GIS Tools – Converting 800 .hdf4 Files to Tiff Using GRASS, QGIS, SAGA, GDAL, and R

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I've have MODIS ndvi data (MODIS_Grid_16 days NDVI) from the iberian peninsula, from February of 2000 to August of 2012 (really big data).

How do I batch convert about 800 .hdf MODIS files to .tif ?

I can use QGIS/GRASS/SAGA/GDAL/R but I have no experience with bash scripts.

Best Answer

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 names of the .hdf files (they should be stored in your workspace)

gdal_translate("Your_Image.hdf","Your_Image_Out.tif",sd_index=1) #extracts the NDVI band (sd_index=1) and converts to .tiff

gdalwarp("Your_Image_Out.tif","Your_Image_OutWGS84.tif",s_srs="+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs",t_srs="+proj=longlat +datum=WGS84 +no_defs",srcnodata=-3000,dstnodata=-3000) #handles the conversion to WGS84

If all these commands work for you, wrap them up in a loop and you're good to go.

Related Solutions

[GIS] How to process MODIS ndvi data using QGIS/GRASS/R

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 "extract" function. Here is a mock example:

require(raster)
require(rgdal)
require(sp)

setwd("C:/data")

# Read points
  pts <- readOGR(getwd(), "MyPoints")

# Create raster stack using wildcard in list.files
ndvi.files <- list.files(getwd, pattern="img$", full.names=FALSE)
  ndvi <- stack(ndvi.files)

# Extract point values and merge with sp point object 
pts@data <- data.frame(pts@data, extract(ndvi, pts) )
  str(pts@data)

After you have the ndvi values extracted, you could apply the date functionality in R to the names of the ndvi file names (retained in extract) associated with each column. In this way you can write functions to analyze the time-series. Also, keep in mind that you can apply functions to the raster stack itself using the "overlay" function.

QGIS – Reprojecting MODIS-Sphere ESRI GRID File to WGS84 Using QGIS/GDAL

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 (not reprojected!):

So once you assign the custom CRS to the raster, you can set the project CRS on-the-fly to Web Mercator or something else:

Best Answer

Related Solutions

[GIS] How to process MODIS ndvi data using QGIS/GRASS/R

QGIS – Reprojecting MODIS-Sphere ESRI GRID File to WGS84 Using QGIS/GDAL

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