Image Compositing Script in Google Earth Engine

The University of Nevada Reno has removed our website due to accessibility issues. Two years back Joe Brehm and Adriano Matos put together a nice Google Earth Engine script to composite Landsat imagery.  The  script is available through the Earth Engine site.  You'll need a Google login and a Google Drive account before you can run it.  Adriano and Joe did a great job of documenting their script, so it is fairly easy to follow.  The script lets users add several vegetation indices in addition to the reflectance data.

https://code.earthengine.google.com/60bc1e1140539d4d8946351a4a9c180b

Anna Knight wins AGU Outstanding Student Presentation Award

Congratulations Anna Knight on winning Outstanding Student Presentation at the 2018 American Geophysical Union Meeting for her poster “Geomorphic and disturbance controls on vegetation dynamics in Great Basin riparian ecosystems “. Click HERE to view Anna’s poster. You can read more about AGU’s Outstanding Student Presentation by clicking HERE.

AGU is a huge conference with thousands of students attending.  Anna's award is a testament to her hard work and innovative cutting-edge techniques.  Anna is completing her master's thesis under the advisement of Dr. Peter Weisberg.  You can view her webpage by clicking HERE.

New paper - Cheatgrass Die-Offs: A Unique Restoration Opportunity in Northern Nevada

Owen Baughman recently authored "Cheatgrass Die-Offs: A Unique Restoration Opportunity in Northern Nevada" in the journal Rangelands. This nice short piece highlights some of the restoration opportunities presented by cheatgrass die-offs. Cheatgrass die-off is a term that refers when a whole stand of cheatgrass fails to regenerate due to a pathogen.  Usually this results in nearly complete lack of regeneration which can clearly be seen from both high resolution imagery and moderate resolution imagery, such as Landsat. Owen completed his master's thesis in 2014. I'd expect several papers related to his thesis out soon.

This paper also highlights some of the findings from our more detailed paper on remote sensing of cheatgrass die-offs "Development of remote sensing indicators for mapping episodic die-off of an invasive annual grass (Bromus tectorum) from the Landsat archive" in Ecological Indicators. The Great Basin Landscape Ecology Lab continues to explore ways in which remote sensing can be used to map cheatgrass die-offs across the Great Basin and to use imagery to quantify spatial pattern and relate it to climatic and other abiotic factors. Joe Brehm is a current master's student in the lab who is focusing on remote sensing of cheatgrass die-offs for his thesis. I'm looking forward to seeing Joe's findings.

Cheatgrass die-off paper is now in print

Our paper about remote sensing of cheatgrass die-off patches is now in print in Ecological Indicators. To read our paper click HERE or request a PDF from myself or one of the other authors.  Since this paper we've done more mapping of cheatgrass die-offs in Skull Valley of western Utah and have a new graduate student coming on board next month to take the die-off mapping to new heights.

Landsat image pre-processing in ArcGIS - tools for seamless mosaicking

Mosaicking adjacent Landsat tiles often produces visible seam lines at the boundary between the two scenes. The Landsat Toolbox for ArcGIS deals with this problem by selecting non-cloud, non-shadow, and non-snow pixels in the overlapping portions of the scenes and then performing linear regression on each band. To do this the user must first select a "master" or reference image and a "second" image. The values in the second image are adjusted to match the first image. Although this method is relatively simple it is not part of the standard mosaic tool in ArcGIS. As evidenced in the figure below this method is quite effective at removing seam lines producing a visually coherent mosaic.

The upper image contains portions of two overlapping scenes mosaicked using the standard mosaic tool. The lower image was mosaicked using the Landsat Toolbox for ArcGIS.

Landsat image pre-processing in ArcGIS - tools for topographic correction

Correcting Landsat imagery for topographic effects is challenging at best and ignoring the differential illumination resulting from topography can lead to some pretty misleading results, especially when it comes to change analysis. Topographic efffects arise not just from shadowing, but also (more importantly) from different angles of the ground relative to the sun angle. When the sun is directly overhead pixels will be much brighter than when the sun angle is at a much lower angle. In our lab we've used a variant of the empirical-line method for removing topographic effects on Landsat image. In the Landsat Toolbox for ArcGIS 10.1. First, a Landsat metadata on sun angle and azimuth is used to generate a hillshade (illumination) raster that mimics illumination at the time of satellite overpass. Then each band is extracted and a linear regression is used to predict the reflectance as if each pixel were illuminated the same. We've found this method to be simple and quite successful at reducing the differential illumination effects in an image.

The images below illustrate some mountainous terrain before (left) and after (right) topographic correction. You can see that the terrain appears flat aster topographic correction.

New tool - Landsat image pre-processing in ArcGIS - Part I

Landsat is powerful resource for measuring changes on the Earth's surface over the past > 30 years. However, ArcGIS users lack image pre-processing tools available in remote sensing packages, such as ENVI and ERDAS. Through the years I've trialed and error-ed with different image pre-processing workflows with varying success. Some workflows actually ended up making the data worse off than when I began! Recently I decided to post the Landsat Toolbox that I've developed to facilitate my own image processing - http://www.arcgis.com/home/item.html?id=a60b0120a79f45ae990bb85f4d12edee .

The Landsat Toolbox for ArcGIS provides many basic preprocessing tools that can be used to help facilitate change detection and vegetation dynamics studies. This toolbox lessens the need for commercial remote sensing packages, such as ENVI or ERDAS, and brings some image processing functionality directly into ArcMap. Image pre-processing involves steps that may be under-appreciated by some GIS analysts, but are nonetheless important for ensuring reliable outcomes. This toolbox contains tools to do the following:

1) Convert raw DN values to top-of-atmosphere reflectance
2) Perform radiometric normalization using user-selected pseudo-invariant pixels
3) Perform topographic corrections using a digital elevation model
4) Mosaic adjacent scenes using linear regression to ensure a smooth edge-match

Many of the tools in this toolbox require fmask or fmask for R to perform cloud, cloud shadow, and snow masking prior to running. However, you could also do the masking manually by setting any value that you wish to remove to > 0.

https://code.google.com/p/fmask/
https://bitbucket.org/droletg/fmask