NDVI and vegetation volume as predictors of urban bird diversity


  • UNHSP. World Cities Report 2022. (2022). https://unhabitat.org/wcr/.

  • Lai, H., Flies, E. J., Weinstein, P. & Woodward, A. The impact of green space and biodiversity on health. Front. Ecol. Environ. 17, 383–390 (2019).

    Article 

    Google Scholar
     

  • Marselle, M. R., Lindley, S. J., Cook, P. A. & Bonn, A. Biodiversity and health in the urban environment. Curr. Environ. Health Rep. 8, 146–156 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gianfredi, V. et al. Urban green spaces and public health outcomes: A systematic review of literature. Eur. J. Public. Health 31, ckab164638 (2021).

    Article 

    Google Scholar
     

  • Wang, J. et al. Long-term exposure to residential greenness and decreased risk of depression and anxiety. Nat. Mental Health 2, 525–534 (2024).

    Article 

    Google Scholar
     

  • Grabowski, Z. et al. Cosmopolitan conservation: The multi-scalar contributions of urban green infrastructure to biodiversity protection. Biodivers. Conserv. 32, 3595–3606 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Sweet, F. S. T., Apfelbeck, B., Hanusch, M., Garland Monteagudo, C. & Weisser, W. W. Data from public and governmental databases show that a large proportion of the regional animal species pool occur in cities in Germany. J. Urban Ecol. 8, (2022).

  • Olive, A. & Minichiello, A. Wild things in urban places: America’s largest cities and multi-scales of governance for endangered species conservation. Appl. Geogr. 43, 56–66 (2013).

    Article 

    Google Scholar
     

  • Planchuelo, G., von Der Lippe, M. & Kowarik, I. Untangling the role of urban ecosystems as habitats for endangered plant species. Landsc. Urban Plan. 189, 320–334 (2019).

    Article 

    Google Scholar
     

  • Lepczyk, C. et al. Global patterns and drivers of urban bird diversity. In Ecology and Conservation of Birds in Urban Environments 13–33 (2017).

  • Garrard, G. E., Williams, N. S. G., Mata, L., Thomas, J. & Bekessy, S. A. Biodiversity sensitive urban design. Conserv. Lett. 11, e12411 (2018).

    Article 

    Google Scholar
     

  • Weisser, W. W. & Hauck, T. E. Animal-Aided Design—planning for biodiversity in the built environment by embedding a species’ life-cycle into landscape architectural and urban design processes. Landsc. Res. 1–22 (2024).

  • Bekessy, S. A. et al. Transparent planning for biodiversity and development in the urban fringe. Landsc. Urban Plan. 108, 140–149 (2012).

    Article 

    Google Scholar
     

  • De Martino, R., Franchino, R. & Frettoloso, C. A. Stepping stone approach to exploiting urban density. In Technological Imagination in the Green and Digital Transition (eds Arbizzani, E. et al.) 639–647 (Springer, 2023) https://doi.org/10.1007/978-3-031-29515-7_57.

    Chapter 

    Google Scholar
     

  • Aronson, M. F. J. et al. A global analysis of the impacts of urbanization on bird and plant diversity reveals key anthropogenic drivers. Proc. R. Soc. B. 281, 20133330 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lepczyk, C. A. et al. Biodiversity in the city: Fundamental questions for understanding the ecology of urban green spaces for biodiversity conservation. BioScience 67, 799–807 (2017).

  • Sandström, U. G., Angelstam, P. & Khakee, A. Urban comprehensive planning—identifying barriers for the maintenance of functional habitat networks. Landsc. Urban Plan. 75, 43–57 (2006).

    Article 

    Google Scholar
     

  • Kahl, S., Wood, C. M., Eibl, M. & Klinck, H. BirdNET: A deep learning solution for avian diversity monitoring. Ecol. Inf. 61, (2021).

  • Barthel, P. H. & Krüger, T. Liste der Vögel Deutschlands: Version 3.2. (2019).

  • Pérez-Granados, C. A. First assessment of Birdnet performance at varying distances: A playback experiment. Ardeola 70, 221–233 (2023).

    Article 

    Google Scholar
     

  • Arif, M., Hedley, R. & Bayne, E. Testing the accuracy of a BirdNET. Automatic Bird. Song Classifier 7 (2020).

  • Manzano-Rubio, R. Low-cost open-source recorders and ready-to-use machine learning approaches provide effective monitoring of threatened species. Ecol. Inf. 72, (2022).

  • Cole, J. S., Michel, N. L., Emerson, S. A. & Siegel, R. B. Automated bird sound classifications of long-duration recordings produce occupancy model outputs similar to manually annotated data. Ornithol. Appl. 124, 1–15 (2022).


    Google Scholar
     

  • Bota, G., Manzano-Rubio, R., Catalán, L. & Gómez-Catasús, J. Pérez-Granados, C. Hearing to the unseen: Audiomoth and birdNET as a cheap and easy method for monitoring cryptic bird species. Sensors 23, 7176 (2023).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • McGinn, K., Kahl, S., Peery, M. Z., Klinck, H. & Wood, C. M. Feature embeddings from the BirdNET algorithm provide insights into avian ecology. Ecol. Inf. 74, 101995 (2023).

    Article 

    Google Scholar
     

  • Fairbairn, A. J., Burmeister, J. S., Weisser, W. W. & Meyer, S. T. BirdNET is as good as experts for acoustic bird monitoring in a European city. 09.17.613451 Preprint at https://doi.org/10.1101/2024.09.17.613451 (2024).

  • Borker, A. L. et al. Vocal activity as a low cost and scalable index of seabird colony size. Conserv. Biol. 28, 1100–1108 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • Pérez-Granados, C. et al. Vocal activity rate index: A useful method to infer terrestrial bird abundance with acoustic monitoring. Ibis 161, 901–907 (2019).

    Article 

    Google Scholar
     

  • Pérez-Granados, C. et al. Effort needed to accurately estimate vocal activity rate index using acoustic monitoring: A case study with a dawn-time singing passerine. Ecol. Indic. 107, 105608 (2019).

    Article 

    Google Scholar
     

  • Zhao, K., Chen, G., Liu, Y., Møller, A. P. & Zhang, Y. Population size assessment of adélie Penguin (Pygoscelis adeliae) chicks based on vocal activity rate index. Global Ecol. Conserv. 38, e02263 (2022).

    Article 

    Google Scholar
     

  • Hutschenreiter, A. et al. How to count bird calls? Vocal activity indices May provide different insights into bird abundance and behaviour depending on species traits. Methods Ecol. Evol. 15, 1071–1083 (2024).

    Article 

    Google Scholar
     

  • Benedetti, Y. et al. EVI and NDVI as proxies for multifaceted avian diversity in urban areas. Ecol. Appl. N/a, e2808 (2023).

  • Bino, G. et al. Accurate prediction of bird species richness patterns in an urban environment using Landsat-derived NDVI and spectral unmixing. Int. J. Remote Sens. 29, 3675–3700 (2008).

    Article 

    Google Scholar
     

  • Kontsiotis, V. J., Chatzigiovanakis, S., Valsamidis, E., Xofis, P. & Liordos, V. Normalized difference vegetation index as a proxy of urban bird species presence and distribution at different Spatial scales. Diversity 15, 1139 (2023).

    Article 

    Google Scholar
     

  • Leveau, L. M. Primary productivity and habitat diversity predict bird species richness and composition along urban-rural gradients of central Argentina. Urban Urban Green. 43, 126349 (2019).

    Article 

    Google Scholar
     

  • Carlson, T. N. & Ripley, D. A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ. 62, 241–252 (1997).

    Article 
    ADS 

    Google Scholar
     

  • Van Wagtendonk, J. W. & Root, R. R. The use of multi-temporal Landsat normalized difference vegetation index (NDVI) data for mapping fuel models in Yosemite National park, USA. Int. J. Remote Sens. 24, 1639–1651 (2003).

    Article 

    Google Scholar
     

  • Geerken, R., Zaitchik, B. & Evans, J. P. Classifying rangeland vegetation type and coverage from NDVI time series using fourier filtered cycle similarity. Int. J. Remote Sens. 26, 5535–5554 (2005).

    Article 

    Google Scholar
     

  • Yan, E., Wang, G., Lin, H., Xia, C. & Sun, H. Phenology-based classification of vegetation cover types in Northeast China using MODIS NDVI and EVI time series. Int. J. Remote Sens. 36, 489–512 (2015).

    Article 

    Google Scholar
     

  • Huang, S., Tang, L., Hupy, J. P., Wang, Y. & Shao, G. 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
     

  • Hashim, H., Abd Latif, Z. & Adnan, N. A. Urban vegetation classification with NDVI threshold value method with very high resolution (VHR) pleiades. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences vol. XLII-4-W16 237–240 (Copernicus GmbH, 2019).


    Google Scholar
     

  • Fairbairn, A. J. et al. Urban biodiversity is affected by human-designed features of public squares. Nat. Cities. 1–10 (2024).

  • Mühlbauer, M., Weisser, W. W., Müller, N. & Meyer, S. T. A green design of City squares increases abundance and diversity of birds. Basic. Appl. Ecol. 56, 446–459 (2021).

    Article 

    Google Scholar
     

  • Chace, J. F. & Walsh, J. J. Urban effects on native avifauna: A review. Landsc. Urban Plan. 74, 46–69 (2006).

    Article 

    Google Scholar
     

  • Gebremichael, G. et al. Bird community composition and functional guilds response to vegetation structure in Southwest Ethiopia. Forests 13, 2068 (2022).

    Article 

    Google Scholar
     

  • Zhang, Q., Han, R., Huang, Z. & Zou, F. Linking vegetation structure and bird organization: Response of mixed-species bird flocks to forest succession in subtropical China. Biodivers. Conserv. 22, 1965–1989 (2013).

    Article 

    Google Scholar
     

  • Hijmans, R. J. Terra: Spatial Data Analysis. https://rspatial.org/ (2024).

  • Martin, T. E. et al. Variability in the effectiveness of two ornithological survey methods between tropical forest ecosystems. PLoS ONE. 12, e0169786 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chamberlain, S. A. & Boettiger, C. R. Python, and Ruby clients for GBIF species occurrence data. Preprint at https://doi.org/10.7287/peerj.preprints.3304v1 (2017).

  • Chamberlain, S. et al. Rgbif: Interface to the Global Biodiversity Information Facility API. (2025).

  • Mondal, P. Quantifying surface gradients with a 2-band enhanced vegetation index (EVI2). Ecol. Ind. 11, 918–924 (2011).

    Article 

    Google Scholar
     

  • Taubenböck, H., Esch, T., Wurm, M., Roth, A. & Dech, S. Object-based feature extraction using high Spatial resolution satellite data of urban areas. J. Spat. Sci. 55, 117–132 (2010).

    Article 

    Google Scholar
     

  • Wurm, M., Taubenböck, H., Schardt, M., Esch, T. & Dech, S. Object-based image information fusion using multisensor Earth observation data over urban areas. Int. J. Image Data Fus. 2, 121–147 (2011).

    Article 

    Google Scholar
     

  • Deparis, M. et al. Linking plant diversity and urban uses at the City-Block scale to inform urban planning. Land 14, 3 (2025).

    Article 

    Google Scholar
     

  • Snow, D. W. The migration and dispersal of British Blackbirds. Bird. Study. 13, 237–255 (1966).

    Article 

    Google Scholar
     

  • Ferry, C., Frochot, B. & Leruth, Y. Territory and home range of the Blackcap (Sylvia Atricapilla) and some other passerines, assessed and compared by mapping and Capture-Recapture. Stud. Avian Biol. 6, 119–120 (1981).


    Google Scholar
     

  • Cinelli, C., Ferwerda, J. & Hazlett, C. Sensemakr: Sensitivity Analysis Tools for Regression Models. (2024).

  • Naimi, B., Hamm, N., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling. Ecography 37, 191–203 (2014).

    Article 
    ADS 

    Google Scholar
     

  • Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. (2024).

  • Oksanen, J., et al. Vegan: Community Ecology Package. (2020). https://CRAN.R-project.org/package=vegan

  • Tobias, J. A. et al. AVONET: Morphological, ecological and geographical data for all birds. Ecol. Lett. 25, 581–597 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Croci, S., Butet, A. & Clergeau, P. Does urbanization filter birds on the basis of their biological traits. Condor 110, 223–240 (2008).

    Article 

    Google Scholar
     

  • Legendre, P. Lmodel2: Model II Regression. (2018). https://CRAN.R-project.org/package=lmodel2

  • Winiarska, D., Szymański, P. & Osiejuk, T. S. Detection ranges of forest bird vocalisations: Guidelines for passive acoustic monitoring. Sci. Rep. 14, 894 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pérez Granados, C., Bota, G., Albarracín, J., Giralt, D. & Traba, J. Cost-Effectiveness assessment of five audio recording systems for wildlife monitoring: Differences between recording distances and singing direction. Ardeola 66, 311–325 (2019).

    Article 

    Google Scholar
     

  • Budka, M., Jobda, M., Szałański, P. & Piórkowski, H. Acoustic approach as an alternative to human-based survey in bird biodiversity monitoring in agricultural meadows. PLoS ONE. 17, e0266557 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • James Reynolds, S., Ibáñez-Álamo, J. D., Sumasgutner, P. & Mainwaring, M. C. Urbanisation and nest Building in birds: A review of threats and opportunities. J. Ornithol. 160, 841–860 (2019).

    Article 

    Google Scholar
     

  • Derryberry, E. P. & Coomes, C. M. Providing urban birds nutritious food to feed chicks reduces urban versus rural breeding success disparities. J. Anim. Ecol. 89, 1546–1548 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Burt, S. A., Vos, C. J., Buijs, J. A. & Corbee, R. J. Nutritional implications of feeding free-living birds in public urban areas. J. Animm Physiol. Anim. Nutr. 105, 385–393 (2021).

    Article 

    Google Scholar
     

  • Tremblay, M. A. & St. Clair, C. C. Permeability of a heterogeneous urban landscape to the movements of forest songbirds. J. Appl. Ecol. 48, 679–688 (2011).

    Article 

    Google Scholar
     

  • Loss, S. R., Will, T. & Marra, P. P. Direct mortality of birds from anthropogenic causes. Annu. Rev. Ecol. Evol. Syst. 46, 99–120 (2015).

    Article 

    Google Scholar
     

  • Dale, S. Urban bird community composition influenced by size of urban green spaces, presence of native forest, and urbanization. Urban Ecosyst. 21, 1–14 (2018).

    Article 

    Google Scholar
     

  • Dyson, K. Conserving native trees increases native bird diversity and community composition on commercial office developments. J. Urban Ecol. 6, juaa033 (2020).

    Article 

    Google Scholar
     

  • Campos-Silva, L. A. & Piratelli, A. J. Vegetation structure drives taxonomic diversity and functional traits of birds in urban private native forest fragments. Urban Ecosyst. 24, 375–390 (2021).

    Article 

    Google Scholar
     

  • Lee, T. S. et al. A framework to identify priority wetland habitats and movement corridors for urban amphibian conservation. Ecol. Solut. Evid. 3, e12139 (2022).

    Article 

    Google Scholar
     

  • Lee, G., Hwang, J. & Cho, S. A novel index to detect vegetation in urban areas using UAV-Based multispectral images. Appl. Sci. 11, 3472 (2021).

    Article 

    Google Scholar
     

  • van den Berg, A. Staatsbroeders: Hoe Leefomgeving En Bouwstijl Beïnvloeden Vogeldiversiteit (Natuurhistorisch Museum Rotterdam, 2021).

  • Curipaco Quinto, P. Z. & Quispe-Melgar, H. R. Siguas Robles, O. Plant composition, water resources and built structures influence bird diversity: A case study in a high Andean City with homogeneous soundscape. Urban Ecosyst. 27, 1–14 (2024).

    Article 

    Google Scholar
     



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