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The sensible choice of gamma, which generates the output values between the “increasive” trends of fuzzy algebraic sums and the “decreasive” effects of fuzzy algebraic products.
But, how can I come to know, which gamma value is suitable for my output?
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It is a combination of SUM and Product fuzzy logic. use power gamma.
µ(x) = (FuzzySum)γ * (FuzzyProduct)1-γ
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We think to test this hypothesis in Morocco within our research project, but we wonder if the error in the absolute altitude could prevent from using these data to this goal.
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Dear Linh
are you interested in using radar data for soil erosion survey?
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Dear all!
I would like to suggest you take the survey "NSDI FOR YOU AND YOUR COUNTRY":
The results of this survey will help me to research the issues of the implementation of NSDI and get an overview of this policy.
I will be very grateful for your help, cooperation and sharing!
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Indeed, good start
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Actually ,I want to use landuse data in swat model, I download the landuse data from USGS and want to process in Erdas imagine for using swat model. How can one process this data and which steps are necessary for this process? Thanks for your positive response
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hello, i downloaded the collection 2 level 2 data (landsat 5 and 8) from USGS. i cant able to open in erdas imagine 2015 . it comes with error ( unwanted compression type) . please help with this!!!!!. thankyou
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Given the two following patterns: the left or the right, which one is more beautiful than another?
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Left.
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Hello everybody,
I've recently come across taxi trajectory analysis as an interesting topic in GIS/transportation. I thought it would be great if everybody here can share his/her experience in this field and introduce any researchers, good papers or corporations leading in this area.
Regards,
Pooya
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Have a look at my papers for Taxi Trajectory analysis in Munich.
There are also good Papers from TU Berlin and ETHZ:
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Aerial Laser Scanning Data is being more and more widely used across all Europe. Recently Great Britain decided to provide free access to aerial scanning data (starting on September 1st. 2016).
I know that Netherlands also provide free access to their laser scanning data (AHN 2 database of more than 600 billion x,y,z measured points)
I have also learned that Slovenia has released their LIDAR data as well, and also heard that North-Rhine Westfalia decided recently to open access to LIDAR data of their territory.
Are there any other EU States, which also decided to open free of charge access for their LIDAR datasets ?
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A nicely maintained list is here:
List of freely accessible LiDAR data and digital terrain models: https://arheologijaslovenija.blogspot.com/p/blog-page_81.html
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Recently been asked to describe some challenges when designing or implementing a geodatabase and how the challenges were addressed.
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I have seen some papers and scientific scripts mentioning both and claiming application of IoT in GIS but I couldn't find suitable and enough papers. If you know any good paper or have any idea please share with me.
thanks in advance.
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IoT has a GIS nature, every connected object that produces information or data is located in a structured system that has a location, a sequence of data to be recorded and stored (detect and store), searchable and above all has spatial relationships with other objects. This is a naturally GIS approach.
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As is well known, the Discrete Fourier Transform (DFT) is one of the main tools for digital signal analysis in electrical engineering and related fields. Now the question is:
Do you think the the Discrete Fourier Transform is deeply understood in the Geospatial community?
Although DFT was used in several areas of geospatial sciences such the Filtering of Digital Terrain Models, digial image processig, pattern recognition, its theoretical principles and their implications may not be deeply understood in the geospatial community.
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Dear Gamal,
welcome,
Any analog function f(t) or f(x) which is a function of time or position in the sense they vary with time or position or both when drawn in a linted time span or position range one depicts the variations of the amplitude of the function with time and position. Such variations may be called as the profile of the function or its waveform. Such look is abstract and complex.
The scientists were convinced that such complex shapes an be constructed from a unitary periodic wave forms which they discovered to be the sinusoidal waves or the complex exponential signals. So, it is so that any waveform in t or x or any variable is composed from its basic sinusoidal components. So, any waveform can be resolved into its constituting cosinusoidal weaves or complex exponential functions. These sinusoidal components or vibrations can be represented in the frequency domain as they are extending from - infinity to plus infinity. Such representation is called the frequency spectrum of the signal. It is normally designated by X(f).
So, one has devised two equivalent descriptions of the signal, the time domain description and the frequency domain description where the real domain in which the signals happens is the time domain while the frequency domain is the domain where the signal is analysed to its fundamental sinusoidal components.
The Fourier devised the Fourier transform FT to convert the signal from the time domain to the frequency domain. This transform is reversible which means that if one has the frequency components of the signal one can get the time domain signal.
There is great benefit to analyse the signal and get its frequency spectrum. One of great benefit is to determine its frequency range and its bandwidth. One can frequency limit this signal and filter it. One can synthesize it from its frequency components.
So assume that one has x(t) transformed it by FT to X(f),
This from is called analog form which the values are continuous with time.
For digital signal processing one has to transform the analog signal into digital form which can xd= x[n], which is a sequence of values of the x called digitized samples. The sampling rate fs must be accomplished according the Nyquist rate fs>= 2fm where fm is the maximum frequency in x(t),
The Fourier transform of this digital signal x[n] is called the Digital Fourier Transform DFT, X[k] where k is the sample index in the frequency domain.
Th DFT can be considered the sampled frequency domain.
Since DFT is time consuming one has invented the fast Fourier transform FTT to save calculations in DFT.
The most important point is that one can resolve any complex waveform or function into its frequency spectrum content for sake of signal analysis , synthesis and filtering.
Best wishes
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What is space? Is it what is conceived by Newton or by Leibniz? Both Newtonian and Leibnizian views of space are framed under the mechanistic thinking of Descartes back to the 17th century. Christopher Alexander in his life's work - The Nature of Order conceived and developed a brilliant new view of space: space is neither lifeless nor neutral, but a living structure capable of being more living or less living. This new view of space has some far-reaching implications. It implies that we make space or our built environment better, smarter, or more sustainable, or more livable, or more resilient. More importantly, it implies that goodness of space is a matter of fact rather than an opinion. In this paper, we attempted to provide empirical evidence space indeed has different degree of beauty or life:
By asking this question, what I really wanted to know how long it will take for us to adopt Alexandrine view of space. Benoit Mandelbrot said Euclidean shapes are cold and dry, but nature, traditional buildings and artifacts are full of living structures, as noted by Christopher Alexander.
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As an architect it's the biggest question
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Am Trying to model local TEC component at an East Africa Location using GNSS receivers. Anybody with similar experience local TEC especially at an equatorial location. Collaboration is also welcome
Dept. Of Geospatial & Space Technology - UON
C. M. Lwanga
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TEC - the total number of electrons in a rotation cylinder centered around the line of sight receiver–satellite with a cross section of one square meter. The TEC is expressed in so–called TEC Units (TECU). Example: for L1 solutions with an elevation cut–off angle of 15◦ and a prevailing TEC of 10TECU, you may expect a baseline shrinking of about 10·0.10 = 1.0ppm (or 1.0mm/km).
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In the last couple of months, we have seen an explosion in the blockchain technology mass media coverage, which surged the value of the great majority of crypto currencies worldwide.
I am not interested in the speculation side of this market, but more in the distributed de-centralised system that blockchain provides.
Applications of blockchain is wide, but still, only a few of geospatial examples are found on the internet.
Are you aware of, or can you imagine, a good geospatial application that runs on blockchain?
ps: avoid the public parcels ledger (register) example, that is the most obvious, I am looking for originality.
THX!
NIC
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Have you see Jonas Ellehauge's article in GIM International (October 2016)? https://www.gim-international.com/content/blog/blockchain-in-geospatial-applications-2
More on his Web site, including slides: http://blog.awesomemap.tools/blockchainingeospatial/
I particularly like his IoT use cases.
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I am looking for both basic information about this statistical method and appropriate software to run some analysis. Thanks!
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I suggest that you begin with a book, e.g. "Geostatistics", J.-P. Chiles and P. Delfiner, John Wiley and Sons (2nd edition)
For a tutorial paper see
1991, Myers,D.E.Interpolation and Estimation with Spatially Located Data Chemometrics and Intelligent Laboratory Systems 11, 209-228
This paper uses a free software package called GeoEAS (from the US EPA) then as a step up see the open source free R package called gstat. You can download various binaries (Linux, Windows, Mac) and also the source codes if you want. The software comes with tutorial materials and has very good graphics
Many of the other suggestions listed by others are commercial software and are of varying quality. Since they will not give you access to the source code you will have to know much more about the underlying theory to know what they can do and what they can not do. In general R software packages are better than commercial packages. (Do a search in Google for "R package gstat")
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Hello everyone, I'm doing work about solute transportation in soil. There are 40 sample points and in GS+ software, active lag distance is default as half of sample distance. After calculation, only four or five plotted points were showed, but R2 is more than 0.9. Is this analysis reliable? How many plotted points are needed at least?
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If you going to use the variogram for kriging or simulation remember that the purpose of the variogram is purely utilitarian; you are trying to develop a model that captures the spatial covariance of observed values.  The model you specify for kriging or simulation helps control the smoothness and roughness of the resulting surface.  Therefore you want enough variogram points to indicate a range of spatial dependence,  existence and  magnitude of any very small-scale variability ("nugget effect"), and a value for the sill.  Four points might be enough for some datasets, including yours.  For others, forty might not be enough.  You can play around with different lag sizes but remember that as the number of variogram values increases the number of pairs used to calculated each value decreases and the variogram can become more noisy.
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I have used AHP, Frequency Ratio (FR), and Fuzzy Logic (FL) to create land suitability maps in the GIS environment. Do you know other methods?
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If you create land suitability model so you have face pixel mixing problem for accuracy purposes.
Weighted Overall analysis method for land suitability map & sub- pixel is another very good method for pixel mixing of classes.
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I am currently embarking on a research on climate change modeling using geospatial technique, CO2 raster data could be very useful in this research.
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what are the softwares for doing spatial analysis?
To create maps
To compare maps
To do spatial analysis
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QGIS (http://www.qgis.org/zh_CN/site/) is the most commonly used free open source software for analysis and visualization of spatial data. It is user friendly, well-documented, equipped with hundreds of tools, and enables implementation of user's algorithms.
SAGA GIS (https://sourceforge.net/projects/saga-gis/) and GRASS GIS (https://grass.osgeo.org/) are also worth to consider. They can be installed as standalone, but they are additionally integrated in QGIS.
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I've tried with the following software:
1. Sentinel-1 toolbox - I can open the GRD IW data but can't apply the Polarimetry tools. When I use the tools with SLC IW I get errors.
2. PolsarPro - Only SLC IW data can be imported. The Polarimetry tools work fine with this product.
3. Envi Sarscape - Only SLC IW data can be imported with success, when I try to import GRD IW data I get a error.
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I already finished the project. At the time I asked this question  SNAP had not yet been released. Therefore I used Sentinel-1 toolbox for processing and ENVI for image classification.
But I did some research after that and it is possible to extract  Polarimetric variables from the Sentinel-1 images using SNAP and Polsalpro.
I'll leave a link on this matter.
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Space debris include space junk, space waste and defunct objects in orbit around Earth. It includes spent rocket, old satellites and fragments and they pollute space. They usually can be tracked, but how about measuring their quantity and relating the values to threshold limits?
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One of the major problems is that items less than about 10-cm are hard to detect and/or track with terrestrial radar. What about space-based radar? There are satellites in orbit that could do this, should they be re-tasked..
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I am looking for a Python library or API that facilitates the creation of geo-spatial topologies and is able save them to a portable format, such as TopoJSON or GML.
The main goal is to save space in detailed polygonal topologies, with borders defined only once and shared among adjacent polygons.
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Hi Luis
I think GDAL [1] is your best option. You can see the specification in [2] and an example here [3]
Regards
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I am trying to create a tree based on the geographic range centroids for 93 taxa.  I have used cluster analysis in Mesquite to create a couple of trees but I am very new to this and am not sure whether Mesquite is the best program for this, or, while in Mesquite, whether I should be using UPGMA or single-linkage.  Any help that can be provided would be greatly appreciated!
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Dear Jackson,
I think cluster analysis is not a good way to analyse such kind of data. Besides, why only using range centroids? I think it is a quite misleading to only use the centroids and disregard the taxa's actual distribution ranges because you loose a lot of information (e.g., the area of each taxon's distribution and whether distributions overlap or not).
I guess the centroids are based on point data, which you could use for an ecological network analysis as proposed by Strona and Veech (2015; http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12395/abstract).
We recently used this analysis to assess varying spatial patterns of species distributions in geological time. The analysis nicely showed increasing and decreasing range overlap, more or less corresponding to lowering or rising provincialism. Our underyling data structure was a presence-absence matrix based on occurrence data fitted to an equal-area grid of the geographic region of interest.
Best regards,
Thomas
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I have downloaded level 1Gst and level 1T hyperion hyperspectral data for my study area, in order to use in mineral mapping, however, every time I try use FLAASH for atmospheric correction it gives an errors or bad results. Should I use the toolkit then subset (removeing bad bands) then FLAASH or not? Also should I put specific advanced paramters in FLAASH?
How I use FLAASH atmospheric correction using ENVI (what ideal parameters for such data sets)?
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hai first remove bad bands and water vapour 1-7 58-76 165-182 221-242 you have 163 bands around use radiometric calibration tool convert DN to radiance. after that use FLAASH or QUAC my friend.scale factor 0.1 for radiance conversion
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I have parameters from MODIS, MISR and Aernet
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Greetings Mohammed. Lot of work is done on aerosols and space observations. But the spectral signature characteristics [ optical or IR and thermal or MW regions in general] associated with anthropogenic and natural aerosols is not a significant part of any study. For this exclusive mapping of aerosols in different types of ambient settings would be essential. Also the physical parameters of weather play important role. In most of the developing countries which are occupied with large scale construction and transportation, one could observe the dominance of anthropogenic aerosols   A pristine blue and green environment may give a aerosols data not influenced by anthropogenic substances. I think this would be possible to distinguish using MODSI products with extensive ground truth..  
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i am trying to convert a polygon map to raster map so as to reclassify the map but it is not working out well.The original map has four sets of polygon but on converting to raster only two polygons are reflected.
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Did you check if the polygons are multipart-polygons? If so, try to "break" them and do the conversion again.
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I am trying to find out an approach to effectively and accurately identify and extract various horizontal curves (e.g., simple curve, compound curve, reverse curve and spiral curve) and obtain curve location, radius, and curve length in an automated and time-efficient way. 
However, I tried some ways on this study, there are several problems I can't solve right now:
a.      Inconsistency of roadway alignment in the ArcGIS map.
Problem: the inconsistency of roadway alignment in the GIS map results Type I error. Some road segments are curving or smooth in actual roadway but mapped as straight line in GIS map. It will result the obtained curve radius changing dramatically in the same segment.
b. Segments breakpoints in Database
Problem: the improper sequence of segment in database is a major cause that contributes of Type II error.
c.   ArcGIS  map alignment accuracy
The better accuracy of roadway polyline shape file can get better results. However, currently, the available ArcGIS map is not high accurate. That’s the possible reason why the most common methods to identify and extract horizontal curve data are based on Global Positioning System (GPS) data.
Any ideas or suggestion? thanks.
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This paper might also have some good data/information for you:
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In modern LST is a key component. So for estimation of LST it is important to measure it from Geospatial technology
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ASTER
SEVIRI
AVHRR  
VIIRS  
AATSR  
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Is it possible to create a 3D geological model using ArcScene in order to help reserve calculation/estimates ?
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Hi!
I'll work with this subject soon, and share any advise and/or information i find.
I'll check this papers too.
Best regards...
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I want to do an investigate of suit GDEMs selecting for my research on a mountainous forested area.  
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Hey!
If You use ArcGIS the thing is simple.
1. Download the DEM together with the ENVI header (*.hdr)
2. Next in ArcMap use: ArcToolbox - Conversion Tools - From Raster - Raster to Point (this option will extract all DEM's pixels height values to point shapefile - very useful tool)
3. But, if You have Your own set of points and You want to compare height values from two different DEMs exactly in places where these points are located - You can extract height values (for Your set of points) from one DEM and then from another; You have to use in ArMap: ArcToolbox - Spatial Analyst Tools - Extraction - Extract Values to Points.
Good Luck!
Bart
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My name is Sydwell, I am doing second level in Geography, I urge anyone with information to assist me. THANK YOU.
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William is right. I would also mention the opportunities to analyze spatial data and get some indicators that could help you to make some important decisions related to location. Some examples might be the Nearest Neighbor index, or the Local Moran's index.   
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Nangone is "a coral island" referred to as being in Melanesia in the Pacific, somehow connected to Easter Island. Does anyone know how to find it?
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Hallo Nils, die Insel ist vermutlich heute unter anderem Namen bekannt. Sie liegt nicht bei Rapanui (Osterinsel) sondern auf der anderen Seite des Pazifiks in Melanesien. Sie wird allerdings im Buch von Katherine Routledge "The Mystery of Easter Island" erwähnt. Dort findet sich das Zitat über Nangone auf Seite 133: Bishop of Wellington, "Notes on the Maoris and Melanesians" The Journal of the Ethnographical Society, New Series, Vol. 1 Session 1868-69. Vielleicht findet sich dort eine Karte. In jedem Fall ist es eine Koralleninsel, und das trifft für Rapanui nicht zu.
Aloha!
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I have to use to OTSU algo on T-cap Greenness component in order to find out the threshold above/below which i can figure it out as vegetation/agriculture/urban greenery. 
As i have used MATLAB...(with "graythresh" function) and tried ENVI Image Change Workflow... (where i get a change detection difference map with threshold technique of OTSU).
What is the expected out for this...?
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Sometimes if two peaks are not clearly identified in histogram then it may lead to error. I had used a r package (EBImage)  for otsu on a raster. You may try that too.
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I have a daily database of MODIS and I would use a models or functions to estimate moisture,
Thank You
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You can use total column precipitable water vapor. To gain more information, you can refer to the following publication:
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I have downloaded Landsat 7 surface reflectance products, How to use that in the calculation of EVI? and What should be the range of values in output
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I agree with Paulo about the google search.  Or you may simply refer to the landsat documentation for details (EVI is on page 13).  See below.
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My Area of interest is Path 150 to 151 and Row is 45 to 46. I got the right image in WRS1 when I choose the images from WRS2, I get entirely different images or regions of same country. Why does this happen? I used GLCF site to download the image. I am requesting your suggestions and advice.
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Worldwide Reference System (From the above Ref:)
The Worldwide Reference System (WRS) is used to identify the path and row of each Landsat image. The path is the descending orbit of the satellite. Each path is segmented into 119 rows, from north to south. The Landsat MSS sensor had a swath width of 180 km and global coverage required 251 paths. The Landsat TM and ETM sensors have a swath width of 185 km and require only 233 paths for complete coverage. MSS and TM scenes share common rows, but in most cases the paths will be different. Because of this difference, MSS scenes are identified using WRS I while TM and ETM scenes use WRS II path/row designations.
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Strip Map Mode: 80 km swath, 5 x 5 m spatial resolution
Interferometric Wide Swath: 250 km swath, 5 x 20 m spatial resolution
Field inventory is in 120 plot (600m2)
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Hi Somayeh. I think that Sentinel-1 can really be used for estimating AGB in woodlands.
But there are some constraints that you must be aware of:
I think the most relevant constraint is that Sentinel-1 works in C-band. It means that the signal it uses interacts mostly with canopy top and do not allow to get a direct analysis of the ground beneath the trees. Then, forest/woodland trees' volume cannot be assessed by a direct analysis, wich means that the analysis of AGB using Sentinel-1 data will certainly have an error that you must to assess if it is acceptable or not to your research objectives.
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I just get to know about GeoPDF. Can this technique be used by private researchers? If I want to create or edit a GeoPDF file without any commercial software, is it possible (by coding I mean) ? Is GeoPDF open source? Thank you for any one giving me guidance.
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You have a free plug-in for Adobe Reader on
 and
free converter to geotiff
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I am setting up a map template to automate map production at global scale using ArcGIS 10.3, and some problems arise when displaying countries at the verges (longitude) of global map (i.e. Pacific islands such as Fiji). I successfully manage to run the data driven pages tool to produce bulks of maps but when getting those countries at the verge of global map it zooms out to a global map view (because some parts of the country may fall in the "left side/limit" and other in the "right side/limit"). It's a little bit tricky and I thought that maybe using a spherical based coordinate system I will manage to sort it out, won't I? Does anyone come up with other suggestion?
Thanks in advance. 
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Thanks you all guys. I found a solution within Data Driven Pages tool. Jan Burdziej put me in the right direction. Within the Data Driven Pages tool window you have an option to vary the spatial reference system throughout your map production. There are three options, but the simplest one is to create a field in the attribute table of the index layer to specify what coordinate system should be used to display each feature (in my case countries). Use the conventional number assign to each coordinate system or point to the prj field corresponding to each coord system. Obviously the first option is less computing intensive. In optional fields of the Data Driven Pages tool you have the additional options including "spatial reference". Display the dropdown menu and choose "field" option and point to the field which set the CS code. 
As Jan said, it can also be done using arcpy, but this option is quite more simple.
Thanks again for your contribution.
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What is the explanation behind the difference in NDVI values when calculated from DN, radiance and reflectance?
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I'm interested in using projection pursuit for decorrelating variables in geostatistical simulation, but a don't want to start from zero if there is any previous code.
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I think there is an R package for this (open source software)
You might also want to do a search for "Independent Data Analysis"
Finally it is possible to do geostatistical simulation for multiple variables
1989, Myers,D.E.,Vector Conditional Simulation in Geostatistics, M.Armstrong (ed), D. Reidel Pub. (Proceedings of the Third International Geostatistical Congress, Avignon, France 5-9 September 1988), 283-293
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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.
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Dear Hein,
The forest depletion (= deforestation + degradation) rate for Borneo is approx. -3%yr-1. This includes -2.2%yr-1 of deforestation. So, your Bolivian findings are consistent with Borneo data.
 We don't have illegal logging problem in Poland. Forests almost entirely belong to the state. The data for GFRA report are prepared based on data provided by the forest district officers and are reliable.
 Will you be interested to do a mutual project on forest dynamics? (kbecek"gmail.com)
 Here are my paper related to Borneo/Brunei.
 Best Regards,
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I have two SPOT 5 images and they seem to have overlapping pixels, now I would like to coregister them so that pixels align properly.
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I think, u have stereo image, so better u use Leica photogrammetry suite (LPS) tool available in ERDAS..
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I am looking for lat/long for villages of India for a project, where I can find the same.
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It would also be good to define what a "village" is in India, if there is an official description somewhere. The nic.in site appears to divide districts into villages, each with a distinct polygon feature.
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When we analysis some change pattern or spatial density  distribution, we could create a grid with m columns and n rows to do analysis. As we know that the MAUP is an important problem is spatial analysis. If the grid number and grid size were set not correctly, the results may be wrong.
     For example someone cerat a gridnet with 100 columns and 100 rows , and the grid size is 5km*5Km to analysis some point data density distribution. However, few of them tell us why the grid size is 5km? Why just create a  gridnet with 100 columns *100 rows to do analysis?
     So how to set the grid number and grid size according to our experimetal data? In other words, is there any method to determine the grid number and gride size?
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As Kazimierz said, you need to look to the sampling theorem. Or you may need to look to the scale-space representation, which addresses the level of details in your signal (here in your question 100 x 100 matrix).
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 I am planning for a land surface temperature map of 1990. and want to see change in land surface temperature of my study area.  Both 2000 and 2010 temperature are coming good but 1990  Landsat thermal band showing abnormally low temperature compared with 2000 and 2010 temperature calculated for Land surface temperature.
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Dear Rajib,
i also used TVX  approach to check errors while relating the LST with T air (MET data). while comparing the LST with Tair u can get some errors you should first check the exact location of MET station, also its land use  and record its height usually the  met stations record the air temperature at about 2.6 meters above ground. 
Also note that their will be time difference between the satellite over pass and the time at which the MET had recorded the air temperature. Therefore  you should use the AWS data for better comparison as they record the temperature more frequently. 
and if you have gone through above two articles i think it will help you  further more i will very soon add my publication where i have tried to explain all these errors in LST.   
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Does any one know how to query available Landsat and MODIS data within one map?
Sometimes, I do not know the path/row (h/v) corresponding Landsat and MODIS at the study area. I need to find these information at separated website, and it may not corrected when study area is located at the corner of Landsat or MODIS.
So, if there were one website can provide the location map of Landsat and MODIS simultaneously, it will save time.
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Hello Dr. Dongjie Fu,
What you are looking for is readily available @ USGS  http://earthexplorer.usgs.gov/
You don't need to know the path and row details for a specific LANDSAT or MODIS scene as long as you know your study area.
To make your task simple I've uploaded a series of pictures which can walk you through the process of retrieving LANDSAT or MODIS data without knowing any of their orbit details. Just follow these steps:
  1. Make an account in http://earthexplorer.usgs.gov/
  2. Define your search criteria. A number of options are available. As an example I've simply typed the name of my area of interest in the search bar, e.g "Los Angeles" (See Image 1). You can try out other options too.
  3. Define the time period.
  4. Select your Data Sets. Once you have defined your area of interest and date range, you can begin to select the Data Sets, which in my example (See Image 2) is LANDSAT 8 & one of the products of MODIS (MOD17A2)
  5. Finally check the Results. After the desired Data Sets have been selected, check the Results tab. Here, you can alternatively click on the drop down menu to toggle between your selected MODIS and LANDSAT scenes ( See Image 3 & 4)
I hope these steps are easy to follow. If not you can always watch this youtube webinar (~ 50 min) by NASA. 
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I am using the StereoSAR modual of ERDAS IMAGINE to construct a DEM. And it seems the result of "match" step determines the quality of the final DEM. However, my self-defined correlator always performd worse than the pre-defined correlator. I want to ask is there any principle about how to set the parameter of a match correlator?
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Hi,
corelator to corelate the data in master/slave images using window size. If your z in between  range, so you may increase correlation values.
-Ram
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One of my main research interest is to find analytical and numerical solutions to the  non-linearity of the photogrammetric orientation algorithms, which I do believe is one of the main reasons that slows the acceptance of photogrammetry as a generic 3D measurement technology. Now the question is: Does the non-linearity of the photogrammetric orientation algorithms is solved? Or there are some remaining issues?
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Hello Gamal: You might be interested in having a look at the following paper:
Mazaheri, M., and Habib, A., 2015. Quaternion-Based Solutions for the Single Photo Resection Problem. Photogrammetric Engineering and Remote Sensing Journal, Vol. 81, No. 3, pp. 209 – 2017.
This paper presents a quaternion-based closed form and iterative solutions for solving the SPR problem without the need for approximate values or partial derivatives.
Best regards, Ayman.
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It seems to me that ESRI ArcGIS has been dominant in GIS and spatial visualization and analysis applications for some time.  However, some of my students have been talking about other GIS software. I know that QGIS is pretty popular as far as free and alternative GIS software goes.  Has anyone out there used it for research or engineering that can share their experience?  What do you like and dislike about it?  How easy is it to learn?  Is there a reason to use this free software as opposed to proprietary GIS software?
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QGIS is fast developing, easy to learn, multiplatform (linux), customizable (plugins etc.) and open-source (the biggest benefit). If more complicated analysis are needed, you can run there functions from GRASS, SAGA or R. Also its more  user-friendly in many cases (for me). I am using QGIS for years and never need go for ArcGIS again.
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Actually in some areas are cloud present in image.So is it possible i replace cloud pixel values of landsat 8 with landsat 7.In this way i will get near by values for estimating results.
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example using ERDAS model maker
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Secondly please guide me, is it possible in Arc GIS 10.2 that all points will go to respective coordinates in images. so I do not need to individually write coordinate one by one and train it?
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Dear Usman
You can easily carryout gap filling process with Model Builder and apply focal mean with a matrix window of your choice, but preferably 3x3. I am sharing the model, you need input just the input layerstack images.
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I want to separate the soil and rock field. I have ERDAS and ENVI.
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From experience: any unsupervised classification of mid-resolution imagery will included a class corresponding to bare rock, if the latter is present in the area of interest, or more than one class when various types of bare rock (e.g. dolomite versus granite) occur at the surface. Presumably you know your area of interest well enough to locate some bare rocks on the image, a topographic map or along a road to allow for a supervised image classification. Attached is a case; secondarily we lumped the grass and the bare rock classes as neither of them was the target of our research. But the rock class stood out.
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We developed several machine learning techniques for growth stages classification based on MODIS (or later incorporating LANDSAT). Currently, we obtained results on our own annotated dataset based on field survey. Are there some open annotated dataset that we can compare our algorithms to this data?
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Dear Mohamad Ivan Fanany, 
you can find some global land use / land cover produts here
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I know the principle of Modtran doing atmospheric correction,but i doubt is it the same using the Modtran and the FLAASH(a module in ENVI ),even though I know the FLAASH is based on Modtran. Could anyone tell me the connection and difference between using Modtran and using FLAASH when ding atmospheric correation?And can the FLAASH be used doing atmospheric correction for quantitative RS, especially for SST retrieval in case2 waters.
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No, FLAASH has been designed for 0.4-2.5 um, not for thermal bands. Since, water vapor plays the major role in absorption of EM in thermal bands, it is better not to use the models (e.g., MODTRAN) having high uncertainty caused by high variability of water vapor content. That's why, using split window-based method is encouraged. 
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Landsat provides beautiful data of ocean SST at 100 m resolution ... still there is an annoying stripe pattern in the images in the channel 10 (thermal infrared). Does any one has a correction for that?  Here is an example from September 10, 2014 over the west coast of France.
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Hi,
A detailed explanation of the problem is given here: http://landsat.gsfc.nasa.gov/?p=9308
A good source of information on image destriping is "Computer Processing of Remotely-Sensed Images - An Introduction" (2011), by Paul Mather and Magaly Koch.
Best wishes
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If anyone can direct me to publication(s) that talk about how or why soil disturbance would lead to faster establishment of forbs over grasses. Thank you!
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Montalvo, AM, McMillan, PA, Allen, EB (2002) The relative importance of seeding method, soil ripping and soil variables on seeding success. Resoration Ecol 10: 52-67
Ditomaso, JM, Brooks, ML, Allen, EB, Minnich, R, Rice, PM, Kyser, GB (2006) Control of Invasive Weeds with Prescribed Burning. Weed Technology 20: 535-548
Gillespie, IG, Allen, EB (2004) Fire and competition in a southern California grassland: Impacts on the rare forb erodium macrophyllum. J Appl Ecol 41: 643-652
Moyes, AB, Witter, MS, Gamon, JA (2005) Restoration of native perennials in a California annual grassland after prescribed spring burning and solarization. Restoration Ecology 13: 659-666
Heneghan, L, Miller, SP, Baer, S, Callaham Jr, MA, Montgomery, J, Pavao-Zuckerman, M, Rhoades, CC, Richardson, S (2008) Integrating soil ecological knowledge into restoration Management. Restoration Ecol 16: 608-617
Hayes, GF, Holl, KD (2011) Manipulating disturbance regimes and seeding to restore mesic Mediterranean grasslands. Appl Veg Sci 304-315
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Dear all, I have field data of vegetation cover and soil's mechanical Composition and Analysis of soil chemical data. So far I computed a desertification map by remote sensing data. How can I accuracy make an assessment between desertification map and field data? How can I check my result? Please help me.
Which kind of program best in this process erdas or envi? Please send me some lesson. Actually I don't know how do this process.  Thank you
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Getting there: you classified with three imagery-derived variables.
More questions: what input imagery was used for your NDVI, Albedo and GSI?
Desert is presumably defined as a pixel, or group of pixels, with low NDVI (low green biomass), high Albedo and/or high GSI (high sand content) ?
Desertification as decrease in NDVI, increase in albedo and/or GSI?
NDVI and vegetation cover may be statistically tested for correlation, GSI and  proportion of sand at the surface as well; as far I can see you have no proxy for albedo. Albedo and NDVI may be tricky in your desert context as spatial variation in surface lithology (e.g. sand versus rock desert) within your Area-of-Interest may overrule any variation in cover or albedo signal from the "desert"; if so you better stratify your area accordingly. For an example of extraordinary low NDVI values due to calcareous bare rock (arctic-alpine desert) please see attached.
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We are in search of a methodology that is suitable for determining an index of soil quality from the establishment of a geographic information system
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Yes Satya, but it does require fieldwork to examine/collect soils, analyze their properties, and then couple this information with the Remote Sensing data in a GIS system. 
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    I want to use the linear spectral mixture model through end member selection. But now many researchers all use envi remoted sensing software. I found the puried pixels selection tools in envi is inconvenient. For example, there are so many roofs with different colors. The puried pixels usually select one of the roofs as the end member. It dont represent the all roof imperviousness.  Is there the other soft tools to select end member ,eg, erdas. How does it operate?
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Hi Xuegang,
The following URLs are the potential starting points for you. I also refer you to the official tutorial documents for ENVI. By precisely reading these documents (and other similar ones) you will be able to implement your processing easily.
Good Luck!
*ENVI Tutorial (chapter 9 would be your target!):
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I want to calculate LAI in Envi software, 
I am using formula LAI=0.57exp(2.33NDVI)
I don't know how write this formula in band math Envi software, please tell me how I should write it in band math?
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thank you Dear Marco
I did your structure and revitalize NDVI, best regards.
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I have problem in smoothing the slope derived from digital elevation model. I want to do filter for the slope <5 degree in 7 X 7 kernel size, 5-20 degree in 5X5 kernel and >20 degree slope in 3X3. But I am not able to put these kernel size condition in filter tools in ArcGIS Desktop 9.3.
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Dear Rakesh,
use the Focal Statistics Tool from the Spatial Analyst Extension. Use as Neighborhood Type Weight. Here in a text file you can define any size and values for your filter kernel. Define your 3 different filters, apply them on your slope raster and combine the resulting three smoothed rasters using the raster calculator with the con-funcion.
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I'm currently trying to find the change in value between two dates for a couple vegetation indices generated from two different types of Landsat data. The first image is a TM image from 2011 and the second is a 2013 Landsat 8 OLI image. Both images are georegistered and converted to atmospherically corrected TOA values. While doing some preliminary investigating, I found that the values for unchanged landscape features are significantly different between dates. I'm wondering if this is a result of the difference in radiometric resolution between the sensors (8bit vs 12bit). The Landsat 8 values appear to be lower in a number of areas compared to the TM. Whats should I do to accurately difference the images?
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In fact, you were asking a technical issue of image differencing between the images obtained by different sensors on different dates. If the acquisition dates of your images are the same or close to each other in the same month but in different years,  here are some suggestions for your consideration: (1) In my opinion, you have to conduct first atmospheric correction but not only radiometric correction since two images were acquired on different dates by different captors (TM and OLI). TOA means Top of Atmosphere, so that TOA values either in radiance or reflectance are actually at-satelite values but not ground values.  You may apply the FLAASH model (Perkins et al 2005) or the improved DOS model (Chavez 1988 and 1996) for achieving this correction and conversion of each band (in DN) into reflectance taking the path radiance and scattering effects into account. (2) Having so done, you may have to be aware of the fact that OLI images contain the coastal blue band (B1) so the bands to derive NDVI or other indices are different, e.g., for TM, B3 (red) and B4 (NIR) but for OLI they are B4 (Red) and B5 (NIR). In addition, OLI B5 has narrower spectral region (0.851-0.879 um) than that of TM band 4 (0.76-0.90 um). It is normal that NDVI of OLI is slightly lower than that of TM for the same vegetal biome.  (3) After Step 1 and 2, you may choose some pixels or patches at the same places (defined by the same polygons) in two images, where no clear change has occurred, to check their average NDVI. These pixels can be water (no change with time), cropland (wheat or rice land repeatedly cultivated), and cement/concrete built-up in urban squares (no change if the observation time of year is close to each other). If the difference is < 5%, you can consider that there is no change on these checked land cover types between the two images. The two images are usable for differencing. If the difference of these checked biomes is > 10 %, the two images (at least one of them) have problem in quality. Thus differencing techniques may not be applicable. In this case, you can apply "post-classification comparison" or "post-classification differencing" (Wu 2009) for change detection.
I hope these may help you a bit to sort out your problem. Good try.  Here some publications may be of reference for you.
Perkins, T., Adler-Golden, S., Matthew, M., Berk, A., Anderson, G., Gardner, J. and Felde, G., 2005. Retrieval of atmospheric properties from hyper and multispectral imagery with the FLAASH atmospheric correction algorithm. In: Schäfer, K., Comerón, A.T., Slusser, J. R., Picard, R. H., Carleer, M.R. and Sifakis, N. (Eds), Remote Sensing of Clouds and the Atmosphere X, Proceedings of SPIE, Vol. 5979.
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Hi
I have 3 raster layers in GIS, i want to identify the maximum point in each raster, which is simple using cell statistic in ArcGIS. However, i also want to know which raster each maximum value came from. Any Ideas?
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Andrew, the suggestions from Viorel and Alessandro are good, but you need to take some more steps.  After generating the maximum raster and three new rasters, substracting each original raster (1,2 and 3) from the maximum, where 0 would be the cells with the max value in each. For each of them, do the following; set all the cells with non 0 values to null, and the cells with 0 to 1 for raster 1, 2 for raster 2 and 3 for raster 3.  Then combine them in a single new raster, where each cell would indicate the origin raster. Then you can dismiss the intermediate difference rasters and have: the maximum raster, and the origin raster.
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Is digitizing of map features completely dead?
For example, when and why do you still digitize features from a map?
It can be heads digitizing up from a scanned paper map (or image, or whatever) or using a traditional digitizing tablet. It can also just be digital and heads-up (i.e. you never had the paper map).
Do you ever still utilize manual feature digitization to create points, lines, or polygons?
I need this information for a statistical technique I am developing.
Addition.
Any VGIS examples?
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Dear Stuart,
in the archaeological domain this kind of operational approach is "still" very diffuse, due to enormous amount of archives material items, such as paper maps, radex and similar data recording supports, furthermore still too much employed in current field excavations and analysis.
Within our SITAR Project (see at http://sitar.archeoroma.beniculturali.it/ and some other resources at my RG profile), digitization activities represent the most important aspect for the first phase of our geo-data bank implementation, necessary for extracting above all polygonal archaeological features, altimetric points and related geospatial informations for mapping archaeological sites, monuments and complexes, and their fragmental partitions.
By the way, more generally such a necessary approach to data "distillation" implies some issues with regard to data heterogeneity, their completeness and final use and re-use. In this sense, this native heterogeneity generates, more or less deeply and distinctly, some questions on precision/completeness both at topographical and descriptive level, for old and also new data, motivating the implementation of semi-automated workflows in order to compare and re-align fragmentary different analogic source data.
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Recently, USGS provide provisional Landsat 8 surface reflectance product (L8SR), which combined with data from Landsat 7 surface reflectance product (LEDAPS), give a unique opportunity to analyze every eight days the changes of vegetation cycle all over the word.
The problem is that the Landsat 7 images are affected by the problem of the failure of the Scan Line Corrector so many data are not available.
There are Open Source software can gapfill the missing data?
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Hi, you can try our NSPI or GNSPI methods. You can get IDL code from my webpage http://xiaolinzhu.weebly.com/. Please let me know if you meet any problems.
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Using ArcGis
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Fmask is a very powerful tool for cloud mask generation. All details and the tools itself can be found here: http://code.google.com/p/fmask/
I used Fmask for several Landsat 7 ETM+ and Landsat 8 images...
Best wishes Olaf
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Obviously MOIDS VI products are corrected (to some extents) for cloud shadows. But how about topographic shadows? If yes, any information on DEM and methodology used for corrections?
If no, to what extent do you think VIs (particularly NDVI) in C5 are affected by topography and illumination/viewing geometry? As far as the general appearance of the vegetation cover is concerned, my focus is not on "trees", but mostly on grass, shrubs, and subshrubs. Can I also have some suggestions from your own experiences with correcting topographic, viewing/illumination effects on NDVI?
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The MOD13 product is corrected for angular variations in observation and sun-target-sensor variations but not due to topography using a DEM (I believe). It is also important to note that there has occurred a significant degradation of the Terra MODIS sensor affecting VI data. See Wang et al. (2012) Remote Sens. Environ., 119.
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Hi all, I have some data I need to interpolate.
Data are a set of scalar values (measurements); for each value, I have a two-dimensional probability map (ranging from 0 to 1) depicted in the image attached. The map is almost everywhere zero (or random noise) and shows some peaks where the probability of the measure is higher.
Each scalar value has its own two-dimensional probability map.
I want to interpolate these measurements in all the space of the probability map.
My first attempt has been using a function of the distance. For each point P0 where I want to interpolate data, I evaluate the distance between P0 and the points of the map, and I use a ratio between the probability of a measurement and the distance from P0. For each measurement, I evaluate the maximum of this ratio and I eventually evaluate a simple weighted average of the measurements. This method works, but it is really sensitive to the way I evaluate the ratio and the function of the distance.
How can I do this interpolation?
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I don't know if I understand correctly your question.Your idea to construct a weighting matrix distance-based should be correct. What type of variable are you studying? How your samples are distributed in the study area? For example, you could construct a regular point grid and then calculate the distance of your sample points from the nearest point of the grid, and use this distance as weight. Otherwise you can construct an experimental variogram, model it and use the model in the kriging algorithm to estimate your variable at unsampled points and represent the final map in term of  probability. I hope this to be useful. Best regards, Giancarlo
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Regardless of the form or method used, should we take consider all biotic and abiotic factors of the physical environment or only the climat indicator (IC) and topography indicator (ITP) ?
Thank you for answering my question.
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Concerning your second question: as no/insufficient vegetation available for combustion (fuel load) inhibits the occurrence of a fire, I would at least include some quantitative measure of vegetation. Furthermore, humans are the major source of ignition in many ecosystems (and I could imagine in the Mediterraneans as well) - it thus makes sense to somehow account for those (e.g. proximity to roads, accessability,...) when determining fire risk or probability of ignition.  
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       When we use the moving window to calculate density distribution in spatial analysis, the window size is a key parameter. While few of the researches talked about how to determine the window size.
         For example, someone only told us he used a 3*3 window to do analysis. But why is 3*3? Maybe if we change the window size, the result may be different.
        Will you please give me some advices about to determining the window size? Is there any criterion?
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Concur with the others, there is really no universal optimal window size. Really depends on what your object size is, the imagery you are using, and the image characteristics you are aiming to capture. In the paper noted below, we tried to use variograms to provide for customization of the windows and the information generated (Wulder et al. 1998, RSE). Pretty old, so am sure more and better has been done since. In general, IMHO for characterizing local variance conditions fancier methods have not really outperformed simpler and more transparent approaches (e.g., variance within a fixed window).  If you have your objects via other means (say segmentation), within object texture is also a useful option. 
Best of luck with your research. 
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I am using a combined dataset of Landsat TM, ETM+ and Chinese HJ-1 A/B CCD sensors images coincident with the in situ measured Suspended Solid Concentrations (SSC) for my study area Hong Kong. All these sensors are similar in spatial resolution (30 m), bands designations and data bit levels (8 bit). I have used the four bands Blue, Green, Red and NIR to find their correlation with coincident SSC.
I am observing a low correlation between NIR and SSC, while in literature a higher correlation has been reported.
Can someone explain why there is low correlation between NIR band and SSC for my study area? 
Can this be attributed to the specific grain size and particle size distribution of the suspended solids found in this region?
Any comments will be appreciated.
Thanks
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Dear Majid,
Your question is an interesting one. I investigated some literature and I found the following according to Azad Hossain et al. (2006) many factors such as suspended particle size, shape and color can have large influences on spectral properties of suspended particles.
In two different researches NIR is not used in mapping suspended solids and I guess that is why you have low correlation. Instead the visible wavelength especially the green band.
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In Object-based Geostatistical Modeling, is there default set of parameters for Facies modeling for a real field?
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You must apply the indicatrice kriging method (topo_probabilist) , but in all times we find the default set of parameters for Facies, the raison the mineralogical heterogeneities especially with clays and sitstone, when we do the continuous logs facies by the methods of prediction and rebuilding the lithological facies like (Neural Network) by the combination with the real observations on cores and logging measurements, we find a slightly default set of parameters for Facies modeling for a real field,
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      Most of the previous researches focus on city level (i.e. Beijing or Guangzhou ), using remote sing images to obtain land information  and landscape metrics to analyse the changing patterns. Some of previous researches focus on discovering the changing progress in different regions and then compare them. Some of them focus on methodology innovation  such as information extraction or putting forward new landscape metrics.
      However research at Megalopolis level is less than at city level.  What are the differences  in scientific problem, method and  theory between researches at city level and researches at Megalopolis level? Doing research  at Megalopolis level , what (or which aspect) should we pay more attention to than at city level?
      Could anyone give me some tips?
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Thank you Fakhri again. Your advice is that we should divide the whole megalopolis into many subregion, then we research the rules in these subregion. Yes, that's a good idea! However, another question maybe we have to face with is that how to divede the megalopolis?  If we could not provide strong evidence about how to divide the megalopolis, our research results maybe questined. Someone could say that we get the results maybe just by accident. Do you have any idea about that?
Thanks so much!
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my data contains NA pixels
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Then it should be fairly easy to produce a RasterStack from your ncdf fields on which you can use raster::calc() to produce a RasterLayer of pixel averages. See help(calc)
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As I am digitizing polygons in 10.2 ArcGIS, sometimes the vertices are getting deleted automatically and also it does not close as it should by clicking on the last vertex. 
2. The final product has an altered shape that it takes on its own.
3. Even while editing vertices as I add a new vertex it deletes the previous one.
4. Also, I tried converting from lines to polygon. That also does not match exactly.
I am frustrated. Any help would be appreciated. 
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Thank you for replies. It was because I had not defined spatial reference frame. Defined it and now it is working.
Though that was not a problem with earlier versions.
Thanks again.
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I am trying to classify images of different dates in ENVI using a supervised classification. 
I have defined ROIs for one year (2009) and classified that image. However, when classifying other images (from 1970 - 2013) do I have to define new ROIs for each image or can I use the ones from the 2009 image,
Essentially does ENVI store the spectral reflectance info from my original 2009 ROIs on other images when doing supervised classification or does it simply recalculate spectral reflectance for each class for each year - meaning that I must have new ROIs for each year?
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Since a same LULC may have different radiometric values along time (because images may be taken with different sensors, different sun illuminations etc) you should redefine ROIs. However, beyond the problems mentioned in other answers (registration etc), another problem may appear if LULC has changed : indeed, all classification algorithms define  the spectral boundaries between classes based on the statistical distribution of radiometric values at each date, so that the boundaries may move. The consequence is that a pixel where no change has occurred can jump over the boundary and become another class. Another approach to overcome this problem would be to run a single big classicifation, both multi-spectral and multi-temporal, over all images, with multitemporal classes (e.g. forest becoming grass between date t1 and t2).
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I need some full waveform experimental LIDAR data.
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Try here with ground truth data
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I have three different satellite Images from three different time periods, one from Landsat 8 (30 m), another from ASTER (15m) and one from MODIS (250 m). The objective here is to do a multi-temporal land cover change detection analysis study with the help of Digital Image Classification. I plan to first do the Image Classification for the three images separately and then resample the coarser spatial resolution images to the geometry of the finer spatial resolution, i.e., MODIS (250 m) to 15m pixel size and Landsat (30m) to 15m pixel size respectively for the sake of comparison. Finally after resampling with a Nearest Neighbor Interpolation method I would go ahead with the change detection process.
I am curious to learn whether this is the right approach or are there better means of achieving the desired result?     
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Hi James,
Why don't you start with the most cited (?) paper in remote sensing: http://www.tandfonline.com/doi/abs/10.1080/01431168908903939#.VH460THF98E
Also there is an excellent chapter in Jensen's DIP book. All these will give you the basic idea of available change detection techniques. 
Since you will be using multi-source/temporal data, along with geometric integrity, you should consider the atmospheric effects: http://www.sciencedirect.com/science/article/pii/S0034425700001693.
After you have some expertise, you can take the problem in sub-pixel or unmixing domain to deal with the different spatial resolutions. I couldn't find any particular work but I will be happy discuss on this!
Enjoy!
-Ani