Remote Sensing
Remote Sensing - Related Videos
12:26
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
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Cited by 34 •
2016
We demonstrate the utility of remotely sensed data and the newly developed Software for Assisted Habitat Modeling (SAHM) in predicting invasive species occurrence on the landscape. An ensemble of predictive models produced highly accurate maps of tamarisk (Tamarix spp.) invasion in Southeastern Colorado, USA when assessed with subsequent field validations.
08:16
Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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Cited by 1 •
2025
Recent advancements in remotely piloted aircraft systems (RPAS) allow sub-meter resolution, ideal for forest recovery monitoring. Integrating artificial intelligence (AI) enables deeper insights from large remotely sensed datasets. This protocol improves monitoring by supporting more efficient assessment and management of forested lands recovering from disturbance.
06:16
Femtosecond Laser Filaments for Use in Sub-Diffraction-Limited Imaging and Remote Sensing
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Cited by 3 •
2019
High-intensity femtosecond pulses of laser light can undergo cycles of Kerr self-focusing and plasma defocusing, propagating an intense sub-millimeter-diameter beam over long distances. We describe a technique for generating and using these filaments to perform remote imaging and sensing beyond the classical diffraction limits of linear optics.
05:03
Remote Sensing Evaluation of Two-spotted Spider Mite Damage on Greenhouse Cotton
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Cited by 15 •
2017
This manuscript describes a multispectral optical sensor that effectively detected damage to early season cotton artificially infested with varying densities of the two-spotted spider mite populations.
04:17
Approximate Number Sense Test
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University A common carnival game is to ask people to guess the number of jellybeans packed into a jar. The chances that anyone will get the exact number right are low. But what about the chances that someone will guess 17 or 147,000? Probably even less than the chances of guessing the correct answer; 17 and 147,000 just seem irrational. Why? After all, if the beans cannot be taken out and counted one-at-a-time, how can someone tell that an...
FAQs
Sensors record electromagnetic radiation that objects either reflect or emit. Differences in these signals allow processing algorithms to distinguish surface characteristics and convert raw measurements into images, maps, or quantitative data. The resulting information supports observation without physical access, which is especially valuable for large, remote, or difficult-to-reach areas.
These resolution types determine what the collected data can reveal. Spatial resolution affects the level of surface detail, spectral resolution relates to distinctions among radiation characteristics, and temporal resolution concerns how frequently an area can be observed. Selecting suitable resolutions influences whether the data are useful for a particular engineering monitoring or assessment task.
Sensor calibration helps ensure that recorded measurements can be interpreted consistently and accurately. Without reliable calibration, variations in the data may be confused with actual changes on the observed surface. Calibration therefore supports the processing of radiation measurements into dependable images, maps, and quantitative information for engineering analysis and evidence-based decisions.
Satellites, aircraft, and drones provide different ways to position sensors over an area, allowing engineers to match observations to the scale and accessibility of a task. The overview identifies all three as platforms for detecting reflected or emitted radiation, while the appropriate choice depends on the area being observed and the required monitoring or assessment purpose.
A typical workflow begins by collecting radiation measurements with a sensor mounted on a satellite, aircraft, or drone. Processing algorithms then transform those measurements into images, maps, or quantitative data. Engineers interpret the processed outputs in relation to the task, such as surveying, infrastructure inspection, environmental monitoring, resource assessment, or disaster response.
Engineers use this approach when direct access is inefficient, impractical, or impossible, particularly across large, remote, or difficult-to-access areas. It can support land surveying, infrastructure inspection, environmental monitoring, resource assessment, and disaster response. By providing processed spatial information, the method contributes to planning, monitoring, and decisions based on observed evidence.
Processed observations can inform planning and monitoring by showing conditions across a broad area and providing quantitative data for analysis. In engineering, these outputs support land surveying, infrastructure inspection, environmental monitoring, resource assessment, and disaster response. Their usefulness depends on selecting appropriate resolutions, maintaining sensor calibration, and interpreting the results accurately.