There is so much raster data hosted on the web. We have access to nearly endless imagery and elevation datasets. While these data are exceptionally rich and useful, they can produce a problem of their own. The downsides of raster data Data requires storage. And storage gets messy. It’s inevitable that we’re going to download…
leafmap_split Visualizing raster data is important for assessing the results of analysis and models. One of the best ways to visualize raster data is to plot it on a map where you can interact with the raster data an other relevant layers. When performing analysis in Python, it can be difficult to create an interactive…
GDAL, the Geospatial Data Abstraction Library, was designed to interact with raster and vector (geospatial) data. Over time it has evolved to include sophisticated data manipulation and analysis tools. The GDAL source code is C++, which makes it fast to run but difficult to install. Many software and applications use GDAL for handling and analyzing…
A shapefile is a file format that represents geographic data in vector format. Shapefiles define points, lines, and polygons. The shapefile format was developed by ESRI and is frequently referred to as an ESRI shapefile. Shapefiles are not the only way to represent vector data. Geopackages, GeoJSON, KML, CSV, and database structures also represent vector…
Relative elevation models (REMs) adjust a DEM so that the elevation is relative to another feature instead of sea level. One common way to create a REM is to make elevation relative to a river bed. Then, the resulting REM displays the height of each pixel above the nearest river location. River REMs produce some…
Have you ever seen those really awesome visualizations of rivers that are made with elevation data? You know, the ones where you can see the channels meander through the floodplain? Those visualizations are made using relative elevation models (REMS), which are also known as detrended DEMs. Visualizing data for rivers in GIS and map visualizations…
Lidar data is awesome! It has so many great uses and produces some amazing visualizations. But it’s not without its problems. Mainly, lidar data can be difficult to work with because file sizes are so large. This means that point clouds are usually tiled and if the area you’re interested in overlaps multiple tiles then…
Geocoding (i.e. geolocating) is the process of converting street addresses to coordinate pairs that can be easily mapped. Addresses are very useful but are difficult to map without converting them to coordinates first. There are many softwares that will geocode addresses to coordinates for you. Some are free and some are paid. Each software has…
With QGIS reprojections can be calculated with the export data tool, the reproject layer tool, and GDAL Warp. Reprojecting layers (i.e., converting them to a different coordinate reference system, or CRS) is often an important step of geospatial analysis to ensure layers are properly registered and perform distance and area calculations. I’ll demonstrate how to use the export, reproject, and warp methods to reproject raster and vector layers.
In cartography and GIS, it is to display two different products side by side to make comparisons. This is a powerful and often necessary feature of any serious GIS software. QGIS makes it possible to display multiple map views side by side. However, there are a few steps that are not well documented that you’ll…
Clipping, extracting to masks, and subsetting rasters are common GIS operations. Using the gdal package for Python it is very easy to automate these processes. This tutorial will demonstrate how to use the Warp() function from the gdal Python API to clip rasters to a specified extent and to clip rasters to a polygon layer. These operations are also often referred to as mask extraction or extract to mask.
GeoPandas is a high-level Python package for reading, writing, analyzing, and manipulating vector (points, lines, and polygons) geographic data. As the name ‘GeoPandas’ implies, it brings pandas data frame functionality to geographic data so that you interact with the data much as you would an attribute table in ArcMap or QGIS or a spreadsheet in…
In geographic information science (GIS), remote sensing is the act of acquiring information about the features of the Earth’s surface from a distance. Remotely sensed data are acquired from many different platforms including, satellites, aircraft, and fixed instruments with many different sensors including, spectral imagery (cameras), lidar, and radar. The most common form of remotely…
Image classification techniques are at the heart of modern remote sensing. Image classification is a method of extracting usable information from images. Without classification, images are just a collection of numbers that represent colors and it’s very difficult to obtain any quantifiable information about a landscape from data like that. With remote sensing techniques, we…
If you’re looking for a way to automate your GIS workflows and products, the QGIS Python API (PyQGIS) may be able to save you a lot of time. With PyQGIS you can write Python code that automates QGIS tasks that range from loading data to creating and saving map layouts. I’ve used PyQGIS to write…
Web maps are a great way to publish and share geospatial data. However, if you’re new to programming and web development it can be difficult to know how to manage, render, and serve your data on the web while also trying to partition and manage the web services you need. Fortunately, Mapbox provides a solution…
GIS software makes it possible to create maps that contain a lot of powerful and important information. However, sharing those maps and information with non-GIS users can be difficult, especially when maps contain multiple layers. Others may not have GIS software or know how to use it, and an image of a map may not…
Image classification is the process of using numerical methods to automate the identification of objects in images and is a common method to interpret satellite imagery. With vast amounts of satellite imagery being collected every day it is next to impossible to manually classify satellite images for their many purposes. Instead we rely on numerical…
Satellite images are important data sources for many remote sensing and GIS analyses. Today many different satellites circle the globe and collect various image types. Images from many of these satellite platforms are available free of charge. This tutorial will demonstrate how to download images from the Landsat (Landsat 9) and Sentinel (Sentinel 2) satellites…
The Geospatial Data Abstraction Library (GDAL) is the standard for managing spatial data formats. GDAL has been incorporated into many different enterprise and open source GIS projects. Because GDAL is open source, it can be used by all. Learning to use GDAL with Python can help you automate workflows and implement custom raster processing solutions….
When working with spatial data it is common to have a file that has columns containing coordinates and attributes. Often points are represented in Excel or CSV (comma-separated values) files. To work with these data with geospatial software (like QGIS or ArcGIS) they need to be converted to a geospatial file format, like a geopackage…
Contour lines are most often used to represent topography on two-dimensional maps and figures. However, they can represent spatially changing values for any variable. Creating, styling, and labeling contour lines can be a little tedious, and it takes a little bit of time to learn exactly which tools to use to do this in QGIS….
QGIS only has one basemap that is included by default, Open Street Map. However, QGIS does have the functionality to support many different types of basemap sources and services. Basemaps can be added to QGIS by copying the URL to a web map service and pasting the URL to the proper data source in QGIS….
Recently, I’ve started using rioxarray to read NetCDF data into xarray format. If you’re not familiar with the xarray python package it’s basically a wrapper (for lack of a better term) around numpy arrays that allows metadata to be included with the arrays (more on this later with an example).
Lidar data are 3D point clouds that can describe Earth’s surface in great detail. However, lidar point clouds present some challenges because of how much data they contain. One fundamental challenge with lidar data is how to effectively extract the points that represent the Earth’s surface (not vegetation, building, or other objects) and create a…