectral resolution is usually measured in nanometers or micrometers. For example, a spectral resolution of 10 nanometers means that each band has a width of 10 nanometers in the electromagnetic spectrum. Higher spectral resolution means more spectral information but more data volume.



  • Temporal resolution refers to the frequency or interval at which a sensor acquires images of the same area. It indicates how often an image can be updated or changed. Temporal resolution is usually measured in hours, days, weeks, months, or years. For example, a temporal resolution of 16 days means that a sensor revisits the same area every 16 days. Higher temporal resolution means more timeliness but more redundancy.



  • Radiometric resolution refers to the number and range of digital values that a sensor can record for each pixel or location in an image. It indicates how much radiometric information can be captured from an image. Radiometric resolution is usually measured in bits or levels. For example, a radiometric resolution of 8 bits means that each pixel can have 256 possible values ranging from 0 to 255. Higher radiometric resolution means more contrast but more noise.



Remote sensing systems and techniques




of remote sensing systems and techniques that can be used to acquire and analyze remote sensing data. Some of the most common and important ones are:


  • Aerial photography is the oldest and simplest form of remote sensing. It involves taking photographs of the Earth's surface from an aircraft using a camera. Aerial photographs can provide high spatial resolution and visual information, but they are limited by weather conditions, flight restrictions, and spectral range.



  • Multispectral remote sensing is the most widely used form of remote sensing. It involves measuring the electromagnetic radiation in several discrete spectral bands or wavelengths using a multispectral scanner or a digital camera. Multispectral images can provide moderate to high spatial and spectral resolution and quantitative information, but they are affected by atmospheric interference, geometric distortion, and spectral mixing.



  • Thermal infrared remote sensing is a form of remote sensing that measures the thermal radiation or heat emitted by the Earth's surface and atmosphere using a thermal infrared scanner or a radiometer. Thermal infrared images can provide low to moderate spatial and spectral resolution and temperature information, but they are influenced by emissivity, atmospheric absorption, and thermal contrast.



  • Microwave remote sensing is a form of remote sensing that uses microwaves or radio waves to penetrate through clouds, vegetation, and soil using a radar or a radiometer. Microwave images can provide low to high spatial and spectral resolution and structural information, but they are complicated by speckle noise, shadowing, and multiple scattering.



  • Lidar remote sensing is a form of remote sensing that uses lasers to measure the distance and elevation of the Earth's surface and objects using a lidar or a laser scanner. Lidar images can provide very high spatial resolution and three-dimensional information, but they are expensive, complex, and sensitive to atmospheric conditions.



What are the benefits and challenges of remote sensing?




Remote sensing has many benefits and challenges for earth observation and resource management. Some of the main benefits and challenges are:


Benefits of remote sensing




Some of the benefits of remote sensing are:


  • Accuracy: Remote sensing can provide accurate and objective information about the Earth's features and phenomena that may be difficult or impossible to obtain by other means.



  • Coverage: Remote sensing can cover large areas and regions that may be inaccessible or hazardous to visit by humans or ground instruments.



  • Timeliness: Remote sensing can provide timely and up-to-date information about the Earth's conditions and changes that may be dynamic or rapid.



  • Cost-effectiveness: Remote sensing can reduce the cost and time of data collection and analysis compared to conventional methods such as field surveys or ground measurements.



  • Integration: Remote sensing can integrate data from different sources, scales, perspectives, and dimensions to create comprehensive and consistent information products.



Challenges of remote sensing




Some of the challenges of remote sensing are:


  • Data quality: Remote sensing data may suffer from errors, uncertainties, biases, gaps, or inconsistencies due to sensor limitations, platform instability, atmospheric interference, geometric distortion, or human intervention.



  • Data availability: Remote sensing data may not be available or accessible for some areas or times due to technical issues, legal restrictions, political conflicts, or economic constraints.



  • Data accessibility: Remote sensing data may not be easy or convenient to access or use for some users due to data format, size, complexity, or compatibility issues.



  • Data interpretation: Remote sensing data may not be easy or accurate to interpret or understand for some users due to data ambiguity, variability, or diversity issues.



  • Data ethics: Remote sensing data may raise ethical issues such as privacy, security, ownership, or responsibility for some users due to data sensitivity, vulnerability, or impact issues.



How can you access and use remote sensing data?




If you are interested in accessing and using remote sensing data for your own purposes, there are many sources and tools that you can use. Here are some tips on how to find, download, and analyze remote sensing data from various sources and tools:


Sources of remote sensing data




There are many sources of remote sensing data that you can use depending on your needs and preferences. Some of the most popular and reliable sources are:


  • Satellites: Satellites are artificial objects that orbit around the Earth and carry sensors that acquire remote sensing data. There are many satellites that provide remote sensing data for various applications, such as Landsat, Sentinel, MODIS, SPOT, QuickBird, and WorldView. You can access satellite data from various online platforms, such as USGS EarthExplorer, ESA Copernicus Open Access Hub, NASA Earthdata, Google Earth Engine, and DigitalGlobe.



  • Aircrafts: Aircrafts are vehicles that fly in the air and carry sensors that acquire remote sensing data. There are many aircrafts that provide remote sensing data for various applications, such as aerial photography, hyperspectral imaging, thermal imaging, and lidar. You can access aircraft data from various online platforms, such as USGS National Map, USDA NAIP, NASA AVIRIS, and NEON AOP.



  • Drones: Drones are unmanned aerial vehicles that fly in the air and carry sensors that acquire remote sensing data. There are many drones that provide remote sensing data for various applications, such as crop monitoring, forest mapping, wildlife surveying, and disaster assessment. You can access drone data from various online platforms, such as DroneDeploy, Pix4D, Agisoft Metashape, and OpenDroneMap.



  • Ground stations: Ground stations are facilities that receive and transmit remote sensing data from satellites or aircrafts. There are many ground stations that provide remote sensing data for various applications, such as weather forecasting, climate modeling, and emergency response. You can access ground station data from various online platforms, such as NOAA CLASS, EUMETSAT EUMETCast, and ISRO MOSDAC.



Tools for remote sensing data analysis




There are many tools for remote sensing data analysis that you can use depending on your skills and objectives. Some of the most common and useful tools are:


  • Software: Software are programs that run on computers and perform various tasks related to remote sensing data analysis. There are many software that provide remote sensing data analysis functions, such as image processing, classification, segmentation, feature extraction, change detection, and accuracy assessment. Some of the most popular software are ArcGIS, ENVI, ERDAS IMAGINE, QGIS, and R.



  • Hardware: Hardware are devices that support the operation of software and enhance the performance of remote sensing data analysis. There are many hardware that provide remote sensing data analysis capabilities, such as processors, memory, storage, graphics cards, and monitors. Some of the most advanced hardware are NVIDIA GPUs, Intel CPUs, SSDs, 4K displays, and VR headsets.



  • Algorithms: Algorithms are sets of rules or instructions that solve problems or perform tasks related to remote sensing data analysis. There are many algorithms that provide remote sensing data analysis solutions, such as image enhancement, compression, registration, fusion, segmentation, classification, and change detection. Some of the most innovative algorithms are deep learning, convolutional neural networks, generative adversarial networks, and reinforcement learning.



  • Models: Models are representations or simulations of reality or phenomena related to remote sensing data analysis. There are many models that provide remote sensing data analysis insights, such as spectral signatures, radiative transfer models, atmospheric correction models, land cover models, and biophysical models. Some of the most accurate models are PROSAIL, MODTRAN, 6S, GLC30, and MOD17.



How can you learn more about remote sensing?




If you want to learn more about remote sensing and its applications, there are many resources that you can use depending on your level and interest. Some of the best resources are:


Books on remote sensing




Books are written documents that contain comprehensive and authoritative information about remote sensing and its related topics. Books can help you learn the fundamentals and principles of remote sensing as well as the advanced and specialized topics of remote sensing. Some of the most recommended books are:


  • Remote Sensing of the Environment by John R. Jensen: This is one of the most comprehensive and authoritative books on remote sensing from an earth resource perspective. It covers the fundamentals of remote sensing from an earth resource perspective and provides a wealth of examples and applications from various fields.



  • Introduction to Remote Sensing by James B. Campbell and Randolph H. Wynne: This is one of the most popular and accessible books on remote sensing from a geospatial perspective. It covers the basics of remote sensing from a geospatial perspective and provides a clear and concise explanation of the concepts and methods of remote sensing.



-art techniques and applications of digital image processing for remote sensing.


Courses on remote sensing




Courses are organized programs that provide structured and interactive learning about remote sensing and its related topics. Courses can help you acquire the skills and knowledge of remote sensing as well as the practice and experience of remote sensing. Some of the most popular and reliable courses are:


  • Remote Sensing and Image Analysis by Penn State University: This is an online course that introduces the fundamentals and applications of remote sensing and image analysis. It covers the topics such as electromagnetic radiation, sensors, platforms, image processing, image interpretation, and image analysis.



  • Remote Sensing for Earth Observation by ETH Zurich: This is an online course that provides an overview and examples of remote sensing for earth observation. It covers the topics such as remote sensing systems, data acquisition, data processing, data analysis, and data applications.



  • Remote Sensing with Drones by Coursera: This is an online course that teaches how to use drones for remote sensing. It covers the topics such as drone technology, drone operation, drone data collection, drone data processing, drone data analysis, and drone data visualization.



Websites on remote sensing




Websites are online platforms that provide updated and interactive information about remote sensing and its related topics. Websites can help you access and use remote sensing data and tools as well as stay informed and connected with remote sensing community. Some of the most useful and relevant websites are:


  • Remote Sensing Portal by USGS: This is a website that provides access to various remote sensing data and products from USGS. It includes the topics such as land cover, land use, vegetation, water, fire, climate, and hazards.



  • Remote Sensing Blog by GIS Geography: This is a website that provides articles and tutorials on various remote sensing topics and applications. It includes the topics such as image processing, image classification, image segmentation, image enhancement, image fusion, and image change detection.



  • Remote Sensing Forum by ResearchGate: This is a website that provides a platform for discussion and collaboration on various remote sensing topics and issues. It includes the topics such as remote sensing methods, remote sensing software, remote sensing applications, remote sensing challenges, and remote sensing opportunities.



Conclusion




Remote sensing is a powerful tool for studying and managing the Earth's resources and environment. Remote Sensing of the Environment by John R. Jensen is one of the most comprehensive and authoritative books on this topic. In this article, we have given you an overview of what remote sensing is, how it works, what are its benefits and challenges, how you can access and use remote sensing data, and how you can learn more about it. We hope that this article has sparked your interest and curiosity in remote sensing and encouraged you to explore more about it. Remote sensing is a fascinating and rewarding field that offers many opportunities and possibilities for learning, research, and application. If you want to learn more about remote sensing, you can start


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