Next Article in Journal
Diving into Diversity: Copepod Crustaceans in Octocoral Associations
Previous Article in Journal
Seasonal Variation in the Organization of Dung Beetle Communities in the Moroccan Middle Atlas (Coleoptera: Scarabaeoidea)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diversity
Volume 15
Issue 11
10.3390/d15111139
diversity-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Normalized Difference Vegetation Index as a Proxy of Urban Bird Species Presence and Distribution at Different Spatial Scales

by
Vasileios J. Kontsiotis
,
Stavros Chatzigiovanakis
,
Evangelos Valsamidis
,
Panteleimon Xofis
and
Vasilios Liordos
*
Department of Forest and Natural Environment Sciences, International Hellenic University, 66100 Drama, Greece
*
Author to whom correspondence should be addressed.
Diversity 2023, 15(11), 1139; https://0-doi-org.brum.beds.ac.uk/10.3390/d15111139
Submission received: 24 October 2023 / Revised: 6 November 2023 / Accepted: 9 November 2023 / Published: 12 November 2023
(This article belongs to the Section Animal Diversity)
Browse Figures
Versions Notes

Abstract

:
Birds are important features of the urban landscape, offering valuable ecosystem services, such as physiological and psychological stress reduction, aesthetic pleasure, and education. Knowing the populations of bird species in cities is important for their successful conservation. The normalized difference vegetation index (NDVI) is a remotely sensed metric used as a green cover proxy. We estimated the abundance of 15 bird species in the urban green spaces of Kavala, Greece, and calculated the NDVI at 19 survey stations with three different spatial scales: 50 m, 200 m, and 500 m circular buffers. NDVI was shown to significantly affect the abundance of 13 species. The 50 m buffer best predicted the abundance of 4 species, the 200 m buffer predicted 7 species, and the 500 m buffer predicted 4 species. Abundance decreased with NDVI for 9 species (urban dwellers) and increased for 6 species (urban utilizers). These findings suggest that NDVI is a reliable predictor of the abundance of bird species in urban areas. More importantly, bird abundance and NDVI associations can be better described if determined at various spatial scales. These findings could be used for the prediction and monitoring of urban bird species populations and incorporated into urban conservation management plans.

1. Introduction

Modern people increasingly choose to live in cities. In 2007, the global urban population exceeded the rural population for the first time, while 57.5% of people currently live in urban areas, it is a percentage projected to reach 68.4% by 2050 [1]. This ever-increasing urbanization has resulted in the creation of large cities and megacities worldwide. Urban areas are detrimental to biodiversity because they occupy previously natural ecosystems [2]. On the other hand, urban green spaces, either remnants of natural areas or artificial ones, are important for wildlife species, acting as an oasis in a sea of concrete [3]. Research has shown that urban green spaces offer nesting sites, food, and shelter for birds and host diverse avian communities [2,4,5,6,7]. As a result, green spaces in cities have a dual crucial role: they are hotspots for biodiversity conservation and provide valuable ecosystem services [3]. Birds are important for urban ecosystem functioning, acting as both predators and prey [8], plant pollinators [9], and indicators of animal diversity and habitat conditions [10,11] because of their conspicuousness and quick response to changes caused by urbanization [12,13]. However, not all bird species readily adapt to novel conditions in urban green spaces. Fischer et al. [14] categorized wildlife’s response to urbanization based on differences in population dynamics in natural and developed areas. Urban avoiders are species that rarely occur in developed areas but may persist in natural areas embedded in urbanized landscapes. Urban dwellers and urban utilizers have adapted, and the former readily exploit novel urban habitats. However, the persistence of dwellers is independent of natural areas, while the persistence of utilizers depends on both developed and natural areas. Visiting or viewing vegetated areas in cities promotes socialization and human health and well-being [15,16]. Wildlife, and especially conspicuous birds, are significant components of green spaces and are important for the improvement of the physical and mental health of urban residents [17,18]. People reported higher levels of happiness, positive emotions, and vitality and lower levels of anxiety in green spaces with high bird diversity [19] and abundance [20,21]. The importance of birds for urban ecosystems and society calls for the protection and enhancement of their populations. The monitoring of species abundance and the use of indicators of their presence and population size are among the strategies used for acquiring data that are suitable for determining their distribution and trends [4,22,23,24]. Such data are critical for managing urban green spaces to secure local populations and increase bird diversity in cities.
The monitoring of bird species in large areas, such as cities, involves many researchers and is time-consuming and expensive. The use of easily retrieved and measured environmental variables could provide the required information on the presence and distribution of bird species in urban areas [25,26]. Data retrieved from multispectral satellite imagery can be used to monitor green cover and primary productivity in any part of the world efficiently and reliably [27]. The normalized difference vegetation index (NDVI) is a vegetation index that combines two opposite canopy characteristics, removing the noise introduced by varying sun angles, topography, clouds, shadows, and atmospheric conditions [28,29,30]. It can be regarded as a methodologically sound way of calculating the physiological activity of plants and is positively correlated with the area index of green foliage, green biomass, and the percentage of green cover [31]. NDVI is one of the most common global vegetation indices and can be used as a reliable indicator for surveys of vegetation cover in urban green spaces [32]. Most studies have reported a positive relationship between vegetation cover and bird diversity in urban areas [33,34,35,36]. NDVI has also been used to predict urban bird diversity in several studies because it is a good indicator of vegetation cover and because of the global availability of data for its reliable calculation. These studies have reported positive relationships between NDVI and bird occurrence, richness, and diversity [24,37,38,39,40,41,42,43,44], though not always [24,44].
Greece is a highly urbanized country, with 80.7% of its current population being urban, a rate that is expected to reach 87.7% by 2050 [1]. These increasing urbanization trends emphasize the importance of green spaces in Greek cities for the well-being of both people and wildlife [45]. Research has shown that urban green spaces in Greece host diverse bird communities [2,4,6,7]. Therefore, inferring the presence and abundance of bird species populations through appropriate indices is cost-effective, easily applicable, and critical for their conservation management. The aim of this study was to use the abundance of urban bird species nesting in the green spaces of a Greek city, Kavala, to assess whether and how NDVI affects the species’ presence and distribution across the city.

2. Materials and Methods

2.1. Study Area

This study was carried out in the core urban area of the municipality of Kavala, northern Greece (Figure 1). The core urban area of Kavala, hereafter just Kavala, extends to about 8.0 km2 and has about 56,500 inhabitants [46]. Kavala is situated between the peri-urban pine forest to the north (Turkish pine Pinus brutia) and the sea to the south. Two pine woodlands are among the most important green spaces of Kavala: the Panagiouda (17.0 ha) to the west and the Pentakosion (1.3 ha) to the east [2]. Otherwise, its urban green spaces mainly consist of small square gardens (<3.0 ha) with native and non-native plants, pavements, playgrounds, cafés, and restaurants.

2.2. Bird Surveys

We surveyed 19 green spaces, representing all categories of green spaces as follows: woodlands (5 survey stations), square gardens (5 survey stations), playgrounds (4 survey stations), and street hedgerows (5 survey stations) (Table 1). One survey station was established at the center of each green space. Survey stations were located at least 250 m apart to avoid the double counting of individuals. We used the single-visit fixed-radius (50 m) point count method to estimate the abundance of each bird species [47].
Bird counts were carried out in three monthly sessions during the breeding season of 2016 (April, May, and June) to reduce the possibility of not recording early and late nesters. Each session was carried out for five days during the second week of April, May, and June. Counts were carried out during the period of maximal diurnal bird activity from 1 h after dawn to 1030 h, according to the recommendations of Bibby et al. [46], with a 5 min silent period before starting the 5 min bird surveys. All fieldwork was carried out in clear and calm weather conditions by one observer (E.V.) to avoid observer effects. Breeding birds were determined when the following activities were observed, following the procedure proposed by [48]: male singing (>3 registrations during the count period), a pair (birds were seen together), or an active nest or family.

2.3. NDVI Estimation

NDVI uses an algorithm that extracts information from two channels of a satellite image, red and near-infrared (NIR) [31,49], and is calculated as follows:
N D V I = N I R − R E D ( N I R + R E D )
NDVI values range from −1 to +1 depending on the relative reflectance of geographic features in the two spectral bands. Vegetated areas tend to give high NDVI values due to the high reflectance of green vegetation in NIR and low reflectance in the red band. Rocks and impervious surfaces have similar reflectance in both bands and give values close to zero, while open water gives negative values [50].
In the current study, NDVI was calculated on a Sentinel-2 image sensed on 10 July 2016, which was the year when in situ data collection took place. A summer image was selected because the vegetation was at its best, deciduous species set their leaves, and new shoots had grown; therefore, in this period, NDVI expresses a better landscape composition. It was not considered necessary to calculate the average NDVI of multiple images due to the non-dynamic nature of the urban landscape in such a short period. The Sentinel-2 mission consists of two polar-orbiting satellites operated by the European Space Agency [51]. They carry the Multispectral Instrument (MSI) sensor, delivering data at spatial resolutions between 10 and 60 m and covering a wide part of the electromagnetic spectrum. The image was obtained by the Copernicus Scientific Hub processed at Level 1C, while an absolute atmospheric correction using the Dark Object Subtraction algorithm (DOS) was applied in the software SNAP 9.0.0. Bands 4 (RED) and 7 (NIR), both at a spatial resolution of 10 m, were employed for the calculation of NDVI.
Mean NDVI values were calculated in three circular spatial scales (buffer zones) fixed at the center of each survey station, with a radius of 50 m, 200 m, and 500 m. The bird species breeding in the green spaces of Kavala were solitary territorials (e.g., Common Chaffinch Fringilla coelebs, tits, warblers, thrushes, nightingales) with territories varying from 0.1 ha to 4.0 ha, or loosely (e.g., European Greenfinch Chloris chloris, European Goldfinch Carduelis carduelis, corvids) to strongly colonial species (e.g., pigeons, martins, swallows) [52]. The size of buffers was selected considering the size of each species’ territories and colonies, the size of the city, and the configuration of grey–green infrastructures in the city [24,42]. The sea surface within the buffer zones was truncated for measurement accuracy.

2.4. Data Analysis

In total, 32 bird species were observed in the green spaces of Kavala (see [4] for details). However, we examined 15 species that were recorded in more than 20% of the survey stations to allow for assessing trends [47]. The correlation of mean NDVI values was not higher than the proposed threshold for suggesting collinearity (r < 0.7) among the three spatial scales (all Spearman rs < 0.572). Correlations were calculated using the function cor.test from stats in R 4.2.2 software [53]. Bird abundances did not show a spatial autocorrelation (Moran’s I varied between −0.08 and 0.29, all p-values > 0.15). Therefore, we examined the relationships of bird abundance for each species with NDVI by performing generalized linear models (GLMs) with Poisson distributions and log link functions using the function glm from stats in R 4.2.2 software [53]. Abundances were count data containing zeroes and, therefore, followed the Poisson distribution [54]. The log link function is a natural fit for count variables that follow the Poisson distribution. We modeled NDVI at different buffers separately because we wanted to assess their relationship with the abundance of each bird species. This procedure resulted in three models for each species. The best of the three models explaining the variation in the data was determined using the Akaike Information Criterion (AICc) [54]. The model with the lowest AICc and the greatest AICc weight (wi) was considered the best model [55].

3. Results

The 15 bird species included 5 urban dwellers, 7 urban utilizers, 2 urban dwellers/utilizers, and 1 urban avoider (Table 2). The Northern House Martin (Delichon urbicum), the House Sparrow (Passer domesticus), the Βarn Swallow (Hirundo rustica), the Feral Pigeon (Columba livia forma urbana), and the Collared Dove (Streptopelia decaocto) were the most abundant, while the Northern House Martin, the House Sparrow, the Collared Dove, and the European Greenfinch were found in more green spaces.
NDVI mean values ranged from 0.203 to 0.600 in the 50 m buffer (mean 0.364 ± 0.105 SD), from 0.163 to m 0.547 in the 200 m buffer (0.274 ± 0.088), and from 0.161 to 0.436 in the 500 m buffer (0.267 ± 0.080). NDVI values were significantly higher in the 50 m buffer than in the 200 m (t = 2.845, df = 36, p = 0.007) and the 500 m buffer (t = 3.202, df = 36, p = 0.003). The 200 m and 500 m buffers did not significantly differ in their NDVI mean values (t = 0.269, df = 36, p = 0.790).
NDVI significantly affected abundance in all the buffers for the House Sparrow, Feral Pigeon, Western Jackdaw (Corvus monedula), and Eurasian Blackbird (Turdus merula) (Table 3). The 200 m buffer represented the best model for the House Sparrow and Eurasian Blackbird, while the 500 m buffer represented the best model for the Feral Pigeon and Western Jackdaw. Abundance decreased with increasing NDVI values for the House Sparrow, Feral pigeon, and Western Jackdaw (Figure 2). By contrast, abundance increased with increasing NDVI values for the Eurasian Blackbird.
NDVI significantly affected abundance in the 50 m and 200 m buffers for the Great Tit (Parus major), the Common Chaffinch, and the European Greenfinch (Table 3). The 50 m buffer represented the best model, with abundance increasing with increasing NDVI values for these three species (Figure 2).
NDVI significantly affected abundance in the 200 m and 500 m buffers for the Common Nightingale (Luscinia megarhynchos) and the Sardinian Warbler (Curruca melanocephala) (Table 3). The 200 m buffer represented the best model for the Sardinian Warbler, while the 500 m buffer represented the best model for the Common Nightingale. Abundance increased with increasing NDVI values for both species (Figure 2).
NDVI significantly affected abundance only in the 50 m buffer for the Barn Swallow (Table 3). Abundance decreased with increasing NDVI values in the 50 m buffer (Figure 2).
NDVI significantly affected abundance only in the 200 m buffer for the Northern House Martin and the Collared Dove (Table 3). Abundance decreased with increasing NDVI values in the 200 m buffer for both species (Figure 2).
NDVI significantly affected abundance only in the 500 m buffer for the European Goldfinch (Table 3). Abundance decreased with increasing NDVI values in the 500 m buffer (Figure 2).
NDVI did not affect the abundance of the Hooded Crow (Corvus cornix) and the Common Starling (Sturnus vulgaris) (Table 3). Model selection determined the 200 m buffer model as the best model. Abundance decreased with increasing NDVI values for both species (Figure 2).

4. Discussion

In agreement with previous and similar research [24,37,38,39,40,41,42,43,44], NDVI significantly affected bird presence and distribution in the city of Kavala. Primary productivity was important at different spatial scales, usually for more than one and for different species. NDVI was related significantly to abundance for four species in the 50 m buffer, for seven species in the 200 m buffer, and for four species in the 500 m buffer. The presence of bird species in European cities might be related to their old history [5,56]. In areas such as Australia, where urbanization is a relatively new phenomenon, several species, especially those threatened, may still live in cities, whereas in much earlier urbanized Europe, many threatened species have already disappeared from cities. Another important factor connected to the presence and distribution of a species in urban areas is the habitat diversity of the regional landscape matrix [5,57,58]. Kavala is surrounded by a pine forest and rural areas. The regional bird diversity might be responsible for the high species richness of Kavala when compared to larger Greek cities [6]. However, Kontsiotis et al. [4] found that bird communities differed between the forest, the ecotone, and the city of Kavala. This trend has also been reported for other European cities [5,58,59]. Although this city’s history and the natural areas in the regional landscape matrix are crucial, more local factors finally shape the composition of urban bird communities, such as the local landscape matrix, green space size, and the amount and permeability of built areas [5,7,13,60,61]. When we come down to species, their life history traits determine their ability to colonize, adapt, and thrive in urban environments [14]. Within cities, green cover might differ in amount and spatial arrangement at different spatial scales, thus differentially affecting the presence and distribution of bird species, as evidenced by our findings and those of other studies [23,37,38,39,40,41,42,43,44]. For example, the Barn Swallow nests almost exclusively in a few abandoned large warehouses in Kavala. Thus, green cover around their nests is increasing with the increasing distance from warehouses [4]. As a consequence, NDVI affected Barn Swallow abundance more at the smaller 50 m buffer than the larger 200 m and 500 m buffers. Different cities might most likely offer different resources to birds, both in quantity and quantity [5,6,7,13,57,58,61,62]. Leveau and Isla [24] studied the relationship of NDVI with the presence of bird species in Mar de Plata, Argentina, and found that it could not predict species presence and distribution in other Argentinean cities even with similar NDVI values (Miramar and Balcarce), and they attributed this to factors related to city size, such as noise and the distance to rural areas. Habitat suitability studies for urban birds should be city-specific and also examine different spatial scales to produce valid information.
The abundance of six bird species increased with increasing NDVI and were distributed in green spaces with values higher than 0.35 to 0.40. The Sardinian Warbler is a farmland species that also nests in shrubs and brambles in forest understories or openings [52]. The survival of this species in urban areas depends on the size, composition, and connectivity of green spaces in cities [61,63,64]. The Eurasian Blackbird is a forest species that nests in bushes and low in bushy trees; the Common Chaffinch and European Greenfinch mostly nest in trees but also in shrubs and hedges, often in colonies; the Blue Tit nests in holes in trees; and the Common Nightingale nests low in bushes [52]. These species prefer forested habitats but also utilize open woodlands and cultivated areas with trees and bushes. They also use parks and gardens in cities, considered as urban utilizers in terms of their response to urbanization [14]. These six species largely depend on primary productivity, and the differences in their abundance with different NDVI values are indicative of differences in habitat requirements [23].
The abundance of nine bird species decreased with increasing NDVI and was distributed in green spaces with values lower than 0.30 to 0.25. Most of these species are urban dwellers adapted to city life whose survival is not dependent on immigration from adjacent natural areas [14]. The Collared Dove prefers nesting in trees but can readily use ornamental plants and building ledges, while the Common Starling and Western Jackdaw nest in holes in trees and buildings [52]. The Northern House Martin, Barn Swallow, and House Sparrow nest, almost exclusively in building cavities [52,59]. The Feral Pigeon and Collared Dove nest in the roofs and ledges of buildings [52]. The European Goldfinch is also a species adapted to city life [4]. However, it is dependent on natural areas, just as the other finches in the area, and we did not expect its abundance to decrease with increasing NDVI values. As finches are also adapted to nesting in parks and gardens and the European Goldfinch nests in loose colonies in areas with scattered trees and bushes [52], it might be that Kavala’s green spaces towards the city center offer such opportunities, resulting in an increasing presence of this species in the more urbanized parts of the city.
Although cost-effective and easy to extract, NDVI is only a general proxy for many factors that decide bird abundance, such as green cover and green space size [7,22,35]. Also, it cannot always predict abundance other than in the studied urban areas [24]. NDVI proved an important indicator of bird presence and distribution in the green spaces of Kavala. However, we could not validate our predictions due to the small number of green spaces studied.

5. Conclusions

We used NDVI, the most common and remotely sensed proxy of primary productivity, to assess how it affected bird presence and distribution in Kavala. NDVI could determine the presence and distribution of all the bird species at different spatial scales as follows: for four species in the 50 m buffer, for seven species in the 200 m buffer, and for four species in the 500 m buffer. These relationships confirmed previous research in Kavala [2,4,7] and other cities [5,6,12,24,63] concerning the increasing importance of green cover for six avoider or utilizer species and the increasing importance of built areas for nine urban dweller species. These results emphasized the need to examine relationships between remotely sensed indices and bird presence and distribution in urban areas at various spatial scales. Future research should involve the investigation of these relationships at more spatial scales and other cities differing in size and green-built area relationships and configurations.

Author Contributions

Conceptualization, V.L.; investigation, E.V.; methodology, V.J.K., S.C., P.X. and V.L.; software, S.C., P.X. and V.L.; validation, V.J.K., S.C. and V.L.; formal analysis, V.L.; resources, V.J.K., S.C., E.V., P.X. and V.L.; data curation, S.C. and V.L.; Writing—Original draft preparation.; V.J.K., S.C. and V.L.; Writing—Reviewing and editing, E.V. and P.X.; visualization, S.C. and V.L.; supervision, V.J.K., P.X. and V.L.; project administration, V.J.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Data is contained within the article.

Acknowledgments

This project was part of the “Planning and managing urban green spaces” postgraduate program of the Department of Forest and Natural Environment Sciences, International Hellenic University. We are grateful to two anonymous reviewers whose comments and suggestions helped greatly improve the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. United Nations, Department of Economic and Social Affairs, Population Division. World Urbanization Prospects: The 2018 Revision (ST/ESA/SER.A/420). United Nations. Available online: https://population.un.org/wup/Publications/Files/WUP2018-Report.pdf (accessed on 20 October 2023).
  2. Liordos, V.; Jokimäki, J.; Kaisanlahti-Jokimäki, M.-L.; Valsamidis, E.; Kontsiotis, V.J. Niche analysis and conservation of bird species using urban core areas. Sustainability 2021, 13, 6327. [Google Scholar] [CrossRef]
  3. Grimm, N.B.; Faeth, S.H.; Golubiewski, N.E.; Redman, C.L.; Wu, J.G.; Bai, X.M.; Briggs, J.M. Global change and the ecology of cities. Science 2008, 319, 756–760. [Google Scholar] [CrossRef] [PubMed]
  4. Kontsiotis, V.J.; Valsamidis, E.; Liordos, V. Organization and differentiation of breeding bird communities across a forested to urban landscape. Urban For. Urban Green. 2019, 38, 242–250. [Google Scholar] [CrossRef]
  5. Jokimäki, J.; Suhonen, J.; Kaisanlahti-Jokimäki, M.-L. Urban core areas are important for species conservation: A European-level analysis of breeding bird species. Landsc. Urban Plan. 2018, 178, 73–81. [Google Scholar] [CrossRef]
  6. Tzortzakaki, O.; Kati, V.; Kassara, C.; Tietze, D.T.; Giokas, S. Seasonal patterns of urban bird diversity in a Mediterranean coastal city: The positive role of open green spaces. Urban Ecosyst. 2018, 21, 27–39. [Google Scholar] [CrossRef]
  7. Liordos, V.; Jokimäki, J.; Kaisanlahti-Jokimäki, M.-L.; Valsamidis, E.; Kontsiotis, V.J. Patch, matrix and disturbance variables negatively influence bird community structure in small-sized managed green spaces located in urban core areas. Sci. Total Environ. 2021, 801, 149617. [Google Scholar] [CrossRef] [PubMed]
  8. Cohn, J.P. Urban wildlife. BioScience 2005, 55, 201–205. [Google Scholar] [CrossRef]
  9. Klein, A.M.; Vaissière, B.E.; Cane, J.H.; Steffan-Dewenter, I.; Cunningham, S.A.; Kremen, C.; Tscharntke, T. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B Biol. Sci. 2007, 274, 303–313. [Google Scholar] [CrossRef]
  10. Lepczyk, C.A.; Warren, P.S. Urban Bird Ecology and Conservation; University of California Press: Berkeley, CA, USA, 2012. [Google Scholar]
  11. Xu, X.; Xie, Y.; Qi, K.; Luo, Z.; Wang, X. Detecting the response of bird communities and biodiversity to habitat loss and fragmentation due to urbanization. Sci. Total Environ. 2018, 624, 1561–1576. [Google Scholar] [CrossRef]
  12. Chace, J.F.; Walsh, J.J. Urban effects on native avifauna: A review. Landsc. Urban Plan. 2006, 7, 46–69. [Google Scholar] [CrossRef]
  13. Pellissier, V.; Cohen, M.; Boulay, A.; Clergeau, P. Birds are also sensitive to landscape composition and configuration within the city centre. Landsc. Urban Plan. 2012, 104, 181–188. [Google Scholar] [CrossRef]
  14. Fischer, J.D.; Schneider, C.; Ahlers, A.A.; Miller, J.R. Categorizing wildlife responses to urbanization and conservation implications of terminology. Conserv. Biol. 2015, 29, 1246–1248. [Google Scholar] [CrossRef] [PubMed]
  15. Cox, D.T.C.; Shanahan, D.F.; Hudson, H.L.; Fuller, R.A.; Gaston, K.J. The impact of urbanization on nature dose and the implications for human health. Landsc. Urban Plan. 2018, 179, 72–80. [Google Scholar] [CrossRef]
  16. Soga, M.; Gaston, K.J. Extinction of experience: The loss of human–nature interactions. Front. Ecol. Environ. 2016, 14, 94–101. [Google Scholar] [CrossRef]
  17. Fuller, R.A.; Irvine, K.N.; Devine-Wright, P.; Warren, P.H.; Gaston, K.J. Psychological benefits of green space increase with biodiversity. Biol. Lett. 2007, 3, 390–394. [Google Scholar] [CrossRef] [PubMed]
  18. Lerman, S.B.; Warren, P.S. The conservation value of residential yards: Linking birds and people. Ecol. Appl. 2011, 21, 1327–1339. [Google Scholar] [CrossRef] [PubMed]
  19. Cameron, R.W.F.; Brindley, P.; Mears, M.; McEwan, K.; Ferguson, F.; Sheffield, D.; Jorgensen, A.; Riley, J.; Goodrick, J.; Ballard, L.; et al. Where the wild things are! Do urban green spaces with greater avian biodiversity promote more positive emotions in humans? Urban Ecosyst. 2020, 23, 301–317. [Google Scholar] [CrossRef]
  20. Shwartz, A.; Turbé, A.; Simon, L.; Julliard, R. Enhancing urban biodiversity and its influence on city-dwellers: An experiment. Biol. Conserv. 2014, 171, 82–90. [Google Scholar] [CrossRef]
  21. Wolf, L.J.; Zu Ermgassen, S.; Balmford, A.; White, M.; Weinstein, N. Is variety the spice of life? An experimental investigation into the effects of species richness on self-reported mental well-being. PLoS ONE 2017, 12, e0170225. [Google Scholar] [CrossRef]
  22. Ferenc, M.; Sedláček, O.; Fuchs, R. How to improve urban greenspace for woodland birds: Site and local-scale determinants of bird species richness. Urban Ecosyst. 2014, 17, 625–640. [Google Scholar] [CrossRef]
  23. Tryjanowski, P.; Morelli, F.; Mikula, P.; Krištín, A.; Indykiewicz, P.; Grzywaczewski, G.; Kronenberg, J.; Jerzak, L. Bird diversity in urban green space: A large-scale analysis of differences between parks and cemeteries in Central Europe. Urban For. Urban Green. 2017, 27, 264–271. [Google Scholar] [CrossRef]
  24. Leveau, L.M.; Federico, I.; Isla, F.I. Predicting bird species presence in urban areas with NDVI: An assessment within and between cities. Urban For. Urban Green. 2021, 63, 127199. [Google Scholar] [CrossRef]
  25. Rocchini, D.; Balkenhol, N.; Carter, G.A.; Foody, G.M.; Gillespie, T.W.; He, K.S.; Kark, S.; Levin, N.; Lucas, K.; Luoto, M.; et al. Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges. Ecol. Inform. 2010, 5, 318–329. [Google Scholar] [CrossRef]
  26. Rocchini, D.; Boyd, D.S.; Féret, J.B.; Foody, G.M.; He, K.S.; Lausch, A.; Nagendra, H.; Wegmann, M.; Pettorelli, N. Satellite remote sensing to monitor species diversity: Potential and pitfalls. Remote Sens. Ecol. Conserv. 2016, 2, 25–36. [Google Scholar] [CrossRef]
  27. Wang, H.; Zeng, Z.-C. Satellite observations of urban greenery phenology in downtown Beijing at meter to kilometer scales. In Proceedings of the IGARSS 2022–2022, Kuala Lumpur, Malaysia, IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 17–22 July 2022; pp. 6268–6271. [Google Scholar] [CrossRef]
  28. Xofis, P.; Spiliotis, J.A.; Chatzigiovanakis, S.; Chrysomalidou, A.S. Long-term monitoring of vegetation Dynamics in the Rhodopi Mountain Range National Park-Greece. Forests 2022, 13, 377. [Google Scholar] [CrossRef]
  29. Huang, S.; Tang, L.J.; Hupy, 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. 2021, 3, 1–6. [Google Scholar] [CrossRef]
  30. Zhengxing, W.; Chuang, L.; Alfredo, H. From AVHRR-NDVI to MODIS-EVI: Advances in vegetation index research. Acta Ecol. Sin. 2003, 23, 979–987. [Google Scholar]
  31. Anguluri, R.; Narayanan, P. Role of green space in urban planning: Outlook towards smart cities. Urban For. Urban Green. 2017, 25, 58–65. [Google Scholar] [CrossRef]
  32. Li, X.; Gong, P.; Liang, L. A 30-year (1984–2013) record of annual urban dynamics of Beijing City derived from Landsat data. Remote Sens. Environ. 2015, 166, 78–90. [Google Scholar] [CrossRef]
  33. Jasmani, Z.; Ravn, H.P.; van den Bosch, C.C.K. The influence of small urban parks characteristics on bird diversity: A case study of Petaling Jaya, Malaysia. Urban Ecosyst. 2017, 20, 227–243. [Google Scholar] [CrossRef]
  34. Strohbach, M.W.; Lerman, S.B.; Warren, P.S. Are small greening areas enhancing bird diversity? Insights from community-driven greening projects in Boston. Landsc. Urban Plan. 2013, 114, 69–79. [Google Scholar] [CrossRef]
  35. MacGregor-Fors, I.; Schondube, J.E. Gray vs. green urbanization: Relative importance of urban features for urban bird communities. Basic Appl. Ecol. 2011, 12, 372–381. [Google Scholar] [CrossRef]
  36. Beninde, J.; Veith, M.; Hochkirch, A. Biodiversity in cities needs space: A metaanalysis of factors determining intra-urban biodiversity variation. Ecol. Lett. 2015, 18, 581–592. [Google Scholar] [CrossRef] [PubMed]
  37. Bino, G.; Levin, N.; Darawshi, S.; Van Der Hal, N.; Reich-Solomon, A.; Kark, S. Accurate prediction of bird species richness patterns in an urban environment using Landsat-derived NDVI and spectral unmixing. Int. J. Remote Sens. 2008, 29, 3675–3700. [Google Scholar] [CrossRef]
  38. Cooper, W.J.; McShea, W.J.; Forrester, T.; Luther, D.A. The value of local habitat heterogeneity and productivity when estimating avian species richness and species of concern. Ecosphere 2020, 11, e03107. [Google Scholar] [CrossRef]
  39. Callaghan, C.T.; Major, R.E.; Lyons, M.B.; Martin, J.M.; Kingsford, R.T. The effects of local and landscape habitat attributes on bird diversity in urban greenspaces. Ecosphere 2018, 9, e02347. [Google Scholar] [CrossRef]
  40. Nieto, S.; Flombaum, P.; Garbulsky, M.F. Can temporal and spatial NDVI predict regional bird-species richness? Glob. Ecol. Conserv. 2015, 3, 729–735. [Google Scholar] [CrossRef]
  41. Katoh, K.; Matsuba, M. Effectiveness of nature reserves for bird conservation in urban parks in Tokyo. J. For. Res. 2021, 32, 2011–2022. [Google Scholar] [CrossRef]
  42. Leveau, L.M. Primary productivity and habitat diversity predict bird species richness and composition along urban-rural gradients of central Argentina. Urban For. Urban Green. 2019, 43, 126349. [Google Scholar] [CrossRef]
  43. Leveau, L.M.; Isla, F.I.; Bellocq, M.I. Predicting the seasonal dynamics of bird communities along an urban-rural gradient using NDVI. Landsc. Urban Plan. 2018, 177, 103–113. [Google Scholar] [CrossRef]
  44. Leveau, L.M.; Isla, F.I.; Bellocq, M.I. From Town to Town: Predicting the Taxonomic, Functional and Phylogenetic Diversity of Birds Using NDVI. Ecol. Indic. 2020, 119, 106703. [Google Scholar] [CrossRef]
  45. Liordos, V.; Foutsa, E.; Kontsiotis, V.J. Differences in encounters, likeability and desirability of wildlife species among residents of a Greek city. Sci. Total Environ. 2020, 739, 139892. [Google Scholar] [CrossRef] [PubMed]
  46. Greek Statistical Authority. Population Census 2011. 2011. Available online: http://www.statistics.gr/portal/page/portal/ESYE/PAGE-census2011 (accessed on 20 October 2023).
  47. Bibby, C.; Burgess, N.; Hill, D.; Mustoe, S. Bird Census Techniques, 3rd ed.; Academic: London, UK, 2000. [Google Scholar]
  48. Ferry, C.; Frochot, B. L’avifaune nidificatrice d’une foret de chenes pedoncules en Bourgogne: Etude de deux successions ecologiques. Terre Vie 1970, 2, 153–250. Available online: https://www.persee.fr/doc/revec_0040-3865_1970_num_24_2_4591 (accessed on 4 November 2023). [CrossRef]
  49. USGS. NDVI, the Foundation for Remote Sensing Phenology. 2018. Available online: https://www.usgs.gov/special-topics/remote-sensing-phenology/science/ndvi-foundation-remote-sensing-phenology (accessed on 20 October 2023).
  50. Christidis, N.; Jones, G.S.; Stott, P.A. Dramatically increasing chance of extremely hot summers since the 2003 European heatwave. Nat. Clim. Chang. 2015, 5, 46–50. [Google Scholar] [CrossRef]
  51. Drusch, M.; Del Bello, U.; Carlier, S.; Colin, O.; Fernandez, V.; Gascon, F.; Hoersch, B.; Isola, C.; Laberinti, P.; Martimort, P.; et al. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens. Environ. 2012, 120, 25–36. [Google Scholar] [CrossRef]
  52. Snow, D.W.; Perrins, C.M. The Birds of the Western Palearctic.; Oxford University Press: Oxford, UK, 1998. [Google Scholar]
  53. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2020; Available online: https://www.R-project.org/ (accessed on 20 October 2023).
  54. Burnham, K.P.; Anderson, D.R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd ed.; Springer: New York, NY, USA, 2002. [Google Scholar]
  55. Mazerolle, M.J. AICcmodavg: Model Selection and Multimodel Inference based on (Q)AIC(c): R Package Version 2.0-4. 2016. Available online: http://CRAN.R-project.org/package=AICcmodavg (accessed on 20 October 2023).
  56. Covas, R.; Blondel, J. Biogeography and history of the Mediterranean bird fauna. Ibis 1998, 140, 395–407. [Google Scholar] [CrossRef]
  57. Lepczyk, C.A.; La Sorte, F.A.; Aronson, M.F.; Goddard, M.A.; MacGregor-Fors, I.; Nilon, C.H.; Warren, P.S. Global patterns and drivers of urban bird diversity. In Ecology and Conservation of Birds in Urban Environments; Murgui, H., Hedblom, M., Eds.; Springer: Cham, Switzerland, 2017; pp. 13–33. [Google Scholar]
  58. La Sorte, F.A.; Tingley, M.W.; Hurlbert, A.H. The role of urban and agricultural areas during avian migration: An assessment of within-year temporal turnover. Glob. Ecol. Biogeogr. 2014, 23, 1225–1234. [Google Scholar] [CrossRef]
  59. Ives, C.D.; Lentini, P.E.; Threlfall, C.G.; Ikin, K.; Shanahan, D.F.; Garrard, G.E.; Bekessy, S.A.; Fuller, R.A.; Mumaw, L.; Rayner, L.; et al. Cities are hotspots for threatened species. Glob. Ecol. Biogeogr. 2016, 25, 117–126. [Google Scholar] [CrossRef]
  60. Jokimäki, J.; Suhonen, J.; Kaisanlahti-Jokimäki, M.L. Urbanization and species occupancy frequency distribution patterns in core zone areas of European towns. Eur. J. Ecol. 2016, 2, 23–43. [Google Scholar] [CrossRef]
  61. Ortega-Álvarez, R.; MacGregor-Fors, I. Living in the big city: Effects of land-use on bird community structure, diversity, and composition. Landsc. Urban Plan. 2009, 90, 189–195. [Google Scholar] [CrossRef]
  62. Pereira, H.M.; Daily, G.C.; Roughgarden, J. A framework for assessing the relative vulnerability of species to land-use change. Ecol. Appl. 2004, 14, 730–742. [Google Scholar] [CrossRef]
  63. Savard, J.P.L.; Clergeau, P.; Mennechez, G. Biodiversity concepts and urban ecosystems. Landsc. Urban Plan. 2000, 48, 131–142. [Google Scholar] [CrossRef]
  64. Donnelly, R.; Marzluff, J.M. Importance of reserve size and landscape context to urban bird conservation. Conserv. Biol. 2004, 18, 733–745. [Google Scholar] [CrossRef]
Diversity 15 01139 g001
Figure 1. Maps of (a) Kavala, Greece, with numbered marks indicating the survey stations (N = 19) (Google Earth; Image Landsat/Copernicus, Data SIO, NOAA, U.S. Navy, NGA, GEBCO; ©2020 Google, Image©2021 TerraMetrics, Image©2021 Maxar Technologies, inset: ESRI, HERE, Garmin, NOAA, USGS), and (b) NDVI map showing the 50 m, 200 m, and 500 m buffers around survey station 13 (Arc GIS Pro, v. 3.1.3).
Figure 1. Maps of (a) Kavala, Greece, with numbered marks indicating the survey stations (N = 19) (Google Earth; Image Landsat/Copernicus, Data SIO, NOAA, U.S. Navy, NGA, GEBCO; ©2020 Google, Image©2021 TerraMetrics, Image©2021 Maxar Technologies, inset: ESRI, HERE, Garmin, NOAA, USGS), and (b) NDVI map showing the 50 m, 200 m, and 500 m buffers around survey station 13 (Arc GIS Pro, v. 3.1.3).
Diversity 15 01139 g001
Diversity 15 01139 g002
Figure 2. Representation of the relationships of NDVI with abundance for 15 bird species based on the best model explaining variation in the data (see Table 2). Shadowed areas represent 95% confidence intervals.
Figure 2. Representation of the relationships of NDVI with abundance for 15 bird species based on the best model explaining variation in the data (see Table 2). Shadowed areas represent 95% confidence intervals.
Diversity 15 01139 g002
Table 1. Type, number of species, and NDVI values at different spatial scales at the 19 survey stations of Kavala. Green space numbers correspond to the numbers in Figure 1.
Table 1. Type, number of species, and NDVI values at different spatial scales at the 19 survey stations of Kavala. Green space numbers correspond to the numbers in Figure 1.
Mean NDVI Values
Green Space NumberGreen Space TypeNumber of Species50 m Buffer200 m Buffer500 m Buffer
1Square garden120.3670.3720.428
2Woodland110.3990.2660.294
3Playground100.3550.2280.242
4Street hedgerow120.3760.2070.285
5Woodland90.5220.2740.308
6Playground100.3030.3210.302
7Woodland90.6000.5470.349
8Woodland110.4370.3470.323
9Square garden50.3010.2020.223
10Playground100.2360.1800.168
11Square garden80.3020.1660.164
12Square garden90.4540.1630.161
13Street hedgerow60.2600.2760.229
14Street hedgerow90.4230.2820.209
15Playground10.2550.2720.199
16Square garden70.2840.2300.283
17Woodland110.4950.2890.218
18Street hedgerow60.2030.2660.254
19Street hedgerow110.3450.3230.436
Table 2. Common and scientific names, mean abundance, incidence (percentage of plots a species was observed in), and urbanization category (data from Kontsiotis et al. [4]) of the 15 bird species recorded in 5 or more of the 19 green spaces of Kavala.
Table 2. Common and scientific names, mean abundance, incidence (percentage of plots a species was observed in), and urbanization category (data from Kontsiotis et al. [4]) of the 15 bird species recorded in 5 or more of the 19 green spaces of Kavala.
Common NameMeanSEIncidence (%)Urbanization Category
Northern House Martin (Delichon urbicum)13.6841.35594.7Dweller
Βarn Swallow (Hirundo rustica)7.7373.15947.4Dweller
House Sparrow (Passer domesticus)9.8951.83289.5Dweller
Feral Pigeon (Columba livia forma urbana)5.0532.37452.6Dweller
Collared Dove (Streptopelia decaocto)4.7370.61189.5Dweller/Utilizer
Western Jackdaw (Corvus monedula)1.5790.73531.6Dweller
Hooded Crow (Corvus cornix)1.6320.31768.4Utilizer
Common Starling (Sturnus vulgaris)2.0000.68842.1Dweller/Utilizer
Eurasian Blackbird (Turdus merula)0.8420.36931.6Utilizer
Common Nightingale (Luscinia megarhynchos)1.0530.34636.8Utilizer
Great Tit (Parus major)1.5260.35368.4Utilizer
Sardinian Warbler (Curruca melanocephala)0.6320.25626.3Avoider
European Goldfinch (Carduelis carduelis)2.2110.43668.4Utilizer
Common Chaffinch (Fringilla coelebs)2.0530.57557.9Utilizer
European Greenfinch (Chloris chloris)1.8950.40473.7Utilizer
Table 3. Results of generalized linear models (GLMs) performed to assess the effect of NDVI on bird abundance at three spatial scales (50 m, 200 m, 500 m). Underscored text indicates the best model.
Table 3. Results of generalized linear models (GLMs) performed to assess the effect of NDVI on bird abundance at three spatial scales (50 m, 200 m, 500 m). Underscored text indicates the best model.
Common NameEstimateSEzAICcΔAICcwi
Northern House Martin
NDVI500.0550.6050.091143.728.850.01
NDVI200−2.3500.825–2.849 ***134.860.000.97
NDVI500–0.6810.812–0.838143.018.150.02
Barn Swallow
NDVI50–6.9571.028–6.765 ***304.960.001
NDVI200−0.871.010−0.861357.5752.610.00
NDVI5001.6041.0221.570359.2154.240.00
House Sparrow
NDVI50−4.1890.817−5.126 ***159.7310.290.01
NDVI200−6.6461.164−5.709 ***149.440.000.99
NDVI500−4.7921.076−4.453 ***166.9417.490.00
Feral Pigeon
NDVI504.1480.9254.484 ***246.5299.050.00
NDVI200−19.7212.889−8.617 ***157.7110.250.01
NDVI500−21.5012.52−8.531 ***147.460.000.99
Collared Dove
NDVI50−0.4871.0431−0.46798.806.220.04
NDVI200−3.5501.488−2.385 *92.570.000.92
NDVI500−0.5671.376−0.41298.846.270.04
Western Jackdaw
NDVI50−4.4702.067−2.163 *105.9326.250.00
NDVI200−13.4753.502−3.848 ***91.7412.060.00
NDVI500−18.9124.174−4.531 ***79.680.001
Hooded Crow
NDVI500.5861.7270.33968.300.730.28
NDVI200−2.0762.355−0.88267.570.000.41
NDVI500−1.252.388−0.52468.130.560.31
Common Starling
NDVI50−0.5131.606−0.320107.551.950.20
NDVI200−3.0332.234−1.358105.590.000.53
NDVI5001.7912.0010.895106.871.280.28
Eurasian Blackbird
NDVI505.9602.2562.641 **57.3410.540.01
NDVI2008.6511.8884.583 ***46.810.000.97
NDVI5008.8982.8943.075 **54.928.110.02
Common Nightingale
NDVI503.6512.0361.79363.6310.910.00
NDVI2006.1511.8113.396 ***57.344.620.09
NDVI5009.7822.6063.754 ***52.720.000.91
Great Tit
NDVI505.2981.6753.163 **59.920.000.89
NDVI2004.2031.6612.532 *64.204.280.10
NDVI5002.0292.2790.89068.878.950.01
Sardinian Warbler
NDVI504.5252.6101.73448.205.180.04
NDVI2007.1222.553.158 **43.020.000.59
NDVI5009.0093.3442.694 **43.990.970.36
European Goldfinch
NDVI501.8031.4441.24880.907.600.02
NDVI200−2.8322.104−1.34680.407.110.03
NDVI500−6.8132.425−2.809 **73.290.000.95
Common Chaffinch
NDVI506.3971.4484.419 ***74.840.000.98
NDVI2005.1281.3613.768 ***82.307.460.02
NDVI5003.5231.9121.84290.8115.980.00
European Greenfinch
NDVI504.1091.5112.719 **68.750.000.78
NDVI2003.4511.5582.216 *71.622.860.18
NDVI5002.4002.0311.18274.545.780.04
* p < 0.05, ** p < 0.01, *** p < 0.001.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

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 2023, 15, 1139. https://0-doi-org.brum.beds.ac.uk/10.3390/d15111139

AMA Style

Kontsiotis VJ, 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. 2023; 15(11):1139. https://0-doi-org.brum.beds.ac.uk/10.3390/d15111139

Chicago/Turabian Style

Kontsiotis, Vasileios J., Stavros Chatzigiovanakis, Evangelos Valsamidis, Panteleimon Xofis, and Vasilios Liordos. 2023. "Normalized Difference Vegetation Index as a Proxy of Urban Bird Species Presence and Distribution at Different Spatial Scales" Diversity 15, no. 11: 1139. https://0-doi-org.brum.beds.ac.uk/10.3390/d15111139

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop