2015/01/19

Landsat 8 Surface Reflectance: a Comparison of DOS1 Correction and USGS High Level Data Products


Recently USGS has announced the availability of provisional Landsat surface reflectance products, through the EarthExplorer website (see here).
In particular, the Landsat Surface Reflectance High Level Data Products for Landsat 8 is generated from the L8SR algorithm (for more information read http://landsat.usgs.gov/CDR_LSR.php). You can download the product guide from here.
These high level data products are very useful for environmental analysis, especially for supervised classifications. In fact, classification of images converted to surface reflectance can improve accuracy, for instance when several images are used for land cover change assessment.

The Semi-Automatic Classification Plugin (SCP) for QGIS allows for the conversion of Landsat images to TOA (Top Of Atmosphere) reflectance, which does not correct the atmospheric effects. Also, the SCP implements the image based method DOS1 (i.e. Dark Object Subtraction 1) (Chavez, 1996) for converting Landsat images from DN to surface reflectance. Of course, DOS1 method is very simple because it doesn't require any information about atmospheric conditions, but the results are not as accurate as the Landsat Surface Reflectance High Level Data Products.

In this post I try to compare DOS1 surface reflectance to Landsat Surface Reflectance High Level Data Products, calculating the spectral signature, NDVI, and spectral angle of several samples.
In order to assess the results of DOS1 correction, I converted a Landsat 8 image acquired over central Italy on 12th June 2014 (LANDSAT SCENE ID = LC81910312014163LGN00). Also, the Landsat Surface Reflectance High Level Data Product of the same scene was downloaded from the EarthExplorer website (data available from the U.S. Geological Survey) which is shown in the following figure.

The Landsat 8 Surface Reflectance image (data available from the U.S. Geological Survey) 

With SCP, the original Landsat image was converted to surface reflectance using the method DOS1 and to TOA reflectance (see my previous post for further information).
Then, several ROIs of 1 pixel size were created in a random fashion over different land cover classes, and I have calculated the spectral signatures thereof (using the SCP functions). I exported the spectral signatures to csv files, which were imported into a spreadsheet for comparing DOS1 reflectance to the Landsat Surface Reflectance High Level Data Product .

2015/01/06

Semi-Automatic Classification Plugin v.4.0 Beta Available


The beta version of the Semi-Automatic Classification Plugin v.4.0 "Frascati" is available through the the Facebook group and the Google+ Community. The main new features of the plugin are described in my previous post here.


2015/01/02

Semi-Automatic Classification Plugin v.4.0 "Frascati": an Overview of New Features

During the last few months I have been developing a new version of the Semi-Automatic Classification Plugin (SCP) for QGIS. This new SCP version 4.0 codename "Frascati" (dedicated to the ESA ESRIN located in Frascati (Italy), which is the European centre of Earth observation missions, and it is involved in the development of the Copernicus Sentinel satellites in particular for the acquisition, distribution and exploitation of data) will be officially released on February 16th 2015.

I have tried to add several new functions both to the user interface and the processing tools. In particular, a new SCP menu in located in the main menu bar of QGIS. One of the main achievements is the optimization of the Maximum Likelihood algorithm, which is considerably faster than before.
Another major change is that temporary ROIs are displayed as overlay, avoiding the loading of temporary shapefiles in QGIS. Also, new options allow for the automatic refresh of temporary ROIs changing ROI parameters.
A combo box allows for the rapid visualization of RGB color composites, which is useful for image interpretation.
New custom functions are available for splitting raster bands, converting classifications to shapefile, reclassifying classifications (also using Python expressions).
I have summarized the main changes in the following video.



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I am working in the fields of GIS and Remote Sensing.