Hot answers tagged remote-sensing
18
I would have to say that the most complete software environment for Machine Learning and nonparametric modeling is R. This is a big field in statistics, spanning K-NN, Kernel smoothing, General Additive Models, weak learners, support vectors, neural nets, semi-parametric spline regression, imputation, etc... I would highly recommend reading: Hastie, T., R. ...
10
You can't 'remove' clouds from optical imagery, what you see is what you get; they are photographs and there is no optical data recorded from below the clouds in the same way that there is no data underneath building roofs.
If you use remote sensing data of a longer wavelength than light such as microwave, the water particles in the clouds do not absorb the ...
10
There are a few good ones around:
Orfeo Toolbox
GRASS
SAGA GIS
All with the bonus of being able to be used though the QGIS interface using the SEXTANTE plugin like so http://blog.orfeo-toolbox.org/uncategorized/otb-inside-sextante-inside-qgis
7
Please mention the sensor of Landsat 5, is it MSS or TM? Assuming it is Thematic Mapper data, you have visible red and shortwave infrared data. You can directly infer from the band reflectance values about where vegetation patches lie and hence moisture content.
Band 3 (Red) can help you discriminate vegetation slopes and Band 5 (SWIR) can help you ...
7
First problem:
You're looking at a mixture of minima. One gigantic tree with an acre-sized crown looks quite a lot, interpreted on a point / kernel density basis, like a field with no trees at all. You will end up with high values only where there are small, rapidly growing trees, at edges and in gaps in the forest. The tricky bit is, these dense smaller ...
7
I'd strongly recommend scikits-learn for Python. It supports supervised and unsupervised classification and the documentation is excellent (particularly check out the Machine Learning for Astronomical Data Analysis tutorial and the accompanying YouTube video (note: this is 3 hours long)).
The project is under active development, with the last version being ...
6
For processing of Landsat, I can recommend GRASS. I tried many others.
You may need to refine your question with regard to the type of imagery you propose to use. There are workflows which have been more or less developed and implemented in various software.
Not only the type of imagery, but the purpose of the processing and final analysis. For Landsat, ...
6
Generally speaking, if you have a Single Image (Single or Multi Band), you cannot get Elevation information from it directly.
If you have a Stereo Image Pair, you might be able to get some Elevation values from it, but even those are not accurate without a rough base DEM, or Benchmark.
You also get satellite data, which consists exclusively of a DEM. For ...
6
There are published coefficients available for MSS, TM5 ETM+7, QuickBird and IKONOS but I do not believe that anybody has derived coefficients for Rapid Eye. Here is a paper that describes how the authors derived the coefficients for Quickbird (http://www.asprs.org/a/publications/proceedings/pecora16/Yarbrough_L.pdf).
5
The "best" software is somewhat subjective and dependent on your needs. All of the options provided thus far are worth exploring. I would like to add SPRING software to the current suggestions. This is a very robust free GUI-driven software for remote sensing. All of the functionality that you mentioned is available.
5
The problem with your KDE appraoch is that it smoothes the whole area and thus closes gaps you might want to find.
When I read that you used NDVI for tree crown detection, I wonder how the crown-polygons look like? are these really single polygons with tree-species ID linked to it?
If you have the luxury to have polygons for every single tree crown and ...
4
You could try the gdal_fillnodata tool which is also available in QGIS via the Raster->Analysis->Fill nodata menu. It uses an inverse distance weighting (IDW) interpolation. I just tried both that method and the single date Triangulation interpolation (in ENVI) and gdal_fillnodata looked much better. If you want to merge multiple dates, you might have to ...
4
I've used Enhanced Vegetation Index (EVI) data extensively for analyzing agricultural areas. Although I've never used it with NAIP imagery, all you need is red, blue, and IR data.
For your purposes, the biggest advantage of EVI is that it does not "saturate" as easily as NDVI--it offers more contrast (dynamic range) when examining highly vegetated areas ...
4
Hello Processing Full Waveform Lidar,
If you already have the data from a waveform system the best software would be from the vendor of the lidar system.
If it is already processed but you want to do additional analysis - a very popular program for that is MatLab.
The best information available is really found doing a websearch of white papers and ...
4
Bulk Download
One can also follow the instructions given at
Landsat Scenes: Bulk Download, part of USGS' EarthExplorer web-service.
After selecting the scenes of interest within from either the USGS Global Visualisation Viewer or EarthExplorer, one has to save/create the scene IDs of interest as a list (each ID entry should be a single line) in a pure .txt ...
4
FullAnalyze is open source waveform software. http://fullanalyze.sourceforge.net/
SPD is a waveform file format with open source processing software. http://www.spdlib.org/doku.php
4
Your question assumes that machine learning algorithms for land classification are somehow distinct from software used for other machine learning applications. There are some applications that require special treatment because of unusual characteristics, but there is no reason I know of to think that land use needs special treatment. If land use data can ...
4
To acquire NAIP for Oregon that includes the NIR band you must contact the OGEO office directly (gisgis.state.or.us or 503-378-2166). The NIR is not available on the download site.
When you refer to "historic" NAIP including the NIR band you are going to be somewhat out of luck. Many states still do not include NIR in their contracts and the USDA-APFO ...
4
in qgis for contrast stretching:
Right click your layer > Properties > Style , at the lower right Contrast Enhancement to Stretch To MinMax
and in Raster menu you have lots of thing for imagery as Raster to vector,contour, clipper, proximity...
i hope it helps you...
4
To better understand, I have done a similar segmentation in GRASS GIS 7 using the new i.segment. In my opinion these lines appear where the image data were mosaiked due to a non-perfect histogram matching (or whatever) being used.
In short: Orfeo or any other segmentation software may deliver better results when the initial mosaiking is improved to avoid ...
3
A good place to read and ask detailed questions about real full waveform LiDAR (-; is the discussion forum around the PulseWaves format. Many folks have only a vague understanding of what full waveform LiDAR exactly is and often talk about its derivatives (e.g. extra returns, echo width values, ...) rather than the actual full waveform data. Join ...
3
what is the format of your waveform lidar data? I have worked with it in ENVI format, which can be converted to matlab array form using freadenvi()
http://www.mathworks.com/matlabcentral/fileexchange/4918
3
Consider integrating DEMs into your research on soil moisture/exposure. I have used the following indices in the past for regression models (Davies et al. 2010):
Site exposure index = slope∗cos(pi∗(aspect−180)/180) (Balice et al. 2000)
Heat load index = 0.039 + [0.808 * cos(l) * cos(s)] – [0.196*sin(l)*sin(s)] – [0.482*cos(a)*sin(s)] (McCune and Keon ...
3
R is also suitable as a GIS. Many of the standard GIS functionality is available in pure R, e.g. interpolation (gstat, automap, fields), raster operations (raster, sp), or polygon operations (rgeos). In addition, many of the statistical techniques (e.g. regression, PCA, classification), can be used also for spatial data and are readily available in R. For ...
3
My favorite discovery this year has been the Orfeo Toolbox and the associated program: Monteverdi.
http://orfeo-toolbox.org/otb/monteverdi.html
Lots of options for Remote Sensing work and very helpful documentation. Oh, did i mention it is free and o-s
Enjoy,
sa
3
What software do you have? A real quick solution is Global Mapper (which costs $350+). If you have this it is simply file-->export raster --> choose file type and then in the 3rd tab (gridding) you can import a vector file with your quad or define it. It is also possible in arcpy and Grass (will add details if you want but it all depends on what software you ...
3
There is a group out of Duke University that have developed some interesting script tools for ArcGIS, including random forest models.
Marine Geospatial Ecology Tools
3
I would recommend calculating soil moisture indices from Landsat TM bands. MTRI has an interesting article on creating soil moisture index (SMI) from Landsat TM 5. Also, I would recommend exploring soil moisture estimates using TM band 6 (Thermal IR). Attached is a good tutorial on calculating indices from Landsat TM bands using ArcGIS 9.x (as you ...
3
near infrared band in satellites built for cartography have a meaninfull result bewteen water and other structures. My guess is results based on the size of object would be less than meaningfull, lots of false positives, think flocks of white birds or any other surface or near subsurface objects as well as differences in water temprature, shallow water pans, ...
3
IR is usually most useful for mapping vegetation and water, in as much as water does not reflect ir. My guess is that both your water and beacon will not reflect ir. If you have a relatively homogenous background of water you might try a segmentation approach. You will need a few pixels per feature, so if they are 20-30m structures than 5-m pixels will be ...
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