Estimation of Land Surface Temperature with Landsat Thermal Infrared Band: a Tutorial Using the Semi-Automatic Classification Plugin for QGIS


 Updated tutorial at https://fromgistors.blogspot.com/2016/09/estimation-of-land-surface-temperature.html


This post is a tutorial for the estimation of Land Surface Temperature using a Landsat image acquired over Paris (France), using the Semi-Automatic Classification Plugin for QGIS, which allows for supervised classifications.

Before the tutorial, please watch the following video that illustrates the study area and provides very useful information about thermal infrared images, and their application (footage courtesy of European Space Agency/ESA). Also, a brief description of the area that we are going to classify is available here.


Minor Update: Semi-Automatic Classification Plugin v. 2.3.3


Minor update of the Semi-Automatic Classification Plugin for QGIS 2 to version 2.3.2.

The changelog:

-fixed a bug with the processing of the band 8 of Landsat 8
(http://hub.qgis.org/issues/9284), which now is skipped

This is a minor update, which fixes a bug with the conversion to reflectance of the band 8 (panchromatic) of Landsat 8.
Since this band is not used in semi-automatic classifications, it is skipped from the conversion to reflectance. Therefore, it is possible to input the original USGS dataset directly, without removing band 8.
The updated Semi-Automatic Classification Plugin is already available through the QGIS repository, or can be downloaded here.

Semi-Automatic OS: Video tutorials in English and Portuguese


In this post I would like to share several video tutorials about the installation and configuration of the Semi-Automatic OS in English and Portuguese.
Many thanks to the author of these video tutorials Jorge Santos, who has a YouTube channel http://www.youtube.com/user/jorgepsantos2002 where you can find several interesting tutorials.

I remember you that the Semi-Automatic OS is a lightweight virtual machine for the land cover classification of remote sensing images and GIS analyses, which includes the Semi-Automatic Classification Plugin 2.3.1 for QGIS 2.0.1, already configured along with all the required dependencies (Orfeo Toolbox, SAGA GIS, GDAL, Numpy and Matplotlib).
You can download the Semi-Automatic OS virtual machine (about 500 MB) from here.

Installation of the Semi-Automatic OS in VirtualBox



Land Cover Classification of Cropland: a Tutorial Using the Semi-Automatic Classification Plugin for QGIS


In this post, I am presenting you a tutorial for the land cover classification of cropland. In particular, we are going to classify a Landsat image acquired over the US state of Kansas, near the city of Ulysses, using the new version 2.3.2 of the Semi-Automatic Classification Plugin for QGIS, which allows for supervised classifications (an updated tutorial is available here).

Before the tutorial, please watch the following video that illustrates the study area and provides very useful information for the interpretation of the image in false colors (footage courtesy of European Space Agency/ESA). Also, a brief description of the area that we are going to classify is available here.


Minor Update: Semi-Automatic Classification Plugin v. 2.3.2


Minor update of the Semi-Automatic Classification Plugin for QGIS 2 to version 2.3.2.

The changelog:

-fixed a bug with the calculation of the classification report

This is a minor update, which fixes a bug with the calculation of the classification report.
The updated Semi-Automatic Classification Plugin is already available through the QGIS repository, or can be downloaded here.
And Happy New Year!
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