Electric Conductivity Analysis of a River Pathway using QGIS 3 and the Profile Tool Plugin

Electrical Conductivity (EC) has been used to analyze the content of dissolved salts in water. EC refers to the ability to transmit electricity (Ikeda et al. 1991). Pure water has low values of EC because the electricity is conducted by the ions in solution; therefore, the greater the concentration of ions in water, the greater the value of EC. This tutorial explains how to analyze the EC of a river using the Profile Tool plugin in QGIS 3.0.

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How to visualize time series in QGIS 3.0 with the Time Manager Plugin - Tutorial

Time series in hydrology can be analyzed to a) detect a trend due to another random hydrologic variable, b) develop and calibrate a model, c) predict future characteristics of a variable (Machiwal & Jha 2012). The application of time series analysis is diverse; for instance, it can be used to evaluate global trends of soil moisture (Dorigo et al. 2012), to analyze river discharges (Papa et al. 2012), to detect glacial lake outburst floods (Veh et al. 2018) or to detect rainfall patterns (Wang et al. 2016).

The visualization of the data variability over the time can be a useful tool to identify patterns or to compare the behavior of different samples. The use of software for Geographic Information Systems (GIS) allows to identify the location of the samples and to compile the information that the samples have. Open-source software like QGIS offers excellent tools to achieve this objective. This tutorial will explain how to use the Time Manager Plugin.

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Comparison of IMERG Precipitation with Station Information with QGIS, Python and Pandas - Tutorial

There are tools for temporal data analysis like Python, IPython and Jupyter; there are tools for spatial data analysis like QGIS. But, are there tools for spatio-temporal analysis? Unfortunately no, but there are good approaches to manage spatial data in Jupyter or to run IPython in QGIS3. These approaches aren't a complete ansqwe to the current demands of big data processing in few computational time with simple scripts, but by sure it will help to shape better solutions.

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Direct NASA IMERG precipitation images download in QGIS3 with Python

The new version of QGIS is QGIS3 and it runs with Python 3 which introduces some change on the interaction with webservers with package “requests”. For those that are new to the IMERG images, those are some kind of the new TRMM images with precipitation estimation from multiple passive microwave (PMW) sensors on various precipitation-relevant satellites starting in March 2014. The IMERG images have a pixel resolution of 0.1 degrees and a temporal scale of 30 minutes; on the current panorama of precipitation estimates based on satellite-gauge, the IMERG data product with the highest spatial and temporal resolution available over the last 4 years.

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Massive Operations on Rasters with QGIS3 and Python - Tutorial

There are new available tools and resources to understand climate change, land use dynamics, water cycle and other parts of our physical environment. Many spatial data come on raster format and are available on web servers, those servers have a image register every year, every month, every day, hour, half hour or minute. If we want to assess a physical phenomena we have to be able to analyze large set of data.

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How to download and use the LocClim software to estimate climate - Tutorial

LocClim is a software developed by the Agrometeorology Group of the Food and Agriculture Organization of the United Nations. The software provides an estimate of climatic conditions at different location regardless of the availability of observations. This software is of great importance if you want to know the climatic conditions of certain location and you do not have available observation points. It is possible to modify the stations that provide the data, so you can control the accuracy of the estimates. This tutorial demonstrates how to download the New LocClim software, how to find a location and how to export the resulting data.

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Downloading and preprocessing Sentinel 2 images using the Semi-Classification Plugin in QGIS 3.0

Sentinel-2 carries an innovative wide swath high-resolution multispectral imager with 13 spectral bands (443–2190 nm) (European Space Agency). Sentinel images have a swath width of 290 km and a spatial resolution of 10 m (four visible and near-infrared bands), 20 m (six red edge and shortwave infrared bands) and 60 m (three atmospheric correction bands) (Satellite Imaging Coorporation).The images are very powerful and are of great relevance for land and water management because analyses can be done from the spectral bands, natural color and false color image can be visualized and spectral indices can be obtained. Therefore, downloading them for free is an incredible opportunity for scientists and analysts to meet their goals. This tutorial demonstrates how to download and preprocess Sentinel 2 images using the Semi-Classification Plugin in QGIS 3.0. In addition, how to install the plugin is explained.

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Labelling and Clipping using Hydrologic Information of a Basin in QGIS 3.0 - Tutorial

Presenting Geographic Information Systems (GIS) is of great importance to transmit the correct ideas of the analyzed information. QGIS 3.0 is a powerful software to design and present maps. GIS can contain diverse and vast information; however, the analyst should decide what to present and how to present it. Therefore, labelling is a skill that must be developed when creating maps. This tutorial shows how to clip features to the shape of the study area and how to label the features of interest. The study area is a basin within the state of Tlaxcala, Mexico and it has a channel network shapefile and points with the name of the channel network features. The explanation includes how to create an expression to label the features using conditionals and how to change the style of the labels using QGIS 3.0.

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How to obtain a Basin, Channel Network and Flow Accumulation from a Digital Elevation Model with no sinks in SAGA GIS and QGIS 2.18 – Tutorial

A basin is defined as a topographic region in which all water drains to a common area. Identifying basins within a study area can be beneficial for land and water management because priority areas can be defined and the hydrology of the area can be visualized. Delimiting a basin can be done by using Geographic Information Systems such as QGIS and SAGA GIS. To do so, a Digital Elevation Model (DEM) is required since the drainage network is determined by the elevation and slope of the terrain. This tutorial demonstrates how to reproject a DEM raster in QGIS, how to fill the sinks of a DEM, to calculate flow accumulation, to obtain the channel network and the basin limits with SAGA GIS based on the DEM using the Upslope Area interactive tool.

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Downloading a Digital Elevation Model (DEM) from NASA EarthData –Tutorial

The terrain properties of a study area are of great importance due to the vast information they provide for water and land management. The elevation values can be obtained by advanced spaceborn techniques such as the use of remote sensing from satellites. On June 29, 2009, NASA and the Ministry of Economy, Trade, and Industry (METI) of Japan released a Global Digital Elevation Model (GDEM) to users worldwide at no charge as a contribution to the Global Earth Observing System of Systems (METI and NASA, 2011). This tutorial is a demonstration of how to download a Digital Elevation Model (DEM) from the ADVANCED SPACEBORNE THERMAL EMISSION AND REFLECTION RADIOMETER (ASTER) Version 2. In addition, how to merge two DEM rasters and how to change the style of them in QGIS 2.15 will be shown.

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Arid Zone Determination on Global Scale with IMERG Imagery and QGIS 3 - Tutorial

An arid region is defined as a land that has little to no rain and it is too dry to support extensive vegetation. Arid regions are common in our planet and those are located on specific latitudes as a product from the atmospheric global circulation, topography and other factors. Even tough arid regions are too dry, it doesn't mean that an extensive amount of people do live in these arid regions or their economical activities depend on those areas.

As zones located close to rivers are vulnerable to flooding, the arid zones and its water resources are vulnerable to global warming. Changes on the spatial and temporal precipitation distribution with an increase on temperature can lead to drastic changes on the surface flow rates and the groundwater flow regime.

As a deeper introduction to the existence of arid regions, there is a animation from the NASA's Global Precipitation Measurement mission that unifies the precipitation measurements of 12 satellites and integrates them into a Integrated Multisatellite Retrievals for GPM data product (IMERG). The animated data visualization is from April though September 2014 and shows the global distribution of precipitation as rain (liquid) or snow (frozen). 

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How to decrease the Raster Resolution in QGIS3 - Tutorial

A raster is a rectangular pattern of rows and columns with a spatial georeferentiation. On a raster each cell contains a value that is uniform in the cell geospatial extension and its the most used spatial data model when we deal and analyze spatially distributed values as land cover, precipitation or population density.

Spatial analysis on raster deals with raster resolution or cell size. As smaller the cell size, the result from the analysis will be finer however the raster file size will be much larger; if we use coarse cells, the raster file sizes will be smaller but the results could be poor for the objectives of the analysis. An equilibrium in between the raster resolution and the raster file size has become a topic to have in mind for beginner GIS users as well as for experienced GIS users.

QGIS is a free and powerful Geographical Information Software (GIS) software. QGIS comes with a variety of tools to manage vector and raster spatial data and its capabilities can be increased when it couples with Grass, SAGA GIS, OTB and its plugins. This tutorial show the simple procedure to change the raster resolution in QGIS 3.

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Exploring the QGIS 3 options to create maps - Tutorial

Map creation is one of the most or maybe the most popular application of Geographical Information System (GIS) software as QGIS. There has been an evolution in the layout options along the previous versions and now QGIS 3 has a whole set of map elements as legends, attribute tables, arrows, lines, scales among others. Each map element has a entire set of tools to be customized to the canvas spatial reference or other spatial data properties and styled to the desired output. Map design quality is upon the user and his experience and knowledge of the available tools in QGIS 3. This tutorial shows the procedure to create a layout / map, reviews the map objects, implements some objects in a applied case and export the map as pdf. 

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How to add a Google Map/Terrain/Satellite Layer in QGIS 3 - Tutorial

QGIS is a open source and powerful Geographic Information System (GIS) software. The latest version of QGIS is QGIS 3.0 that comes with many and exciting new features for the old and new users. As the previous versions of QGIS, the software is really intended to make more spatiall analysis and management with less effort, however this version has new tricks and a new order to locate tools. In this tutorial we will show the complete procedure to import a Google Map layer to the QGIS 3 canvas as XYZ tiles.

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Introduction to Spatial Analysis in Python with GeoPandas - Tutorial

GeoPandas is the geospatial implementation of the big data oriented Python package called Pandas. GeoPandas enables the use of the Pandas datatypes for spatial operations on geometric types. The library is a combination of a set of geospatial packages in Python as Shapely, Fiona together with well known and powerful Python libraries as Numpy and Matplotlib.

For normal Geographical Information System (GIS) users, GeoPandas enables a new way to interact with geospatial data, since it allows us to handle a more variety of queries, listing, indexing and data translation in less time, and even with less computer requirements. This tutorial show some examples of data manipulation and analysis with GeoPandas for polygons and lines from Guayaquil City. 

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How to Calculate the Vegetation Index NDVI from Sentinel 2 Imagery with PyQGIS

Vegetation indexes are calculated from the plant radiation in certain ranges of the visible and infrared spectrum. There are many indexes based on different band combination formulas, one of the most common indexes is the Normalized Deviation Vegetation Index (NDVI) because it was of the first vegetation index and it can be applied to images from current and old satellites. This tutorial shows the complete procedure to represent in QGIS the red and near infrared (NIR) images from a clipped Sentinel 2 image with PyQGIS and then calculate the NDVI using the processing package.

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Non Overlapping Areas Determination from Multiple Layers with QGIS and PyQGIS - Tutorial

Geospatial process are involved in most part of our activities; because of that it is important to optimize the time spent by the GIS specialist and to improve the quality of the spatial analysis. PyQGIS is the Python extension in QGIS, this framework allows us to manage the QGIS tools together with Python functions and even with external Python packages improving the speed and quality of our geoprocessing and spatial representation. 

In this tutorial we will show the complete procedure to determine the non overlapping areas of an area of interest from 7 different layers.

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Introduction to GeoJSON and its advantages compared to ESRI Shapefile

When dealing with spatial data for a project or a study sometimes the data format and data interoperatibility can be key to the success of the research or the map quality. For decades the ESRI Shapefile has been the most used format to exchange and to work with spatial data. From the Internet development a new geospatial data interchange format has been created to represent geographic features, their properties, and their spatial extends. This tutorial is a introduction to the GeoJSON data format with a practical work in QGIS 3 and a comparison of spatial data in both GeoJSON and ESRI Shapefile formats.

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How to import a Water Table from MODFLOW in QGIS with Python - Tutorial

Advances in groundwater modeling with MODFLOW allow us to have higher refinements on the representation of the water heads and water table as well as more capabilities in the representation of physical process related to groundwater flow. On a regional scale, we can deal with models of more than 500K elements and most times we need to represent this data on a GIS software for further study or the creation of figures for the end users, stakeholders and reports. By the use of Python scripts we can speed up the process of model output representation on a GIS software as QGIS.

Python scripts can be a little bit long and very declarative, but the process time is much smaller than the traditional clicking process on the GUI interface. The purpose is to store these scripts and use then every time one have to process the MODFLOW output data. 

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