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Tools and methods for monitoring the health of the urban greenery

Nature Sustainability (2024)Cite this article

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Abstract

Urban greenery supports cities in achieving Sustainable Development Goals, but it is increasingly affected by multiple stressors impacting its health. Owing to the high costs of greenery inspection and monitoring, local governments often lack adequate data to effectively manage their urban greenery and prevent damage. In this Review, we present an overview of technology-supported methods and tools to measure the health of urban greenery and discuss the space–time resolution trade-offs associated with the various methods presented. To inform researchers and policymakers in global cities, we highlight how high-resolution urban greenery health data can support in achieving Sustainable Development Goals at scale.

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Fig. 1: Space–time resolution of classes of methods with respect to SDG requirements.
Fig. 2: Examples of three imaging-based methods used to estimate urban greenery health attributes.

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References

  1. Escobedo, F. J., Kroeger, T. & Wagner, J. E. Urban forests and pollution mitigation: analyzing ecosystem services and disservices. Environ. Pollut. 159, 2078–2087 (2011).

    Article  CAS  PubMed  Google Scholar 

  2. Nowak, D. J., Greenfield, E. J., Hoehn, R. E. & Lapoint, E. Carbon storage and sequestration by trees in urban and community areas of the United States. Environ. Pollut. 178, 229–236 (2013).

    Article  CAS  PubMed  Google Scholar 

  3. Kirnbauer, M., Baetz, B. & Kenney, W. Estimating the stormwater attenuation benefits derived from planting four monoculture species of deciduous trees on vacant and underutilized urban land parcels. Urban For. Urban Green. 12, 401–407 (2013).

    Article  Google Scholar 

  4. Wong, N. H., Tan, C. L., Kolokotsa, D. D. & Takebayashi, H. Greenery as a mitigation and adaptation strategy to urban heat. Nat. Rev. Earth Environ. 2, 166–181 (2021).

    Article  ADS  Google Scholar 

  5. Gregory McPherson, E. Accounting for benefits and costs of urban greenspace. Landsc. Urban Plan. 22, 41–51 (1992).

    Article  Google Scholar 

  6. Hobbie, S. E. & Grimm, N. B. Nature-based approaches to managing climate change impacts in cities. Phil. Trans. R. Soc. B 375, 20190124 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Nourmohammadi, Z., Lilasathapornkit, T., Ashfaq, M., Gu, Z. & Saberi, M. Mapping urban environmental performance with emerging data sources: a case of urban greenery and traffic noise in Sydney, Australia. Sustainability 13, 605 (2021).

    Article  Google Scholar 

  8. Pyky, R. et al. Individual and environmental factors associated with green exercise in urban and suburban areas. Health Place 55, 20–28 (2019).

    Article  PubMed  Google Scholar 

  9. Moreira, T. C. L. et al. Assessing the impact of urban environment and green infrastructure on mental health: results from the São Paulo megacity mental health survey. J. Expo. Sci. Environ. Epidemiol. 32, 205–212 (2022).

    Article  PubMed  Google Scholar 

  10. Blicharska, M. et al. Biodiversity’s contributions to sustainable development. Nat. Sustain. 2, 1083–1093 (2019).

    Article  Google Scholar 

  11. Bartesaghi Koc, C., Osmond, P. & Peters, A. Towards a comprehensive green infrastructure typology: a systematic review of approaches, methods and typologies. Urban Ecosyst. 20, 15–35 (2017).

    Article  Google Scholar 

  12. Childers, D. L. et al. Urban ecological infrastructure: an inclusive concept for the non-built urban environment. Elementa 7, 46 (2019).

    Google Scholar 

  13. Nitoslawski, S. A., Galle, N. J., van den Bosch, C. C. K. & Steenberg, J. W. Smarter ecosystems for smarter cities? A review of trends, technologies, and turning points for smart urban forestry. Sustain. Cities Soc. 51, 101770 (2019).

    Article  Google Scholar 

  14. IPCC: Summary for Policymakers. In Climate Change 2021: The Physical Science Basis (eds Allan, R. P. et al.) (Cambridge Univ. Press, 2021); https://doi.org/10.1017/9781009157896.001

  15. Hilbert, D. R., Roman, L. A., Koeser, A. K., Vogt, J. & van Doorn, N. S. Urban tree mortality: a literature review. Arboric. Urban For. 45, 167–200 (2019).

    Google Scholar 

  16. Wu, C. K. et al. An IoT tree health indexing method using heterogeneous neural network. IEEE Access 7, 66176–66184 (2019).

    Article  Google Scholar 

  17. Kuser, J. Handbook of Urban and Community Forestry in the Northeast (Springer, 2013).

  18. Hand, K. L. & Doick, K. J. Understanding the Role of Urban Tree Management on Ecosystem Services (UK Forestry Commission, 2019).

  19. Solotaroff, W. Shade-trees in Towns and Cities: Their Selection, Planting, and Care as Applied to the Art of Street Decoration; Their Diseases and Remedies; Their Municipal Control and Supervision (Wiley, 1912).

  20. Bárta, V., Hanuš, J., Dobrovolný, L. & Homolová, L. Comparison of field survey and remote sensing techniques for detection of bark beetle-infested trees. For. Ecol. Manage. 506, 119984 (2022).

    Article  Google Scholar 

  21. Beery, S. et al. The auto arborist dataset: a large-scale benchmark for multiview urban forest monitoring under domain shift. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 21294–21307 (IEEE, 2022).

  22. Fuentes, S., Tongson, E. J. & Viejo, C. G. Urban green infrastructure monitoring using remote sensing from integrated visible and thermal infrared cameras mounted on a moving vehicle. Sensors 21, 295 (2021).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  23. Houborg, R. & McCabe, M. F. High-resolution NDVI from planet’s constellation of earth observing nano-satellites: a new data source for precision agriculture. Remote Sens. 8, 768 (2016).

    Article  ADS  Google Scholar 

  24. Li, X. et al. Assessing street-level urban greenery using Google Street View and a modified green view index. Urban For. Urban Green. 14, 675–685 (2015).

    Article  Google Scholar 

  25. Branson, S. et al. From Google maps to a fine-grained catalog of street trees. ISPRS J. Photogramm. Remote Sens. 135, 13–30 (2018).

    Article  ADS  Google Scholar 

  26. Kwok, C. et al. Detection of structural tree defects using thermal infrared imaging. In Proc. 40th Asian Conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS (2019).

  27. Preisler, Y., Tatarinov, F., Grünzweig, J. M. & Yakir, D. Corrigendum: Seeking the ’point of no return’ in the sequence of events leading to mortality of mature trees. Plant Cell Environ. 45, 1333 (2022).

    Article  CAS  PubMed  Google Scholar 

  28. Hammond, W. M. et al. Dead or dying? Quantifying the point of no return from hydraulic failure in drought-induced tree mortality. N. Phytol. 223, 1834–1843 (2019).

    Article  CAS  Google Scholar 

  29. Hammond, W. M., Johnson, D. M. & Meinzer, F. C. A thin line between life and death: adial sap flux failure signals trajectory to tree mortality. Plant Cell Environ. 44, 1311–1314 (2021).

    Article  CAS  PubMed  Google Scholar 

  30. Flexas, J. et al. Photosynthetic limitations in mediterranean plants: a review. Environ. Exp. Bot. 103, 12–23 (2014).

    Article  CAS  Google Scholar 

  31. Schiermeier, Q. Climate change made europe’s mega-heatwave five times more likely. Nature 571, 155–156 (2019).

    Article  CAS  PubMed  Google Scholar 

  32. Houston, D. R. Stress related to diseases. Arboric. J. 8, 137–149 (1984).

    Article  Google Scholar 

  33. Trugman, A. T., Anderegg, L. D., Anderegg, W. R., Das, A. J. & Stephenson, N. L. Why is tree drought mortality so hard to predict? Trends Ecol. Evol. 36, 520–532 (2021).

    Article  PubMed  Google Scholar 

  34. Oldfield, E. E. et al. Growing the urban forest: tree performance in response to biotic and abiotic land management. Restor. Ecol. 23, 707–718 (2015).

    Article  Google Scholar 

  35. Leong, E. C., Burcham, D. C. & Fong, Y.-K. A purposeful classification of tree decay detection tools. Arboric. J. 34, 91–115 (2012).

    Article  Google Scholar 

  36. Vidal, D. & Pitarma, R. Infrared thermography applied to tree health assessment: a review. Agriculture 9, 156 (2019).

    Article  Google Scholar 

  37. Grossiord, C. et al. Plant responses to rising vapor pressure deficit. N. Phytol. 226, 1550–1566 (2020).

    Article  Google Scholar 

  38. McCarthy, H. R. & Pataki, D. E. Drivers of variability in water use of native and non-native urban trees in the Greater Los Angeles area. Urban Ecosyst. 13, 393–414 (2010).

    Article  Google Scholar 

  39. Marchionni, V., Guyot, A., Tapper, N., Walker, J. & Daly, E. Water balance and tree water use dynamics in remnant urban reserves. J. Hydrol. 575, 343–353 (2019).

    Article  ADS  Google Scholar 

  40. Ramcharan, A. et al. Using transfer learning for image-based cassava disease detection. Front. Plant Sci. 8, 1852 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Näsi, R. et al. Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft. Urban For. Urban Green. 30, 72–83 (2018).

    Article  Google Scholar 

  42. Näsi, R. et al. Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level. Remote Sens. 7, 15467–15493 (2015).

    Article  ADS  Google Scholar 

  43. Borges, E. et al. Bioimpedance parameters as indicators of the physiological states of plants in situ a novel usage of the electrical impedance spectroscopy technique. Adv. Life Sci. 6, 74–86 (2014).

  44. Pitarma, R., Crisóstomo, J. & Ferreira, M. E. Contribution to trees health assessment using infrared thermography. Agriculture 9, 171 (2019).

    Article  CAS  Google Scholar 

  45. Goh, C. L., Abdul Rahim, R., Fazalul Rahiman, M. H., Mohamad Talib, M. T. & Tee, Z. C. Sensing wood decay in standing trees: a review. Sens. Actuators A 269, 276–282 (2018).

  46. Potamitis, I., Rigakis, I., Tatlas, N.-A. & Potirakis, S. In-vivo vibroacoustic surveillance of trees in the context of the IoT. Sensors 19, 1366 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  47. Just, M. & Frank, S. Evaluation of an easy-to-install, low-cost dendrometer band for citizen-science tree research. J. For. 117, 317–322 (2019).

    Google Scholar 

  48. Drew, D. M., Drew, D. M. & Downes, G. M. The use of precision dendrometers in research on daily stem size and wood property variation: a review. Dendrochronologia 27, 159–172 (2009).

    Article  Google Scholar 

  49. Rocha, E. & Holzkämper, S. Assessing urban climate effects on Pinus sylvestris with point dendrometers: a case study from Stockholm, Sweden. Trees https://doi.org/10.1007/s00468-020-02082-8 (2021).

  50. Shabandri, B., Madara, S. R. & Maheshwari, P. IoT-based smart tree management solution for green cities. Internet Things Anal. Agric. 2, 181–199 (2020).

    Google Scholar 

  51. Wei, Y. et al. Proximity environmental feature based tree health assessment scheme using Internet of Things and machine learning algorithm. Sensors 19, 3115 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  52. Wang, H. et al. NB-IoT based tree health monitoring system. In 2019 IEEE International Conference on Industrial Technology (ICIT) 1796–1799 (IEEE, 2019).

  53. Torresan, C. et al. A new generation of sensors and monitoring tools to support climate-smart forestry practices. Can. J. For. Res. 51, 1751–1765 (2021).

    Article  Google Scholar 

  54. Catena, A. & Catena, G. Overview of thermal imaging for tree assessment. Arboric. J. 30, 259–270 (2008).

    Article  Google Scholar 

  55. Catena, A. Thermography reveals hidden tree decay. Arboric. J. 27, 27–42 (2003).

    Article  Google Scholar 

  56. Smigaj, M., Gaulton, R., Barr, S., Suarez, J. & Suarez, J. C. UAV-borne thermal imaging for forest health monitoring: detection of disease-induced canopy temperature increase. ISPRS Int. Arch. Photogramm. Remote Sens. https://doi.org/10.5194/isprsarchives-xl-3-w3-349-2015 (2015).

  57. Majdák, A., Jakuš, R. & Blaženec, M. Determination of differences in temperature regimes on healthy and bark-beetle colonised spruce trees using a handheld thermal camera. IForest 14, 203 (2021).

    Article  Google Scholar 

  58. Burcham, D., Leong, E., Fong, Y. & Tan, P.-Y. An evaluation of internal defects and their effect on trunk surface temperature in Casuarina equisetifolia L. (Casuarinaceae). Arboric. Urban For. 38, 277–286 (2012).

    Article  Google Scholar 

  59. Jiménez-Bello, M., Ballester, C., Castel, J. & Intrigliolo, D. S. Development and validation of an automatic thermal imaging process for assessing plant water status. Agric. Water Manage. 98, 1497–1504 (2011).

    Article  Google Scholar 

  60. Ballester, C., Jiménez-Bello, M., Castel, J. & Intrigliolo, D. S. Usefulness of thermography for plant water stress detection in citrus and persimmon trees. Agric. For. Meteorol. 168, 120–129 (2013).

    Article  ADS  Google Scholar 

  61. Nagy, A. Thermographic evaluation of water stress in an apple orchard. J. Multidiscip. Eng. Sci. Technol. 2, 2210–2215 (2015).

    Google Scholar 

  62. Smigaj, M., Gaulton, R., Suarez, J., Suarez, J. C. & Barr, S. Use of miniature thermal cameras for detection of physiological stress in conifers. Remote Sens. 9, 957 (2017).

    Article  ADS  Google Scholar 

  63. Kim, J. Y. & Glenn, D. M. Multi-modal sensor system for plant water stress assessment. Comput. Electron. Agric. 141, 27–34 (2017).

    Article  Google Scholar 

  64. Huang, S. et al. A commentary review on the use of normalized difference vegetation index (ndvi) in the era of popular remote sensing. J. For. Res. 32, 1–6 (2021).

    Article  Google Scholar 

  65. Qu, Y. Leaf Area Index: Advances in Ground-Based Measurement 359–378 (Springer, 2019); https://doi.org/10.1007/978-3-662-48297-1%5C_11

  66. Wang, L. et al. Precise estimation of NDVI with a simple NIR sensitive RGB camera and machine learning methods for corn plants. Sensors 20, 3208 (2020).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  67. Lausch, A., Erasmi, S., King, D. J., Magdon, P. & Heurich, M. Understanding forest health with remote sensing -Part I—a review of spectral traits, processes and remote-sensing characteristics. Remote Sens. 8, 1029 (2016).

    Article  ADS  Google Scholar 

  68. Karnieli, A. et al. Comments on the use of the vegetation health index over mongolia. Int. J. Remote Sens. 27, 2017–2024 (2006).

    Article  Google Scholar 

  69. Mak, H., Hu, B. & Hu, B. Tree species identification and subsequent health determination from mobile LiDAR data. In 2014 IEEE Geoscience and Remote Sensing Symposium 1365–1368 (IEEE, 2014).

  70. Degerickx, J., Roberts, D., McFadden, J., Hermy, M. & Somers, B. Urban tree health assessment using airborne hyperspectral and LiDAR imagery. Int. J. Appl. Earth Obs. Geoinf. 73, 26–38 (2018).

    ADS  Google Scholar 

  71. Wu, J., Yao, W., Polewski, P. & Polewski, P. Mapping individual tree species and vitality along urban road corridors with LiDAR and imaging sensors: point density versus view perspective. Remote Sens. 10, 1403 (2018).

    Article  ADS  Google Scholar 

  72. Li, X. & Ratti, C. Mapping the spatial distribution of shade provision of street trees in Boston using Google Street View panoramas. Urban For. Urban Green. 31, 109–119 (2018).

    Article  ADS  Google Scholar 

  73. Chen, X. et al. Evaluating greenery around streets using Baidu panoramic street view images and the panoramic green view index. Forests 10, 1109 (2019).

    Article  Google Scholar 

  74. Li, X., Ratti, C. & Seiferling, I. Quantifying the shade provision of street trees in urban landscape: a case study in Boston, USA, using Google Street View. Landsc. Urban Plan. 169, 81–91 (2018).

    Article  Google Scholar 

  75. Feng, Y. et al. Detection and health analysis of individual tree in urban environment with multi-sensor platform. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 7548–7551 (2018).

  76. Luketich, A. M., Papuga, S. A. & Crimmins, M. A. Ecohydrology of urban trees under passive and active irrigation in a semiarid city. PLoS ONE 14, e0224804 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Marchin, R. M., Esperon-Rodriguez, M., Tjoelker, M. G. & Ellsworth, D. S. Crown dieback and mortality of urban trees linked to heatwaves during extreme drought. Sci. Total Environ. 850, 157915 (2022).

    Article  ADS  CAS  PubMed  Google Scholar 

  78. Sishodia, R. P., Sishodia, R. P., Ray, R. L., Ray, R. L. & Singh, S. K. Applications of remote sensing in precision agriculture: a review. Remote Sens. 12, 3136 (2020).

    Article  ADS  Google Scholar 

  79. Ramsfield, T. D., Bentz, B. J., Faccoli, M., Jactel, H. & Brockerhoff, E. G. Forest health in a changing world: effects of globalization and climate change on forest insect and pathogen impacts. Forestry 89, 245–252 (2016).

    Article  Google Scholar 

  80. Morgenroth, J. & Östberg, J. in Routledge Handbook of Urban Forestry (eds Ferrini, F. et al.) 33–48 (Taylor & Francis, 2017).

  81. Roman, L. A., McPherson, E. G., Scharenbroch, B. C. & Bartens, J. et al. Identifying common practices and challenges for local urban tree monitoring programs across the United States. Arboric. Urban For. 39, 292–299 (2013).

    Google Scholar 

  82. Song, X. P., Tan, P. Y., Edwards, P. & Richards, D. The economic benefits and costs of trees in urban forest stewardship: a systematic review. Urban For. Urban Green. 29, 162–170 (2018).

    Article  Google Scholar 

  83. Akbari, H. & Kolokotsa, D. Three decades of urban heat islands and mitigation technologies research. Energy Build. 133, 834–842 (2016).

    Article  Google Scholar 

  84. Lee, S., Moon, H., Choi, Y., Yoon, D. K. & Yoon, D. K. Analyzing thermal characteristics of urban streets using a thermal imaging camera: a case study on commercial streets in Seoul, Korea. Sustainability 10, 519 (2018).

    Article  Google Scholar 

  85. Coutts, A. M. et al. Thermal infrared remote sensing of urban heat: hotspots, vegetation, and an assessment of techniques for use in urban planning. Remote Sens. Environ. 186, 637–651 (2016).

    Article  ADS  Google Scholar 

  86. Venter, Z. S., Krog, N. H. & Barton, D. N. Linking green infrastructure to urban heat and human health risk mitigation in Oslo, Norway. Sci. Total Environ. 709, 136193 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  87. Linda E. Kruger, M. A. S. Getting to know ourselves and our places through participation in civic social assessment. Soc. Nat. Resour. 13, 461–478 (2000).

    Article  Google Scholar 

  88. Conrad, C. C. & Hilchey, K. G. A review of citizen science and community-based environmental monitoring: issues and opportunities. Environ. Monit. Assess. 176, 273–291 (2011).

    Article  PubMed  Google Scholar 

  89. Vogt, J. M. & Fischer, B. C. in Urban Forests, Ecosystem Services and Management (ed. Blum, J.) 153–186 (Taylor & Francis, 2017).

  90. Van Herzele, A., Collins, K. & Tyrväinen, L. Involving People in Urban Forestry—A Discussion of Participatory Practices throughout Europe 207–228 (Springer, 2005); https://doi.org/10.1007/3-540-27684-X_9

  91. Seiferling, I., Naik, N., Ratti, C. & Proulx, R. Green streets—quantifying and mapping urban trees with street-level imagery and computer vision. Landsc. Urban Plan. 165, 93–101 (2017).

    Article  Google Scholar 

  92. Zhang, Y., Li, S., Fu, X. & Dong, R. Quantification of urban greenery using hemisphere-view panoramas with a green cover index. Ecosyst. Health Sustain. 7, 1929502 (2021).

    Article  Google Scholar 

  93. Xia, Y., Yabuki, N. & Fukuda, T. Development of an urban greenery evaluation system based on deep learning and Google Street View. In Proc. 25th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) 783–792 (2020).

  94. O’Keeffe, K. P., Anjomshoaa, A., Strogatz, S. H., Santi, P. & Ratti, C. Quantifying the sensing power of vehicle fleets. Proc. Natl Acad. Sci. USA 116, 12752–12757 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  95. Uittenbogaard, R. et al. Privacy protection in street-view panoramas using depth and multi-view imagery. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 10573–10582 (IEEE, 2019).

  96. Pittaluga, F., Zivkovic, A. S. & Koppal, S. J. Sensor-level privacy for thermal cameras. In 2016 IEEE International Conference on Computational Photography (ICCP) 1–12 (IEEE, 2016).

  97. Xiao, Q. & McPherson, E. G. Tree health mapping with multispectral remote sensing data at UC Davis, California. Urban Ecosyst. 8, 349–361 (2005).

    Article  Google Scholar 

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Acknowledgements

We thank all the members of the MIT Senseable City Lab Consortium for funding this research: Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA) with Carabinieri Forestali, Dubai Future Foundation, Toyota Woven City, UnipolTech, Volkswagen Group America, FAE Technology, MipMap, GoAigua, Shell, ENEL Foundation, Kyoto University, Weizmann Institute of Science, KTH Royal Institute of Technology, AMS Institute, and the cities of Helsingborg, Stockholm and Amsterdam. A.G. thanks Renswoude Foundation, FAST Delft and EFL Stichting for their financial support.

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  1. These authors contributed equally: Akshit Gupta, Simone Mora.

Authors and Affiliations

  1. Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, USA

    Akshit Gupta, Simone Mora, Fàbio Duarte & Carlo Ratti

  2. Technische Universiteit Delft, Delft, The Netherlands

    Akshit Gupta & Venkatesha Prasad

  3. Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway

    Simone Mora

  4. Harvard University, Cambridge, MA, USA

    Yakir Preisler

  5. ABC Department, Politecnico di Milano, Milan, Italy

    Carlo Ratti

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Contributions

Conceptualization: A.G., S.M. and Y.P. Data curation: A.G. Methodology: S.M. Formal analysis: A.G., S.M. and Y.P. Writing—original draft: A.G., S.M., Y.P. and F.D. Writing—review and editing: A.G., S.M., Y.P., F.D., V.P. and C.R. Supervision: S.M. Project administration: S.M., F.D. and V.P. Funding acquisition: C.R.

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Gupta, A., Mora, S., Preisler, Y. et al. Tools and methods for monitoring the health of the urban greenery. Nat Sustain (2024). https://doi.org/10.1038/s41893-024-01295-w

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