Processing historical NOAA AVHRR data with precise geometric correction

We published a new article in MDPI Remote Sensing journal on new method to accurately process time series of AVHRR Local Area Coverage (LAC) data. You can read the entire paper here:

We developed a new workflow which can read all the AVHRR LAC level 1B  data over all the NOAA satellites, calibrate them, applied clock drift corrections, geometrically correct them using automated feature matching technique called SIFT and finally applied split window technique to the thermal bands to derive lake surface water temperature as a case study. We found that the SIFT based geometric correction followed by gcp filter using m.gcp.filter addon in GRASS GIS 7 is very efficient in performing image to image corrections on thousands of images.

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When the Indian tiger pays a visit to villages!!!!!

In summer 2009, we conducted a field work in Kanha Tiger reserve, as part of the ground truthing exercise to study the effects of eco-tourism and to understand the zone of interaction around the park for better management. It was an afternoon on a scorching hot day, the open jeep on which we were exploring the core area, suddenly stopped. Sensing wildlife nearby, I started looking around. Everyone whispered Tiger! Tiger!; still I cannot see it. After few seconds, looking at where the cameras around me are zoomed, I realised that the mighty Tigress was resting right ahead on the track blocking us.Order nike air max

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Processing ERA-Interim dataset in ncdf using GDAL and GRASS GIS

Processing big data efficiently has become a necessity of the hour in ecological and climate change research. Now there are enormous number of public data available online, which demands high level of processing capabilities too.  In this post I will show you how we can process ESA-Interim data which is global atmospheric reanalysis from 1979 to present developed by ECMWF.

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GRASS GIS 7.0 RC1 is released

The first release candidate of feature rich GRASS 7.0 is released on January 14, 2015. The new candidate release is an output of 6 years of development after the last release of GRASS 6.4.0. One of the main feature in GRASS 7.0 is its new python user interface replacing the old tcl/tk based GUI.
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Changes and improvements on the latest version can be read here: GRASS 7.0 RC1
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