TerrAdapt:Cascadia Documentation
TerrAdapt:Cascadia
  • QUICK START GUIDE
  • HOW TO USE TERRADAPT:CASCADIA
    • Public License and Citation
    • Appropriate Uses and Key Limitations
    • TerrAdapt:Cascadia Map Portal
    • TerrAdapt:Cascadia Dashboard
  • DATA INPUTS
    • Data Inputs Overview
    • Remote Sensing Data
    • Climate
    • Energy and Transportation Infrastructure
    • Topography, Hydrology, & Soils
  • METHODS AND VALIDATION
    • Methods Overview
    • Landcover
      • Taxonomy
      • Training Data
      • Covariates
      • Model Development
      • Model Validation
    • Forest Structure
      • Training Data
      • Covariates
      • Model Development
      • Model Validation
    • Rangeland Fractional Cover
      • Training Data
      • Covariates
      • Model Development
      • Model Validation
    • Change Detection and Ecological Disturbance Modeling
      • Taxonomy
      • Covariates
      • Training Data
      • Model Development
    • Human Footprint
    • Habitat
      • Species Distribution Modeling
      • Ecosystem-based Models
      • Core Habitat
      • Habitat Centrality
    • Connectivity
      • Mapping Connectivity Networks
      • Corridors
      • Corridor Centrality
      • Mapping Barriers
Powered by GitBook
On this page

Was this helpful?

  1. DATA INPUTS

Remote Sensing Data

PreviousData Inputs OverviewNextClimate

Last updated 1 year ago

Was this helpful?

Landsat Multispectral Imagery

Many of TerrAdapt:Cascadia's data products are directly or indirectly derived from multispectral imagery hosted in the Google Earth Engine data catalog. We use 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; ) 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. They also contain intermediate bands used in calculation of the ST products, as well as QA bands.

We post-process 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 use the Continuous Change Detection and Classification (CCDC; ) 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 can be 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 of between years.

Zhu et al, 2014
Landsat
Schmidt et al. 2013
Landsat multispectral imagery band wavelengths (figure from NASA)