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Remote Sensing Applications - Science topic

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Questions related to Remote Sensing Applications
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Are there any GPS Analysis Softwares that run on Windows? It would be preferrable if the software can be acquired at low cost or for free. All the GPS analysis tools I'm coming across run on Linux. Unfortunately, I'm not very good with Linux and it's proving to be a challenge running the programs. A GPS analysis tool that runs on WIndows would be very helpful at this point.
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Of course the answer depends a lot on which kind of analyses you need to do.
Anyway, as already mentioned by others, RTKLIB is free and open source software with a graphical user interface running on Windows. It's written in C in case you need to modify the source code. http://www.rtklib.com/
GpsTools was developed by the same author of RTKLIB, but it's written in MATLAB (mixed with compiled C programs). http://gpspp.sakura.ne.jp/gpstools/help/gpstools.htm
Another free and open source solution is goGPS (but only for single-frequency analyses): http://www.gogps-project.org/
The MATLAB version of goGPS is more complete, while its Java version implements only the main positioning engine. goGPS MATLAB comes with a graphical user interface, while goGPS Java has to be included in your project as a library.
goGPS is a work-in-progress, so it may require the user to tweak the code here and there.
As one of goGPS developers, I would be happy to get back code contributions / bug fixes / suggestions. Collaboration proposals on goGPS development are welcome.
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What are the best remote sensing techniques for mapping freshwater (upstream, prairy pothole, etc.) wetlands?
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Using a water index such as MNDWI (for images with SWIR band) or NDWI (for images without SWIR band) to highlight water features, and then separating wetland form other features with proper threshold values.
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To estimate carbon stock of the tree systems in agriculture fields or urban trees with direct remote sensing methodologies accurately.
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That really depends on the technique you use.
Full wave form lidar is very expensive, but provides a very good estimate of above ground woody biomass (irrespective of whether the tree is alive, dead or in variable condition).
SAR also provides good estimates, but needs a thorough understanding of backscatter characteristics of both the trees and the surrounding landscape, but is likely to be problematic in urban areas due to the array of different scattering elements.
Traditional airborne or spaceborne high resolution optical will allow you to identify/count the number of trees and you'll need to have some sort of allometric relationship between crown size and wood volume to estimate carbon stock. The more stratified and locally validated your allometric relationship...the more reliable the result.
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I processed, using SeaDAS, a complete year of L2 files (with hi-res - 500 m). Also using the seaDAS software, I created the L3 bin files with 8 days temporal average but the .hdf file is completely different from the .hdf of the level 2 and I can't understand how to open it and map the ocean color products using the matlab software.
Without using seadas, is it correct doing a weekly average just with gridded and interpolated L2 files with matlab software. If yes, how is the correct procedure to do it?
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Read Help doc in Matlab about: "HDF Import Tool", maybe useful.
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High-resolution data play a vital role in geospatial technology especially for regular environmental monitoring. Is there any detailed processing steps to generate DEM from sentinel-1 images?
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MODIS is not the best instrument to derive a DEM
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In addition, I want to process and interpret aerials - what are the steps in doing this if I want to compare different years?
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I agree with Akhtar in using ArcGIS. When loading the polygons and the SPOT images, check their projection, coordinate system, and datum. Then use the projection tools to reproject the polygons in the same system of the raster images (not the opposite). About the second question on comparing aerial photos among different years, you should first classify them, and then perform a change detection analysis among the different years. Make sure that the aerial photos are in the same projection, datum, etc., and then choose an appropriate classification method which will depend on case by case.
Which methods we can apply to assess forest fire burn severity and its impacts on vegetation?
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Gralewicz, Nelson, and Wulder (2012) found that it takes about 20 years after a burn for a forest to return to pre-fire conditions in terms of spatial patterns. However, vegetation dynamics of a forest system after a forest fire event are difficult to predict and the ultimate effects of fire disturbance requires more intensive study (Chapin et al., 2010).
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Please see attached an example for an experimental approach to determine fire behaviour, which has a strong influence on the the response of vegetation. This can be combined with pre- and post-burn inventories of vegetation.
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Like Jason can give about oceans.
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You can extract depth information from satellite images (or orthophotos) of clear shallow waters. You can use just optical bands, as Leo wrote above. It is possible to use even Google images. Just, you need to have several points with known depth for calibration.
This possibility has been examined by many authors:
Lyzenga, D.R., 1978. Passive remote sensing techniques for mapping water depth and bottom features. Applied Optics 17 (3), 379-383.
Clark, R.K., Fay, T., Walker, C., 1987. Bathymetry calculations with Landsat 4 TM imagery under a generalized ratio assumption. Applied Optics 26 (19), 4036-4038.
Benny, A.H., Dawson, G., 1983. Satellite imagery as aid to bathymetric charting in the Red Sea. The Cartographic Journal 20 (1), 5-16.
Stove, G., 1985. Use of high resolution satellite imagery in optical and infrared wavebands as an aid to hydrographic and coastal engineering. Proceedings Conference on Electronics in Soil and Gas, London, January 1985 (Twickenham: Cahners Exhibitors), pp. 509-530.
Stumpf, R.P., Holderied K., Sinclair M., 2003. Determination of water depth with highresolution satellite imagery over variable bottom types. Limnology and Oceanography 48 (1, part 2), 547-556.
A review of passive remote sensing of shallow-water bathymetry Yarbrough, L.D., Easson, G., 2003. Comparison of techniques for deriving bathymetry from
remotely sensed data. AMRS – Conference 2003: Hyperspectral Issues for Coastal Zone Environments.
Baban, S.M.J., 1993. Evaluation of different algorithms for bathymetric charting of lakes using Landsat imagery. International Journal of Remote Sensing 14 (12), 2263-2273.
Bramante, J.F.; Raju, D.K., Min T.S., 2010. Derivation of bathymetry from multispectral imagery in the highly turbid waters of Singapore’s south islands: A comparative study. DigitalGlobe 8 – Band Research Challenge 2010.
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Are any countries installing remote sensors on commercial airliners to monitor oil spills, algal blooms,or other undesirable processes/features in coastal waters or inland seas (e.g. Baltic Sea, Mediterranean Sea, etc.)
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i want to explore the usefulness of satellite Remote Sensing for monitoring desertification
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There isn't a 250 m thermal product in MODIS; however, there are techniques where you can fuse both MODIS and Landsat products to get better spatial and temporal resolution.
See this article.
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What sensors can be used on AUVs to detect and map plumes of oil seeps beyond 500 meter depth?
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dear Victor,
as you may know, the problem of oil spill detection on water is still partly unsolved, especially for what concerns deep oil leaks and thickness detection,
you may be interested to look at this review to have a general listing of all the methods and their literature references, even though the paper is not really recent:
But the literature on the argument is very large so I am far from complete in my answer!
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Has anyone any experience in combined seismological (seismometer)-geodetical (GPS) and interferometric (InSAR Corner Reflector) permanent stations?
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Very good question !!!! : )
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We have a very dense topography file (text- xyz format) that we will use to build a medium resolution DEM. We have used gOcad to decimate the file. The problem is that this takes so much time
Is there a freeware that allows us to do this faster??
Thanks in advance
Emilio
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GRASS GIS r.in.xyz is VERY fast. It can create a DEM from millions of LiDAR points in a few seconds. And of course it is free and open-source
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The EO-! Hyperion hyperspectral imager has only moderate spatial resolution of 30 m and a relatively poor signal-to-noise ratio. How suitable is it for mapping coastal vegetation, such as salt marshes and mangroves? I have read several articles on that subject, but would like to get more opinions and discussion of results.
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I agree with the previous answers. The success depend on many issues including the nature of vegetation that you want to map i.e. the extent, the mixing between different type of vegetation and also on what kind of properties that you will map from these vegetation. If the vegetation
Based on its spectral resolution, hyperion is a good sensor. If the area under investigation is large enough, and the cloud cover is low, it should be able to provide many valuable information about wetland vegetation, especially when its coupled with field data or spectral library.
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I digitized my study area, a catchment area of a river in Nepal, from Google Earth. For further analysis to be done in my research I tried to clip the MODIS image with the polygon feature of my study area, but that give an empty result with a layer with no data. Should I be more aware while entering the digitized data in my project, undergo re-projection, conversion and transformation, or is there some problem in my data or software? I have a research project to detect the temporal change in vegetation by using hyper temporal image.
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when digitizing in google earth, the data should be saved as *.kml file.
This file should be imported to arcgis through import utility.
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Can mangrove cover (extent) and main species composition be mapped with multispectral scanners or must one use hyperspectral techniques? What spatial resolution would be required for global mangrove mapping using satellites?
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Multispectral instruments such as Landsat and ASTER have been proven in Belize, where Cherrington et al. aimed to measure at change in mangrove extent over multiple decades (see link to technical report from 2010). Spatial resolutions here ranged from 30m (Landsat's multispectral bands ranging from blue to near-infrared) to 15m (ASTER's green, red, and near-infrared channels). In brief, spectral mixture analysis allowed for the identification of mangroves. It should be noted that a detailed baseline mangrove map that included extensive field reports provided a reference for validation.
Globally, Giri et al. (2011) mapped mangroves using Landsat as well (30 m spatial resolution).
To identify dominant species (or to distinguish one species from the next), greater spectral resolution would be needed. Unfortunately, while the MODIS and MERIS instruments have better spectral resolution than instruments like Landsat and ASTER, their spatial resolution is lacking (~250m - 1km) for the fine level of detail that mangrove mapping requires. Hyperion is a hyperspectral instrument on board the Earth Observing-1 satellite and provides 30m spatial resolution, but the signal to noise ratio can limit its utility.
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There have been attempts made to estimate with airborne hyperspectral sensors the salinity of river plumes due to the rough inverse relationship of salinity to suspended sediment concentration (in some rivers). It seems that these techniques are much less reliable than using airborne microwave radiometry. Does anyone have experience trying to do that? How accurate were the results? (Multispectral sensors are more readily available than microwave radiometers in most parts of the world).
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I agree with Emmanuel that the relationship to CDOM is probably a more promising approach to take. Our Aquatic Remote Sensing Group at CSIRO recently published work on the relationship of CDOM to salinity for river plumes impacting the Great Barrier Reef. Check out:
Schroeder, T., Devlin, M., Brando, V., Dekker, A., Brodie, J., Clementson, L., and McKinna, L. (2012). Inter-annual variability of wet season freshwater plume extent into the Great Barrier Reef lagoon based on satellite coastal ocean colour observations. Marine Pollution Bulletin, 65: 210-223.
Abstract:
Riverine freshwater plumes are the major transport mechanism for nutrients, sediments and pollutants into the Great Barrier Reef (GBR) lagoon and connect the land with the receiving coastal and marine waters. Knowledge of the variability of the freshwater extent into the GBR lagoon is relevant for marine park management to develop strategies for improving ecosystem health and risk assessments. In this study, freshwater extent has been estimated for the entire GBR lagoon area from daily satellite observa- tions of the Moderate Resolution Imaging Spectroradiometer (MODIS) between 2002 and 2010. To enable a reliable mapping of freshwater plumes we applied a physics-based coastal ocean colour algorithm, that simultaneously retrieves chlorophyll-a, non-algal particulate matter and coloured dissolved organic mat- ter (CDOM), from which we used CDOM as a surrogate for salinity (S) for mapping the freshwater extent.
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What are the most effective methods for remotely detecting and mapping coastal freshwater springs? The water temperature of these springs changes rapidly to an intermediate temperature between the temperatures of the background water and the air temperature.
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Dear Victor,
Infrared and hyperspectral data will be good for detecting freshwater. However, geophyical mapping may help some times for fresh water hunting.
Hope this will be helpful for you
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I'm currently creating a public list of databases for hyperspectral material signatures and hope you can help me to make it of more worth for the scientific community.
The ones i already have:
- ASTER
- U. S. Geological Survey, Digital Spectral Library
- Ferwerda: A Web-based open-source database for the distribution of hyperspectral signatures
- Hüni: SPECCHIO
- Pfitzner: Issues to consider when designing a spectral library database
- Rasaiah: Building better hyperspectral datasets: The fundamental role of metadata protocols in hyperspectral field campaigns
- Ruby: Spectral signatures database for remote sensing applications
- Satterwhite: Hyperspectral Signatures (400 to 2500 nm) of Vegetation, Minerals, Soils, Rocks, and Cultural Features: Laboratory and Field Measurements
- SpecMIN: REFERENCE MINERAL SPECTRAL LIBRARY AND MINERAL SPECTRAL IDENTIFICATION SYSTEM
- Vegetation Spectral Library
Which databases are known to you that contain hyperspectral material Images and/or signatures?
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here's a link to the U.S. Geological Survey Spectral Libraries...http://speclab.cr.usgs.gov/spectral-lib.html
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150 km^2 only.
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Indeed, some good correspondence between satellite retrievals and in-situ observations can be obtained. See:
Albergel et al. 2009: An evaluation of ASCAT surface soil moisture products with in-situ observations in Southwestern France. Hydrol. Earth Syst. Sci., 13, 115–124.
Prigent, C., F. Aires, W. B. Rossow, and A. Robock (2005), Sensitivity of satellite microwave and infrared observations to soil moisture at a global scale: Relationship of satellite observations to in situ soil moisture measurements, J. Geophys. Res., 110, D07110, doi:10.1029/2004JD005087.
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Identification of the promising area that can further be asessed for ore estimation.
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I second Richard Gloaguen's comment. Additionally, using freely available Landsat ETM+ and Landat 7 imagery is a good place to start. There's a small body of literature available from authors around the world on the band ratios that worked in their study areas for epi-and mesothermal gold deposits.
In unvegetated areas, the bands and band ratios that highlight alteration products found on the surface, mainly clays, were reasonably good indicators and predictors of subsurface and nearsurface veins.
Check out the following paper "Primary analysis for enhancing the Iron Oxide and alteration minerals, using ETM+ data: a Case study of Kuh-e-Zar gold deposit, NE Iran" S Saadat, M Ghoorchi - IRANIAN JOURNAL OF EARTH SCIENCES (IJES), 2009 (http://www.sid.ir/En/VEWSSID/J_pdf/1019920090106.pdf) and others like it.
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We are using MNDWI, NDWI, VIUPD and several indices to delineate water boundary from non-water features. We try to implement these methods for floods or lakes and try to choose some best threshold methods.
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The turbidity, organic and inorganic matter content of the water, and the nature of the atmospheric layer between the sensors and the water body (often influenced by time of the year) and the nature of the surface at the boundary between water and land could introduce challenges. These variables would affect the albedo of the water body and make it more difficult to distinguish. There have been a number of studies utilising bio-optical and radiation transport models to enhance mapping accuracy. 
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In order to asses potential zones for ground water extraction in hard rock formation.
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There are quite a lot of methods that will apply on different settings with more or less success... We are currently working on several approaches so if you are interested contact me. But basically, we use preprocessed DEMs and PAN-data to extract linear features that we further merge using polynomial fits and or splines. It works quite well.
But, to be honest, one of the most important step is still to pre process the data in order to be able to extract the right stuff... There is a huge gap between image discontinuities and geological structures...
We do better than that now but look at: Mallast, U., Gloaguen, R., Geyer, S., Rödiger, T., & Siebert, C. (2011). Derivation of groundwater flow-paths based on semi-automatic extraction of lineaments from remote sensing data. Hydrology and Earth System Sciences, 15(8), 2665–2678. doi:10.5194/hess-15-2665-2011
It might give you some ideas...
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OMI level2 products contain geophysical information at ground pixel resolution and derived from level 1B product.
Level 2G (L2G) datasets contain one day's worth of the Level 2 data (typically 14 orbits) ordered by ground position rather than by time.
What does it mean by ordered by ground position rather than by time?
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OMI's Level 2 products are stored as two-dimensional arrays in the instrument's frame of reference. The first dimension of these arrays has 60 entries in across-track direction, which are obtained at the same time (push broom scanner). The second dimension has about 1600 entries in along-track direction. In along-track direction, entries are sorted by time from the first to the last measurement in an orbit. As there are about 14 orbits per day, you will find 14 files for each day.
The Level 2G product are also stored in a two-dimensional array, but with first and second dimension being longitude and latitude, respectively. Each entry of these arrays has a list of all measurements obtained at this longitude-latitude position. The Level 2G product contains all data of a single day in one file.
To summarize, the Level 2 product is using measurement time as array dimensions and the Level 2G product is using longitude-latitude ground position as array dimensions. Thus Level 2 data are ordered by time, while Level 2G is ordered by ground position.
I hope my explanations help to answer your questions.
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I have data range (lets say) from 0.1 to 1.0 and I want to divide it into three different ranges: low, medium and high values. Is there any automatic method to select these ranges?
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Yes, you can. Most of the GIS/RS software should do it. If you have access to Arc GIS you can use Arc Map and categorize the data (classification). Thanks.
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I need more information about how to run SAGA GIS as a software to analyze coastal morphodynamic using lansat data.
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Thank you for the answer.
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DigitalGlobe arranged a challenge for WorldView-2 satellite applications in the years 2010 and 2011. The contributions were free for download at their website, but unfortunately this is no longer possible.
Does anyone know where I can find a compilation of articles submitted to the WorldView-2 8-band challenge? I have already downloaded some of the articles so I can share them on request.
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Thx for the adress, but it seems this server isn't online anymore...
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Satellite-derived vegetation indices can be used to define the start and end of the growing season.
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Hi Martin,
I can mention two large networks of ground measurements.
1) The VALERI Initiative
The objectives of the VALERI project are to provide high spatial resolution maps of biophysical variables (LAI, fAPAR, fCover) estimated from ground measurements to validate products derived from satellite observations.
For this purpose, the VALERI project offers:
• a methodological framework designed for the derivation of the high spatial resolution maps;
• a pool of instrumentation and tools for ground measurements and processing;
• a network of sites distributed over the Earth’s surface;
• a database of processed and available high spatial resolution maps.
2) FLUXNET
In the CO2 Eddy covariance FLUXNET network (global) several sites are equipped with optical instruments to measure in situ NDVI. For more details and a description of the approach see the publication here below:
Cheers,
Frank
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Example: I have two areas as follow: Area 1: latitudes: 10S-->32S Area 2: latitudes: 28S-->50S There is an intersection area, that is, 28S to 32S. I wonder what is the best practice for joining interpolated areas and generate only one image.
Please note that each area is allocated in a netCDF file. They are in a regular grid.
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As the method of first choice I would suggest a linear interpolation.
In your example: in the common area 28S-> 32S value of each pixel set as a weighted average of the two images with respect to latitude, ie:
X1 - the value of an image 1
X2 - the value of image 2
X - the value of the merged image
fi - latitude
If fi <= 28
X (fi) = X1 (fi)
If fi> = 32
X (fi) = X2 (fi)
If fi> 28 and fi <32
X (fi) = X1 * (32-fi) / (32-28) + X2 * (fi-28) / (32-28)
If you are using NetCDF, you can do the calculations for example in ArcMap (version> 9) or in MATLAB
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I am looking for research papers on carbon stock estimation using RS and GIS techniques.
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Is it possible to get information on estimation of the biomass of mediterranean type bushlands (maquis, chapparals etc) using remote sensing and/or GIS techniques?
I have asked a question, but stiil waiting for a reply:
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Currently working with a software developed by the imaging spectrometer owner for images taken outdoors in mid morning sunlight. Wondering if we need to include a factor of incoming solar radiation or if there was a known reflectance page used in each image, would that negate the need. Hope I explained this satisfactorily.
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Have you read up at all on BDRF? I think there are a number of formulae for correcting for the angle of your incoming solar radiation.
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I will appreciate if someone can refer me to relevant papers that talk about upscaling
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Thanks Abdelaziz, may I have your email address if possible?
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I would like to calculate convective inhibition to check the stability, layer stability and stability of the atmosphere.
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I think this link contains everything you need for Matlab: http://homepage.uibk.ac.at/~c7071028/metpack.zip
Anyway, there is a special package dedicated for R programming language, which is called Radiosonde (http://www.image.ucar.edu/Software/RadioSonde/) with nice skewT examples. R programming language is in some aspects very similar to Matlab syntax, so maybe using part of this code may be really helpful also in your case.
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Can anyone tell me if any of the new image fusion methods (STARFM, ESTARFM...) are implemented anywhere, or are available for beeing applied without needing to program them?
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You can contact Jeff Masek at NASA to obtain the STARFM software.
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This calculation is one of the procedures for SEBAL algorithm implementation.
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Based in the information from USGS (http://landsat.usgs.gov/ESUN.php) you should not use ESUN but the information provided in the metadata.
In the GRASS manual is a method described how to do it.
Hope this helps?
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What is the status of relating remotely sensed land cover (land use) change in a watershed to the water quality change of streams and bays receiving the run-off, assuming that key model inputs (e.g.precipitation, hydrology, etc.) are known?
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Dear Victor,
The relationship depends much on the change in land use and the change in activities and management of that land. The run-off quality is highly influenced by the retention time and contact with polluted substances. In general land cover change from nature to roads or cities results in lower quality of the run-off water. Roads will add oil and others, buildings can increase the concentration of e.g. lead and copper in the run-off water (from e.g. drain pipes). Key is, what kind of change is it, what was the prior situation and what is the difference and what is the surface area of that change.
In my upcoming project I will more or less take this relation into account, regarding soil structure improving measures and their effect on the watersystem. Effects are all ready known on a rough scale, but the affiliated costs and benefits aren't. I will focus on the latter.
Hopefully this helps you. If you have more questions don't hesitate to ask.
Greetings,
Jeroen
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I am presently working on a problem which needs change detection studies in forestry. Is there any other advanced or new methodology to differentiate forests other than normal change detection algorithms (classification, image ratioing/differencing etc.)?
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The issue is rather complex and depends a lot on the forest characteristics. While in general dense forests can be easily detected the problem becomes critical in open forests such as savannahs, degraded forests etc. Here, in the optical domain the background reflected energy often masks the signal from trees resulting in classification errors. This raises as well the definition of the class "forest", as for instance in tropical countries agro-forestry (e.g. rubber, cocoa, orchards) are more considered as agriculture, while mainly comprise trees. What can help are time series (esp. when seasonal changes are present) which will allow to differentiate for instance agriculture practice from permanent forestry.
Nevertheless, open forests remain a challenge in the optical domain.
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I am a beginner in Remote Sensing
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VIIRS is a new and very wide dataset, you can find it here
Also EUMETSAT products are available here
Running HBV Light Model - need suggestions for data preparation?
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I want to apply HBV Light model in a mountain watershed of Nepal. The altitude of the watershed ranges from 50m to 7800m. I have 11 years of flow data, of which I am thinking to use first 6 years for calibration and next 5 years for validation. And, I have 3 precipitation stations with about 35 years of rainfall and temp data. The stations are distributed between 800 - 1200m altitude. What would be the best way to prepare ptq data from all these 3 precipitation station?
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I've sent the paper - strange that Durhm University doesn't subscribe to Hyrology Research as this is the journal sponsored by BHS! At 1400 sq km, you will still have a problem with interpolating 3 gauges, but the satellite products are probably not going to be much use. If you can get other gauges in the general area, you might be able to determine a relationship between elevation and rainfall, and use this to help with the interpolation. Seems to be common to think that only gauges within the catchment can be used to estimate the areal rainfall. This is not the case, and you should always look for data in neighbouring areas. At the least this will limit edge effects from the interpolation.
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I have retrieved CO2 concentrations from EnviSat sciamachy's data sets. But for particular months (June, July, August and September) the data quality is poor. In the sense there were very few points in the area of my interest (whole India). But for the remaining months the more or less the data covering is satisfactory.
I feel that Interpolation is not a good idea to find out the missing information. Is there any other way or idea to find out the missing data points (contains CO2 concentrations) in this region.
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I suggest looking at this very interesting paper:
"A three-dimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations", freely available here:
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I am trying to apply the general split-window algorithm ( Ts= T1 + A ( T1 - T2 ) + B) for estimating LST. But I do not know how I can calculate the coefficients A and B.? Could you please provide me with the way of calculating them?
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Sure. I will post it here as soon as it comes out.
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I am using Landsat-8 to estimate land surface temperature. But the problem is that the Landsat-8 has two thermal bands which are band 10 and 11. My question is what band I should use to estimate the LST. Can I take the average of both bands? And apply this method (http://landsat.usgs.gov/Landsat8_Using_Product.php). Or is there an algorithm that can be used for 2 bands?
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Before doing anything you should probably take into consideration some of the calibration issues currently being dealt with for Band 11 (http://landsat.usgs.gov/calibration_notices.php)
There is a correction factor that can be applied to Band 10 but it is suggested you currently steer clear of Band 11 and the split window correction process until further investigation is done on the calibration.
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Deep ocean dynamics and thermal/salinity structures cannot be observed by satellite remote sensors directly, but can be estimated with the help of models using satellite data. Which processes and structures can be estimated?
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Data assimilation is a natural approach for merging observations (in-situ and satellite) and model data to obtain estimates of either observed or unobserved variables. For instance, we are interested in obtaining estimates of the North Atlantic meridional overturning circulation (NAMOC) by applying that methodology. Preliminary results suggest that this could be a successful endeavor.
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Hi Solomon, it's an interesting question, one I'll respond to with a different question :) What sort of land cover change are you trying to detect? land use intensification, tree clearing, urban expansion, shifts in grazing pressure, vegetation responses to climate dynamics? All of the above?
Depending on what sort of change you're trying to detect, and your existing skills there are a range of options:
Easiest: Identify the best available GIS data from the three decades, research the methods and classification schemas used to generate the GIS layers and then create look up tables to cross walk between classifications. Once you've standardised the three classifications then a standard GIS change analysis will give you a first order estimate of the changes that have taken place. Advantages, relatively quick and straightforward and requires intermediate GIS skills and no remote sensing expertise. The major limitation is the availablity and comparability and thematic/temporal/spatial limitations of the coverages of available GIS layers.
Moderate: Source surface reflectance corrected Landsat data from the three decades in question, 1 cloud free scene per decade, preferably acquired at a time of year when the dominant land cover classes are easy to discriminate (ie dry season rather than wet season), perform the same image classification technique to all data and then compare results through time. The national land cover dataset for the US is a good example of this approach http://www.mrlc.gov/nlcd2001.php
Advantages, the stable nature of Landsat allows you to hindcast the classification provided you have decent field data for current land cover classes (worst case you can do this from Google Earth with it's date specific imagery and some image interpretation). Disadvantages, you need a reasonable level of remote sensing and image processing techniques to undertake such an approach and need access to earth observation image processing software i.e. ENVI, Erdas, ERMapper, GRASS to run the classification. Another disadvantage is that you have no insight into when the change occurred between image dates, and you may fail to detect short live land cover change events/signals. This is especially problematic in tropical environments where the shift from forest to oil palm (both have strong green canopy signal once established) can occur in a short space of time.
These approaches are at the cutting edge of land cover mapping but require a strong understanding of both the time series analysis techniques and land cover change types that you're trying to detect, and access to high performance computing so that you can bring change detection algorithms to bear on terrabytes of data.
Good luck!
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I have calculated the IVI or the frequency, dominance etc. of the various species found in a Forest Management Unit. Now can we represent these on the FMU map using GIS technology? I just pondered over it but couldn't find any solution.
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I wouldn't suggest to apply any direct spatial interpolation method before checking whether there is any spatial pattern or not. I would recommend to perform trend surface analysis and Mantel test before you proceed. If there is any spatial pattern, it's also important to identify what actually describes such spatial variability. There is a recent good paper on it if you are interested:
Peres-Neto, P. R.; Legendre, P., Estimating and controlling for spatial structure in the study of ecological communities. Global Ecol. Biogeogr. 2010, 19, (2), 174-184.
Hope that helps.
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We are a small team of ocean color researchers from the Southeast Asia region. Ocean color technology is rather new to most of the people here. Aiming to encourage more young scientist involves in the ocean color research, we have voluntary prepared an information portal that will disseminate the research findings, scholarships, jobs, funding opportunity information which hosted at http://oceancolor.marufish.com
We would like to invite you to visit the website and kindly provide comment to further improve it. Thank you and see you at the Ocean Color Information Portal for Southeast Asia region.
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Thanks for this information. I'm currently working in Vietnam in the frame of different project dealing with ocean color, my main research topic. I'm based at Spatial Technological Institut in Hanoi and promote ocean color in Vietnam in collaboration with different researchers and institution (ESA, CNES, NASDA, IRD, USTH).
Hope to hear from you,
best
hubert loisel
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Evapotranspiration.
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It is very useful, especially ASTER satellite. There is a model called "SEBAL, Surface Energy Balance Algorithm for Land" deals with estimating the evapotranspiration. Also, it can be correlated with the vegetation cover using vegetation indices (VI), such as NDVI and SAVI.
Please find below some useful links:
Please don't hesitate if you want to contact me for more information about this issue.
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How accurately can one estimate cloud height and water vapor using Thermal Infrared data from AVHRR and GOES satellites?
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Here is a recent publication that should help answer your question:
Di Michele et al, 2013: Quality Assessment of Cloud-Top Height Estimates From Satellite IR Radiances Using the CALIPSO Lidar. IEEE Transactions on Geoscience and Remote Sensing, Volume:51 , Issue: 4, Pages 2454 - 2464.
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Whether hybrid polarisation has potential in identifying partially submerged crops?
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maybe this pub will help some. part of the document discusses coastal flood inundation mapping with SAR and touches on influences of HH and VV polarizations.
Ramsey, E., III, D. Werle, Y. Suzuoki, A. Rangoonwala, and Z. Lu, 2012. Limitations and Potential of Optical and Radar Satellite Imagery to Monitor Environmental Response to Coastal Emergencies in Louisiana, USA. Journal of Coastal Research, 28(2), 457–476.
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I have been monitoring the characteristic properties of the Densu River in Ghana and have been comparing this to the population dynamics of local Odonatofauna. In addition to that, I want to use satellite imagery to enhance the accuracy of this study but I feel it would be very expensive.
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colour can be assessed from satellite images, especially due to suspended sediments and algal blooms
p.s. based on these there is possible to make some rough correlations, but if You want to set-up some kind of water-quality classes than You will have to combine the spectral analyses with field-data (physico-chemical and biological parameters from the same periods when the respective images were taken)
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I am interested in air quality and pollution. My thoughts so far is to use remote sensing to estimate the pollution and to measure the trends over the last 30 or 50 years. Also can I present the gases destruction over a country in a separate layers?
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Dear Ali,
It is a very good question, specially for those who are working in the field...
Moreover, you can read my paper entitled " APPLICATION OF DUBAISAT-1 IMAGERY" which shows some of DubaiSat-1 Environmental Applications.
Best Regards,
Saeed
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I'm making a classification (OBIA) supported software Definiens 7, only to separate the vegetation of other covers. I'm using WorldView-2 sensor with the bands of the visible and infrared. However I need articles showing what the best descriptors for my rating. I work in a city in the state of Rio de Janeiro (Brazil), where the Atlantic rainforest is predominant.
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As your objective is just to seperate the vegetation from others, I suggest you calculate the NDVI, then conduct segmentation and classification by setting a threshold.
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landsat
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You can get Landsat metadata for this site
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Forest degradation remains a very complex issue. Forest degradation is understood in different ways in different geographical and cultural contexts. Its measurement and quantification of its intensity and extent remain a matter of scientific debate, and one of the main issues about it is how to "measure" forest degradation (loss of productivity, carbon, biodiversity, etc ) using remote sensing techniques
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I will strongly disagree with the predecessors. Satellite images will never directly allow you to measure forest degradations. Sadly, RS is a bit cursed because it deals with data that are easily represented as images, fancy pictures with nice colours. One can easily forget that it is a bit more complicated than that.
As as said in a previous comment, one has to be extremely cautious with change detection based on RS.
To summarise: find a proxy for your type of "degradation", find the proper data/technique and then, maybe, you will find that RS is adequate.
On the other hand, there is no real debate in the RS community regarding the potential of RS for any kind of land degradation monitoring. RS has, without any doubt, a huge potential. What people do though, is trying to find suitable methodologies for all the different ecological/climatic/... settings at different time and spatial scales, with limited data availability.
I can't tell you how to measure forest degradation because you have first to define "degradation".
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Particularly for for canopy structure and biomass estimation analysis
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There is no specific answer to your questions. In our last research on that topic we found out that the MLR (multiple linear regression) of textures and PolInSAR decompositions and coherence gives the best results. But I guess we are only beginning to understand the matter ;-)
Anyway, I can tell you that backscatter coefficients only will not do at all, in any polarisations.
We are currently publishing a few papers in that directions. Stay tuned.
But here some current thoughts:
Liesenberg, V., & Gloaguen, R. (2013). Evaluating SAR polarization modes at L-band for forest classification purposes in Eastern Amazon, Brazil. International Journal of Applied Earth Observation and Geoinformation, 21, 122–135. doi:10.1016/j.jag.2012.08.016
Wijaya, A., Reddy Marpu, P., & Gloaguen, R. (2010). Discrimination of peatlands in tropical swamp forests using dual-polarimetric SAR and Landsat ETM data. International Journal of Image and Data Fusion, 1(3), 257–270. doi:10.1080/19479832.2010.495323
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I am doing a research on diurnal UHI change over the last 20 years by using bi-week data from Landsat 5, but the sensor passes over the study site from 6 to 7 Am and I'm not sure if that would be a good data. I would appreciate any suggestions.
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It is on process now, no published yet. We have a research on Moscow case study, but only in Russian. Here http://www.geosys.ru/images/site/journal/3-2011/vihod_3_2011.pdf - you can find an abstract of Moscow UHI study in English/
Here: Experience of satellite images application for ecological research of Moscow area. I. Labutina, E. Baldina, M. Grischenko, T. Khaibrakhmanov; -http://zikj.ru/index.php/en/archive/issue12 - the study of Moscow with our Moscow UHI as a fragment.
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I currently have a Landsat 5 image, but the resolution seems too high for this project.
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its better go for some other data than Landsat since it is very coarse to map mangroves. Mangroves cover along coast line there will be a chance of miscalculation of area with coarse resolution. u will be face mixed pixel problem
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I have some orbit PMW rain rate products of different polar satellites. In order to know which one is the most accurate, I want to compare them with the hourly rain gauge data. But as you know, the hourly rain gauge data is an hourly accumulated precipitation, but PMW rain rate is an instantaneous value. Can I compare PMW rain rate with hourly rain gauge data? If I can't, how can I compare the orbit PMW rain rates of different polar satellites?
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I would start by looking at what others have done toward creating integrated MW precipitation products, as differences in performance/calibration from different sensors must be dealt with in this context. For instance, see what was done at Remote Sensing Systems (http://www.ssmi.com):
Hilburn, KA, FJ Wentz, 2008, Intercalibrated passive microwave rain products from the unified microwave ocean retrieval algorithm (UMORA), Journal of Applied Meteorology and Climatology, 47, 778-794.
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In benchmarking, an area with crude oil deposit, what is the best remote sensing operation to be employed?
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i think thermal remote sensing will give some clue.. or you can try high resolution data which will provide the detail picture and extract the features by taking separated from others.
pls have a look
or
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I would like to apply an Optimal Interpolation methodology to provide a sea surface temperature (SST) composition based on thermal AVHRR and microwave TMI. The interpolator will be used to merge these two data sources. Blending two different measurements of SSTs requires some specific data treatment, that is, is data filtering or smoothing necessary? In case it is necessary, what kind of filter is more appropriate?
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There is ongoing development on that issue in the DINEOF software suite, (http://modb.oce.ulg.ac.be/mediawiki/index.php/DINEOF ) , A " EOF-based method to fill in missing data from geophysical fields, such as clouds in sea surface temperature". Which also handle mutlivariate satellite information .
As i'm only indirectly involved in the project i can only redirect you to the website, where you can find papers and documentation about the tool, and eventually download the tool itself.
Good work !
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How do I get satellite images from indian satellites between 1990 and 2000?
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Thank u Selavm and Tashi:)
Remote estimation of lue
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Thanks Gloaguen and Kaicun.
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Read this: Goerner, A., Reichstein, M., and Rambal, S., 2009, Tracking seasonal drought effects on ecosystem light use efficiency with satellite-based PRI in a Mediterranean forest, Remote Sensing of Environment, 113(5), 1101-1111, doi:10.1016/j.rse.2009.02.001 As far as I know, you will have to get the parameters from is-situ data.
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In a landsat image, can paddy fields have a cyan colored reflection in FCC in the month of March?
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I think it would be difficult to distinguish paddy from other vegetation using landsat image
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Minerals exploration
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Please go through this paper
The application of ASTER remote sensing data to porphyry copper and epithermal
gold deposits
Amin Beiranvand Pour, Mazlan Hashim
doi:10.1016/j.oregeorev.2011.09.009
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I am in need for new techniques that give accurate results in alteration zone mapping in mineral exploration
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Consider using L band radar if your areas are covered with sand. L band sees through sand to materials below due to it's long wavelength. Keep in my that with all other data products, you are seeing only the surface sand/dirt/ veg/ rock whatever. Aster is great, but I am a fan of believing the more data, the better the project!
I use a variety of methods when exploring data. I like using Sprectral Angle Mapper (SAM) but I also like using Minimum Noise transform (MNF). Both are available in ENVI.
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There is a need in my project to identify the tree canopy cover percentage at village level using the remote sensing images
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Hi there,
Depending on your term of canopy cover percentage, the analysis may varies. If you're talking about cover percentage, then, you should consider the nature (i.e. diameter, composition) of the tree that you want to identify. It will determine the specification of image that can be effectively used.
using very high spatial resolution data won't be effective because each pixel is sub-canopy extent, and thus, at per-pixel analysis, there will be many pixels that have 100% cover because the pixel consist only tree leaves.
the term percentage itself means that it is the percentage of area covered by tree canopy per unit area. Thus, you should define your unit of analysis before progressing. If not, quantifying the percentage will be difficult and the result will be difficult to compare.
If you have already finish with the aforementioned issues, there are many method that can be used to improve satellite-based canopy cover percentage identification such as vegetation index and unmixing. Furthermore, to obtain a real quantiative estimate of canopy cover, you should at least perform field survey, or if you use medium spatial resolution image, you can use higher resolution data to get canopy cover information. Afterward, you can perform empirical modeling using vegetation index, PC bands, image fractions or other approach.
And if you want to separate tree from background reflectance i.e. grass, you can try applying PCA with vegetation mask on. This will maxed out the variation of vegetation pixels and object separation will be easier.
Hope this help.
Thank you.
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A common task in remote sensing is to produce land cover maps for which usually a kind of supervised classifier is used. To train this classifier a set of reference data is required. My questions is about the sampling and plot design to select polygons / pixels to be used as train data. What designs are used and how do they workout with regard to sample size, efficiency and statistical constrains (e.g. probability sampling) ?
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First of all, check somehow all papers written by Foody and Stehman. These will answear all your questions.
Generally, there is no "perfect" way. It depends on your application, classifier, study area, budget, limitations, and many more ...
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I would like to evaluate effectiveness on food early warning systems using remote sensing and i am looking for methodologies and articles
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There is a European project about it with good information:
Good luck
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Minimum mapping unit: 20 meters,
Classification from remote sensing and ancillary data,
It will be a tool for biodiversity research.
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Its quite obvious that different users will have different needs regarding habitat characterization. Two issues seem noteworthy:
One should be aware of the difficulties in classifying a habitat from remote sensing data, even with the use of good ancillary data - field work is most recommended. From an ecologic point of view its critical to have a clear notion of the bioclimatic and biogeographic context of each habitat (hence the need for good ancillary data), together with a set of diagnostic species that will assist you to make a correct diagnostic on the field regarding the presence/absence of a certain habitat. Having said that, its also evident that this is a valid approach for most habitats (meaning NATURA 2000 habitats), but not all of them: Cavities (habitat 8310) have no diagnostic species and are mostly dependent on geological/geomorphological context and habitats like Estuaries (habitat 1130) are composed of a complex mosaic of plant communities, many of them included in other NATURA 2000 habitats.
Finally, from the manager point of view some patch metrics may be of high interest, especially if trends over time can be studied using these metrics. This may require retrospective studies but because of the reasons I mentioned earlier, many limitations may occur in this process. Also a good diagnostic of the (potential) threats and disturbance sources (some of which may be useful to the habitats persistence) may be of high interest.
In the Portuguese case some of this information was put together in what was called: 'NATURA 2000 characterization and management files', that can be found here (http://www.icn.pt/psrn2000/caract_habitat.htm ) but because these are in Portuguese, a synthesis of this may be found here (https://www.researchgate.net/publication/231521517_The_application_of_the_Habitats_Directive_in_Portugal). Hope you find it useful.
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What is the algorithm I should follow to calibrate the thermal channel of satelite images to Celsius. I have one Landsat, one AVHRR and one modis. How do I compare the results?
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this algorithm may help you retrieve the temperature from Landsat image
Ts = γ [e-1 (ψ1 Lsensor + ψ2) + ψ3] + δ
with
γ = {c2 Lsensor / Tsensor2 + [λ4 / c1 Lsensor + λ-1]}-1
and
δ = - γ Lsensor + Tsensor
where :
Ts – surface temperature calculated using generalized single-channel method;
Lsensor – at-sensor radiance in W m-2 sr -1 μm-1;
Tsensor – at-sensor brightness temperature in K;
γ – effective wavelength (11.457 μm for TM6 band);
e – emissivity;
c1, c2 – constants (c1 = 1.19104 108 W μm4m-2 sr -1; c2 = 14387.7 μm K);
ψ1, ψ1, ψ1 – atmospheric functions obtained as a function of the total atmospheric water vapour content (W)
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I am not sure what the difference is between satellite data ATLAS, MODIS, Landsat, ASTER and others. What are the reasons for using one and not the other?
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I would like to do a premise first. Usually the urban heat island (UHI) phenomenon is intended as the difference of Air temperature (AT) in the urban areas in respect of surrounding peripheral or rural ones. From airborne/satellite satellite what can be "directly" inferred is the Land Surface Temperature (LST), which is sometimes used as input for modeling to retrieve AT. A term used to remind this difference is the Surface Urban Heat Island (SUHI), which is related to the difference of LST.
So, in trying to answer to your question I'll refer to satellite data to calculate LST as a proxy to then model AT.
To make a choice (or an integration) between the sensors some parameters to focus are: 1) spectral resolution, 2) spatial resolution and 3) temporal resolution.
1) Concerning the spectral resolution, you should consider that at least one band with wavelength in the Thermal Infra Red range (~8-12micrometers) (TIR) is required. With 1 TIR band (e.g. Landsat family) you can apply single-channel algorithms: needs a reliable calibration, very sensible to the area of application and type of material, very dependent on the removal of the atmospheric effects. With 2 TIR (e.g. MODIS and AVHRR or the old (A)ATSR or the future VIIRS-OLCI) bands you can apply split windows algorithm, mitigating atmospheric effect, but the type of material is still critical (emissivity). With >3 TIR bands (e.g. ASTER or usuallly airborne sensors like the ATLAS or AHS40) you can apply advanced algorithms (like the Temperature Emissivity Separation - TES), but still the atmospheric correction is critical.
2) Concerning the spatial resolution you can go from the <=5 meters of airborne sensors, to the 60/120m of Landsat, the 90m of ASTER and then there is a jump to the 1km resolution of MODIS/AVHRR, up to the 3-4km of MSG.
3) Finally concerning the temporal resolution, the golden rule is usually: higher resolution means lower repetition frequency (e.g. >2 daily acquisitions for MODIS/AVHRR/MSG, about every 16 days ASTER/Landsat).
Then depending on the application you have to do a trade off between those 3 elements.
For a big city (e.g. of >20km2), 1km data could be enough for historical analysis and/or monitoring. This could be integrated with few acquisitions at high resolution for a "zonation" of the city and to have a more precise characterization in terms of emissivity.
For medium and small cities 1km become no more enough and it is more difficult to find data suitable for monitoring, while you can more easily found ASTER/Landsat or airborne data for a limited historical study or again zonation.
Hope to be of help.
PS
I suggest you to give a look to the Urban Heat Island and Thermography ESA project (www.urbanheatisland.info) and to Sobrino, J. A., Jiménez-Muñoz, J. C., Sòria, G., Romaguera, M., Guanter, L., Moreno, J., Plaza, A. and Martínez, P., 2008. Land Surface Emissivity Retrieval From Different VNIR and TIR Sensors. IEEE Transactions on Geoscience and Remote Sensing, 46, 316 – 327
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GPP~ chlorophyll index * PAR
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Thanks for the reply first.
It's about terrestrial GPP and by Chlorphyll index I mean the index derived from reflectance at 750 nm and 710 nm, which is derived as CI_re = (R750nm/R710nm -1, where R750 and R710 are reflectance at 750 nm and 710 nm respectively.
Regards,
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test
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Error matrix is used to find out the accuracy of classified raster layer. for more information you can go through this papers.
you can find out lot from google search
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I want to use support vector machine and random forest to discriminate 3 different levels of insect defoliation from data collected from ground based hyperspectral sensors. I also wish to resample my ground based data to different sensors and compare the 2 algorithms above using indices derived from the data.
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The scikit-learn library for python is best and easy to use–many machine learning algorithms a implemented in this library.
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Does anyone how to open downloaded AVHRR data form the Comprehensive Large Array-data Stewardship System (CLASS) in IDRISI Software. I could not import files with format of GC.
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Dear Ali,
Sorry for not being able to get back to you promptly. I've downloaded the data. It seems that it was in Level 1 format. You need to use software from the website first. Then convert it to normal desktop image processing format.
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I am comparing methods for data filling for Landsat ETM+ SLC-off
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You can download it from here --> http://geography.osu.edu/grads/xzhu/
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In ArcGis software, we can create the drainage from DEm but we have to provide a basic drainage layer. I was just curious to know if there is any such model through which we can generate the drainage of an area only with the DEM in ERDAs IMagine?
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we can also use aster DEM to create drainage layer as it is also of more resolution than SRTM provided in the cgiar website which has resolution of 90 m. At times the major drawback with the aster DEM fails in the way as to which algorithm has been used for the "fill" command in the ArcHydro tool in ArcGIS. If proper algorithm is provided for the Aster DEM data also then the drainage extraction from this data can provide better results than any other DEM data of less resolution. In SRTM the algorithm used for "Fill" command has given a better result for drainage delineation.
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Hough Transforms are useful for linear feature detection but are there other methodologies and how do we analyze connectivity in the linear features.
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You can have a look at this paper [1]. You certainly need to preprocess your images, e.g. using [2].
[1] C. Sun and P. Vallotton, Fast Linear Feature Detection Using Multiple Directional Non-Maximum Suppression. Journal of Microscopy, 234(2):147-157, May 2009.
[2] R. Su, C. Sun, C. Zhang, and T. D. Pham, Linear Feature Enhancement based on Morphological Operation and Gabor Function, In Image and Vision Computing New Zealand, Dunedin, New Zealand, 26-28 November 2012, pp.91-96.
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What are the most cost-effective techniques for tracking subsurface oil plumes (or concentrations)? If one uses AUVs (ocean gliders, etc.), what detectors or other instrumentation should they carry? Assume two different cases, shallow waters and deep waters.
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Hi Victor, a couple of options come to mind:
Fluorescence can be used to track hydrocarbons. I believe Turner Designs makes submersible sensors for this purpose. I am not sure of Turner's depth and platform capabilities.
In situ particle sizing instruments (Sequoia Scientific LISST-100X and -Deep) have also been used for oil plume tracking and for examining the effects of dispersants on the oil in situ and in test facilities (a couple sources below). The LISST-Deep is capable of deployment to 3000m. These instruments can be profiled, and can be adapted to various platforms, though I do not know of any researchers deploying specifically on an AUV for oil studies.
Li Z, Lee K, Kepkay P, King T, Yeung W, Boufadel MC, Venosa AD (2008): Wave Tank Studies on Chemical Dispersant Effectiveness: Dispersed Oil Droplet Size Distribution. In: Davidson WF, Lee K, Cogswell A (Eds.): Oil Spill Response: A Global Perspective. NATO Science for Peace and Security Series C: Environmental Security, Springer, pp. 143 – 157.
doi:10.1007/978-1-4020-8565-9
Li Z, Lee K, King T, Boufadel MC, Venosa AD (2010): Effects of temperature and wave conditions on chemical dispersion efficacy of heavy fuel oil in an experimental flow-through wave tank. Marine Pollution Bulletin 60(9): 1550-1559.
doi:10.1016/j.marpolbul.2010.04.012
Trudel K, Belore RC, Mullin JV, Guarino A (2010): Oil viscosity limitation on dispersibility of crude oil under simulated at-sea conditions in a large wave tank. Marine Pollution Bulletin 60(9): 1606-1614.
doi:10.1016/j.marpolbul.2010.01.010
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SAR and multispectral imagers seem to be best for tracking (mapping) surface oil slicks. What is the added value of including a thermal infrared scanner? Can thermal IR provide rough estimates of oil slick thickness? How reliable would be the TIR results?
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Hi Victor, I don't think that there is any point using TIR. The simplest reason is spatial resolution. Because of low SNR the footprint of TIR sensor is usually 2 times the size of VIR and 4 times of PAN...
As oil slicks are dominantly detected by their geometric properties (SAR roughness, form and size...) you would not add any specific information to the algorithm with TIR.
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Using commercial airborne lidar systems, what is the range of accuracies one can obtain for topographic mapping on land (or sandy beach above water) ? Assume there is no vegetation cover.
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The companies promotional material after the trial is attached
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There are many publications on soil moisture mapping with microwave radiometry. How useful is thermal infrared data for estimating approximate soil moisture? How accurate would such estimates be?
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Actually, the same authors are very active in this field:
Hain, Christopher R., John R. Mecikalski, Martha C. Anderson, 2009: Retrieval of an Available Water-Based Soil Moisture Proxy from Thermal Infrared Remote Sensing. Part I: Methodology and Validation. J. Hydrometeor, 10, 665–683. doi: http://dx.doi.org/10.1175/2008JHM1024.1
Yilmaz, M.T., W.T. Crow, M.C. Anderson and C. Hain, "An objective methodology for merging satellite and model-based soil moisture products," Water Resources Research, 48, W11502, doi:10.1029/2011WR011682, 2012.
Hain, C.R., W.T. Crow, M.C. Anderson and J.R. Mecikalski, "An EnKF dual assimilation of thermal-infrared and microwave satellite observations of soil moisture into the Noah land surface model," Water Resources Research, 48, W11517, doi:10.1029/2011WR011268, 2012.
... and continue to publish on that
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How rapidly does the error in soil moisture measurement with microwave radiometers increase as the percentage of vegetation cover increases? What percentage of vegetation cover (density) is acceptable if the soil moisture accuracy must be 90% or better? The vegetation consists of grasses or scrub/shrub.
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It is outside my field
R.G,Rastogi
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Just like what you see in the attachment (the first answer). Now, I have a question that how can I conduct two data: AVHRR and MODIS? In fact, AVHRR would be consistent with MODIS in time series. However, owing to that the quality of AVHRR was lower than that of MODIS, the value of AVHRR was bigger than that of MODIS in the same time. For example, (I think that) the value of AVHRR should be 177.5 (unit) in 1999, but now the value of AVHRR is actually 244.0 (unit). Now, I want to know whether or not having a function or method is universal and can be used in my research to help solve the consistent question.
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Some of the trends in your AVHRR data look like they are related to sensor or orbital drift.
Here is one paper where the authors discuss this problem and their attempts to correct for it. This may help you find a solution.
Tucker,CJ, Pinzon, JE, Brown, ME, Slayback, DA, Pak, EW, Mahoney, R, Vermote, EF, and Saleous, NE. 2005. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing 26:4485-4498.
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What is the range of accuracies for airborne lidar measurements of land topography and water bathymetry? Assume no vegetation cover on land and low turbidity of water!
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Emmanuel,
I guess the 5/beam attenuation coefficient distance includes total pulse travel distance down and up, right?
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What range of errors will be introduced by soil roughness and vegetative cover variations in microwave radiometry of soil moisture?
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Surface roughness has influence on air dynamics by influencing its velocity due to friction. In high level of roughness it may also divert direction of wind flow through gullies or narrow troughs. As is known that wind speed, with humidity level and sunshine bear heavily in the rate of evaporation greater the wind velocity, the larger amount of the local moisture in the air, due to vapour pressure, will move away and increasing the process of evaporation of soil moisture. Surface roughness mostly retard the wind velocity therefore decreases the intensity of evaporation of soil moisture. However, in some situations roughness may act conversely. For example, if the wind is directed into a narrow valley, its velocity shall increase so may increase evaporation of soil moisture on valley floor.
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What is the vertical accuracy of lidar bathymetry and topography measurements, assuming low water turbidity and no vegetative cover on land?
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Victor, it has been a long time since I played with giant lasers, so I'm not sure this helps, but back in the day (ca. 1999) topographic LIDAR vertical resolution was about 15 cm. See e.g. Meridith, Andrew W., David Eslinger, and Dirk Aurin. An evaluation of hurricane-induced erosion along the North Carolina coast using airborne LIDAR surveys. Coastal Services Center, 1999.
Full-text available on my RG page.
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I want to carry out rule based classification of landsat image using the CART package in R. I would like some links to tutorials for the same.
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What is the difference between land use and land cover? How can we make land use map from high resolution satellite imagery? Does making confusion matrix to access the accuracy of land use make any sense, or it is applicable to access the accuracy of land cover only?
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Dear Joshi,
from my stand of fiew, the following definition is possible:
Land Cover
Land cover is a natural scientific category. That means that we describe the fact what is located on a specified area (e.g. wood, soil, rock).
Land Use
In contrast to this, land use is more anthropocentrically defined. That means that we give an impression what we do on a specified area. We give an information of the intensity (e.g. intensive, extensive, not used) of our usage.
With best wishes, Erik!
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Is there any software available which updates the current global sea-surface temperature periodically?
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If there is, I think the place to look would be with NOAA. The GTSPP may be what you are looking for.
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As one of MODIS product, MOD15A2 is easy to download from several websites. But how about MOD15A1 (the daily product)? Where can I get it?
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I want to extract Hypsometric Integral curves from ASTER GDEM for a mountaineous terrain, with the help of Hypsometry tools in ArcGIS 10. I have searched through a lot of papers and queries but was unable to find a suitable procedure and layout map for extracting the hypsometric curve. I came through CalHypso which is compatible with ArcGIS 9.x only, and as, I am using ArcGIS 10, I am unable to move forward. Any kind of help will be highly appreciated.
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If you are still interested in calculating hypsometric curve look at attached image and try to do the same.
Steps:-
Delineate watershed in ARCGIS or what ever tool like to do so.
Export the table information of DEM into ASCII and import the same into MS Excel and try to arrange as arranged in the attached image file
Value represents the elevation information and arranged in descending order, count represents number of pixel of particular elevations. Area is represented in terms of pixel (since pixel size is fixed and hence represent fixed area)
Elevation above baseline = A2-MIN(A:A)
Area Above given line of elevation = number of pixel above that particular elevation
Normalised area = (D2-MIN(D:D))/(MAX(D:D)-MIN(D:D))
Normalized Elevation = (C2-MIN(C:C))/(MAX(C:C)-MIN(C:C))
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I want to carry out changes of detection between 2 images: 1. landsat 3 mss image and 2. landsat 5 tm image. If anyone could also tell me how to calculate the earth sun distance or where to calculate this from, it would be very helpful.
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I want to carry out LULC change detection of landsat images. Do I need to convert DN to radiance or reflectance values?
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Vaijayanti,
If you are still in the process of looking for documentation, here after is the reference to a paper recently released:
"Reconstructing satellite images to quantify spatially explicit land surface change caused by fires and succession: A demonstration in the Yukon River Basin of interior Alaska", Huang et al., 2013, Rem Sensing Environ, at the address
Even if this paper deals with LULC changed caused by fires, I consider that reading it will help you in a proper selection of the further steps in your study.
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With the (theta)n+1 calculation, do we fix the value of (theta)n and (phi)n of the reference pixel, or do we take their values from adjacent pixels? It is not clear to me. Please see Hagberg's paper attached to understand my question.
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I am not wholly familiar with the method, but just by following the math in the paper, which indicates that Eqn. 15 is derived by differentiation of Eqn. 13, it seems that (theta)n+1 does not refer to value from adjacent pixel, rather it is a small deviation from the actual (theta)n value. Essentially: delta(theta) = [(theta)n+1] - [(theta)n], where delta(theta) is the small deviation that is used to define the differentiation approximation. Similarly for delta(phi).
I don't know how exactly this equation is used in simulation or phase-to-height conversion, perhaps that would clear things up more.
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I want to know which algorithms to use to calculate phenological metrics. There is one 'Delayed moving average' but I don't know what it does or how to carry out this method.
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Also maybe have a look at a practical we have for msc students which explores some of the issues ...http://www2.geog.ucl.ac.uk/~plewis/geogg124/phenology.html
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For instance: change in depth due to artisanal dredging over a period of years. Where do technologies like aquasat and MODIS come in?
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LiDAR has been used to measure depth successfully in coastal and other clear waters. Hence its good for bathymetry in such waters. However if there are any chemical or suspended solids that are leading to differences in light dispersal within the water column then LiDAR is no use at all. For Australia that means most inland waters cant apply this technology becuase we highly dispersive clays.
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I want to know of any portals from which SPOT images can be downloaded or purchased
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Related to purchase you have to contact with the local vendor.
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A LANDSAT image has been classified using OB classification by setting rules. However due to uncertainty in the reference data I would like to include fuzziness in the accuracy assessment.
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Dear all, thank you for your answers. I found the method to perform the fuzzy accuracy assessment in this paper: Gopal, S., and Woodcock, C. E., 1994, Accuracy assessment of Thematic Maps using fuzzy sets I: theory and methods. Photogrammetric Engineering and Remote Sensing, 60, 181±188.
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Can anyone suggest GIS-RS experts, who will be able to provide training and facilitation to agricultural development project in Bangladesh?.
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What kind of course are you thinking of? basic level or advanced? to do what precisely.