A framework for integrating spatiotemporal deep learning methods with landsat for annual land cover and impervious surface mapping
Remote Sensing of Environment
Links
- More information: Publisher Index Page (via DOI)
- Open Access Version: Publisher Index Page
- Download citation as: RIS | Dublin Core
Abstract
Land cover information is essential for understanding Earth’s surface dynamics and how vegetation, water, soil, climate, and terrain interact. The National Land Cover Database (NLCD) has been the authoritative source for consistent U.S. land cover mapping. To extend NLCD’s temporal resolution and reduce production latency, we developed the Land Cover Artificial Mapping System (LCAMS)—a prototype spatiotemporal deep learning framework piloted as the foundation for the new Annual NLCD.
LCAMS builds on concepts from legacy NLCD and the U.S. Geological Survey Land Change Monitoring, Assessment, and Projection (LCMAP) initiatives. It employs a loosely coupled two-stage architecture consisting of independent but functionally interdependent spatial and temporal models. Spatial models extract per-year information from Landsat data, while the temporal models refine the spatial outputs to enforce inter-annual consistency—critical for reliable land change monitoring. LCAMS produces annual 30 m resolution land cover and impervious surface outputs, with region-specific fine-tuning to generalize across diverse landscapes and temporal dynamics.
Validation was conducted using an independent dataset of 1925 randomly sampled plots from five U.S. Landsat Analysis Ready Data (ARD) tiles spanning 1985-2021, selected for spatial and temporal variability. This dataset was used consistently to evaluate LCAMS, Legacy NLCD, and LCMAP. Using the NLCD legend, LCAMS achieved 72.1 ± 1.60% overall agreement, compared to 71.1 ± 1.7% agreement for Legacy NLCD. Using the LCMAP legend, LCAMS achieved 83.4 ± 1.22% agreement, compared to 84.6 ± 1.11% agreement for LCMAP. Overall, LCAMS delivers comparable accuracy while offering higher thematic resolution, longer temporal coverage, and automated production of annual 30 m CONUS land cover.
Suggested Citation
Fleckenstein, R., Wellington, D.F., Jin, S., Tollerud, H.J., Brown, J.F., Dewitz, J., Pastick, N.J., Barber, C.P., O'Brien, A., and Spanier, M., 2026, A framework for integrating spatiotemporal deep learning methods with landsat for annual land cover and impervious surface mapping: Remote Sensing of Environment, v. 338, 115347, 24 p., https://doi.org/10.1016/j.rse.2026.115347.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | A framework for integrating spatiotemporal deep learning methods with landsat for annual land cover and impervious surface mapping |
| Series title | Remote Sensing of Environment |
| DOI | 10.1016/j.rse.2026.115347 |
| Volume | 338 |
| Publication Date | March 05, 2026 |
| Year Published | 2026 |
| Language | English |
| Publisher | Elsevier |
| Contributing office(s) | Earth Resources Observation and Science (EROS) Center |
| Description | 115347, 24 p. |