National Land Cover Database 2019: A new strategy for creating clean leaf-on and leaf-off Landsat composite images
ISPRS Journal of Photogrammetry and Remote Sensing
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Abstract
National Land Cover Database (NLCD) 2019 is a new epoch of national land cover products for the conterminous United States. Image quality is fundamental to the quality of any land cover product. Image preprocessing has often taken a considerable proportion of overall time and effort for this kind of national project. An approach to prepare image inputs for NLCD 2019 production was developed to ensure efficiency and quality of operational production. Here, we introduce a new and comprehensive strategy to produce clear Landsat composite images for NLCD 2019 production. First, we developed a new median-value compositing method. Second, we designed parameter settings for selecting images and pixels to generate 4 composite images (leaf-on, leaf-off, primary reference, and complementary reference) for a target year based on the US Landsat Analysis Ready Data surface reflectance dataset. Third, we developed a method, referred to as Detection and Filling with Simulated Image, to detect and replace clouds and cloud shadow pixels to produce the final clean leaf-on and leaf-off image composites. This image compositing and processing strategy was implemented for the entire conterminous United States to produce images for NLCD 2019. Our image results and NLCD 2019 change detection and land cover products, which were released in July 2021, showed this new strategy to be effective and efficient.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | National Land Cover Database 2019: A new strategy for creating clean leaf-on and leaf-off Landsat composite images |
| Series title | ISPRS Journal of Photogrammetry and Remote Sensing |
| DOI | 10.34133/remotesensing.0022 |
| Volume | 3 |
| Year Published | 2023 |
| Language | English |
| Publisher | AAAS |
| Contributing office(s) | Earth Resources Observation and Science (EROS) Center, Advanced Research Computing (ARC) |
| Description | 0022, 13 p. |
| Google Analytic Metrics | Metrics page |