Assessing the Accuracy and Potential for Improvement of the National Land Cover Database’s Tree Canopy Cover Dataset in Urban Areas of the Conterminous United States

Abstract

The National Land Cover Database (NLCD) provides time-series data characterizing the land surface for the United States, including land cover and tree canopy cover (NLCD-TC). NLCD-TC was first published for 2001, followed by versions for 2011 (released in 2016) and 2011 and 2016 (released in 2019). As the only nationwide tree canopy layer, there is value in assessing NLCD-TC accuracy, given the need for cross-city comparisons of urban forest characteristics. Accuracy assessments have only been conducted for the 2001 data and suggest substantial inaccuracies for that dataset in cities. For the most recent NLCD-TC version, we used various datasets that characterize the built environment, weather, and climate to assess their accuracy in different contexts within 27 cities. Overall, NLCD underestimates tree canopy in urban areas by 9.9% when compared to estimates derived from those high-resolution datasets. Underestimation is greater in higher-density urban areas (13.9%) than in suburban areas (11.0%) and undeveloped areas (6.4%). To evaluate how NLCD-TC error in cities could be reduced, we developed a decision tree model that uses various remotely sensed and built-environment datasets such as building footprints, urban morphology types, NDVI (Normalized Difference Vegetation Index), and surface temperature as explanatory variables. This predictive model removes bias and improves the accuracy of NLCD-TC by about 3%. Finally, we show the potential applications of improved urban tree cover data through the examples of ecosystem accounting in Seattle, WA, and Denver, CO. The outputs of rainfall interception and urban heat mitigation models were highly sensitive to the choice of tree cover input data. Corrected data brought results closer to those from high-resolution model runs in all cases, with some variation by city, model, and ecosystem type. This suggests paths forward for improving the quality of urban environmental models that require tree canopy data as a key model input.

Satellite‐based evidence highlights a considerable increase of urban tree cooling benefits from 2000 to 2015

“…The dataset is processed at an annual scale and for 2000, 2005, 2010, and 2015 (referred to as the 4 years thereafter), all of which were used for analyzing the spatial and temporal patterns of TCE. Tree cover derived from 30-m Landsat sensors can be underestimated to varying degrees as indicated by previous studies (Nowak & Greenfield, 2010;Pourpeikari Heris et al, 2022) investigating the accuracy of the National Land Cover Dataset (NLCD). However, the global Landsat VCF tree cover layers used in this study reduce this underestimation as shown in two US cities: San Francisco and Washington selected as examples (Figure S2a,b).…”

Incorporating Social and Policy Drivers into Land-Use and Land-Cover Projection

“…However, there remain considerable false projections among Shrub/Herbaceous and Barren land as these two LULC classes are the most transient land uses in the region due to prevailing forestry practices as well as activities such as solar panel installment, establishment of road construction projects or mining activities. Moreover, land-use classes with similar reflectance in satellite images may lead to classification confusion among some LULC classes, hence there are classification errors associated with these two land uses that can negatively affect the overall accuracy level of projected maps [56,76,77].…”

Global risk of precipitation deficits for tree-based cooling in cities

“…Reliance on 1 km spatial resolution satellite data presents limitations to inference in individual cities, though commonly used for global-scale analyses. MODIS may underestimate tree cover in urban and sparsely-treed environments 22,23 and LST may overestimate the effects of trees on human thermal comfort 24 . However, alternative high-resolution commercial urban tree cover products are sparsely available and are costprohibitive for many organizations.…”