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10

There is a new, 30 meters resolution SRTM DTM coming out. As stated on the NASA JPL official page, The next release is planned for later in 2014, and it is expected to include all of South America plus North America south of the United States. It is incomplete, for now, it has only limited coverage. You can read an article about SRTM coverages here ...


9

ESRI has a pretty good help section on LiDAR (below). For more formal details on LiDAR, I would recommend the following books: Topographic Laser Ranging and Scanning: Principles and Processing Airborne and Terrestrial Laser Scanning Remote Sensing and Image Interpretation LiDAR Laser Returns Laser pulses emitted from a lidar system reflect from ...


8

I'm guessing you never worked with SAR data before, so I'll break your question down into parts I can answer: 1) Create high resolution DEMs in GIS The process of creating a DEM just from SAR data is quite complex and requires a lot of processing power and memory. I don't know of a GIS software that implements DEM creation due to these constraints. 2) ...


8

The term spectral signature refers to the relationship between the wavelength (or frequency) of electromagnetic radiation and the reflectance of the surface. The signature is affected by several things including the material composition and structure. Some parts of the EMR spectrum, such as the microwave region, are more sensitive to surface structure than ...


7

you can find the H and V index in all MODIS product file name. These indices refer to the grid below (from the MODIS Website). For instance you have H8V6 (MOD17A3.A2000001.h08v06.055.2011276103801.hdf).


7

those are different things. Image classification is the process of creating a thematic image where each pixel is assigned a number representing a class (can include the class 'unclassified'). In an aerial image the classes can be soil, vegetation, water etc. image classification algorithms examples are k-means or ISO-DATA. Pattern recognition is the ...


6

A spectral signature is some measurable quantity (e.g., reflectivity, emissivity), which varies as a function of wavelength and can be used to identify a material. To obtain a signature, the quantity must be measured at a sufficient number of wavelengths (and at fine enough spectral resolution) such that the material can be discriminated from other ...


6

Take an agricultural field as a simple example. Unless we're interested in precision agriculture, in which case characterizing the variability within the field is important, then we're more likely just interested in knowing that the patch of land designated by that field is being used to grow soy, or corn, or whatever crop. That is, from an information ...


5

As someone who did feature capture from imagery for a while, I would caution you against expecting a pool at a spring. The majority of the ones I've encountered (both in capture and on the ground in person) don't have one. We often referred to ancillary sources to suggest/confirm a spring. Depending on your purposes, USGS quad sheets or hydrography datasets ...


5

This varies greatly on the characteristics of the scene. Fire scar mapping studies using Landsat-5 TM have used the following three band combinations: Spain: Bands 4, 5, 7 CHUVIECO, E., and CONGALTON, R., 1988, Mapping and inventory of forest fires from digital processing of TM data. Geocarto International, 4, 41–53. Amazonia: Bands 3, 4, 5 PEREIRA, ...


5

Landsat and Modis are optical sensors, which means that they provide digital numbers of reflected materials that are within the electromagnetic spectrum. These values correspond to the wave length of the corresponding satellite band. To get elevation from just the raw values would be impossible. The only potential means to collect elevation information would ...


5

I've been in LiDAR processing for a couple of years now. The best approach we've found is to classify the suspect water points to something other than ground. Should be easy just classifying based on intensity (near nadir points will have high intensity, whereas turbid water will be close to 0) and laser shots are usually absorbed near shore anyway. ...


5

I've had to map ditches from 1 m LiDAR derived DEMs of agricultural landscapes before. It's certainly a challenging task to come up with a workflow that is suitable. You're ability to successfully extract a ditch network will depend on a number of factors. For example, are you only interested in roadside ditches? If so, are the roads on embankments (as is ...


5

Answer for others so confused people as I am: To know how to deal with downloaded raw Landsat data - what else in pre-processing do I need? Firstly check their processing level in_MTL.txt file (included in downloaded Landsat image: http://landsat.usgs.gov/Landsat_Processing_Details.php) Processing level = DATA_TYPE L1T - terrain corrected processing. ...


5

There is no specific GIS software for doing this: most will handle the RGB image and the Lidar data. Basically, NDVI is (NIR - RED)/(NIR + RED). Most of the time, aerial Lidar gives you the NIR value (to be checked in metadata) and the first band of your RGB image gives you the RED value. Just make sure that your data are calibrated to reflectance (or, if ...


4

You may check the "pyMODIS" software, a Free and Open Source Python library to work with MODIS data. It can bulk-download, mosaik and reproject: http://gis.cri.fmach.it/pymodis/


4

Not that I know. Sometimes the sensors are named and then the definition change (e.g. Advanced Very High Resolution Radiometer would not be called VHR anymore, but it was in 1978) Your definition is quite practical but does not tell you what you have between the two ranges (e.g. 15 bands like MERIS, Rapideye red Edge is 40 nm... I would put those two in the ...


4

there are a few examples of animal counting by remote sensing (whales, gnu, crocodiles, seals...), but they used higher resolution satellite images (<1m) or aerial photographs (see this paper) and there was a clear spectral difference with the background (sometimes in UV or infra-red)). As a rule of thumb, you should have around 10 pixels to detect an ...


4

You can try 'lasoverlap.exe' to quickly both visualize and quantify the overlap. And you can use 'lasoverage.exe' to remove the extra layers when there is overlap among points based on the scan angle. Both these modules are part of LAStools, which come with a toolbox for both ArcGIS and QGIS. Attached is a visualization of an example visualization produced ...


4

Panchromatic images are created when the imaging sensor is sensitive to a wide range of wavelengths of light, typically spanning a large part of the visible part of the spectrum. Here is the thing, all imaging sensors need a certain minimum amount of light energy before they can detect a difference in brightness. If the sensor is only sensitive (or is only ...


4

I just finished writing a script to accomplish this task using the free and open-source GIS Whitebox Geospatial Analysis Tools (download here), for which I am the lead developer. The source code of the script can be found here. Although the script is not yet part of the current official Whitebox release (v. 3.2.1) you can get an early version of it by ...


4

Forgive me if this is too basic an answer: panchromatic and infrared are mutually exclusive. Panchromatic means all visible light, which is generally considered to range 0.4μm to 0.7μm in wavelength. Near (or reflected) infrared energy is generally considered to range 0.7μm to 0.9μm in wavelength, just beyond visible. See Infrared vs. Panchromatic - Mt. ...


4

There is a considerable body of literature on individual crown detection in spectral and lidar data. Methods wise, perhaps start with: Falkowski, M.J., A.M.S. Smith, P.E. Gessler, A.T. Hudak, L.A. Vierling and J.S. Evans. (2008). The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data. ...


4

NDVI is for vegetation/non vegetation discrimination. So if your vegetation is always coniferous forest, then it should be the most efficient method in your case. Otherwise you will have confusions with crop, grassland and deciduous forests. In a montainous area, single reflectance thresholds will be problematic due to the hillshade (clearly visible on ...


4

Not a complete answer, but I don't have enough reputation to comment on your post: The Supplementary Materials (SM) for the Science article provides references to a number of different journal-articles that outline various parts of the methodology. The SM can be found here Extending the time-series to include Landsat-5 (and potentially Landsat-8 to make ...


4

Matt Hansen's team has a paper published on forest cover change in Eastern Europe that goes back to 1985 - see Eastern Europe's forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive http://www.sciencedirect.com/science/article/pii/S0034425714004817 I'm also checking with colleagues on whether Matt Hansen's algorithm is available ...


4

Have a look at nlayers(s). The returned number of layers will equal 28 - at least for the above example with 4 multi-layer objects encompassing 7 layers each. Applying stack to multiple multi-layer files results in one huge 'RasterStack' object, i.e. all the single multi-layer objects are appended to one another. If you would like to have separate stacks ...


4

In general, there are two approaches to classification: pixel-based and object-based: Pixel-based: Each spatial pixel is evaluated by itself against a set classification parameters. In this case, pansharpening the image will not help you at all. Object-based / Segmentation: In this approach pixels are evaluated as groups and segmented into groups based on ...


4

The full details can be found on the USGS FAQ. Here is a short answer. Band numbers in Landsat 5 TM and Landsat 7 ETM+ correspond with the same wavelength (approximately). Unfortunately, ​Landsat 5 Thematic Mapper (TM) operational imaging ended in November 2011. Landsat 5 MSS was powered back on in 2012 and collected data until January 2013. However, there ...


4

Yes, there are free object-oriented (segmentation) software available. A few that come to mind are Spring, ITK, Orfeo toolbox and GRASS GIS. I would however point out that image segmentation is a poor direction to peruse when trying to model fractional cover. A segmentation algorithm is designed to minimize within unit variance and maximize between unit ...



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