LandsatLinkr (LLR) is an automated Landsat image processing system designed to spatially and spectrally link MSS, TM, ETM+, and OLI images through time. It easily and efficiently processes hundreds of images to produce annual cloud-free image composites from which to build 42+ year spectral chronologies. The resulting spectrally consistent composite images can be used individually as inputs to mapping or modeling projects, to investigate simple image-to-image change, or as inputs to complex change detection algorithms such as LandTrendr.
LandsatLinkr is written in the R programming language and distributed as an R package for straightforward and convenient installation. The system features an interactive interface for simple operation with no R experience needed. It is relatively fast and provides an option to run the system in parallel using 2 cores to reduce standard processing time by half.
It was developed for landscape change research as a way to easily incorporate the entire Landsat archive into spatially and spectrally consistent 42+ year time-series stacks. As such, it is constantly changing to meet new needs and is not considered end-point software. However, the outputs are generically useful to almost anyone interested in working with Landsat time series data, so we provide it freely, encourage its use, and welcome feedback.
Basically, LLR takes hundreds of images downloaded from USGS servers and completes the following procedures with one command.
Each step produces outputs which are independently useful, so even if you only need a couple of images processed to surface reflectance, for example, and don't care about annual composites or time-series stacks, LLR is still relevant.
LLR composite image data can be viewed using the LLR-TimeMachine application, an interactive spectral chronology visualization and exploration tool.
We also provide code (LLR-LandTrendr) to easily run the LLR composite imagery through the LandTrendr algorithm.
LandsatLinkr was developed by the Laboratory for Applications of Remote Sensing in Ecology at Oregon State University, Department of Forest Ecosystems and Society with funding from USDA Forest Service and USGS.