QGIS 3 has a lot of new features and new tools for geoprocessing and spatial analysis, however there are some tools from QGIS 2 that hasn´t migrated yet. An example of this break was the OpenLayers plugin, one of the most popular and first plugins in QGIS 2 that is not available in the new version of QGIS.

OpenLayers plugin provide maps from Google, OSM and Bing in QGIS 2. Basemaps in QGIS 3 can be inserted as a XYZ Tiles on a process described on this tutorial. However, QuickMapServices plugin allow the search and implementation from a set of basemaps on different formats (TMS, WMS, WFS and GeoJSON) in QGIS 3.

Flow simulation is an important task for engineering and disaster management. From the free softwares available for flow simulation, HEC-RAS is a strong alternative for its continuous development, the capability to simulate many flow types, and the interaction with other softwares like QGIS or MODFLOW.

QGIS is a free and open source software Geographical Information System (GIS) application. There is a environment of available plugins in QGIS that perform specific tasks of analysis, representation and preprocessing. There are QGIS plugins related to HEC-RAS, in this tutorial we will use the Q-RAS plugin to develop a basic example of flow model geometry construction in QGIS and steady flow simulation in HEC-RAS.

With current technology and the availability of remote sensing tools through different servers makes it possible to determine or estimate the areas that are flooded or could be, the focus of this tutorial in which a methodology to determine flood zones will be described from the calculation of the NDVI and compare the results with the use of two servers, Sentinel 2 and Landsat 8.

The vegetation indices are obtained from area and satellite images and can be used to estimate changes in the state of vegetation, biomass, leaf area index and chlorophyll concentration. The determination of vegetation indices is calculated from the relationship between the reflectance of the electromagnetic spectrum. While biomass presents various methodologies to be able to estimate that they are based on field measurements that despite being a direct method are still very limited. Currently, the use of remote sensors provides a method to generate information on biomass.

In this tutorial the estimation and relationship of the biomass of a study area with the Normalized Difference Vegetation Index (NDVI) and the Normalized Red-Green Difference Index (NGRDI) will be carried out.

Mining and the treatment of metals are a significant source for the contribution of pollutants in various natural resources such as lakes. The impact of a pollutant on the lakes depends on the physical and chemical conditions that affect its toxicity and the degree of exposure related to concentration and time. The contribution of metals can be incorporated by precipitation, mine filtration, soil washing, leaching and percolation.

Remote sensing allows the interpretation of dynamic processes in a study area at a spatial and temporal level. This information is collected from the surface that is captured by a sensor located on board a satellite in orbit around the earth. In this way, you can compare images and analyze the changes of a study area.

Different space agencies have facilitated access to information provided by satellites through servers and web portals, where satellite images can be downloaded for the analysis and interpretation of resources, for this instance, the quality of seawater can be analyzed through the classification of specific footprints using geographic information systems tools such as QGIS 3 and the Semi-Automatic Classification plugin.

Image from earth surface are available from satellite images and unmanned aerial vehicules (UAV). Image resolution ranges from 10 meters on free available datasets as Sentinel2, less than 1 meter on commercial satellite imagery to 0.05m (or even less) from UAVs.

Scikit-image is a library for image processing in Python. Currently there are few QGIS plugins that run Scikit-image, we found one that extracts geological features as fractures and represent fault dip, dip direction, and strike in stereonets and rose diagrams. This tutorial show the procedure to install GeoTrace on QGIS 3 with its dependencies, runs a geological fault trace on a modified raster, calculate fault strike azimuths as a separate attribute field represent them as a strike rose.

For a normal GIS user, QGIS 3 brings a lot of new tools, new forms to perform spatial analysis but also it doesn´t bring (yet) some options available in QGIS 2. This is the case for the representation of HDF raster files that is not available in QGIS 3 but it is available in QGIS 2. Research have been performed to address this issue and many options were evaluated to open the HDF files and perform a geotransformation from Sinusoidal Coordinate System to Geographical Coordinate System.

The solution came from the powerful gdal library and some core Python functions. This tutorial show the procedure to open a layer of a MOD13A2, a MODIS data product for vegetation evaluation and reproject it to geographical coordinate system (lat/lon).

There are several satellites with different characteristics that acquire multispectral images of the surface of the earth. In this case, the Sentinel 2 images are particularly useful for the monitoring of land cover and can be provided free of charge from SCP.

In this tutorial we will perform the evaluation of spectral signatures using the Semi-automatic Classification complement in version 6, which is a free open source plugin for QGIS 3 that allows the supervised and unsupervised classification of remote sensing images.

Spatial interpolation techniques used to evaluate estimations of physical and chemical constituents in areas where they are not estimated (Murphy et al., 2009). This tutorial will show how to interpolate data from point data to obtain a raster that covers the study area and, then, to obtain contour lines from the raster.

The Inverse Distance Weighted (IDW) method is one of the most used due to its simplicity. IDW expect that each point has a nearby impact that reduces with distance. It gives more weight to the points nearest to the forecast area. QGIS has the ability to perform this interpolation method by using data points and the result is displayed as a raster file.

The root mean square error (RMSE) has been used as a standard statistical parameter to measure model performance in several natural sciences. The parameter indicates the standard deviation of the residuals or how far the points are from the regression or modelled line. The following figure shows the residuals as green arrows and its location between the point data and the regression line.