Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Shortcomings of the normalized difference vegetation index as an exposure metric


The health benefits of exposure to trees and plants is a rapidly expanding field of study. Research has shown that exposure is associated with improvements in a wide range of health outcomes including cardiovascular disease, birth outcomes, respiratory disease, cancer, mental health and all-cause mortality1. One of the challenges that these studies face is characterizing participants’ exposure to trees and plants. A common approach is to use the normalized difference vegetation index, a greenness index typically derived from satellite imagery. Reliance on the normalized difference vegetation index is understandable; for decades, the imagery required to calculate the normalized difference vegetation index has been available for the entire Earth’s surface and is updated at regular intervals. However, the normalized difference vegetation index may do a poor job of fully characterizing the human experience of being exposed to trees and plants, because scenes with the same normalized difference vegetation index value can appear different to the human eye. We demonstrate this phenomenon by identifying sites in Portland, Oregon that have the same normalized difference vegetation index value as a large, culturally significant elm tree. These sites are strikingly different aesthetically, suggesting that use of the normalized difference vegetation index may lead to exposure misclassification. Where possible, the normalized difference vegetation index should be supplemented with other exposure metrics.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Elm tree in Portland, Oregon.
Fig. 2: Nine scenes with the same mean NDVI as heritage elm tree.
Fig. 3: Nine scenes with the same NDVI (mean and SD) as heritage elm tree.
Fig. 4: a. LiDAR point cloud of heritage elm tree. b. Heritage elm tree superimposed on NDVI raster.

Data availability

High-density Oregon Department of Geology and Mineral Industries (DOGAMI) LiDAR data were obtained from LIDAR CONSORTIUM PROJECT DATA/OLC METRO 2014/ Landsat 8 data were obtained from Google Earth Engine36. The 3-m multispectral, satellite imagery was obtained from Planet37.


  1. Twohig-Bennett, C. & Jones, A. The health benefits of the great outdoors: a systematic review and meta-analysis of greenspace exposure and health outcomes. Environ. Res. 166, 628–637 (2018).

    Article  CAS  Google Scholar 

  2. Woodcock, C. E. et al. Free access to Landsat imagery. Science 320, 1011 (2008).

    Article  CAS  Google Scholar 

  3. Tigges, J., Lakes, T. & Hostert, P. Urban vegetation classification: benefits of multitemporal RapidEye satellite data. Remote Sens. Environ. 136, 66–75 (2013).

    Article  Google Scholar 

  4. Chaix, B. et al. GPS tracking in neighborhood and health studies: a step forward for environmental exposure assessment, a step backward for causal inference? Health Place 21, 46–51 (2013).

    Article  Google Scholar 

  5. Passchier-Vermeer, W. & Passchier, W. F. Noise exposure and public health. Environ. Health Perspect. 108, 123–131 (2000).

    PubMed  PubMed Central  Google Scholar 

  6. Weilnhammer, V. et al. Extreme weather events in Europe and their health consequences–a systematic review. Int. J. Hyg. Environ. Health 233, 113688 (2021).

    Article  Google Scholar 

  7. Donovan, G. H., Gatziolis, D., Jakstis, K. & Comess, S. The natural environment and birth outcomes: comparting 3D exposure metrics derived from LiDAR to 2D metrics based on the Normalized Difference Vegetation Index. Health Place 57, 305–312 (2019).

    Article  Google Scholar 

  8. Gascon, M. et al. Normalized Difference Vegetation Index (NDVI) as a marker of surrounding greenness in epidemiological studies: the case of Barcelona city. Urban For. Urban Green. 19, 88–94 (2016).

    Article  Google Scholar 

  9. Reid, C. E., Kubzansky, L. D., Li, J., Shmool, J. L. & Clougherty, J. E. It’s not easy assessing greenness: a comparison of NDVI datasets and neighborhood types and their associations with self-rated health in New York City. Health Place 54, 92–101 (2018).

    Article  Google Scholar 

  10. Donovan, G. H., Gatziolis, D., Longley, I. & Douwes, J. Vegetation diversity protects against childhood asthma: results from a large New Zealand birth cohort. Nat. Plants 4, 358–364 (2018).

    Article  Google Scholar 

  11. Hystad, P. et al. Residential greenness and birth outcomes: evaluating the influence of spatially correlated built-environment factors. Environ. Health Perspect. 122, 1095–1102 (2014).

    Article  Google Scholar 

  12. Triguero-Mas, M. et al. Natural outdoor environments and mental and physical health: relationships and mechanisms. Environ. Int. 77, 35–41 (2015).

    Article  Google Scholar 

  13. Nowak, D. J., Crane, D. E. & Stevens, J. C. Air pollution removal by urban trees and shrubs in the United States. Urban For. Urban Green. 4, 155–123 (2006).

    Article  Google Scholar 

  14. James, P., Banay, R. F., Hart, J. E. & Laden, F. A review of the health benefits of greenness. Curr. Epidemiol. Rep. 2, 131–142 (2015).

    Article  Google Scholar 

  15. Li, D. & Sullivan, W. C. Impact of views to school landscapes on recovery from stress and mental fatigue. Landsc. Urban Plan. 148, 149–158 (2016).

    Article  Google Scholar 

  16. Velarde, M., Fry, G. & Tveit, M. Health effects of viewing landscapes – landscape types in environmental psychology. Urban For. Urban Green. 6, 199–212 (2007).

    Article  Google Scholar 

  17. Ulrich, R. S. Visual landscapes and psychological well‐being. Landsc. Res. 4, 17–23 (1979).

    Article  Google Scholar 

  18. Ulrich, R. S. View through a window may influence recovery from surgery. Science 224, 420–421 (1984).

    Article  CAS  Google Scholar 

  19. Kaplan, S. The restorative benefits of nature: toward an integrative framework. J. Environ. Psychol. 15, 169–182 (1995).

    Article  Google Scholar 

  20. De Vries, S., Van Dillen, S. M., Groenewegen, P. P. & Spreeuwenberg, P. Streetscape greenery and health: stress, social cohesion and physical activity as mediators. Soc. Sci. Med. 94, 26–33 (2013).

    Article  Google Scholar 

  21. Sugiyama, T., Leslie, E., Giles-Corti, B. & Owen, N. Associations of neighbourhood greenness with physical and mental health: do walking, social coherence and local social interaction explain the relationships? J. Epidemiol. Community Health 62, e9 (2008).

    Article  CAS  Google Scholar 

  22. Saelens, B. E., Sallis, J. F., Black, J. B. & Chen, D. Neighborhood-based differences in physical activity: an environment scale evaluation. Am. J. Public Health 93, 1552–1558 (2003).

    Article  Google Scholar 

  23. Ball, K., Bauman, A., Leslie, E. & Owen, N. Perceived environmental aesthetics and convenience and company are associated with walking for exercise among Australian adults. Prev. Med 33, 434–440 (2001).

    Article  CAS  Google Scholar 

  24. Coombes, E., Jones, A. P. & Hillsdon, M. The relationship of physical activity and overweight to objectively measured green space accessibility and use. Soc. Sci. Med. 70, 816–822 (2010).

    Article  Google Scholar 

  25. Fan, Y., Das, K. V. & Chen, Q. Neighborhood green, social support, physical activity, and stress: assessing the cumulative impact. Health Place 17, 1202–1211 (2011).

    Article  Google Scholar 

  26. Ribe, R. G. The aesthetics of forestry: what has empirical preference research taught us? Environ. Manag. 13, 55–74 (1989).

    Article  Google Scholar 

  27. Liddicoat, C. et al. Naturally-diverse airborne environmental microbial exposures modulate the gut microbiome and may provide anxiolytic benefits in mice. Sci. Total Environ. 701, 134684 (2020).

    Article  CAS  Google Scholar 

  28. Haahtela, T. et al. The biodiversity hypothesis and allergic disease: World Allergy Organization position statement. World Allergy Organ. J. 6, 3 (2013).

    Article  Google Scholar 

  29. Mohammed, G. H. et al. Natural and Stress-induced Effects on Leaf Spectral Reflectance in Ontario Species (Ontario Ministry of Natural Resources, 2000).

  30. Barron, S. et al. What do they like about trees? Adding local voices to urban forest design and planning. Trees Forests People 5, 100116 (2021).

    Article  Google Scholar 

  31. Lohr, V. I. & Pearson-Mims, C. H. Responses to scenes with spreading, rounded, and conical tree forms. Environ. Behav. 38, 667–688 (2006).

    Article  Google Scholar 

  32. Portland Parks and Recreation. Heritage Trees of Portland (2020).

  33. Rojas-Rueda, D., Nieuwenhuijsen, M. J., Gascon, M., Perez-Leon, D. & Mudu, P. Green spaces and mortality: a systematic review and meta-analysis of cohort studies. Lancet Planet. Health 3, e469–e477 (2019).

    Article  Google Scholar 

  34. Strunk, J. L. et al. Evaluation of pushbroom DAP relative to frame camera DAP and lidar for forest modeling. Remote Sens. Environ. 237, 111535 (2020).

    Article  Google Scholar 

  35. Jiang, B. et al. Remotely-sensed imagery vs. eye-level photography: evaluating associations among measurements of tree cover density. Landsc. Urban Plan. 157, 270–281 (2017).

    Article  Google Scholar 

  36. Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).

  37. Planet Team. Planet Application Program Interface: In Space for Life on Earth (Planet, 2022);

Download references


This work utilized data made available through the NASA Commercial Smallsat Data Acquisition (CSDA) Program.

Author information

Authors and Affiliations



G.H.D., D.G., M.D. and J.D. developed the original idea. G.H.D. and D.G. conducted the analysis. G.H.D., D.G., M.D., Y.L.M., J.P.D. and J.D. wrote and edited the paper.

Corresponding author

Correspondence to Geoffrey H. Donovan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Plants thanks Peter James and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Donovan, G.H., Gatziolis, D., Derrien, M. et al. Shortcomings of the normalized difference vegetation index as an exposure metric. Nat. Plants 8, 617–622 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing