Chris Holdgraf

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.

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.

When you plot time series data using the matplotlib package in Python, you often want to customize the date format that is presented on the plot. Learn how to customize the date format on time series plots created using matplotlib.

Sometimes you need to take time series data collected at a higher resolution (for instance many times a day) and summarize it to a daily, weekly or even monthly value. This process is called resampling in Python and can be done using pandas dataframes. Learn how to resample time series data in Python with Pandas.

Sometimes you have data over a longer time span than you need for your analysis or plot. Learn how to subset your data using a begin and end date in Python.

Python provides a datetime object for storing and working with dates. Learn how you can convert columns in a pandas dataframe containing dates and times as strings into datetime objects for more efficient analysis and plotting.

Python provides a datetime object for storing and working with dates. Learn how you can convert columns in a pandas dataframe containing dates and times as strings into datetime objects for more efficient analysis and plotting.

In this lesson you will review how to customize matplotlib maps created using vector data in Python. You will review how to add legends, titles and how to customize map colors.

When you plot time series data in matplotlib, you often want to customize the date format that is presented on the plot. Learn how to customize the date format in a Python matplotlib plot.

In this lesson you will learn how to adjust the x and y limits of your matplotlib and geopandas map to change the spatial extent..

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.

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.

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.

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.

This lesson covers how to crop a raster dataset and export it as a new raster in Python

Sometimes two shapefiles do not line up properly even if they cover the same area because they are in different coordinate reference systems. Learn how to reproject vector data in Python using geopandas to ensure your data line up.

This lesson introduces what vector data are and how to open vector data stored in shapefile format in Python.

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.

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.

There are a suite of powerful open source python libraries that can be used to work with spatial data. Learn how to use geopandas, rasterio and matplotlib to plot and manipulate spatial data in Python.

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.

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.

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.

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.

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.

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.

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.

Raster metadata includes the coordinate reference system (CRS), resolution, and spatial extent. Learn about these metadata and how to access them in Python

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.

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.

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.

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.

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.

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.

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.

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.