Once R and RStudio are installed (in
Install Git, Bash Shell, R & RStudio
), open RStudio to make sure it works and you don’t get any error messages. Then,
install the needed R packages.
Install/Update R Packages
Please make sure all of these packages are installed and up to date on your
computer prior to the Institute.
The rhdf5 package is not on CRAN and must be downloaded directly from
Bioconductor. The can be done using these two commands directly in your R
console.
From the section titled HDF-Java 2.1x Pre-Built Binary Distributions
select the HDFView download option that matches the operating system and
computer setup (32 bit vs 64 bit) that you have. The download will start
automatically.
Open the downloaded file.
Mac - You may want to add the HDFView application to your Applications
directory.
Windows - Unzip the file, open the folder, run the .exe file, and follow
directions to complete installation.
Open HDFView to ensure that the program installed correctly.
**Data Tip:**
The HDFView application requires Java to be up to date. If you are having issues
opening HDFView, try to update Java first!
Install QGIS
QGIS is a free, open-source GIS program. Installation is optional for the 2018
Data Institute. We will not directly be working with QGIS, however, some past
participants have found it useful to have during the capstone projects.
To install QGIS:
Download the QGIS installer on the
QGIS download page here. Follow the installation directions below for your
operating system.
Windows
Select the appropriate QGIS Standalone Installer Version for your computer.
The download will automatically start.
Open the .exe file and follow prompts to install (installation may take a
while).
Open QGIS to ensure that it is properly downloaded and installed.
Select the current version of QGIS. The file download (.dmg format) should
start automatically.
Once downloaded, run the .dmg file. When you run the .dmg, it will create a
directory of installer packages that you need to run in a particular order.
IMPORTANT: read the READ ME BEFORE INSTALLING.rtf file!
Install the packages in the directory in the order indicated.
GDAL Complete.pkg
NumPy.pkg
matplotlib.pkg
QGIS.pkg - NOTE: you need to install GDAL, NumPy and matplotlib in order to
successfully install QGIS on your Mac!
**Data Tip:** If your computer doesn't allow you to
open these packages because they are from an unknown developer, right click on
the package and select Open With >Installer (default). You will then be asked
if you want to open the package. Select Open, and the installer will open.
Once all of the packages are installed, open QGIS to ensure that it is properly
installed.
LINUX
Select the appropriate download for your computer system.
Note: if you have previous versions of QGIS installed on your system, you may
run into problems. Check out
Verifiability and reproducibility are among the cornerstones of the scientific
process. They are what allows scientists to "stand on the shoulder of giants".
Maintaining reproducibility requires that all data management, analysis, and
visualization steps behind the results presented in a paper are documented and
available in full detail. Reproducibility here means that someone else should
either be able to obtain the same results given all the documented inputs and
the published instructions for processing them, or if not, the reasons why
should be apparent.
From Reproducible Science Curriculum
## Learning Objectives
At the end of this activity, you will be able to:
Summarize the four facets of reproducibility.
Describe several ways that reproducible workflows can improve your workflow and research.
Explain several ways you can incorporate reproducible science techniques into
your own research.
Getting Started with Reproducible Science
Please view the online slide-show below which summarizes concepts taught in the
Reproducible Science Curriculum.
Reproducibility spectrum for published research.
Source: Peng, RD Reproducible Research in Computational Science Science (2011): 1226–1227 via Reproducible Science Curriculum
The Nature Publishing group has also created a
Reporting Checklist
for its authors that focuses primaily on reporting issues but also includes
sections for sharing code.
Recent open-access issue of
Ecography
focusing on reproducible ecology and software packages available for use.
A nice short blog post with an annotated bibliography of "Top 10 papers discussing reproducible research in computational science" from Lorena Barba:
Barba group reproducibility syllabus.
After completing this tutorial, you will be able to:
Define hyperspectral remote sensing.
Explain the fundamental principles of hyperspectral remote sensing data.
Describe the key attributes that are required to effectively work with
hyperspectral remote sensing data in tools like R or Python.
Describe what a "band" is.
Mapping the Invisible
About Hyperspectral Remote Sensing Data
The electromagnetic spectrum is composed of thousands of bands representing
different types of light energy. Imaging spectrometers (instruments that collect
hyperspectral data) break the electromagnetic spectrum into groups of bands
that support classification of objects by their spectral properties on the
earth's surface. Hyperspectral data consists of many bands -- up to hundreds of
bands -- that cover the electromagnetic spectrum.
The NEON imaging spectrometer collects data within the 380nm to 2510nm portions
of the electromagnetic spectrum within bands that are approximately 5nm in
width. This results in a hyperspectral data cube that contains approximately
426 bands - which means big, big data.
Key Metadata for Hyperspectral Data
Bands and Wavelengths
A band represents a group of wavelengths. For example, the wavelength values
between 695nm and 700nm might be one band as captured by an imaging spectrometer.
The imaging spectrometer collects reflected light energy in a pixel for light
in that band. Often when you work with a multi or hyperspectral dataset, the
band information is reported as the center wavelength value. This value
represents the center point value of the wavelengths represented in that band.
Thus in a band spanning 695-700 nm, the center would be 697.5).
Imaging spectrometers collect reflected light information within
defined bands or regions of the electromagnetic spectrum. Source: National
Ecological Observatory Network (NEON)
Spectral Resolution
The spectral resolution of a dataset that has more than one band, refers to the
width of each band in the dataset. In the example above, a band was defined as
spanning 695-700nm. The width or spatial resolution of the band is thus 5
nanometers. To see an example of this, check out the band widths for the
Landsat sensors.
Full Width Half Max (FWHM)
The full width half max (FWHM) will also often be reported in a multi or
hyperspectral dataset. This value represents the spread of the band around that
center point.
The Full Width Half Max (FWHM) of a band relates to the distance
in nanometers between the band center and the edge of the band. In this
case, the FWHM for Band C is 5 nm.
In the illustration above, the band that covers 695-700nm has a FWHM of 5 nm.
While a general spectral resolution of the sensor is often provided, not all
sensors create bands of uniform widths. For instance bands 1-9 of Landsat 8 are
listed below (Courtesy of USGS)
