To carry out this task in ArcGIS Pro, we have the “Create grid” tool from the “Data management tools – Sampling” set. This tool allows us to generate such rectangular or quadrangular cell networks using polyline or polygon geometries, as well as (optional) point geometries indicating cell centres. Besides that, this grid can be oriented in any direction we want.

We’ll start creating a grid of vertically oriented quadrangular cells. For that, all we need as imput data is a layer (entity class or shapefile) containing the surface area where we want to generate the grid. In this case, this will be a polygon layer with a single entity containing the surface area of a hill for which we want to generate a network of samplig plots.

Once the layer is incorporated to our map, we run the “Create grid” tool. First, we must specify the extension of it. For this, the “Template extension” drop-down menu offer us this options:

– Default: the extension will be the maximun size that can be determined by the rest of the entered data (the data extension itself shall remian fixed at 0).
– Current view extent.
– As specified below: Minimum and maximum X and Y values must be introduced.
– Browse…: A layer stored in the system must be chosen. Its extension will be the one used by the grid.
– Same as layer: One of the opened layers in the map will be selected. Its extension will be used for the grid.

In this case, we’ll select: “Same as layer: Monte (Hill)”.

This way the grid will have, as minimum (we’ll see why minimum), the extension of the layer for which we want to generate. We see that upon selection, the checkboxes “Original coordinates of the grid”, “Y axis coordinates” and “Opposite corner of the grid” have been completed, in addition to the boxes corresponding to the extension of the template.

The original coordinate of the grid is the point from which the grid is generated, which corresponds to the bottom left corner of it. The opposite corner of the grid correspond with the upper righ corner and the Y coordinate refers to, aligned with the orignal coordinate of the grid, gives place to the line that determine the direction of it. By default, a Y point located 10 metres north fromt he original coordinate is generated, which gives place to a vertical grid. If the original coordinates or the ones in the opposite corner are modified, the final extension of the grid will adjust.

Hereafter, we must choose either the size (height and width) of the cells or the number of columns and rows that must be build inside the previously indicated extension. In this case, we have introduced the same distance for both height and width of the cells (250 metres), which will result in quadrangular cells.

Finally, we must choose what type of geometry we want the grid to be generated in (Polyline or polygon), as well as pointing out if we want an additional point layer containing the center of every cell (“Create label points” checkbox). In this case, we check this box because said points will be the center of our sampling plots.

The only thing left to do is to define the name and location where it will be saved and run the tool. The result will consist on two layers (one the grid itself and the other one the centers of each cell) on which the grid will have a vertical orientation and its extension will only match with the previously established if none of the columns or rows is left incomplete, in which case it will be completed beyond the pre-established extension.

To generate a grid with a different orientation, we must modify the “Y axis coordinate” parameter so that , together with the point of origin, the line whose direction the grid will follow is defined. We must consider that on this case, the extension will no longer reference the surface area on which the grid will be generated, but rather continue referencing the rectangular/quarangular area that would occupy in case this was vertical. Therefore, we must think of a strategy to define both the extension and the direction of the grid so it falls over the desired surface area and orientation.

In our case, two polygon layers have been generated. On the first one a vertical rectangle has been created with a extension big enought to cover the hole target surface area (Hill) and whose lower left corner matches with the desired point of origin of the grid. On the second layer, a rectangle was also created, with its lower left corner also matching the point of origin of the future new grid, but it is oriented on the direction we want to build the grid.

The layer that contains the vertical rectangle will be used to define the extension of the grid, while the other one will be used to define the Y axis coordinate by extracting the coordinate of the upper left corner of it. After defining the rest of parameters, we run the tool and we’ll have our new grid cell, oriented in the desired direction and covering all the target surface area.

It is worth mentioning that although our diagonal rectangle covers the hole target surface area, it’s not necessary. In fact, a line could be used or even we can introduce manually the coordinates of the point we want.

As an additional step, we might want to restrict the grid to only the target surface area, which does not need to be rectangular or quadrangular. For that, we use the “Cut” tool (Analysis tools – Extract) selecting the grid layer as “Input dataset or entity” and the target surface area layer as “Cutting entities”.

After running the tool, we will now have our cell grid confined to our target surface area.

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Crear una red regular de celdas en ArcGIS Pro.

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We will focus on this integrated QGIS functionality which can be of great interest in geological, engineering and evironmental studies.

When we try to obtain a topographic profile, we can start from two elements:

• On one side, the layer that contains the height or elevation information. This layer can come in vector (contour lines, lidar point clouds, etc.) or raster (DEM) data type.
• On the other side, the cutting limit line along which we obtain the profile. This cutting line can be virtual and be drawn on a canvas, or belong to an existing layer already loaded into our layer panel.

This time, we’ll describe how a topographic profile obtained from a DEM can be represented.

For that, we’ll first enable the tool on the “View” menu and “Elevation profile”. The “Elevation profile” panel will open.

Next step will be to configure the elevation properties of the layer to be represented (DEM). For that, we will double-click on the layer to display the layer properties window, where the “elevation” tab is located.

Activate the checkbox for the “Elevation surface representation” (1). This window allows to specify a scale factor (2) in case you want to exaggerate the elevation profile or need to apply a conversion factor of the elevation information and a displacement (3).

On the lower part of the window, we can define how the topographic profile will be represented, through either a line style or a polygon (4).In both cases we hace a complete control of the symbology.

Once the properties of the elevation layer have been configured, we can go back to the “Elevation profie” panel, where we’ll see our DEM layer loaded. We select the “Capture curve” tool (5) and draw a virtual cut line on the DEM. We rigth-click to finish the digitisation of the cut line.

Finally, we get the representation of the topographic profile. By moving the cursor along the profile (6) we can see the elevation and displacement information represented on cartesian axes, and in the canvas on the profile line it shows us the position of the cursor (7) in order to be able to identify its location on the map.

–

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Original post:

Generación de perfil topográfico integrado en QGIS.

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The autosave allows saving the changes over our project or layer being edited on a scheduled way every few minutes, as we work.

This way we can avoid the loss of information due to programme crashes, electrical flow failures, etc…. anything that could cause the loss of our work.

For that, we have available the “AutoSaver” plugin. We just need to access the add-ons section, search and install the plugin.

Once it is installed, open it and check the verification box to enable the autosave of the map project (1).

Once the autosave of the project is enabled, we will also have the possibility to extend the autosave to the layers in editing (3).

There is also the possibility of creating a backup file (.bak), to create a backup copy external to the map document (2).

In the lower part of the window we can define the time interval (expressed in minutes) on the basis of which the autosave is carried out (4).

After everything is configured, all that remains is to accept (5).

Hope you get the most out of this plugin, both simple and essential.

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El autoguardado en QGIS.

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In this case, we’ll work with roads, towns and municipalities data from the Chiapas state, Mexico.

First thing, we upload our data into QGIS.

In order to start working with the plugin, we must install it from the QGIS add-on section.

Once installed, we can start working with our data. We can access the add-on as follows:

Next step will be to broaden the attribute table with more information. For exmple, in the “Town” layer we will add a field with the municipality name. This will be done with the Join tool located in the layer properties. Let’s remember that this Join is made only virtually, so the layer must be exported if we want to keep this joined data permanently.

Following up, we will combine the Roads and Municipalities layer to define an administrative boundary. This is in order to obtain an estimate of the lengh of a road type within each municipality.

Once all fo this is done, we can continue with the “Group stats” plugin. Using this tool, we’ll calculate the sum of the lengh for each road type in each of the municipalities.

First, we must select the vector layer to calculate the statistics (1).After that, we drag the attribute fields to locate them in the columns (2) and rows (3) section. In the Value section, keep in mind to drag a numeric value that needs to be analysed and next to it the statistic we want to calculate with those values (4). It is important to mention that only one statistic can be chosen. Then, the calculation can be made and it can be displayed in a table which is usually a two-entry table.

Once the results have been obtained, they can be exported in CSV format so they can be consulted later if necessary.

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El plugin «Group Stats» de QGIS.

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Many times, when we need satellite images, we don’t need only one, or the one we want is of such a size that the servers do not offer us the possibility of accessing such large downloads. In this second case, we proceed to download a mosaic of several images of the same size.

SDI servers put at our disposal two Http request methods (HttpGet and HttpPost). For an easy automate download of the data, we’ll use the first one. HttpGet requets allows us to add parameters to the url, shaping what is known as uri. For that, after the url adress, the beginning of the request chain is marked with a question mark (“?”) and it shows pairs of data name and value separated by an “=”, separating each pair of data name and value from the next by an “&”. This allows, for example, a JavaScript script to incorporate as variables the data of the parameters added to the url. For exqmple, if we want to know which layers a WMS service has, we can perform a HttpGet request to the SDI server of the city of Munich, to tell us what services it offers through the GetCapabilities request.

URL:
https://geoportal.muenchen.de/geoserver/gsm/wms?

Request chain:
SERVICE=WMS&REQUEST=GetCapabilities

URI:
https://geoportal.muenchen.de/geoserver/gsm/wms?SERVICE=WMS&REQUEST=GetCapabilities

As a result, it will give us a XML document with information about the service and the data at our disposal. Among these data are the layers and their names, available coordinate systems, layer boundaries and resolutions, etc.

We can extract from this XML all the necessary data to perform a GetMap request to the server to give us back a raster with the desired image. We could get a square image of one thousand pixels on each side of the 200×200 metre squares shown in the previous image through the following HttpGet request:

https://geoportal.muenchen.de/geoserver/gsm/wms?SERVICE=WMS&REQUEST=GetMap&VERSION=1.0.0&srs=EPSG:32632&STYLES=&bbox=685483,5335385,685683,5335585&layers=luftbild&format=image/png&width=1000&height=1000

This request incorporates the following parameters: service type (SERVICE=WMS), request (REQUEST=GetMap), version (VERSION=1.0.0), reference system (srs=EPSG:32632), boundary coordinates of the image to be downloaded (bbox=685483,5335385,685683,5335585), name of the layer (layers=luftbild), format in which the image is downloaded (format=image/png) and number of pixels in width and height that the downloaded image will have (width=1000&height=1000). This 200×200 square over the terrain and of a thousand pixels on a side, will give us the same pixel size as the original orthophoto (20cm). If the request is well-formulated, the browser will open it for us directly, and if not, it will download a text file containing an error message.

We can see that performing a request to get a single image is relatively simple, but we can realise that if we want to do this by hand to obtain all the images corresponding to the 121 boxes shown before, it would be a titanic task. Let alone if we were to go into larger numbers.

To make this task accessible and that the number should not matter, we will prepare with Python a small script that will download for us all these images to the desired folder and that will distinguish each of them in its file name according to its position in the downloaded grid.

We will use the “request” and “time” libraries to perform the requests and to manage the time these are made, in order to avoid that, if we make too many requests in a very short space of time, our requests are detected as an attack to the server. We will also choose the destination folder for our orthophotos.

Then, we introduce the maximum and minimum coordinates of all the surface area from which we want to download and the size in metres of the side of the square of the “patches” we are going to download. With them, we would obtain the boundary coordinates of the first frame to download, adding the side to the minimum x and y coordinates, also starting the counter that will give us the position of each image within the grid, in this case, being the first one, it will have the position 1.1.

We will now create a loop that will first iterate through the frames along the X axis until the maximum X of the frames to be downloaded, passing at each X stop through another loop that will iterate through all the contained frames between the minimum and maximum values for the Y coordinates.

While the X loop is dedicated to update the counters and coordinates at the end of every loop over the Y axis, it is the last one that will stop individually on each square from which we will request a download. This is why this second loop will contain, in addition to the update of the coordinates and counters corresponding to each Y coordinate stop, all the code corresponding to complete the request to the server. Here is where the “magic” happens.

As you can see, this “magic” has little magic and it’s not even very complex. What it does is to locate for each request the maximum and minimum X and Y coordinates for each “patch” and create the name of the file using the repetition counter of each “countx” and “county” loop. Besides that, we included a “print” so it shows us the progress of the script execution, and after every download request we wait 5 seconds to request the next one, so we can avoid being banned over too many simultaneous requests. Without this 5-seconds wait, with much lesser number we would still be excluded.

Now we could, for example, integrate these images in a supervised training session creating masks on the same place and extracting the analogue coordinate patches. If we want to rebuild a bigger image joining all the downloaded images, we should follow the procedure “unpatchify” described in this post:

División de Imágenes en Python con Patchify.

Original post:

Descarga multiple de imagenes de satelite gratuitas con Python.

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Assume we have a large file, such as a world limits polygon type layer, that we want to label with the names of the countries.

We do not want the labels to look like that.

Instead we want this.

Lets see how we can achieve this.

• Go to the Layer Proprieties (right-click it in the Layers Panel > Proprieties) and navigate to the Labels TAB.
• Select the labeling method > Single Labels.
• Next, select the attribute table field to use for labeling. In this case, we’ll go with the one that saves the names of the world’s countries.
• Choose a Font and a Style.
• For the Size, go to the Data defined override function. This function overwrites the labeling settings by using attribute table entries or expressions based on them.

• By selecting the Edit option, the Expression String Builder opens. In this section, we will write an expression that will allow us to label the world counties while considering the area values stored in the Shape_Area field.
• The expression will be as follow:

scale_linear(“Shape_Area”, minimum(“Shape_Area”), maximum(“Shape_Area”), 1, 100)

• This function transforms a given value from an input domain to an output range using linear interpolation. More specifically, the function scales the names of world countries to a font size between 1 and 100 by taking into consideration the minimum and maximum area values (stored in the Shape Area field).

• Click Ok.
• The dialog closes and the Data defined override button change its appearance meaning that the rule is taken into consideration while labeling.

• Click OK.  The Layer Proprieties closes. The result should look like in the following image.

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For more information check: 

• QGIS Documentation – 15.1.4.4. Using data–defined override for labeling
• QGIS Documentation – 12.2.17.23. scale_linear

Check Marks Credits: https://www.freepik.com/free-vector/flat-check-mark-cross-stickers_18141294.htm

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One easy way to do that is to use the LASzip, a free open source product of rapidlasso GmbH. The LASzip library is built into the LAStools, and while the LAStools is commercial software, the LASzip is available as an LGPL-licensed stand-alone software library.

*Keep in mind that the LAStools need to be installed on your computer. An easy way to do that is to go and download it by using the Direct link to LAStools.zip. Unzip the file to find the LASzip application in the bin folder.

Launch LASzip and follow the steps described in the image below.

We have 24 LAZ (.laz) files depicting the area around Toledo city (south of Madrid). The files are LIDAR-derived 3D point clouds colored in true colors (RGB).

A new window will appear after you click the DECOMPRESS button.

Click Start.

Once the decompression process is complete, navigate to the location of the LAZ (.laz) files in your Windows environment. You’ll notice that a new LAS (.las) file was created for each LAZ (.laz) file (with the same name).

Now we can work (open or add the LAS (.las) files) directly in ArcGIS Pro.

Open a new ArcGIS Pro project.

To work with all the LAS (.las) files at once we need to create a LAS dataset using the Create Las Dataset tool.

The LAS Dataset is created. A LAS dataset keeps track of one or more LAS files located on disk.

Next, select the option to filter the 3D cloud points and display only the ground points. Follow the instructions in the image below.

Now, with only the ground points displayed, we can proceed and create a DEM for the area. Access the tool LAS Dataset To Raster.

The DEM is created.

Specify a proper symbology.

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In the next steps, we will exemplify how to add two maps from this valuable site to ArcGIS Pro and ArcMap.

Scroll down and from all the available options, we will choose a map from the Europe category.

It is important to note that signing up for an account and logging in allows you to access all of the collection’s features. The registration process is simple and can be done by hitting the Register button.

Considering all the available maps, we chose Europe’s overland and sea communications from 1922. If you are not able to find it, filter the results by the year (1922).

By clicking on the map, a new web page will open. Once opened, on the left, you will be able to see all the map media information (collection name, author, date, title, scale, etc.).

Next, Click on VIEW IN GEOREFERENCER button. This will “georeference” the map and place it over real world locations. Now, you can navigate and explore the map.

Several options are available:

1. Find a place option,
2. Changing the basemap,
3. Beyond the adjustable transparency, you can analyse map points and their coordinates or adjust map size,
4. In the Overlay & Compare box there are several options to visualize and compare the map with other added maps.

1.

2.

3.

4.

After we’ve explored all of these features, we can click on What next? button and start export, embed, draw and more!

By going to Use in GIS apps section, we can proceed and visualize the historical map in ArcGIS (ArcGIS Pro or other GIS software) as a WMTS service. Click on Get Links button.

Copy the Web Map Tile Service (WMTS) link.

In ArcGIS Pro we will navigate to the Insert menu Connections > Server > New WTMS Server. We will use the link copied to create a new WMTS Server connection.

Once created, the new connection will appear in the Catalog pane in both Servers and Folders section.

Next, just add it as a new layer to your map view. The historical map can now be explored in ArcGIS Pro as a WMTS.

Source: The David Rumsey Map Collection – https://www.davidrumsey.com/home/the-collection

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This plugin provided by OpenPlanetary allow us to access various basemaps of Mars and The Moon. To explore its functionalities install the plugin in QGIS via the Plugins Manager.

Once installed, the OpenPlanetary Tile Loader icon is displayed in the Plugins Toolbar, as illustrated in the picture below:

Once opened, you can add Mars and Moon basemaps into your QGIS Project.

Let’s add the Mars Colour MOLA elevation basemap into our project.

Mars Shaded Grayscale MOLA Elevation

OPM Mars Basemap v0.2

In this way, we can get much closer to the surface of these celestial bodies and learn a lot more about them.

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Sources: 

Original post: Nuevo “plugin” de QGIS: “OpenPlanetary Tile Loader” – https://www.cursosgis.com/nuevo-plugin-de-qgis-openplanetary-tile-loader/

OpenPlanetary Tile Loader plugin – https://plugins.qgis.org/plugins/optileloader/

The post “OpenPlanetary Tile Loader” QGIS plugin first appeared on GIS Course | TYC GIS Training.

First of all, open the QGIS software and install the QuickMapServices plugin by going to the Plugins menu toolbar > Manage and Install plugins. The QGIS Plugins manager opens.

Type “QuickMapServices” in the search box and proceed by installing the plugin.

Once installed, an icon should appear in the Web Toolbar, as demonstrated in the image below:

When we click on this icon, we will see that we are limited to only NASA and OpenStreetMap basemaps. However, we can work this out by selecting the Settings option from this menu.

In the new window that will open, go to the “More services” tab and click on the Get contributed pack. This will add new services/basemaps to your QuickMapServices Plugin.

As a result, we have a list of 14 providers who offer a variety of services/basemaps.

Let’s explore the main basemaps available here.

• The ESRI package is one of the most comprehensive, with 15 available basemaps to add.

• The Google package includes some well-known options.

• In addition, the OSM package includes some interesting options.

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Sources: 

Original post: Quick Map Services: un plugin de uso sencillo para utilizar mapas base en QGIS
– https://www.cursosgis.com/quick-map-services-un-plugin-de-uso-sencillo-para-utilizar-mapas-base-en-qgis/

QuickMapServices plugin – https://plugins.qgis.org/plugins/quick_map_services/ 

The post QuickMapServices Plugin – an easy way to add basemaps in QGIS first appeared on GIS Course | TYC GIS Training.