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
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.
