Digital Geography

Accessing Landsat and Sentinel-2 on Amazon Web services

The cloud has made it easier to process large amount of data, and satellite imagery processing benefits from cloud processing too. One of the cloud services that offers access to satellite images, and abilities to process them in the cloud – no more need to download it to your computer and process it there – is Amazon Web Services. If you’ve never worked with cloud processing, getting started with AWS can be a bit daunting. This tutorial gives beginners an introduction to accessing satellite images – Landsat and Sentinel-2 – on AWS.

Short Announcement: Landsat 8 Data Users Handbook

Landsat 8 is the successful successor of Landsat 5/ Landsat 7. It was launched on February 11, 2013. Since then we saw a major increase in possibilities of getting access to the data and integrations in software packages. Now the USGS published an in-depth document to support the basic understanding for the Landsat 8 observatory and its science data products.

First images from recently launched Sentinel 2 satellite

The earth observation satellite Sentinel-2 with Sentinel-2A and Sentinel 2B was launched on June 23rd 2015 from space centre Kourou in French Guiana. The mission is part of the Copernicus mission by ESA. The liftoff was recorded by ESA and can be watched here. Sentinel will be a complement to the multispectral satellites of the Landsat mission and SPOT. All observation systems in combination reveal a higher temporal resolution by shifted orbits and large stripe widths (Sentinel: 290km). Sentinel has a repetition rate of 10 days, Landsat 8 of 16 days but in combination with Landsat 7 a 8 day…

Landsat in Love with QGIS: the newest coup from Luca

The normal way of getting Landsat data for your GIS projects often was: visit a Landsat data mart like, earthexplorer or WIST, search for your area and time and download/order your desired data. Once you’ve done this, you were prepared to add, analyse and publish this data/results with QGIS. Luca Congedo from the blog “From GIS to Remote Sensing” .

short announcement: Earth Engine by Google

On the 9th of May Google introduced an astonishing new technology that makes the good old flickr function in Erdas Imagine quite useless. With the Earth Engine from Google Earth you will be able to see the world change since 1984. They have compiled about 900 terabyte of Landsat Data. So they stitched, balanced the contrast, analysed and ordered about 2’000’000 images of the world to provide a comprehensive look into Earth’ history: Erdenet (Mongolia): coal mining lake Ugii Nuur (Mongolia): drying and dying Jänschwalde (Germany): coal mining Las Vegas (USA): urban sprawl Additionally you are able to browse the…

short announce: Reverb @NASA the new WIST

Sorry to talk about this important tool with a little delay. As I was lecturing at the university I was using the Warehouse Inventory Search Tool (WIST) by NASA to collect my raster data and download them. It was a great tool but looked a little old. The biggest difference to services like or the SRTM Tile Grabber is, that most data was ordered and delivered by FTP. It was not a big deal as most data was available for free but I think for a lot of users this was not “convenient” enough… Nevertheless Reverb- the new WIST-…

Landsat Data Continuity Mission

Looking Back Most people using GIS or remote sensing data came across the data obtained by the landsat satellite family. It all started with the Landsat 1 mission on July 23, 1972. The Landsat missions aquired millions of pictures which made available to public usage in 1992. Sensors developed and provided more and more details in terms of spectral and ground resolution.

The End of the Landsat 5 Era

If you have been working in the space remote sensing community for any length of time you have almost certainly crossed paths with Landsat 5. After 29 years of watching Earth, the U.S. Geological Survey announced that Landsat 5 would be retired slowly over the coming months. Of course most people are looking forward to Landsat 8 (LDCM), but it is worth it to look back for a bit on Landsat 5, this stalwart icon of Earth remote sensing. More information from the USGS can be found at the official release As always, thanks for reading.

unsupervised classification of a Landsat image in R: the whole story or part two

The main question when using remote sensed raster data, as we do, is the question of NaN-treatment. Many R functions are able to use an option like rm.NaN=TRUE to treat these missing values. In our case the kmeans function in R is not capable to use such a parameter. After reading the tif-files and creating of a layer stack we will go on with a work-around to solve the missing values problem of the non-covered areas of a Landsat picture.

NDVI / SAVI calculation using Landsat scenes: DNs vs reflectance values

In one of my last seminars relating remote sensing techniques I was blamed for calculationg the NDVI and SAVI directly with the ModelMaker in Erdas Imagine using digital numbers (so-called DNs) instead of reflectance values. So I analyzed the influence of using DNs instead of reflectances. You can see some little thoughts and results on that here…