Hot answers tagged landsat
14
I agree with @vascobnunes opinion but if you want to define certain objects you have to use LANDSAT TM because more classification needs more bands as (R, G, B, NIR, MIR, TIR, FIR)... and my choice is that you should use LANDSAT TM (I gave same information in the following explanation) for vegetation.
The important thing in this case is that you should look ...
12
I am in Canada so if I need this imagery I can get it free at Geobase.
Elsewhere you should be able to download from USGS direct. You will need to register on both sites. Here are NASA links to download free Landsat data.
11
Landsat is available back to the 80s, it may overlap the dates of your project, excepting of course the 1950s.
edcsns17.cr.usgs.gov/NewEarthExplorer/ will let you easily browse the archive, once you apply for a username.
With that in mind you could potentially get a series of three satellite scenes, two of which tie in with the aerial imagery.
For ...
10
If you only have SPOT 5 and Landsat TM to choose from, money is not a problem and for a small area of 30 000ha, I would agree that SPOT5 is the best choice, although Landsat would have some strong advantages:
SPOT5:
2,5 m spatial resolution
3 spectral bands (Green, Red, Near Infra-red)
about 2,64€ per sqkm for new acquisitions
good revisit time
biggest ...
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 ...
6
You can download the scene boundaries here;
http://landsat.usgs.gov/tools_wrs-2_shapefile.php
You could use these as they are or generate an "inside" buffer shapefile to ensure that you trim all bad data in all bands.
Create a separate file for each scene (use split by attributes tool if there are many images to be processed).
Then clip the rasters (CLIP ...
5
I would recommend using image segmentation with the free software SPRING, available from the Brazilian National Institute for Space Research. Documentation is available here and tutorials are available here. Image segmentation produces high classification accuracy compared to purely pixel based classification methods (e.g. ISODATA, Maximum Likelihood, etc). ...
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
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 ...
3
Thanks to @markusN and @whuber for pointing out some of my glaring oversight on my approach of dealing with this problem.
At first I thought what I needed was a mathematical r.mapcalc formula that could magically offset a mask around - which is the wrong thought-process. What I really need is to first apply specific raster commands to the cloud raster, and ...
3
The manual way to do this is to use the official WRS-2 path/row scene boundaries
Download the WRS-2 shape file from USGS’ Path/Row Shapefiles dedicated web-page
Select the path/row tile of your interest and use it as a mask to clip border fringes (this might involve rasterising the vector tile)
To answer, however, your question directly about an ...
3
Segmented object (aka trained) classification can be used very succesfully for this problem, but I don't know GRASS enough to tell you what capabilities it has in this area. You'd get polygons though, so you'd still have to thin them or use a mean or some other transformation.
You'll get even better results if you have a near-infrared band or composite ...
3
Keep in mind - no one procedure is necessarily going to provide the "best result." Image interpretation is critical, both before and after classification. You will likely find urban areas misclassified as something else and non-urban areas classified as being urban.
You have two basic approaches:
1) Supervised classification: this involves selecting ...
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
Problems with southern hemisphere projections for LANDSAT data in GRASS/GDAL seems to exist since long. Here are some discussions in this regard:
[GRASS5] Problems with UTM/southern hemisphere
r.in.gdal unclean import of southern UTM zone
Win7 GRASS 7 & i.landsat.toar, here there's a mention as follows in question
I have taken care to reproject ...
3
I find the USGS NewEarthExplorer to be the easiest way to download Landsat scenes. As I discuss in a blog post, it has a far more 'modern' interface which makes it easier to select appropriate images for your needs.
3
USGS is what I use.
Note that high quality processing can be achieved in GRASS, I encourage you to carry out atmospheric correction. There are a lot of "primers" out there about calculating NDVI etc which completely overlook atmospheric effects. You can achieve quantitative analysis this way.
2
Apologies if this 2nd answer is a faux-pas and I should've edited my previous answer but BWill has asked a different question in his answer.
I have just tested some USGS landsat imagery (as downloaded) in ArcGIS, ENVI, QGIS and GDAL. ArcGIS and ENVI correctly identify the projection and place it correctly in the southern hemisphere. GDAL correctly ...
2
I contacted USGS;
Hi USGS when I use Landsat imagery in GDAL GIS (GRASS and QGIS) I have trouble with scenes which should be projected into the southern hemisphere. What protocol is USGS working to? Is it consistent with EPSG? thanks.
Their reply;
Thank you for contacting the USGS Earth Resources Observation and Science (EROS) Center.
The southern ...
2
USGS Landsat imagery is supplied with a Northern Hemisphere UTM projection. Scenes that are in the Southern Hemisphere have negative Y coordinates, this means they will display in the correct location south of the equator. Basically, don't worry about it. If it bothers you, reproject your data.
2
The correct definition of 55S is:
# WGS 84 / UTM zone 55S
<32755> +proj=utm +zone=55 +south +ellps=WGS84 +datum=WGS84 +units=m +no_defs <>
but you have omitted +south above. Hence it fails.
I have been using UTM on southern hemisphere in GRASS without problems.
2
If you want to make automatic classification go with Landsat, I think that any automatic classification of 2.5 m resolution imagery (depending on the type and diversity of area) will make you deal with a lot of artifacts unless you're really good at it :).
The purpose of such dataset is the main thing to consider. Just visualization? Very detailed area ...
2
Use a version of GDAL that includes HDF4 support. As you don't specify what OS you're using, I'm assuming Windows. The OSGEO4W package includes GDAL with HDF4 support (note that Tamas' GIS Internals GDAL binary packages do not come with HDF4 built in).
Use gdalinfo to get an overview of the data structure, pass it the *_HDF.L1G file as an argument:
...
2
Let's narrow down the methods of classification to two major groups: object-oriented classification and pixel-based classification. The attached tables are from a publication titled Comparison of Pixel-Based and Object-Oriented Classification Approaches using Landsat-7 ETM sPECTRAL Bands. The highlighted row in Table 3 shows that object-oriented ...
2
I've searched high-and-low, and I think I've discovered the root of my problems. I believe I got the solution for them now - but it's a little bit messy. I'm sure there are better ways to solve them. Do share if you know an easier way!
ROOTS OF PROBLEMS:
The output of i.landsat.toar is in floating point. I've realized that when I use floating point ...
2
While ArcGIS can certainly perform the work you want, if you have access to other software, I would use those. I have not used GRASS before for image classification, but I know it is more than capable. From my knowledge, Ecognition is more for object based classification. I have used ENVI for image classification a few years ago and it does the job really ...
1
One thing I check explicitly for L5 is that the date of acquisition in the mtl/txt file matches the date of the file to be processed. You have to be aware of the naming convention of the files to do this.
I don't use a standard version of grass, but I expect the module should be the same for you, see this image
Also I just ran a Landsat 7 scene from ...
1
According to this addendum (PDF) to the documentation, the PROJ.4 library supports a "LANDSAT" projection which is "for use with LANDSAT satellite data and is a limited form of the more general Space Oblique Mercator projection". It is quite specific to Landsat, as the expected parameters include the satellite number and the path number (since the orbits ...
1
I've never done it myself but try:
GDAL,
OSSIM or
GRASS.
These should all be able to read it and write GeoTIFF. Make sure HDF file format support is enabled in the software you choose. You might have to create the world file yourself.
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