- Pankaj Roy added an answer:Is there any way to acquire free WorldView imagery for scientific research?
I am intending to start a new research and I need very good spatial resolution imagery, therefore I am searching for websites or other sources that offer WorldView imagery for free, does anyone have a hint?
Already you have got lots of information regarding your query. Still I suggest you to visit Bhuvan for limited excess.Following
- Jibran Khan added an answer:Difference between ASAR Wide Swath and Alternating Polarization for vegetation monitoring?
Can someone please elaborate the difference between ENVISAT ASAR Wide Swath (WS) data and ASAR Alternating Polarization (AP) data such as HV or VH when they are used for vegetation monitoring for the region of interest?
Thank you Dibyajyoti for your feedback.Following
- Mosab Hawarey added an answer:Anyone aware of any research, published / unpublished, in any language; over the usage of spectral signature to trace/track/locate living organisms?
I am interested in any scientific research, whether published or unpublished, in any language at all, that has tackled the issue of usage of spectral signature (of any kind) in order to trace / track / locate / position any living organism, whether humans or else. Any credible information will be highly appreciated.
Your answer is crystal clear, but it's relevant to GPS/GNSS not spectral signature. Remote sensing is a general term and as you know there's GNSS-based remote sensing now, and many even consider Lidar and Photogrammetry as types of RS... but I am highly specific on the RS I am after: based in spectral signature of any kind.Following
- Alfredo Cosntain added an answer:What could be the hydrodynamic and morphological modeling strategy for the braided rivers with limited bathymetry information ?
I have been trying to understand morphological dynamics of the Braided rivers (Brahmaputra) using 2D/3D models. The main hurdle is the unavailability of detailed bathymetry information for braided reaches of the river. When I use 2D models such as CCHE-2D, it frequently crash when simulated with coarse bathymetry data. Is there any way I can improve my modeling strategy to get better results over the Braided reaches using remote sensing or any other information.
If you consider that the trench of stream is in dynamic equilibrium , there is certain general conditions that may be stated by means of tracers. Thouhg this view is not detailed it can help to guide some hydraulic and morphologic insights to model it properly.Following
- Mark Brown added an answer:Has anyone used small UAVs to monitor bird colonies?I am now thinking of using a small remote controlled helicopter like an ardrone outfitted with a hd camera and GPS to document the start of nesting and to get a count. The islands are small, wide open, have low vegetation, the chopper has a very low noise. Not sure how the birds will react.
We used two types of drones on Kelp Gull colonies in South Africa. A silent fixed wing "glider" worked well, but the quad copter was too noisy and caused too much disturbance. Glider worked well, had a few non-incubating birds follow it, but most incubating birds ignored it...Following
- Mustafa Üstüner added an answer:Has anyone worked with Sentinel-1 C-Band Synthetic Aperture Radar (SAR) satellite data in the context of Natural Hazards & Disaster Risk Management?
Although there are some RADAR based sensors out there which are quite helpful in Emergency and Disaster Response situation, they are not freely available. Now it seems Sentinel-1 is the first one which I know is free! The answer to a cloud free day and night Satellite Imagery is finally here. I would like to know how Sentinel-1 C-band Synthetic Aperture Radar (SAR) which builds on European Space Agency’s and Canada’s heritage SAR systems on ERS-1, ERS-2, Envisat and Radarsat can be used for Natural Hazards and Disaster Risk Management, especially in Flood situations.
'Sentinel-1 carries an advanced radar instrument to provide an all-weather, day-and-night supply of imagery of Earth’s surface. As a constellation of two satellites orbiting 180° apart, the mission images the entire Earth every six days. The mission will benefit numerous services. For example, services that relate to the monitoring of Arctic sea-ice extent, routine sea-ice mapping, surveillance of the marine environment, including oil-spill monitoring and ship detection for maritime security, monitoring land-surface for motion risks, mapping for forest, water and soil management and mapping to support humanitarian aid and crisis situations.'
Kindly support your answers with relevant research papers.
Thank you for your kind & quick answer and your collaboration.
Many friendly greetings to Napoli from Istanbul.
- Rabia Tabassum added an answer:How to use SEBAL model for evapotranspiration?Any software needed to run it, or just equation can be solved to find values for evapotranspiration?
By using remote sensed data (satellite images) evapotranspiration can be calculated with the help of Arc GIS software. see this link
- Jared Keyes asked a question:Can anyone help me find bathymetric river data for the Philippines and/or Indonesia?
Dear ResearchGate community,
I have searched online and contacted the proper ministries/organizations, and looked into the possibility of finding river depth through remote sensing techniques (Ex. LiDAR) but have found nothing, and was just wondering if anyone could help me out.
Thank you in advance for your time and attention,Following
- Oscar Bautista added an answer:How can I generate a precipitation from MODIS data?
Thanks in advance for your replies.
For recent dates you can use the data of the Global precipitation measurement (GPM), you can find the GIS product here ftp://jsimpson.pps.eosdis.nasa.gov/data/imerg/gis/
in this link you will find 3 hourly , daily and 7 days compositesbefore you can acces you need to register on the GPM website.Following
- Maria V. Kozlova added an answer:Is it possible to do dark object subtraction for Landsat 8 images in ArcGIS 10.2v?
I am using Landsat 8 images in my MSc research. Since I am new in the field of remote sensing, I don't know how to do it. The software that I am now getting familiar with is ArcGIS. I have tirelessly searched for solutions but with no success.
Any help on this will be invaluably considered.
This can be easily done with GRASS GIS by i.landsat.toar module.
And don't forget of Pat Chavez's suggestions.
ArcGis is not really suitable soft for satellite imagery analysis.Following
- Amir Aghassi added an answer:How do I solve of the "flaw" error from ASTER data?
If the PC transformation on an ASTER swir data be done, an error comes up called "flaw". You see the orbital path effect as a distinct band in some PC images when loaded as gray scale image. It affects spectral based image processing.
The term "flaw" has been not introduced at the software, bu Dr. Agar (Australian remote sensing doer) calls it "flaw". PC is simple, but the error after applying SFF, or SAM is more evident. It is not possible or hard to be to correct this error I think.Following
- James Varghese added an answer:How can one interpret Orbiting Carbon Observatory-2 (OCO-2 ) data?
NASA’s first dedicated Earth remote sensing satellite to study atmospheric carbon dioxide from Space was launched successfully in July, 2014. Its primary objective is to monitor sources and sinks of carbon dioxide. The first science data appears like bar codes. This link http://earthobservatory.nasa.gov/IOTD/view.php?id=84159 provides more information. Has anyone worked on sample OCO-2 data provided by https://co2.jpl.nasa.gov/
Thanks Ankit for your comments.
It has been almost a year now since I posted this question. Back then, the instrument was undergoing calibration phase if I remember. Ever since then, I haven't been keeping a track of the updates.Following
- Hein Van Gils added an answer:Is the FAO Forest Resources Assessment in your country using satellite Remote Sensing?
To my surprise many countries appear to report to the FAO every 5-years on forest extent statistics without using satellite Remote Sensing. I would appreciate to learn about countries that do use RS in their FRA. And for those countries that do not, about their motivation.
For an example of uncertainties in FRA forest extent statistics, please refer to my profile.
Somehow deforestation and spontaneous reforestation (attached Majella paper) rates at landscape level show a rather narrow range. Any idea why?
My experience on forest extent data provided by forest district officers is that these represent forest land (forest use; forest reserves) rather than forest cover (attached Rwanda paper). Also data from Indonesia often refer to forest land rather than forest cover in my experience. No problem, unless you compare between countries and over time within countries.Following
- Coral Marie Roig-Silva added an answer:How can I view and exploit SIR-C images downloaded from the USGS site?I need to exploit these images for oil spill detection and identification and can't do that with ENVI software.
I recently used ENVI, If it is a multilook image the data needs to be synthesised. You can refer to the ENVI help for details.Following
- Mustafa Üstüner added an answer:How can I deal with different number of pixels for Training data on Image Classification?
In some studies, different numbers of pixels per class were used for training. Therefore, the training data are unbalanced. However, many studies have shown that unbalanced training sets do bias the results. Should we use equal number of pixel per class for training everytime on image classification? Any answer will be appreciated!
Thank you all for your supports and attentionFollowing
- Frank Veroustraete added an answer:What is the temperature sensitivity range for Landsat 8 Thermal Infrared Sensor (TIRS)?
I have been analyzing a Landsat 8 (July 2014) scene of Danakil Depression located in the Afar Region near the border with Eritrea, Djibouti and Ethiopia in Africa. The area of interest is Dallol where a geothermally active sulfur spring is located. It was found that the Land Surface Temperature (LST) of the area of interest (rectangular box) derived from mono window algorithm from Band 10 ranged from 40 °C to 47 °C. For the whole Landsat scene the LST ranged from -11.15°C to 72.2°C. I think these numbers may be incorrect. But I'm not sure how to verify them. I became sceptical of the results after I found that the lava lake temperature of Erta Ale, the famous active volcano showed only 72°C in the LST. I'm curious to find out whether there is any temperature range for TIRS with an acceptable degree of error. Is there a saturation point above and below which the thermal sensor may not work? I remember there was a higher range for ETM+ Thermal Band 6 at 320°K, not sure about the lower limit. Is there any similar range for L8 TIRS? I did not find any numbers in the USGS links.
Here some news from USGS on the Landsat 8 Thermal sensor.
- August 22, 2013 - Landsat 8 Thermal Infrared Sensor (TIRS) Calibration Notice Discrepancies have been noted between calibrated Landsat 8 TIRS Bands 10 and 11 data, and surface-water temperature measurements collected to validate thermal band calibration. Surface-water temperatures derived from TIRS data, after correction for atmospheric transmission and emissivity, are warmer than measured surface-water temperatures by 2K or more. These discrepancies also may not be consistent across the focal plane. This indicates a possible bias or other error in TIRS calibration that places the calibration uncertainty beyond the specified performance of 2 percent. Users are cautioned to be aware of potential impacts to their analyses and results. The calibration team continues to analyze TIRS data and compare results to surface-water temperature measurements to discover the source of the discrepancy. Updates to TIRS calibration coefficients will be incorporated into Landsat 8 data processing as soon as the discrepancy is sufficiently understood. Details will be provided on this website as they become available.
October 25, 2013 - Upcoming change in Landsat 8 Radiometric CalibrationIn the near future, calibration changes will be made that will affect the Thermal Infrared Sensor (TIRS) Band 10 and all of the Operational Land Imager (OLI) bands onboard Landsat 8. The calibration for TIRS Band 10 will be lowered by a constant 0.32 W/(m2 sr µm) for every TIRS Band 10 pixel. This adjustment is being made due to significant discrepancies as compared to surface water temperature measurements. Studies are ongoing to better characterize the source of the calibration errors, and if possible, provide a more accurate scene-dependent correction. No adjustment will be made to TIRS Band 11, as indications are that its calibration is further off and more variable. Until indicated otherwise, users should work with TIRS Band 10 data as a single spectral band (like Landsat 7 Enhanced Thematic Mapper Plus (ETM+)) and should not attempt a split-window correction using both TIRS Bands 10 and 11. Prior to this reprocessing effort, users can subtract 0.32 W/(m2 sr µm) from the TIRS Band 10 Top-of-Atmosphere (TOA) radiance data to improve the accuracy of their current image products or to avoid downloading a new image product after reprocessing occurs. Once a more accurate scene-dependent correction is determined, a second purge and reprocessing will take place.
The OLI radiance-to-reflectance conversion coefficients will be adjusted for the cirrus band (Band 9) to account for on-orbit performance. The prelaunch derived coefficients were calculated using heliostat measurements, which were expected to be in error because little sunlight reaches the ground at these wavelengths. This adjustment changes the reflectance by about 7 percent in the cirrus band. Additionally, the precision of the other spectral bands’ radiance-to-reflectance conversion coefficients will be increased, changing the reflectance by up to 0.3 percent.
The relative gains of single detectors on the edges of each OLI Sensor Chip Assembly (SCA) will be updated to correct slight striping that is typically not visible. This update will affect all OLI spectral bands.
January 6, 2014 - Landsat 8 Reprocessing Details Several calibration parameter updates will be implemented as part of the Landsat 8 data reprocessing on February 3, 2014 that will result in improved product quality. This reprocessing campaign includes all previously implemented calibration parameter updates that have been implemented since launch. These changes are described in more detail below.
The OLI detector linearization correction coefficients have been refined for all spectral bands to decrease striping in imagery over dark uniform areas. The absolute radiometric accuracy is not significantly affected. The OLI radiance conversion coefficients are also improved slightly by correcting for a slight error in pre-launch calibration that resulted in product radiance values with as much as a +2% error. A slight improvement to the absolute accuracy of the reflectance values, mainly the cirrus band, will also be implemented. The relative gains will also be updated to reduce striping in the cirrus band, but this will not affect the absolute radiometric accuracy. The previously mentioned TIRS temperature offset will also be implemented on February 3, 2014. These offsets remove 0.29 W/m^2/sr/um from TIRS band 10 and 0.51 W/m^2/sr/um from TIRS band 11, relative to products processed prior to February 3, 2014. These offsets will remove an average error introduced by stray light coming from outside the TIRS field of view and will improve the data accuracy for typical growing season data (10° to 30° C) where the surrounding areas are similar in temperature. The RMS variability of this correction is 0.12 W/m^2/sr/um for TIRS band 10 and but remains greater at 0.2 W/m^2/sr/um for TIRS band 11. Due to the larger calibration uncertainty associated with TIRS band 11, it is recommended that users refrain from relying on band 11 data in quantitative analysis of the TIRS data, such as the use of split window techniques for atmospheric correction and retrieval of surface temperature values.
Based on the analysis of on-orbit data, the alignment between the TIRS instrument and the OLI instrument will be adjusted according to these time periods:
Effective Date Range; Change in TIRS alignment; Launch-date -> 09/19/2013; 12 microradians; 09/20/2013-09/30/2013; 25 microradians; 10/01/2013-11/27/2013;10 microradians
There is no change to the TIRS alignment in products after the calibration update on November 27, 2013, which already accounted for this adjustment.
January 23, 2014 - Immediate TIRS Data Adjustment. An incorrect bias adjustment for Thermal Infrared Sensor (TIRS) data was discovered in the latest release of the Landsat 8 processing system, which has caused a significant error in the TIRS products produced from Landsat 8 scenes acquired from January 14, 2014 to January 23, 2014. (The Operational Land Imager (OLI) bands are not affected.) All Landsat 8 scenes processed between January 14 and January 23 will be purged from the online cache and will become available for on demand order processing. An estimated 3 percent absolute radiometric error is observed and the errors do cause significant striping and banding in bands 10 and 11.
January 29, 2014 – Landsat 8 Reprocessing to Begin February 3, 2014 On February 3, 2014, the entire Landsat 8 archive will be cleared from the online cache and reprocessed to take advantage of calibration improvements identified during its first year of operation. All Landsat 8 scenes will be removed from the online cache at this time and these data will be reprocessed starting with the most recent acquisitions and proceeding back to the beginning of the mission. Data will then become available for download. Scenes waiting to be reprocessed will also be available for on-demand product orders. Reprocessing is expected to take approximately 50 days.
The Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) instruments onboard the satellite have proven to be outstanding sensors; however, all sensors have required radiometric and geometric refinements to ensure good calibration and data continuity. This reprocessing campaign includes all previously implemented calibration parameter updates implemented since launch. These changes are described in more detail below.
Changes to Operational Land Imager (OLI) data
All spectral bands of the OLI sensor have shown minor striping, noticeable primarily in dark uniform areas; the striping is worse in coastal aerosol (CA) and short wave infrared (SWIR) bands. Striping has been reduced by making refinements to the detector linearization correction in the Radiometric Look-Up Table (RLUT). The relative radiometric accuracy is improved by this change; however, the absolute accuracy remains unchanged.
An additional change to the relative gains in the Calibration Parameter File (CPF) will be updated to further reduce striping in the cirrus band. This change will not affect the absolute radiometric accuracy.
The OLI radiometric calibration is being updated to reflect better knowledge of the prelaunch test data, on which the original calibration was based. The reflectance will change at most 0.8 percent, except for the cirrus Band 9, which will change by 7.3 percent (see table). This calibration update will make the reflectance appear brighter than when these data were processed with the previous radiometric calibration. These changes apply only to data converted to reflectance. An improvement to the absolute accuracy of the radiance values, around 2 percent for most bands, will also be implemented.
OLI Bands; Radiance Change (%); Reflectance Change (%)
1 (Coastal Aerosol) -2.1; 0.8
2 (Blue) -2.0; 0.4
3 (Green) -1.7; 0.0
4 (Red) -1.9; 0.3
5 (NIR) -1.4; 0.0
6 (SWIR1) -2.2; 0.4
7 (SWIR2) 1.5; 0.5
8 (Pan) -1.6; 0.2
9 (Cirrus) 0.7; 7.3
Changes to Thermal Infrared Sensor (TIRS) data
The TIRS temperature offsets noted on November 14, 2013 Calibration Notice on http://landsat.usgs.gov/calibration_notices.php is a primary driver for this reprocessing effort. These offsets remove 0.29 W/m2/sr/um (~2.1 K) from Band 10 and 0.51 W/m2/sr/um (~4.4 K) from band 11, relative to products processed prior to February 3, 2014. The offsets represent an average error introduced by stray light coming from outside the TIRS field of view and will improve the data accuracy for typical growing season data (10° to 30° C) where the surrounding areas are similar in temperature. The Root Mean Square (RMS) variability of this correction is 0.12 W/m2/sr/um (~0.8 K) for Band 10 and remains greater at 0.2 W/m2/sr/um (~1.75 K) for Band 11. Due to the larger calibration uncertainty associated with Band 11, it is recommended that users refrain from relying on Band 11 data in quantitative analysis of the TIRS data, such as the use of split window techniques for atmospheric correction and retrieval of surface temperature values.
TIRS offset changes
TIRS Bands Radiance Offset [W/m2/sr/um]; Temperature Offset [K @ 300K]
10 -0.29 +/- 0.12; -2.1 +/- 0.8
11 -0.51 +/- 0.2; -4.4 +/- 1.75
Here ends the log of calibration notices of the Landsat OLI sensor. Evidently you can infer the temperature saturation range from these log data. And don't forget the info Michel Verstraete already provided.
Cheers and good luck with OLI.
- Adam Steer added an answer:What is the best way to integrate LiDAR point cloud with aerial optical image?Which algorithm works best while integrating LiDAR with optical image
I'll assume for part one that you want to pull colour information from images and apply it to a point cloud.
If you have access to the Terrasolid suite, there are tools to first coregister imagery with LiDAR, and then extract colour values for the LIDAR point cloud from coregistered imagery.
For the imagery, you need a way to apply an apriori geolocation - either ground points or camera positions and orientations. I've used the second approach - no ground control, but camera centre location (plus heading, pitch, roll).
I've used this in an airborne context - it's a very good tool but fairly expensive.
If your imagery and LiDAR are already coregistered, LAStools also offers a method to extract colour data and apply it to the point cloud (LAScolor).
For part two, I'll assume you want to drape already-coregistered imagery over a LIDAR cloud to make a pretty terrain model.
Terrasolid also offers this capacity (TerraModel). I am almost certain that FUSION can also do the job, but I'm not a FUSION user, I've just dabbled at the edges.
Right now I'm working on a quick-and-dirty program to coarsely coregister (direct georeferencing) some images and LiDAR over a flat surface - but it is not such an easy task. Preferably I'd use an existing tool but I have operational (no ground control, which most methods require) and funding constraints.
I hope my assumptions are somewhat correct, and you find this useful!
- Ershad Ud Dowlah Pahlowan added an answer:Can anyone help to find a reliable sources that provides synchronously multisource data (high resolution SAR & optical) and corresponding maps?
These data need for road extraction purpose and the data of anywhere would be great!
You can download data from this link
The Sentinel-1 Scientific Data Hub provides free and open access to a Rolling Archive of Sentinel-1 Level-0 and Level-1 uFollowing
- John Shaw added an answer:Might they also be a result of obstacle spacing and bedform maturity?If the bedforms are obstacle erosional marks, long distances between obstacles in the flow direction would cause long bedforms. If the bedforms are erosional, they might become shorter with time such that initial lineations would be longer.
Thank-you, Sujit. Your answer leaves the question of a collective term for forms produced by current erosion. 'Erosional bedforms' seems appropriate, as distinct from 'depositional bedforms'. I wonder if you have an alternative suggestion?Following
- Hein Van Gils added an answer:What are the effects of grid resolution on data accuracy?
For an example the city of New York.
How will layers grid resolution impact the ability to determine whether the city of New York has a higher temperature than the surrounding areas?
In several answers it is apparent that surface temperatures are measured from space or airborne platforms for an entire Area of Interest. However, in many environmental studies temperatures are extrapolated from point data to grids (e.g. WorldClim). In the latter case the raster size in relation to the point density and the terrain configuration will impact on accuracy/reliability.Following
- Giulio Ceriola added an answer:How can I change 0 value pixels to NoValue in Erdas Imagine?
The only 0 values in the picture are the ones serving as the background - it's a frame perpendicular to left and upper edge of the picture, which has a rounded corner in the upper-left part. I need to remove this backround, probably by changing 0 values to NoValue or masking it. What's the shortest way to do it?
I'va attached a picture illustrating the problem.
By setting the NoData value you don't remove the 0 values, but simply tell the system to not consider them for certain things like statistics calculation, background drawing, raster operations, etc. Nevertheless the content of the pixel is still 0.
If you would like such pixel to have e.g. a NA value, you can write a simple model (e.g. from the Spatial Modeler) assigning to each pixel with 0 value the new desired value. For NA you can try e.to g. make a division putting the raster value at denominator.Following
- Giacomo Montereale-Gavazzi added an answer:Change detectionIf I have classified 2 images of the same area but different times (supervised classification) then how can I determine change detection between them quantitatively? Please recommend for both Erdas Imagine and ENVI.
Why not use a custom-made R code (free and open access and you truly know what you are doing) and compute a transition matrix to then extract Gross Gain, Gross Loss, Persistence, Class Exchange and several meaningful ratios? I suggest you read the Paper on Benthic Habitat Change Detection by Rattray et al. 2013! Best solution is in there and in its bibliography!!Following
- José M.C. Pereira added an answer:Is it possible to access to geostationary satellite data?
Hi every one. I need free geostationary data (TIR band) in a particular period (e.g. December 2003, ) . Is it possible to access to this data? Can anyone introduce any websites to access to them? Thank you for your help.
You may want to try the EUMETSAT Land Surface Analysis Satellite Applications Facility, http://landsaf.meteo.pt/
- Tomáš Brunclík added an answer:Landsat 8 atmospheric correction SW?Does there exist a software already able to do atmospheric correction of Landsat 8 imagery to ground reflectance, preferably a free?
To return to the topic, I just have published new version of my atmospheric correction script.
Most imortantly it features orthogonal regression, which should work better than ordinary least squares for the type of data used. But I have to warn Windows users, the script does not work for me in WinGRASS. It may work for you, but most probably will not, as it seems to be caused by a problem in the Windows version of GRASS. Linux and Mac users should be fine.Following
- André Beaudoin added an answer:Can anyone tell me that why L-band PALSAR data is suitable for fire monitoring?
I am trying to search on of the publication of L. L. Bourgeau-Chavez, F. Siegert, E.S. Kasischke on L-band PALSAR data utilization for fire monitoring. Can anyone tell me that why L-band PALSAR data is suitable for fire monitoring?
Hello Tasmeen, this PALSAR-based change detection paper, though in boreal forests, could be of interest for mapping fires in your context:Following
- Umma Jannat added an answer:Anyone focused on precognition or intuition research possibly within human-computer interaction?
Does anyone also measure difference between probability and higher success when humans are intuitively selecting right answer?
- Raghava Rayudu added an answer:Where can I get free LIDAR data?
Using LIDAR to model rainfall interception is advantageous to other interception models in many respects (e.g., Gash, Revised Gash, Rutter, and etc.). While published models on rainfall interception have shown to be highly accurate, most require many parameters and long-time measurements.
To understand regional or watershed-level eco-hydrological processes, LIDAR can provide both the spatial coverage and resolution needed to address such questions.
hai, you can get data from nasa
you need to sign up for this site and proceedFollowing
- Volkan Yilmaz added an answer:Satellite sensor that provides both PAN and MS image?
MS is normally called as Multi spectral image and PAN is panchromatic image . What is the abbreviation of PAN.
IKONOS, Quickbird, Geoeye, Hyperion, Landsat, Worldview, SPOT, Orbview are some of the sensors provide both multispectral and panchromatic bands.Following
- Rebecca Lemons added an answer:Remote sensing data or images vs remotely sensed data or images. Are they both OK?
I have used in the past both "remote sensing" and "remotely sensed" as qualifiers to refer to data acquired by airborne or spaceborne sensors. However, I feel increasingly uneasy about "remotely sensed", because I find it convoluted and even a bit pedantic. I would like to convince co-authors to give up using "remotely sensed", and I offer two arguments:
- A digital picture of a lake taken from the top of a mountain was indeed remotely sensed, but it's clearly not what we would understand as a remote sensing image.
- 'remotely sensed' is a secondary jargon derived from the primary jargon 'remote sensing'. Why should we use yet one more jargon when the original one can also work as an adjective?
Can you come up with any other supporting argument, or if you prefer 'remotely sensed', can you come up with a counterargument?
They are the same thing. Generally what we see currently in the literature is that the terms are interchangeable. I have seen where old photographs were used to as remote sensed to create maps of historic vegetation. Additionally currently technologies such as those images taken in your phone have location data associated with them. So pictures from a camera and those taken via a satellite are not much different, only the angle of view. After all satellites are just fancy cameras that can pick up a broader spectrum than the visual bands that your hand held camera does.Following
About Remote Sensing
Group focused on Earth Science Remote Sensing