Most scientific data are geographically located and thus have a spatial component. GIS skills are thus important for working with scientific data. R and Python are free and open scientific programming languages that you can use to work with GIS data and do tasks that you may already do with tools like ArcGIS or QGIS.
In the lessons below, learn how to open, manipulate and plot spatial data in the R programming language. Also learn to use tools like Leaflet and ggplot to create custom and interactive maps. Finally learn how to use remote sensing data like Landsat, NAIP and MODIS in R. Come back later this spring for lessons in Python!
Learn how to calculate seasonal summary values for MACA 2 climate data using xarray and region mask in open source Python.
last updated: 19 Jan 2022
Climate datasets stored in netcdf 4 format often cover the entire globe or an entire country. Learn how to subset climate data spatially and by time slices using xarray and regionmask in open source python.
last updated: 19 Nov 2021
Historic and projected climate data are most often stored in netcdf 4 format. Learn how to open and process MACA version 2 climate data for the Continental United States using the open source python package, xarray.
last updated: 19 Nov 2021
MACA V2 climate data provides but historica and future predictions of climate variables using different models. Learn how to download netcdf 4 format programatically using open source Python and open the data with xarray.
last updated: 19 Nov 2021
In this lesson you will learn the basics of what CMIP5 and MACA v 2 data are and how global climate data are downscaled to higher resolutions to support regional analysis.
last updated: 19 Nov 2021
In this lesson you will learn about that netcdf 4 data format which is a format, commonly used to store climate data. In later lessons you will learn how to open climate data using open source Python tools.
last updated: 12 Nov 2020
Practice your skills creating maps of raster and vector data using open source Python.
last updated: 21 Jan 2022
Practice your skills plotting time series data stored in Pandas Data Frames in Python.
last updated: 15 Sep 2020
Complete these exercises to practice the skills you learned in the file formats chapters.
last updated: 11 Sep 2020
Vector data is one of the two most common spatial data types. Learn to work with vector data for earth data science.
last updated: 23 Sep 2020
Raster data is one of the two most common spatial data types. Learn to work with raster data for earth data science.
last updated: 16 Sep 2021
Two of the major spatial data formats used in earth data science are vector and raster data. Learn about these two common spatial data formats for earth data science workflows in this chapter.
last updated: 23 Sep 2020
MODIS is remote sensing data that is stored in the HDF4 file format. Learn how to open and use MODIS data in HDF4 form in Open Source Python.
last updated: 27 Jan 2022
MODIS is remote sensing data that is stored in the HDF4 file format. Learn how to view and explore HDF4 files (and their metadata) using the free HDF viewer provided by the HDF group.
last updated: 19 Jan 2022
Learn how to find and download MODIS data from the USGS Earth Explorer website.
last updated: 19 Nov 2021
MODIS is a satellite remote sensing instrument that collects data daily across the globe at 250-500 m resolution. Learn how to import, clean up and plot MODIS data in Python.
last updated: 19 Nov 2021
A set of activities for you to practice your skills using Landsat Data in Open Source Python.
last updated: 27 Jan 2022
Learn how to find and download Landsat 8 remote sensing data from the USGS Earth Explorer website.
last updated: 19 Jan 2022
Most remote sensing data sets contain no data values that represent pixels that contain invalid data. Learn how to handle no data values in Python for better raster processing.
last updated: 27 Jan 2022
Landsat remote sensing data often has pixels that are covered by clouds and cloud shadows. Learn how to remove cloud covered landsat pixels using open source Python.
last updated: 27 Jan 2022
Learn how to open up and create a stack of Landsat images and crop them to a certain extent using open source Python.
last updated: 27 Jan 2022
Landsat 8 data are downloaded in tif file format. Learn how to open and manipulate Landsat 8 data in Python. Also learn how to create RGB and color infrared Landsat image composites.
last updated: 11 Jun 2021
The Normalized Burn Index is used to quantify the amount of area that was impacted by a fire. Learn how to calculate the normalized burn index and classify your data using Landsat 8 data in Python.
last updated: 14 Oct 2021
A vegetation index is a single value that quantifies vegetation health or structure. Learn how to calculate the NDVI vegetation index using NAIP data in Python.
last updated: 28 Jan 2021
A vegetation index is a value that quantifies vegetation health or structure. Learn how to calculate the NDVI and NBR vegetation indices to study vegetation health and wildfire impacts in Python.
last updated: 28 Jan 2021
When making maps, you often want to create legends, customize colors, adjust zoom levels, or even make interactive maps. Learn how to customize maps created using vector data in Python with matplotlib, geopandas, and folium.
last updated: 21 Jul 2020
Sometimes vector data are missing attribute data, and it can be helpful to clean up your data. Learn how to handle missing attribute data in Python using GeoPandas.
last updated: 11 Sep 2020
A spatial join is when you assign attributes from one shapefile to another based upon its spatial location. Learn how to perform spatial joins in Python.
last updated: 11 Sep 2020
When you dissolve polygons, you remove the interior boundaries of a set of polygons with the same attribute value and create one new merged or combined polygon for each attribute value. Learn how to dissolve polygons in Python using GeoPandas.
last updated: 28 Jan 2021
Sometimes you may want to spatially clip a vector data layer to a specified boundary for easier plotting and analysis of smaller spatial areas. Learn how to clip a vector data layer in Python using GeoPandas and Shapely.
last updated: 28 Jan 2021
Often when spatial data do not line up properly on a plot, it is because they are in different coordinate reference systems (CRS). Learn how to reproject a vector dataset to a different CRS in Python using the to_crs() function from GeoPandas.
last updated: 11 Sep 2020
Often when spatial data do not line up properly on a plot, it is because they are in different coordinate reference systems (CRS). Learn how to reproject a vector dataset to a different CRS in Python using the to_crs() function from GeoPandas.
last updated: 11 Sep 2020
This lesson covers how to crop a raster dataset and export it as a new raster in Python
last updated: 04 Sep 2019
Sometimes you need to manipulate multiple rasters to create a new raster output data set in Python. Learn how to create a CHM by subtracting an elevation raster dataset from a surface model dataset in Python.
last updated: 04 Sep 2019
This lesson introduces the raster geotiff file format - which is often used to store lidar raster data. You will learn the 3 key spatial attributes of a raster dataset including Coordinate reference system, spatial extent and resolution.
last updated: 04 Sep 2019
In this lesson you will review how to find and download USDS NAIP imagery from the USGS Earth Explorere website.
last updated: 28 Jan 2021
Learn how to open up a multi-band raster layer or image stored in .tiff format in Python using Rasterio. Learn how to plot histograms of raster values and how to plot 3 band RGB and color infrared or false color images.
last updated: 28 Jan 2021
Learn the basics of how addidative colors models are used to render RGB images in Python.
last updated: 28 Jan 2021
Interactive maps allow you to easily explore data. Learn how to create interative leaflet maps embedded in a Jupyter Notebook using Python and folium.
last updated: 21 Jan 2022
When making maps, sometimes you want to zoom in to a specific area in your map. Learn how to adjust the x and y limits of your matplotlib and geopandas map to change the spatial extent that is displayed.
last updated: 21 Jan 2022
When making maps, you often want to add legends and customize the map colors. Learn how to customize legends and colors in matplotlib maps created using vector data in Python.
last updated: 21 Jul 2020
When plotting raster and vector data together, the extent of the plot needs to be defined for the data to overlay with each other correctly. Learn how to define plotting extents for Python Matplotlib Plots.
last updated: 21 Jan 2022
Sometimes you want to customize the colorbar and range of values plotted in a raster map. Learn how to create breaks to plot rasters in Python.
last updated: 21 Jan 2022
Folium is a Python package that can be used to create interactive maps in Jupyter Notebook. Learn how to create interactive maps with raster overlays in Python using Folium.
last updated: 21 Jan 2022
A hillshade is a representation of the earth's surface as it would look with shade and shadows from the sun. Learn how to overlay raster data on top of a hillshade in Python.
last updated: 21 Jan 2022
When plotting rasters, you often want to overlay two rasters, add a legend, or make the raster interactive. Learn how to make a map of raster data that has these attributes using Python.
last updated: 21 Jul 2020
This lesson defines 3 lidar data products: the digital elevation model (DEM), the digital surface model (DSM) and the canopy height model (CHM).
last updated: 11 Sep 2020
Rasters are gridded data composed of pixels that store values, such as an image or elevation data file. Learn how a lidar data point cloud is converted to a raster format such as a GeoTIFF.
last updated: 11 Sep 2020
This lesson covers what a lidar point cloud is. You will use the free plas.io point cloud viewer to explore a point cloud.
last updated: 11 Sep 2020
This lesson reviews what Lidar remote sensing is, what the lidar instrument measures and discusses the core components of a lidar remote sensing system.
last updated: 11 Sep 2020
Sometimes you will work with multiple rasters that are not in the same projections, and thus, need to reproject the rasters, so they are in the same coordinate reference system. Learn how to reproject raster data in Python using Rasterio.
last updated: 09 Nov 2020
Sometimes a raster dataset covers a larger spatial extent than is needed for a particular purpose. In these cases, you can crop a raster file to a smaller extent. Learn how to crop raster data using a shapefile and export it as a new raster in open source Python
last updated: 09 Nov 2020
Reclassifying raster data allows you to use a set of defined values to organize pixel values into new bins or categories. Learn how to classify a raster dataset and export it as a new raster in Python.
last updated: 09 Nov 2020
Often you need to process two raster datasets together to create a new raster output and then save that output as a new file. Learn how to subtract rasters and create a new GeoTIFF file using open source Python.
last updated: 09 Nov 2020
You can perform the same raster processing steps in Python that you would in a tool like ArcGIS. Learn how to process spatial raster data using Open Source Python.
last updated: 05 Nov 2020
Raster data are gridded data composed of pixels that store values, such as an image or elevation data file. Learn how to open, plot, and explore raster files in Python using Rasterio.
last updated: 05 Nov 2020
Challenge your skills. Practice opening, cleaning and plotting raster data in Python
last updated: 19 Aug 2021
Metadata describe the key characteristics of a dataset such as a raster. For spatial data, these characteristics including the coordinate reference system (CRS), resolution and spatial extent. Learn about the use of TIF tags or metadata embedded within a GeoTIFF file to explore the metadata programatically.
last updated: 19 Aug 2021
Raster metadata includes the coordinate reference system (CRS), resolution, and spatial extent. Learn about these metadata and how to access them in Python
last updated: 06 Nov 2020
Histograms of raster data provide the distribution of pixel values in the dataset. Learn how to explore and plot the distribution of values within a raster using histograms.
last updated: 06 Nov 2020
Raster data are gridded data composed of pixels that store values, such as an image or elevation data file. Learn how to open, plot, and explore raster files in Python.
last updated: 09 Nov 2020
Rasters are gridded data composed of pixels that store values. Learn more about the structure of raster data and how to use them to store data, such as imagery or elevation values.
last updated: 05 Nov 2020
Coordinate Reference System (CRS) information is often stored in three key formats, including proj.4, EPSG and WKT. Learn more about the ways that coordinate reference system data are stored including proj4, well known text (wkt) and EPSG codes.
last updated: 11 Sep 2020
Geographic coordinate systems span the entire globe (e.g. latitude / longitude), while projected coordinate systems are localized to minimize visual distortion in a particular region (e.g. Robinson, UTM, State Plane). Learn more about key differences between projected vs. geographic coordinate reference systems.
last updated: 11 Sep 2020
A coordinate reference system (CRS) defines the translation between a location on the round earth and that same location, on a flattened, 2 dimensional coordinate system. Learn how to explore and reproject data into geographic and projected CRS in Python.
last updated: 11 Sep 2020
Vector data are composed of discrete geometric locations (x, y values) known as vertices that define the shape of the spatial object. Learn more about the structure of vector data and how to open vector data stored in shapefile format in Python.
last updated: 11 Sep 2020
In this lesson you learn the steps to create a map in R using ggplot.
last updated: 03 Sep 2019
You can use R as a GIS. Learn how to create a map in R using ggplot in this R maps tutorial.
last updated: 03 Sep 2019
In this lesson you will explore how to import and work with MODIS remote sensing data in raster geotiff format in R. You will cover importing many files using regular expressions and cleaning raster stack layer names for nice plotting.
last updated: 03 Sep 2019
In this lesson you review how to calculate difference normalized burn ratio using pre and post fire NBR rasters in R. You finally will classify the dNBR raster.
last updated: 13 Mar 2020
In this lesson you review the normalized burn ratio (NBR) index which can be used to identify the area and severity of a fire. Specifically you will calculate NBR using Landsat 8 spectral remote sensing data in raster, .tif format.
last updated: 13 Mar 2020
Often data have missing or bad data values that you need to replace. Learn how to replace missing or bad data values in a raster, with values from another raster in the same pixel location using the cover function in R.
last updated: 03 Sep 2019
In this lesson you will review how to find and download Landsat imagery from the USGS Earth Explorere website.
last updated: 03 Sep 2019
In this lesson, you will learn how to deal with clouds when working with spectral remote sensing data. You will learn how to mask clouds from landsat and MODIS remote sensing data in R using the mask() function. You will also discuss issues associated with cloud cover - particular as they relate to a research topic.
last updated: 30 Mar 2020
In this lesson you will review how to adjust the extent of a spatial plot in R using the ext() or extent argument and the extent of another layer.
last updated: 03 Sep 2019
In this lesson you learn to use the par() or parameter settings in R to plot several raster RGB plots in R in a grid.
last updated: 03 Sep 2019
In this lesson you review how to remove those pesky borders from a raster plot using base plot in R. We also cover adding legends to your plot outside of the plot extent.
last updated: 03 Sep 2019
In this lesson you will cover the basics of using Landsat 7 and 8 in R. You will learn how to import Landsat data stored in .tif format - where each .tif file represents a single band rather than a stack of bands. Finally you will plot the data using various 3 band combinations including RGB and color-infrared.
last updated: 08 Jan 2020
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last updated: 03 Sep 2019
NDVI is calculated using near infrared and red wavelengths or types of light and is used to measure vegetation greenness or health. Learn how to calculate remote sensing NDVI using multispectral imagery in R.
last updated: 03 Sep 2019
In this lesson you will learn the basics of using Landsat 7 and 8 in R. You will learn how to import Landsat data stored in .tif format - where each .tif file represents a single band rather than a stack of bands. Finally you will plot the data using various 3 band combinations including RGB and color-infrared.
last updated: 03 Sep 2019
In this lesson you learn how to open up a multi-band raster layer or image stored in .tiff format in R. You are introduced to the stack() function in R which can be used to import more than one band into a stack object in R. You also review using plotRGB to plot a multi-band image using RGB, color-infrared to other band combinations.
last updated: 03 Sep 2019
It is important to compare differences between tree height measurements made by humans on the ground to those estimated using lidar remote sensing data. Learn how to perform this analysis and calculate error or uncertainty in R.
last updated: 03 Sep 2019
This lesson reviews how to extract pixels from a raster dataset using a vector boundary. You can use the extracted pixels to calculate mean and max tree height for a study area (in this case a field site where tree heights were measured on the ground. Finally you will compare tree heights derived from lidar data compared to tree height measured by humans on the ground.
last updated: 03 Sep 2019
In this lesson you break down the steps required to create a custom legend for spatial data in R. You learn about creating unique symbols per category, customizing colors and placing your legend outside of the plot using the xpd argument combined with x,y placement and margin settings.
last updated: 30 Mar 2020
In this lesson you learn how to reproject a vector dataset using the spTransform() function in R.
last updated: 03 Sep 2019
This lesson discusses ways that coordinate reference system data are stored including proj4, well known text (wkt) and EPSG codes.
last updated: 13 Mar 2020
Geographic coordinate reference systems are often used to make maps of the world. Projected coordinate reference systems are use to optimize spatial analysis for a region. Learn about WGS84 and UTM Coordinate Reference Systems as used in R.
last updated: 13 Mar 2020
Coordinate reference systems are used to convert locations on the earth which is round, to a two dimensional (flat) map. Learn about the differences between coordinate reference systems.
last updated: 03 Sep 2019
This lesson reviews the core files that are required to use a shapefile including: shp, shx and dbf. It also covers the .prj format which is used to define the coordinate reference system (CRS) of the data.
last updated: 03 Sep 2019
This lesson introduces what vector data are and how to open vector data stored in shapefile format in R.
last updated: 30 Mar 2020
You can clip a raster to a polygon extent to save processing time and make image sizes smaller. Learn how to crop a raster dataset in R.
last updated: 13 Mar 2020
This lesson presents how to classify a raster dataset and export it as a new raster in R.
last updated: 13 Mar 2020
A canopy height model contains height values trees and can be used to understand landscape change over time. Learn how to use LIDAR elevation data to calculate canopy height and change in terrain over time.
last updated: 03 Sep 2019
A GeoTIFF is a standard file format with spatial metadata embedded as tags. Use the raster package in R to open geotiff files and spatial metadata programmatically.
last updated: 03 Sep 2019
This lesson introduces the raster geotiff file format - which is often used to store lidar raster data. You learn the 3 key spatial attributes of a raster dataset including Coordinate reference system, spatial extent and resolution.
last updated: 03 Sep 2019
This lesson introduces the raster geotiff file format - which is often used to store lidar raster data. You learn the 3 key spatial attributes of a raster dataset including Coordinate reference system, spatial extent and resolution.
last updated: 13 Mar 2020
This lesson reviews how a lidar data point cloud is converted to a raster format such as a geotiff.
last updated: 03 Sep 2019
This lesson covers what a lidar point cloud is. We will use the free plas.io point cloud viewer to explore a point cloud.
last updated: 08 Jan 2020
This lesson reviews what lidar remote sensing is, what the lidar instrument measures and discusses the core components of a lidar remote sensing system.
last updated: 30 Mar 2020
This lesson covers how to overlay raster data on a hillshade in R using baseplot and layer opacity arguments.
last updated: 03 Sep 2019
This lesson covers creating a basemap with the ggmap package in R. Given some ongoing bugs with ggmap it also covers the map package as a backup!
last updated: 30 Mar 2020
You often want to understand the relationships between two different types of data. Learn how to use regression to determine whether there is a relationship between two variables.
last updated: 12 Feb 2021
To explore uncertainty in remote sensing data, it is helpful to compare ground-based measurements and data that are collected via airborne instruments or satellites. Learn how to create scatter plots that compare values across two datasets.
last updated: 12 Feb 2021
For many scientific analyses, it is helpful to be able to select raster pixels based on their relationship to a vector dataset (e.g. locations, boundaries). Learn how to extract data from a raster dataset using a vector dataset.
last updated: 02 Feb 2021
Uncertainty quantifies a range of values within which a measurement value could be within, considering a specified level of confidence. Learn about the types of uncertainty that you can expect when working with tree height data both derived from lidar remote sensing and human measurements and learn about sources of error including systematic vs. random error.
last updated: 02 Feb 2021