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7

If you know R, you can use the package "gdalUtils" and run gdal_translate to do that. If you are on Linux make sure to install GDAL. If you are on Windows, you're good to go. These are the basic commands to handle the conversion to .tiff and the reprojection to WGS84. out.files <- list.files(getwd(), pattern="hdf$", full.names=FALSE) #create a list with ...


7

The project website hosts the MOD16 dataset on an FTP server. As FTPs allow directory listings you can easily download complete folders without having to click individual links. This can be done with most FTP clients - a popular one would be FileZilla. Just right click the folder you want and select download. edit: The question now specifies that only one ...


6

All of the detailed information about MOD12 data can be found in the Algorithm Theoretical Basis Document (ATBD). On page 23, it says that the forest classes require >60% coverage of the pixel.


6

You need the subdataset full name from the query on the file: gdalinfo MOD04_L2.A2003001.0005.051.2010313005421.hdf >2003.txt With the subdataset name, you get the GCP coordinates in pixel and lon/lat: gdalinfo HDF4_EOS:EOS_SWATH:"MOD04_L2.A2003001.0005.051.2010313005421.hdf":mod04:Image_Optical_Depth_Land_And_Ocean >>2003a.txt With the ...


6

If you look at the product page at LPDAAC, under Layers there is a table that lists each of the bands in the dataset and their characteristics. For the NDVI layer, it is a 16-bit signed integer with a fill value of -3000, and a valid range from -2000 to 10000. However, there is also a scale factor of 0.0001, or 1/10,000. This means that a value of 10000 ...


6

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).


6

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 ...


6

The Readme.docx inside the zip file gives some hints on the projection, but maybe misleading. The raster is in an interrupted Goode Homolosine projection, based on the MODIS sphere. So I created the following custom CRS: +proj=igh +R=6371007 +no_defs reprojected a Natural Earth shapefile into it, and it fits to the raster data with the same CRS assigned ...


5

HDF4 support is not available in GDAL by default. If you're using the GDAL binaries and python libraries from GISInternals, these do not have HDF4 support compiled in. HDF4 support is compiled in the OSGeo4W GDAL binaries. You can test if you have HDF4 support with the gdalinfo command: gdalinfo --format hdf4 You can also batch the MRT from within your ...


5

So MODIS is a complex instrument and it has been a while since I worked with the data. So bear that in mind. If we look at band 1, the data is pretty good: But as you point out with band 7 the data does not line up. I had to check it myself because I was certain it had to do with the different channels per band (which is why the raw RSR file have from ...


5

As mentioned in the files metadata, the modis source data has a scale factor of 0.02. QGIS applies it on-the-fly. But the reprojection with gdalwarp destroys the scale factor information, hence the result is unscaled. You have to apply it manually to get the correct results.


4

A quick Google has turned up MODIS Snow and Ice Project, which appears to give resolutions of 500m. You could also look at Landsat data. Due to the high albedo of snow, it should be a fairly easy process to threshold highly reflective values, then average them out by collecting a time-series of images and applying map algebra. Alternatively, you could ...


4

I got your email. Sadly I don't have experience creating MODIS cloud masks. I've used the MOD35 cloud mask data, but I've never done the calculations myself. That being said, I believe that MOD09 reflectance data are already corrected for cloudcover. There is a 2-bit word in the MOD09 QA layer for "Cloud state" that can be either Clear, Cloudy, Mixed, or ...


4

Most MODIS QA data (including the Cloud Mask data) are not stored as separate raster bands, where each band is a grid where each cell is one value of one QA data field. Instead, the QA data are concatenated into strings of bits. So instead of having Band 1 be 00 and Band 2 be 11, they just concatenated them (right-to-left) as 1100 which is a completely ...


4

Information on how to convert the digital values ("digital numbers" or DN) in satellite remote sensing data that is operationally produced and distributed into physical quantities of interest is generally found in the data user guide. In this case, you need the user guide for the MODIS 11 Level 2 product. Under "scientific data sets (SDS" there is a table ...


4

You can use the "stack" or "brick" function(s) in the raster package to create a single raster object of your NDVI time-series. The names of each NDVI file will be retained in the object and the rasters will be held out-of-memory, making it memory safe. You can then retrieve the raster values, associated with a point feature, across the series using the ...


4

You cannot do this in any reliable fashion. The reason is that every MODIS product (like LST) is created from a number of observations (basically, the MOD01 radiance product) and you do not know how that is done. The MOD11A2/MYD11A2 products do not give you the number of observations that go into each product, nor the extremes. But if you really want to do ...


4

It looks like GDAL is describing the outer edge of the 'origin pixel' and Arcmap is refering to the center of the origin pixel. If you add half the resolution of a pixel they'll match fine. This definition is often different with different software, it doesnt really matter, though you should know what you're looking at so you can take it into account. One ...


4

Just a tiny error, as far as I can see. Your substrings are incorrect. This can be seen by comparing the result from a 'which(df$bits="0000100001000100")' with a number of observed unique values, which can be seen in ArcGIS when colouring the tif-file by unique values. 00001000 01000100 = 2116, and there are 3891233 of that number in both ArcGIS and R. This ...


4

The best established Landsat cloud detection algorithm used today is fmask published by Zhu&Woodcock. It is not written in R but in MATLAB, it can be downloaded as MATLAB code or as a compiled C executable. The source code however is openly available, so you could try to rewrite it in R (ceholden already did it for python).


4

In Windows (run OSGeo4W shell): Scaling: for %i in (*.tif) DO gdal_translate -scale -2000 10000 -0.2 1 %i outputs\%i You might find recalculating instead better: for %i in (*.tif) DO gdal_calc.bat -A %i --outfile=outputs\%i --calc="A*0.0001" --NoDataValue=0 In Ubuntu looping through files is slightly different: for i in *.tif; do gdal_calc -A $i ...


3

The PRISM Climate Group's data is exceptional. Their raster products include precipitation, max temp, min temp, dewpoint and historic data. NASA's MODIS site has a wealth of data as does this USGS site. You will find a wide range of products there from vegetation indices to emissivity and burn data.


3

The WorldClim dataset has a lot of the data you want. It is free for non-commercial use and has interpolated 1 km resolution data on: average monthly mean temperature (°C * 10) average monthly minimum temperature (°C * 10) average monthly maximum temperature (°C * 10) average monthly precipitation (mm)


3

for rainfall you can download the TRMM datasets: http://trmm.gsfc.nasa.gov/


3

You may want to check this page: https://lpdaac.usgs.gov/products/modis_products_table/mcd43a3 32767 is actually the fill value, so that's NO DATA (to be removed prior to rescaling). Then rescale the pixel values with 0.0010.


3

Unfortunately, mapping burned areas--especially across large geographic areas--is not a simple task. However, there is a lot of good documentation on how burned area products are created in peer reviewed publications. For a regional perspective, I would refer you to the Monitoring trends in burn severity (MTBS) site. MTBS utilizes Landsat data to map all ...


3

You may take a look at "pyModis", a Free and Open Source Python library to work with MODIS data. It can bulk-download, mosaik and reproject: http://www.pymodis.org


3

Hard to say without looking at the data, but there are some issues that you need to take into account. First, there's angular variation not corrected by the MxD13 data, and its effect can be different for grasses (continuous canopies if you want) and forests (discontinuous). You can use the MODIS NBAR product (MCD43?) to use nadir-reflectance. This product ...


3

I routinely do this with GDAL and a simple bash script. You can also do ith with python, but I think this is quite straightforward and easier to understand #!/bin/bash # This script reprojects and subsets a bunch of HDF files stored in # a given dir (WORKDIR). The output is a GeoTIFF formatted file. # WORKDIR="./" # Where all HDF files are stored ...


3

R is an excellent programming language designed for data manipulation and statistical computation. There is a very nice package called sp that adds good support for handling spatial data. The spsample function can pull a statistical sample from spatial data using 7 different methods. The sampling area may be controlled by specifying a bounding box and/or ...



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