This tutorial covers the complete procedure to create a land cover change raster from a comparison of generated vegetation index (NDVI) rasters by the use of Python and the Numpy and Rasterio libraries. Results of the NDVI for given years and NDVI change are plotted on Jupyter Lab as color grid and contour grid.
Read MoreGeopandas is an amazing library for spatial analysis since it combines the spatial tools from Shapely and Fiona with the versatility of Pandas Dataframes. Rasterio is one of the most complete libraries to deal with raster files in Python.
We are aware that most geoscientists, water resources specialists and related professionals work on Windows, therefore we are always in the search of new ways to get Python working with all its geospatial capabilities in every computer.
Based on the new Anaconda releases now we can install Geopandas and Rasterio in a easy way.
Read MoreWe are allways on the research of new visualization tools for groundwater models and that would imply the review of different apis, codes and backends. This time we have done an applied example for the interactive generation of cross section from a Modflow 6 model with a voronoi mesh on a Jupyter notebook. The cross section shows the model grid, head distribution as a color grid and contours and its generated from the line delineation over the 2d view of the model grid. The code can be easily adapted to any Modflow model by editing the Python and Flopy code.
Read MoreWe have developed a version of our Angascancha basin case study that runs on MODFLOW 6 with discretized by vertices (DISV). The model implements a Voronoi mesh that is generated from the basin boundary and river network and the refinement levels are defined by a minimum and maximum cell size together with determined refinement steps. The tutorial is complete in all the steps involving the model discretization, construction, simulation and visualization.
Read MoreIf you have a set of drone imagery and you want to know the location and direction of the camera, this tutorial might be interesting for you. We have done an applied example that retrieves the geospatial metadata from the drone camera for a group of images and makes a geostapatial repretacion on a map with the image name available as popup. The tutorial is done under a Jupyter notebook with Python and Folium.
Read MoreAnalysis of groundwater chemistry is a difficult task for the limited set of monitoring samples, the limited samples and the limited analized components. In order to assess the actual extension of a contamination plume or the efficiency of remediation techniques we need new and innovative methods to plot and analyze water chemistry data with open source data. We have done an applied case of interactive VOCs and PFAs representation on a Jupyter notebook with Python, Folium and Ipywidgets. The dataset has more than 3300 samples of 127 points over a period of 30 years and corresponds to a contaminated site of a former airfield.
Read MoreOpen lithology datasets are scarce and most times they come in certain exchange formats that can´t be easily coupled with other softwares. Python has the tools to read and extract data from those files and provide them in more friendly formats as csv or xlsx. With the use of spatial libraries as Fiona or 3D visualization libraries as Pyvistas we can go one step further and process our data as shapefiles or vtks.
Read MoreGempy is an open-source library for modeling geology written in Python. The library is capable of creating complex 3D geological models including structures, fault networks, and unconformities and it can be coupled with uncertainty analysis.
Due to the required packages and specific configuration the installation of Gempy is a challenge on Windows, we have developed an alternative installation process by the use of a Debian kernel under a Windows Subsystem for Linux. This tutorial shows the complete procedure to install Gempy from the Debian kernel setup to the review of the Gempy in Python.
Read MoreStorage, management and analysis of geospatial vector data as an ESRI shapefile is a common procedure of GIS and related professionals. The generation of these spatial files can be done not only on a desktop software but also by Python commands. We have created an applied example that shows the procedure in Python to create point, line, and polygon shapefiles from a csv file by the use of the Fiona library.
Read MorePython is a great tool for spatial analysis and geomachine learning, however sometimes the Windows operating system presents some difficulties to install and run the bunch of Python libraries such as Gdal, Fiona, Geopandas, Rasterio. We are aware that most geoscientists, water resources specialists and related professionals work on Windows, therefore we are always in the search of new ways to get Python working with all its geospatial capabilities in every computer. We have created a tutorial that shows the installation process of the Python geospatial libraries in Windows by the use of a Conda environment; the process is simple on its steps, however the sequence and factors related to the package compatibility are important on the installation.
Read MoreWe did a simple procedure in QGIS but unknown to us that extracts the centroids of lines and polygons with Field Calculator commands inside the attribute table. The procedure is straightforward and does not involve the use of any intermediate layer.
Read MoreOver the last months we have researched the different tools in Python and QGIS available to recognize crops and vegetation as geospatial vector files. We have used a variety of techniques that range from machine learning algorithms with Scikit Learn and Scikit Image to just more innovative band combinations and reclassifications in QGIS. This article shows the summary of the tutorials produced so far that we are sure will be very helpful for GIS professionals and geoscientists.
Read MoreSpatial analysis and machine learning sometimes require massive coding in order to achieve decent results such as identifying plants from a drone orthophoto. We wanted to create a simple workflow for beginner and intermediate Python users to work with these libraries without much pain or frustration. This tutorial has the complete procedure to use a Python class that recognizes plants from an orthophoto based on sample points and creates intermediate plots and identifies plants as point shapefiles.
Read MoreWhile doing research on machine learning algorithms for drone orthophotos we found that crops can be delineated with just standard QGIS with excellent performance. Based on the addition of the blue and red band divided by the green band we can have a new vegetation index where the most healthy crops have low index values (0-1.5) and the dry crops / barren soil have high values (more than 1.9). This tutorial shows the complete procedure on QGIS to perform the delineation from bean plants on a drone orthophoto with a resolution of 5cm.
Read MorePython is a programming language capable of performing calculations for hydrological studies and water resources evaluations. We have done a tutorial for the volume-elevation curve determination of the lake Patillas in Puerto Rico with Python and numerical / spatial libraries as Numpy and Rasterio. Finally, results were compared to the volume-elevation curve form a USGS survey. The procedure was done this time for a lake, but can be easily applied to any reservoir or water body when the bottom elevation is available as a raster file.
Read MorePython is a useful tool for data analysis but also for data representation and as a graphic tool. This is an applied tutorial for the representation of a wind rose with Python from wind speed and direction stored on an Excel spreadsheet. The tutorial explores the options of the library to represent windroses as bars, boxes, polygons or contours.
Read MoreTutorial for the calculation of the solubility and thermodynamic stability of gypsum and anhydrite. The example simulates the dissolution of two minerals in a beaker at equilibrium, and the beaker is heated step-wise from 25ºC to 75ºC. Concentrations and saturation indices for the initial solution and the batch reaction are shown as Pandas dataframes and plotted as bar diagrams on a Jupyter Notebook. Finally a plot of the saturation index for anhydrite and gypsum with temperature is generated from an iteration over the batch reaction steps.
Read MoreThere are three essential things for the representation of geological data on QGIS: the spatial geological information, an appropriate symbology and the software knowledge. Once these three things are available, the potential of QGIS to represent geological maps is unlimited.
We have done a tutorial for the representation of synclines, anticlines, overturned synclines and overturned anticlines on the regional scale with QGIS.
Read MoreThe speciation modeling allows to calculate the distribution of aqueous species in a solution. Phreeqc is capable to simulate this speciation calculation and we are going to demonstrate this capability on a study case of aqueous species in seawater.
We have done a tutorial for the speciation modeling of seawater with Phreeqc that runs under Python in a Jupyter Lab enviroment. The code can run the Phreeqc executable, define the databases and stablish the output files. Results from simulation are available as Pandas dataframes and plots are made for the main components and the distribution of saturation indices.
Read MoreIn our perspective, 3D visualization of geospatial data has been a long desired feature that has been covered in some features from SAGA GIS or in some plugins from QGIS. This time we developed a Python script that converts point / line / polygon ESRI shapefiles (or any vector file) to unstructured grid Vtk format type (Vtu) by the use of the Python libraries Geopandas and Pyvista. The tutorial has files, scripts, and videos that show the whole procedure with some remarks on the software and spatial files and a discussion about the nature of the spatial files that presents some challenges in the data conversion.
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