This tutorial covers the data and set up the data directory you will need for the 2017 Institute on Remote Sensing.
Below you will find a complete list of data tutorials, workshop and science videos hosted on this website. The tutorials use R and other open source tools.
This tutorial covers the data and set up the data directory you will need for the 2017 Institute on Remote Sensing.
Learn the basics of how to use the plotly package to create interactive plots and use the Plotly API in R to share these plots.
This page reviews how to check that github is installed on your computer. It also provides a quick overview of Bash shell. Finally we will setup a working GitHub directory.
This page provides an overview of NEON and the data provided by NEON.
This tutorial introduces the importance of version control in scientific workflows.
This page outlines the tools and resources that you will need to complete the Data Institute activities.
This tutorial teaches you how to fork, or copy, an existing GitHub repository.
This tutorial covers the data and set up the data directory you will need for the Institute.
This tutorial teaches you how to clone or copy a GitHub repository to your local computer.
This tutorial covers the basics of writing a document using the markdown language.
This tutorial covers how to edit a local version of a Git repository and then commit changes to it to be tracked in the Git version control system.
This tutorial covers how to submit a pull request to a repository that you don't have direct push access to in order to suggest changes to content.
This tutorial walks through how to download and visualize Palmer Drought Severity Index data in R. The data specifically downloaded for this activity allows one to to better understand a driver of the 2013 Colorado floods.
This lesson walks through the steps need to download and visualize precipitation data in R to better understand the drivers and impacts of the 2013 Colorado floods.
This lesson walks through the steps need to download and visualize USGS Stream Discharge data in R to better understand the drivers and impacts of the 2013 Colorado floods.
This lesson teaches how to use Digital Terrain Models derived from LiDAR data to create Digital Elevation Models of Differences that allow us to measure the change in elevation of an area after a disturbance event.
This tutorial explains how to set a working directory in R. The working directory points to a directory or folder on the computer where data that you wish to work with in R is stored.
This tutorial reviews the fundamental principles, packages and metadata/raster attributes that are needed to work with raster data in R. It covers the three core metadata elements that we need to understand to work with rasters in R: CRS, Extent and Resolution. It also explores missing and bad data values as stored in a raster and how R handles these elements. Finally, it introduces the GeoTiff file format.
This tutorial explains how to plot a raster in R using R's base plot function. It also covers how to layer a raster on top of a hillshade to produce an eloquent map.
This spatial data tutorial explains the how to open and plot shapefiles containing point, line and polygon vector data in R.
This tutorial explores issues associated with working with rasters in different Coordinate Reference Systems (CRS) / projections. When two rasters are in different CRS, they will not plot nicely together on a map. We will learn how to reproject a raster in R using the projectRaster function in the raster package.
This tutorial provides an overview of how to locate and query shapefile attributes as well as subset shapefiles by specific attribute values in R. It also covers plotting multiple shapefiles by attribute and building a custom plot legend.
This tutorial covers how to subtract one raster from another using efficient methods - the overlay function compared to basic subtraction. We also cover how to extract pixel values from a set of locations - for example a buffer region around plot locations at a field site. Finally, it explains the basic principles of writing functions in R.
This tutorial will demonstrate how to import a time series data set stored in .csv format into R. It will explore data classes and will walk through how to convert date data, stored as a character string, into a date class that R can recognize and plot efficiently.
This tutorial provides an overview of how to create a a plot of multiple shapefiles using base R plot. It also explores adding a legend with custom symbols that match your plot colors and symbols.
This tutorial explores how to import and plot a multi-band raster in R. It also covers how to plot a three-band color image using the plotRGB function in R.
This tutorial covers what metadata are, and why we need to work with metadata. It covers the 3 most common metadata formats: text file format, web page format and Ecological Metadata Language (EML).
This tutorial will cover how to identify the CRS of a spatial vector object in R. It will also explore differences in units associated with different projections and how to reproject data using spTransform in R. Spatial data need to be in the same projection in order to successfully map and process them in non-gui tools such as R.
This tutorial covers how to work with and plot a raster time series, using an R RasterStack object. It also covers the basics of practical data quality assessment of remote sensing imagery.
This tutorial explores working with date and time classes in R. We will overview the differences between As.Date, POSIXct and POSIXlt as used to convert a date/time field in character (string) format to a date-time format that is recognized by R. This conversion supports efficient plotting, subsetting and analysis of time series data.
This tutorial covers how to convert a .csv file that contains spatial coordinate information into a spatial object in R. We will then export the spatial object as a Shapefile for efficient import into R and other GUI GIS applications including QGIS and ArcGIS
This tutorial covers how to efficiently and effectively plot a stack of rasters using rasterVis package in R. Specifically it covers using levelplot and adding meaningful, custom names to band labels in a RasterStack.
This tutorial explores how to deal with NoData values encountered in a time series dataset, in R. It also covers how to subset large data files by date and export the results to a csv (text format) file.
This tutorial covers how to modify (crop) a raster extent using the extent of a vector shapefile. It also covers extracting pixel values from defined locations stored in a spatial object.
This tutorial covers how to extract and plot NDVI pixel values from a raster time series stack in R. We will use ggplot2 to plot our data.
In this tutorial, we will use the group_by, summarize and mutate functions in the `dplyr` package to efficiently manipulate atmospheric data collected at the NEON Harvard Forest Field Site. We will use pipes to efficiently perform multiple tasks within a single chunk of code.
This tutorial uses ggplot2 to create customized plots of time series data. We will learn how to adjust x- and y-axis ticks using the scales package, how to add trend lines to a scatter plot and how to customize plot labels, colors and overall theme.
This tutorial covers how to plot subsetted time series data (e.g., plot by season) using facets() and ggplot2. It also covers how to plot multiple metrics in one display panel.
This tutorial is a data integration wrap-up culmination activity for the spatio-temporal time series tutorials.
This tutorial explains why Julian days are useful and teaches how to create a Julian day variable from a Date or Data/Time class variable.
An brief introduction to the Hierarchical Data Format 5 (HDF5) file/data model. Learn about how HDF5 is structured and the benefits of using HDF5.
Explore HDF5 files and the groups and datasets contained within, using the free HDFview tool. See how HDF5 files can be structured and explore metadata. Explore both spatial and temporal data stored in HDF5!
Learn how to build a HDF5 file in R from scratch! Add groups, datasets and attributes. Read data out from the file.
This lesson will guide you through making a simple shiny app.
This tutorial provides basic code for opening an HDF5 file in Python using the h5py, numpy, and matplotlib libraries.
Create a HDF5 in R from scratch! Add groups and datasets. View the files in the HDFviewer.
Explore, extract and visualize temporal temperature data collected from a NEON flux tower from multiple sites and sensors in R. Learn how to extract metadata and how to use nested loops and dplyr to perform more advanced queries and data manipulation.
Learn about the key attributes needed to work with raster data in non-GUI programs. Examples in R.
Learn about the fundamental principles of hyperspectral remote sensing data.
Open up and explore a hyperspectral dataset stored in HDF5 format in R. Learn about the power of data slicing in HDF5. Slice our band subsets of the data and create and visualize one band.
This tutorial explains the fundamental principles, functions and metadata that you need to work with raster data in R.
Open up and explore hyperspectral imagery in HDF format R. Combine multiple bands to create a raster stack. Use these steps to create various band combinations such as RGB, Color-Infrared and False color images.
This tutorial explains the fundamental principles, functions and metadata that you need to work with raster data, in image format, in R. Topics include raster stacks, raster bricks, plotting RGB images and exporting an RGB image to a GeoTIFF.
This tutorial walks you through creating square polygons from a plot centroid (x,y format) in R.
Extract a single pixel's worth of spectra from a a hyperspectral dataset stored in HDF5 format in R. Visualize the spectral profile.
Explore provisional NEON data that characterizes small mammal abundance and soil N:P ratio data as an example of maps that can be created using ESRI's ArcGIS online platform.
This tutorial presents the basics of using R.
This tutorial teaches the basics of creating a function in R.
This tutorial provides the basics of installing and working with packages in R
Explore the basics of how a LiDAR system works and what a LiDAR system measures.
Understand LiDAR data product formats and learn the basics of how a LiDAR data are processed.
Learn about LiDAR point cloud file formats .las and .laz. Explore LiDAR point cloud data using the free, online Plas.io viewer .
In this tutorial, you will bring LiDAR-derived raster data (DSM and DTM) into R to create a canopy height model (CHM).
Learn to extract data from a raster using circular or square buffers created around a x,y location or from a shapefile. With this will will learn to convert x,y locations in a .csv file into a SpatialPointsDataFrame so that they can be
An overview of the basics needed to begin to exploring converting point data into raster or gridding format.
This page contains capstone activities that complement several spatial data tutorial series.
A NEON science video on using data to understand the causes and effects of a natural disturbance event.
A NEON science video on how scientists measure photosynthesis
A NEON science video on how scientists collect plant phenology data from direct observations, cameras mounted above the canopy, and from satellites in space to better understand changes in the environment
A NEON science video on the basic principles used by optical sensors like Landsat, AVIRIS, and other remote sensing sensors.
A NEON science video on what LiDAR, or light detection and ranging, is, how it works and what types of information it can provide.
A NEON science video on an Introduction to Light Detection and Ranging - LiDAR
Learn to use the NEON API! The National Ecological Observatory Network (NEON) provides an Application Programming Interface (API); this workshop will guide you through using the API to access NEON data in R.
This 5-hr workshop is taught at the 2017 meeting of the Ecological Society of America (ESA) in Portland, OR. Learn fundamental skills in R needed to work with and plot time series data in text format including data.frames, converting text format timestamps to an R date or datetime (e.g. POSIX) class, and aggregating data across different time scales (e.g. hourly vs month).
Our 2017 Institute focuses on remote sensing of vegetation using open source tools to promote reproducible science. The primary computing language of this Institute is Python. This Institute will be held Boulder, CO 19-24 June 2017.
This 3.5-hr workshop, taught at the 2016 meeting of the Ecological Society of America (ESA) in Ft. Lauderdale, FL, will cover how to open, work with and plot tabular (.csv format) time series data in R. Additional topics include working with time and date classes (e.g., POSIXct, POSIXlt, and Date), subsetting time series data by date and time, and created facetted or tiled sets of plots.
This 5-hr workshop, taught at the 2016 meeting of the Ecological Society of America (ESA) in Ft. Lauderdale, FL, will focus on working with spatial time series data in R. Participants will learn to read raster metadata and work with and plot raster stacks containing RGB and time series data. They will also learn how to automate importing a raster time series in R.
NEON's Data Institutes provide critical skills and foundational knowledge for graduate students and early career scientists working with heterogeneous spatio-temporal data to address ecological questions. Our 2016 Institute focused on remote sensing of vegetation using open source tools to promote reproducible science. This Institute was held at NEON headquarters in June 2016.
This two-day workshop, taught at the USGS National Training Center in Denver, CO on 12-13 April 2016, will cover working with spatio-temporal data in R.
This two day workshop, taught at the University of Oslo on March 15-16, 2016, will cover shell and spatio-temporal data in R.
The National Ecological Observatory Network (NEON) hosted a 3-day lesson building hackathon to develop a suite of NEON/Data Carpentry spatio-temporal data lesson tutorials.
This brown-bag style workshop, at ESA 2015 in Baltimore, Maryland, provided an overview of the basic knowledge needed to begin to exploring converting point data into raster or gridding format. Most of the demonstrations were performed in ArcGIS, however, concepts learned can be applied to any tool that supports spatial interpolation functions.
This workshop will providing hands on experience with working hierarchical data formats (HDF5), and (lidar derived) raster data in R. It will also cover spatial data analysis in R.
This workshop introduces remote sensing hyperspectral imagery. We will review the background of the data, how to open it in R and how to perform basic raster calculations. We will also explore raster data in R.
This NEON Brownbag increases participants understanding of Hierarchical Data Formats in the context of developing the NEON HDF5 operational file format. Look here to discover resources on HDF5, code snippets in R, Python and Matlab to use H5 files and some example H5 files for Remote Sensing Hyperspectral data and time series temperature data.
This NEON Brownbag introduces the concept of Hierarchical Data Formats. Learn what an HDF5 file is. Explore HDF5 files in the free HDFviewer. Create and open HDF5 files in R.
This workshop will present how to work with Lidar Data derived rasters in R. Learn how to import rasters into R. Learn associated key metadata attributed needed to work with raster formats. Analyzing the data performing basic raster math to create a canopy height model. Export raster results as a (spatially located) GeoTIFF.
This workshop will offer ecologists an overview of the variety of data formats and types that are often encountered when working with big ecological data and an introduction to available tools in R for working with these formats.
Learn how to use the filter, group_by, and summarize functions with piping in R's dplyr package. And combine these with grepl to select portions of character strings.
This lesson demonstrates ways that scientists identify and use data that they use to study disturbance events. Further, it encourages students to think about why we need to quantify change and different types of data needed to quantify the change. This lesson focuses on flooding as a natural disturbance event with impacts on the local human populations. Specifically, it focuses on the causes and impacts of flooding that occurred in 2013 throughout Colorado with an emphasis on Boulder County.
AboutToday, you will use all of the skills you’ve learned at the Institute,to work on a group project that uses NEON ...
AboutOn Thursday, we will begin to think about the different types of analysisthat we can do by fusing LiDAR and hype...
AboutToday, we will focus on the importance of uncertainty when using remote sensingdata. We will work on a hands-on ...
AboutToday, we will review the basics of discrete return and full waveform lidar data.We will then work with some NEO...
This page details the capstone project that each Data Institute participant will develop and implement during the Institute.
This tutorial cover how to use R Markdown files to document code.
This tutorial introduces how to use the R knitr package to publish from R Markdown files to HTML (or other) file format.