Orthophoto from drones provide us aerial imagery with spatial resolution in the scale of centimeters. With this high definition and cheap orthophotos we can interpret, analyze and quantify objects on a horizontal distribution by means of machine learning libraries for image recognition and cluster analysis.
Read MoreEvaluation of environmental processes requires a spatial temporal approach with the right tools to represent, process and analyze raster data. The process of satellite imagery selection on a web server is highly manual with limited options to manage an intensive amount of datasets. There are packages available to download certain Nasa imagery with Python, but the Earthaccess library allows users to download different dataset with options to login, select by time and location, and define folders to download data.
We have done a complete process to download actual evapotranspiration (MYD16A2) imagery with Python and Earthaccess for a given period and location. The files are stored in a folder and represented with QGIS.
Read MoreWe present our own webapp for the representation of the Piper Diagram, Stiff Diagram and Scholler Diagram. The webapp was developed in Python Django and it is entirely free for everyone. The main objective behind this webapp was to develop a user friendly and minimum requirement tool to create these water quality / hidrogeochemical diagrams. The video tutorial shows the complete procedure to update the working file and then generate the diagrams.
Read MoreHatari Utils is our app for different hydrological analysis. The app provides the geometry of the basin, river network and main river as ESRI shapefiles and based on the output from the delineation we can determine the Time of Concentration under several formulas. The app also gives the mean and median of all time results.
Read MoreModeling flow on open channels and simulating water mixing to evaluate the final concentrations and mixing area was a hard task with conventional open source software. Based on our research and app development we came up with a process to model flow of water and effluent mixing and then calculate the mass flux with the initial concentration of certain chemical components to have a fully 3d simulation of the concentration development with time and distance from source.
Read MoreThe processes of working with digital elevation models (DEMs) and watershed delineation are in constant change over time. Even though the elevation datasets date from 10 years ago; the web servers and tools for data processing have changed in recent years. With online tools such as Hatari Utils, the watershed delineation can be performed in a few steps with practical outputs as vector files and watershed statistics.
Read MoreThis is an applied tutorial for the online representation of a wind rose with Hatari Utils from wind speed and direction stored on an CSV file or inserted in an online form. The tutorial explores the online options of Hatari Utils to represent windroses as bars, boxes, polygons or contours.
Read MoreIf you want to determine the longest channel of a river network automatically this feature Hatari Utils might be of your interest. Hatari Utils is a toolbox for different analysis in water resources and beyond. On the “watershed delimitation” tool you can define the catchment, the river network and now you can define the main channel (longest succession of segments that connect a source to the outlet of the basin) automatically on the catchment delineation.
Read MoreNever before the process of watershed delimitation became so easy. We have developed an online tool for basin and river network extraction from a geospatial raster and export them as ESRI shapefiles. This app allows to process a river basin in few steps on a extremely friendly environment.
Read MoreIf you have a geological contact as a geospatial and you want to calculate/measure the strike and dip at a certain point, this tutorial might help you. The ThreePointMethod is a QGIS plugin that calculates a plane strike and dip direction using the three-point method. It works with a point shapefile and a digital elevation model.
Read MoreGeospatial mapping of environmental variables is key to understand habitats and in this case urban habitats. We have done research in temperature mapping stored in the cloud with Raspberrys and a Istsos server, this time we have coupled a gps sensor to make urban temperature map from the data retrieved from the server. This tutorial shows the procedure to retrieve the data, convert it to a geopandas datagrame, export as ESRI shapefile and undertand the temperature distribution relation with the land cover.
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.
This tutorial is developed under a Ubuntu 20.04.2 image installed from the Windows Store and covers all the steps to setup the user, install core packages, install Python packages and set up local paths to have a complete Gempy enviroment running under Jupyter lab.
Read MoreWe dont call this article a tutorial since not all steps of the sensor settings would be explained. It might be the idea of this article to show the general panorama of the sensor installation on a Raspberry Pi under the Istsos framework that implements the SOS standard.
Read MoreThis tutorial covers a simulation example of groundwater flow and groundwater discharge with the GroundwaterDupuitPercolator component of Landlab. Simulation is run on steady state over a one layer aquifer and results are plotted on charts and grids.
Read MoreBased on the several components of Landlab together with other Python packages some procedures can be established to extract single or multiple stream networks from a digital elevation model (DEM) raster and export them as vector spatial data formats as ESRI shapefiles or plot them in Jupyter Lab.
Read MoreThis 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 More
ONLINE COURSES:
LATEST POSTS
SEARCH
