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15

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.


12

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 ...


8

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

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). ...


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 ...


6

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 ...


6

You may consider GRASS GIS which offers a rather complete processing chain for Landsat including radiance correction for Landsat 8. For details, see http://grasswiki.osgeo.org/wiki/LANDSAT Examples: Landsat 1-5,7,8 data import Auto-enhance colors, natural color composites Calculate Top-of-Atmosphere Reflectance and band-6 Temperature Haze removal ...


6

You need to use the Composite Bands ArcGIS Geoprocessing tool. According to the ArcGIS Help, This tool can also create a raster dataset containing subset of the original raster dataset bands. This is useful if you need to create a new raster dataset with a specific band combination and order.


6

Manually using a stereoscope with a high enough resolution image, it is possible to estimate stand height. Although Landsat has a 30meter resolution and is too crude to estimate tree height. (LIDAR data would be necessary if your set on using Landsat) Depending upon the species your trying to measure NAIP imagery may be a better option, with a 1 meter ...


5

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 ...


5

Since Landsat satellites are not placed a true polar orbit -- they are in a "near polar" orbit -- their heading (azimuth) is never zero. See NASA's Landsat Handbook and Landsat Science. It is closest to zero at the equator (8.2°) but deviates from this the closer it gets to the poles. Thus, yes, knowing the center coordinates (latitude, actually) of the ...


4

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 ...


4

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 ...


4

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 ...


4

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 ...


4

I've only worked with Landsat 7 imagery but I believe the same technique apply for composing: You need to create an RGB composite so use the Composite Bands tool (ArcToolbox -> Data Management Tools -> Raster -> Raster Processing -> Composite Bands) Add the three R G B bands in the RGB order: Band 4 (Red) Band 3 (Green) Band 2 (Blue) If you don't add them ...


4

If you really want to use python, and you need functionality similar to GRASS, perhaps the easiest solution would be to use GRASS via Python. That isn't specific to Landsat8, but I don't think a processing solution should be tied that closely to a specific satellite. You could implement some simple wrappers / higher level functions if you're consistently ...


4

I just thought I'd add that there are some 'pure' Python solutions for several nodes in this workflow, also. Some file reading and basic processing: Spectral Python: http://spectralpython.sourceforge.net/ More classification than you'll find in pure remote sensing and GIS packages: http://scikit-learn.org/stable/ More links I can't share: 6S Python ...


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

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.


3

There seems to be two camps about this one. Some prefer to mosaic before classification, others prefer to classify the images before mossaicking. Personally, I would classify the images first, then mosaic them. Have a look at the discussions on this page and you'll find arguments for and against both methods. Generally, they state that you should ...


3

With respect to a natural color image stack, not much has changed. The short answer is that you can create an RGB natural color image from Landsat 8 Bands 4,3,2. Here's a breakdown of Landsat 8's bands, from the Google Earth Engine team (personal communication): B1: A new band, useful for studying water and atmospheric aerosol concentrations B2: Blue B3: ...


3

There are multiple "gap fill" techniques which essentially take multiple L7-SLC off scenes and combines them to create a gap free image. This may or may not work for you application. For land classification it should be acceptable, assuming you can find other L7 scenes of a similar vintage. You should be very aware of what these procedures are doing and be ...


3

As you've probably found, removing clouds from Landsat imagery is not a trivial problem. There is a decent amount of scholarly research about methods for implementing masking and correction for clouds and their shadows. So due to the complexity of this issue, you probably won't find any one size fits all solutions that work perfectly out of the box. That ...



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