Python scripting allows us to enhance the data management and spatial analysis in QGIS. Most times, spatial data comes from a variety of data providers in a sort of formats and data types that we have to be adapted to the GIS standard. Big data and continuos monitoring create large datasets that have to indexed, sorted and manipulated in a effective way.

QGIS has a Python console and the capability to install external Python packages that run on QGIS session. This time we have installed Pandas in QGIS to handle the data from a sensing device made on a Raspberry Pi 3 and then we have defined the path as a succession of points with PyQGIS scripting.

This tutorial show a mixed procedure with native QGIS tools and PyQGIS commands for the data representation, styling and plotting with spatio-temporal criteria with the TimeManager plugin. Data for this tutorial was downloaded from the National Hurricane Center's Tropical Cyclone Reports that contains information as six-hourly positions and intensities. You can access the whole hurricane database o

QGIS is a great software for the processing/analysis of spatial data, Python is a clear, powerful programming lenguaje; together they can enhance the spatial analysis to solve more complex or more dedicated problems in less time. PyQGIS is the Python environment inside QGIS with a set of QGIS libraries plus the Python tools with the potential of running other powerful libraries as Pandas, Numpy or Scikit-learn. 

This tutorial shows the whole procedure to upload multiple files from a certain pattern and group them on the Layer Panel using PyQGIS commands.

QGIS is a great software for the processing/analysis of spatial data, Python is a clear, powerful programming lenguaje; together they can enhance the spatial analysis to solve more complex or more dedicated problems in less time. PyQGIS is the Python environment inside QGIS with a set of QGIS libraries plus the Python tools with the potential of running other powerful libraries as Pandas, Numpy or Scikit-learn. This tutorial show the basic procedure to upload a shapefile in QGIS with PyQGIS.

Any object on the earth has a "signature" on a different bands or ranges on the visible and invisible spectrum. Base on the values and trends on this "spectral signature" one can classify different types of land cover as lakes, soils, wetlands, exposed rock and glaciers from many free and available multispectral satellite images as Sentinel 2, Aster, MODIS, and Landsat. QGIS is a very powerful open geographical information system software and its plugin Semiautomatic Classification Plugin gives QGIS many capabilities for the download, preprocess, classification and analysis of satellite imagery without using another desktop software. This tutorial deals with the whole procedure for the supervised classification of lakes and glaciers on a clipped Sentinel 2 image and its vectorization as polygons.

Based on the review from our followers, we have noticed that the download procedure to download images from the NASA and USGS web servers had changed. It is not clear the current method to download the selected files, even if you have your username and password set up correctly. Luckly there is an alternative way to download those files with QGIS and the Semiautomatic Classification Plugin (SCP). This tutorial covers the whole procedure to sign up, select, previsualize and download a Landsat 8 in QGIS with the SCP Plugin.

The current availability of strong open source GIS software together with remote sensing data and specific data products as high resolution precipitation and evapotranspiration makes possible the assessment at global scale on short processing time of certain parts of the water cycle. Water surplus is the difference in between precipitation and actual evapotranspiration, that can be translated to the difference in between IMERG imagery (precipitation) and MOD16 imagery (actual evapotranspiration). This raster algebra calculation could be implemented in open source software as QGIS with a set of specific procedures as reprojections, temporary files and raster calculators. This tutorial deal with the water surplus calculation for a andean basin for year 2015.

QGIS is a open source software for spatial analysis and data representation. There are many tools from QGIS for the hydrological/hydrogeological evaluation and modeling. This time we have made a tutorial with the whole procedure of the download process for a AsterDEM only on the extension of an area of study. The DEM was later reprojected on QGIS and processed to get the channel network and drainage basins. The tutorial has the purpose to show how simple is the process of working with QGIS and Processing.

To carry out a series of studies and investigations we have to know both name and location of certain places. This may be available through some web pages, however the use of data is limited and advance geoprocessing tools on softwares as QGIS can not be used. There are several services or APIs that can bring you the spatial location of Google searches. In this tutorial we will use the Google Maps API for the location of Hospitals in a radius of 5 kilometers from a point in Miraflores, Lima. Only open source software tools have been used for this tutorial.

Satellite Imagery has developed a new dimension in the evaluation of water resources, environment, land planning, atmosphere and climate change. There are many satellites and imagery available, many of them being free of cost. At the same time, powerful open source tools for spatial analysis and representation are available.

This webminar wants to give a perspective on search, manage and interpretation of satellite imagery, through practical exercises using QGIS.