Digital Geography

Observing deforestation with Sentinel-1

While preparing for an upcoming presentation at the annual meeting of the American Geophysical Union (AGU) I came across a topic that I thought might make an interesting blog post.  The presentation is about using data from the Sentinel-1 mission for Earth Science applications.  The Sentinel-1 spacecraft are C-band SAR systems launched and operated by the European Space Agency (ESA).  An innovative aspect of this mission is that the collection scenario devised by ESA is systematic and very broad in coverage.  Using Sentinel-1 we can monitor Earth using SAR data like never before.

Create A Raster Layer Index With QGIS And GDAL

When working with raster datasets, it is hard to keep an overview of the raster files in use and their coverage. Most raster data providers may keep metadata in the filename itself, like path, row and a timestamp. But for more convenience, it is possible to create an index of your raster maps. This article will show you, how to accomplish this in QGIS or with the Terminal.

GeoTiff compression comparison

In remote sensing you often have to deal with large datsets because their spatial or temporal resolution is high. A typical Landsat 8 scene clocks in at 0.7 – 1 GB and if you are trying to process satellite images for a continent or even the globe you’re easily looking at multiple terrabytes of input data. I am currently working with MODIS time series data, which will use about 4 TB of space even before any processing is done. Therefore I started looking into compression methods. One of the easiest ways to save space is by employing the compression methods some file…

regular spaced points… interpolation madness…

The geodata department of the city of Berlin offers a great portfolio of free geodata for everyone to use. One dataset is the result of some LIDAR measurements and is offered as a txt with x,y and d values. This is commonly known as xyz data but should not be mistaken as a simple whatever-delimited text file. This dataset is regular spaced and therefore can be threatened easily with QGIS. But let me first show you some interpolation results which can be produced as well. Interpolational madness If you want to create raster data from a point shapefile or a…

reproject and filetype change in python/pyqgis for QGIS plugin

In my current work on the qgis2leaf plugin I had the idea to place raster data on a leaflet map as an image overlay. With this in mind and looking at a webmap I needed to consider a good filesize, a strict projection of EPSG:4326 and a strict filetype as well. So decision was: projecting everythin to EPSG:4326 and changing file type to *.jpg. I know, how to do this in the Terminal and in QGIS. But what options do you have using python/ pyqgis only? Terminal For doing this work in the terminal/shell/command line the one and only choice…

QGIS plugins: SEXTANTE

SEXTANTE by Victor Olaya is a powerful plugin that bundles many methods and applications from QGIS in one place and provides a GUI for your processing work flow which is comparable with the ArcGIS ModelBuilder or the ERDAS Spatial Modeler. With this plugin it is very easy to use your GRASS, SAGA and GDAL tools, self-written R scripts and many more. This makes spatial analysis much easier and increases reproducibility. Especially the combination with R functions provides a completely new dimension of working with a GIS as nearly everything spatially can be converted to a data.frame and be consumed by…

Map Projections, spatialreference.org and gdalwarp

Map Projections The question of map projections and how to reproject data is one that comes up often in discussions with both experienced colleagues and those new to the geospatial profession. I’m not going to go through a complete discussion of map projections here, as there are many resources available on the Internet that can help you. I’m going to focus more on how to move data between projections. At its most simple a map projection is simply a mathematical description of how to take data on the surface of a sphere, that are inherently 3-dimensional, and transform them to…

Python for Geospatial Data Analysis (Part IV)

GDAL Geotransforms and World Files The last post in this series considered how to write a geospatially aware file, in that case a Geotiff. In the example the projection and geotransform were read from a file and written into another file with no modification. That worked for the simple example, but isn’t necessarily the most common use case for that type of subroutine. Often you may want to adjust the data based on your analysis. Most geospatial professionals are probably familiar with world files, or have at least run across them. They are a good way to georeference data that…

Python for Geospatial Data Analysis (Part III)

Writing Geospatial Files In the last post in this thread I began discussing basic syntax and how to open and read a geospatial raster file. This installment in the series will demonstrate how to take the data we read from the file and write it out to a new file. In this case, we won’t change anything in the data, just use it as a means to demonstrate writing a file. In order to write a file, there needs to be a small addition to the subroutine for reading data. In the previous post the subroutine didn’t return the data…

create and edit shapefiles with Python only

Some days ago I’ve presented a way to load and monitor the content of a shapefile using pyshp. But since then I was remembering my work with shapefiles in my basic R-seminars and the way we have used the gdal-library for our data management. So I searched the web and found comparable solutions for the project “where are your customers” in Python. You was probably using open source solutions already or are a user of ArcGIS and was frickling around with the Python-interface (new in ArcGIS since version 10). I would like to show you here some basic steps to…