Geology topics

Mehdi Heris

Publications and source records attributed to Mehdi Heris.

5 recordsLinked to original sources

USGS2026

The influence of tree canopy cover data choices on urban ecosystem accounting

Because urban landscapes are heterogeneous, the methods and spatial resolution used to depict the land surface greatly influence the representation of urban features. Land cover products such as tree canopy cover (TCC) are particularly sensitive to the methodology and resolution used in their creation. Differences in TCC mapping have implications on the outcomes of ecosystem service (ES) models, including those underlying natural capital accounting. Here, we quantify the sensitivity of physical rainfall interception and local climate regulation ES models for 189 U.S. cities to TCC inputs from four TCC products: a) National Land Cover Database (NLCD), b) Enhanced NLCD TCC, c) aggregated city-specific composite, and d) global tree canopy height dataset. We find both city-level and aggregate differences in TCC estimates, from a 38% decrease to a 3% increase relative to an aggregated high-resolution product. These differences result in up to 3% overestimations and 27% underestimation of rainfall interception and 2–56% underestimation of local climate regulation ES. City size, population, and greenness in addition to climatic variation drove differences between TCC products, and this variation requires users to carefully consider the choice of input data for any planned analysis. Though high-resolution data can offer greater nuance and accuracy, more limited spatiotemporal availability can hinder their usefulness for long-term monitoring applications such as natural capital accounting. The differences found in this study provide valuable insights for making informed decisions on data inputs for use in urban ecosystem research and for contextualizing model outcomes.

Contiguous United States↗

USGS2025

An enhanced national-scale urban tree canopy cover dataset for the United States

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 (R 2 ) 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 R 2 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.

conterminous United States↗

USGS2022

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

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.

Remote Sensing↗

USGS2021

Piloting urban ecosystem accounting for the United States

In this study, we develop urban ecosystem accounts in the U.S., using the System of Environmental-Economic Accounting Experimental Ecosystem Accounting (SEEA EEA) framework. Most ecosystem accounts focus on regional and national scales, which are appropriate for many ecosystem services. However, ecosystems provide substantial services in cities, improving quality of life and contributing to resiliency for substantial parts of the population. Our models estimate energy savings for indoor cooling resulting from heat mitigated by trees and rainfall intercepted by trees. Both models cover major cities in the contiguous U.S. and report the results through physical supply and use tables for multiple accounting periods (2011 and 2016). Using conservative assumptions, urban trees provide substantial heat mitigation (4,098 and 4,229 GWh, valued at $523 and $539 million in 2011 and 2016, respectively) and rainfall interception (2,422 and 2,627 million m 3 , valued at $434 and $425 million for 2011 and 2016, respectively). Interannual differences largely reflect variations in weather patterns. Our work shows how Earth observation data can support urban ecosystem accounting. We provide model code within a public repository to facilitate model runs elsewhere, enabling the SEEA EEA and Earth observation user communities to reuse our models and provide feedback for improvement.

Ecosystem Services↗

USGS2021

Lessons learned from development of natural capital accounts in the United States and European Union

The United States and European Union (EU) face common challenges in managing natural capital and balancing conservation and resource use with consumption of other forms of capital. This paper synthesizes findings from 11 individual application papers from a special issue of Ecosystem Services on natural capital accounting (NCA) and their application to the public and private sectors in the EU and U.S. NCA is inherently a data-integration centered exercise, aiming to draw new insights by realigning environmental and economic data into a consistent framework. Drawing primarily on papers from the special issue and other key NCA literature, we identify lessons learned and gaps remaining for NCA’s development and application to decision making. In doing so, we identify eight key similarities and three major differences in NCA development, status, and application between the U.S. and EU. NCA can be highly policy relevant: special issue papers address critical issues including agriculture, water, conservation/land-use planning, climate, and corporate decision making. In both the U.S. and EU, further application is needed to drive demand for the accounts’ production. Based on these experiences, the U.S. and EU can be important leaders in cross-sector, international collaboration toward next-generation environmental economic accounts that advance global NCA practice.

Ecosystem Services↗