Remote Sensing Data
Last updated
Last updated
Many of the input data used to map environmental variables that inform WSRRI's spatial priorities were directly or indirectly derived from Landsat multispectral imagery hosted in the Google Earth Engine data catalog. We used Landsat Level 2, Collection 2, Tier 1 data collected by the Landsat 4, 5, 7, 8, and 9 satellites between 1984 and the current year. This dataset contains atmospherically corrected surface reflectance and land surface temperature measured by the Thematic Mapper (Landsat 4 and 5), Enhanced Thematic Mapper Plus (Landsat 7), and Operational Land Imager (Landsat 8 and 9) instruments. Landsat surface reflectance products are created with the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS; Schmidt et al. 2013) algorithm (version 3.4.0). These images contain 4 visible and near-infrared (VNIR) bands and 2 short-wave infrared (SWIR) bands processed to orthorectified surface reflectance, and one thermal infrared (TIR) band processed to orthorectified surface temperature.
We post-processed every Landsat scene using Google Earth Engine to remove clouds, cloud shadows, and other artifacts based on metadata contained in the image's QA band. We also used the Continuous Change Detection and Classification (CCDC; Zhu et al, 2014) temporal segmentation algorithm implemented in Google Earth Engine to fit a harmonic regression through all Landsat observations from 1984 to present for each pixel within our modeling boundary. The harmonic regression coefficients were used to create synthetic images of the Landsat bands that control for much of the scene to scene noise present in Landsat imagery and facilitating rigorous comparisons between years.
