An enhanced national-scale urban tree canopy cover dataset for the United States
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Abstract
Moderate-resolution (30-m) national map products have limited capacity to represent fine-scale, heterogeneous urban forms and processes, yet improvements from incorporating higher resolution predictor data remain rare. In this study, we applied random forest models to high-resolution land cover data for 71 U.S. urban areas, moderate-resolution National Land Cover Database (NLCD) Tree Canopy Cover (TCC), and additional explanatory climatic and structural data to develop an enhanced urban TCC dataset for U.S. urban areas. With a coefficient of determination (R2) of 0.747, our model estimated TCC within 3% for 62 urban areas and added 13.4% more city-level TCC on average, compared to the native NLCD TCC product. Cross validations indicated model stability suitable for building a national-scale TCC dataset (median R2 of 0.752, 0.675, and 0.743 for 1,000-fold cross validation, urban area leave-one-out cross validation, and cross validation by Census block group median year built, respectively). Additionally, our model code can be used to improve moderate-resolution TCC in other parts of the world where high-resolution land cover data have limited spatiotemporal availability.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | An enhanced national-scale urban tree canopy cover dataset for the United States |
| Series title | Scientific Data |
| DOI | 10.1038/s41597-025-04816-0 |
| Volume | 12 |
| Publication Date | March 24, 2025 |
| Year Published | 2025 |
| Language | English |
| Publisher | Springer Nature |
| Contributing office(s) | Geosciences and Environmental Change Science Center |
| Description | 490, 14 p. |
| Country | United States |
| Other Geospatial | conterminous United States |