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MethodsX
MethodsX
MethodsX
2215-0161
Elsevier

S2215-0161(24)00394-7
10.1016/j.mex.2024.102943
102943
Environmental Science
A simple method to map pollination ecosystem services potential in urban lawns
Pereira Paulo pereiraub@gmail.com
a⁎
Kalinauskas Marius a
Pinto Luis Valenca a
Baltranaite Egle a
Barcelo Damia b
Zhao Wenwu cd
Inacio Miguel a
a Environmental Management Laboratory, Mykolas Romeris University, Vilnius, Lithuania
b Department of Chemistry and Physics, University of Almería, Spain
c State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
d Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
⁎ Corresponding author. pereiraub@gmail.com
31 8 2024
12 2024
31 8 2024
13 10294313 6 2024
29 8 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Urban areas have detrimental impacts on the ecosystems. Nevertheless, they still supply many ecosystem services (ES), such as Pollination, in different urban green spaces (UGS). Lawns are among the most degraded UGS due to very high human impact. Still, flowers such as Dandelions (Taraxacum officinalis) live in these spaces. These flowers are considered a suitable habitat for pollinators. In this work, we develop a methodology to map Pollination ES potential in urban lawns using an Unmanned Aerial Vehicle. A detailed protocol was developed using high-resolution images, consisting of orthomosaic creation, flower vectorisation, field validation, and finally, Pollination ES potential mapping using Kernel and Point Density. This method can be applied to urban lawns and grasslands in Spring and Summer.• A novel method was developed to map pollination potential in lawns.

• Dandelions (Taraxacum officinale) were mapped using UAV high-resolution images.

• The method is helpful to identify areas with pollination potential in urban lawns.

Graphical abstract

Image, graphical abstract

Keywords

Ecosystem services
Mapping
Pollination
Dandelions
Unmanned aerial vehicle
Method name

Pollination potential mapping
==== Body
pmcSpecifications tableSubject area:	Environmental Science	
More specific subject area:	Regulating Ecosystem Services	
Name of your method:	Pollination potential mapping	
Name and reference of original method:	NA	
Resource availability:	DJI Mavic 3M
Computer Intel(R) Core (TM) i9-10900X CPU @ 3.70GHz 3.70 GHz and 64.0 GB Memory
Google Earth Online
DJI Terra Software
ArcGIS Pro (ver. 3.1.2)	

Background

Urban ecosystems are subjected to severe human impacts and high degradation. Land consumption, fragmentation, soil sealing, pollution and biodiversity loss are among the most critical pressures that urban green spaces (UGS) are exposed to [[1], [2], [3], [4]]. Among the different UGS, lawns have had the most considerable impacts. They are subjected to intense management (e.g., intense mowing, leaf litter removal, garbage, erosion, agrochemicals application or other types of pollution from traffic and industry) [[5], [6], [7], [8]]. Despite the pressures, lawns are essential for supplying multiple regulating (e.g., Flood, Erosion, Air Quality and Microclimate Regulation, Water Purification, Carbon Sequestration or Pollination) and cultural (e.g., Recreation) Ecosystem Services (ES), improving wellbeing in cities [[8], [9], [10]]. Pollination is among the most valuable ES since it is critical for food production [11]. In urban lawns, vegetation development that is essential to Pollination is affected by management practices, trampling, climate change and pollution, reducing the capacity of these areas to supply this ES [12,13]. Nevertheless, they are a vital pollinator habitat that can pollinate the surrounding urban gardens [14]. The flora that live in urban laws is highly adapted and can thrive in environments with high human pressure [15]. A good example is Dandelions (Taraxacum genus), a plant from the Asteraceae family that is widely distributed worldwide. Dandelions are native to Asia and Europe but are also present in America, Asia and Oceania. They grow in disturbed environments such as lawns and roadsides. Dandelions are an edible medicinal plant for detoxification, lactation, and swelling. They also have important anti-inflammatory, antioxidant, anti-cancer and analgesic agents [16]. Previous works highlighted that Dandelions in urban lawns are an important source of food to pollinators [17,18].

Mapping ES in urban areas is extensively studied [e.g., 19], and several works have been conducted in mapping regulating ES [e.g., 20]. Nevertheless, few were conducted in mapping pollination ES (classified as Pollination, or 'gamete' dispersal in a marine context, Code 2.3.1.1, according to the Common International Classification of Ecosystem Services (CICES) 5.1v)).1 There are examples of works focused on mapping Pollination ES but with a coarse resolution [21,22]. Attempts have yet to be made to map Pollination ES potential in urban areas at a very high resolution, likely because it is costly in time and resources. In addition, many samples are required to produce accurate maps. Therefore, developing methodologies to map pollination ES potential at a very high resolution is essential. Unmanned Aerial Vehicles (UAVs) can help identify flowers that are critical habitats for pollinators [e.g., 23] and monitor their status during Spring and Summer. This will contribute to understanding Pollination ES potential in urban lawns or other grassland environments. In this work, we aim to develop a methodology to map Pollination ES potential with a UAV's very high-resolution images, using Dandelions (Taraxacum officinalis) distribution as a proxy.

Method details

The method was tested in an urban lawn with an area of 9202.1 m2, located in Vilnius (Lithuania) (Fig. 1A and B) during the Spring season (May 15, 2024). We used a DJI Mavic 3M drone with a multispectral and RGB camera to acquire high-resolution imagery. Drone specificities are shown in Table S1 and on the DJI webpage.2 The framework used in this method is shown in Fig. 2. Before going to the field, a KML of the area of interest was created on Google Earth Online3 (Fig. 2). The KML was exported to DJI Pilot 2 software,4 to create a flight mission (Fig. 3). The mission was set to collect only RGB images. In total, 9202.1 m2 were mapped, and the ground sample resolution (GSD)5 was 1.59 cm. The drone flew a total distance of 3.44 km with a duration of 24 min and 36 s at an altitude of 12 m from the ground and a velocity of 2.3 m/s. In total, 2111 photos were taken during this mission. The images were saved into a micro-SD card and processed in the DJI Terra software6 installed in an Intel(R) Core (TM) i9-10900X CPU @ 3.70 GHz (Table S2). The RGB photos were placed in a folder to process the 2D orthomosaic reconstruction. This process took approximately 7 min. Once this step was completed, the orthomosaic file (.tiff) was exported to the ArcGIS Pro (ver. 3.1.2) environment to identify the Dandelions in the area of interest. A point shapefile was created with the GCS_WGS_1984/VCS:EGM96_Geoid projection. UAV, hardware and software costs are described in Tables S1 and S2.Fig. 1 A and B) Study area and C) vectorised Dandelions (Taraxacum officinale).

Fig. 1

Fig. 2 Methodological framework applied.

Fig. 2

Fig. 3 Flight mission snapshot.

Fig. 3

After the image preparation and shapefile creation, Dandelions were vectorised carefully. During this process, it is important not to misinterpret Dandelions with other surface features (e.g., bare soil, stones). Dandelion flowers have a specific colour (e.g., yellow) and form (e.g., circular) that makes them easy to identify in images with high resolution (Fig. 2). After vectorising all the Dandelions, fieldwork was conducted for validation as misclassifications could occur, especially in areas where flowers were located near pathways (e.g., bare soil) or omissions if located under the trees, where the UAV could not identify them. Overall, 7 Dandelions were misclassified on the pathways. The flowers located under the trees and not identified in the orthomosaic were pinpointed in the field using Google Earth online. Only 10 Dandelions were identified. Therefore, the impacts of canopy cover were residual. Dandelions germinate better in areas exposed to sunlight, as observed elsewhere [24]. The KML created with the flowers identified under the trees was converted to layer in ArcGIS Pro (ver. 3.1.2) using the KML To Layer7 tool and subsequently exported as a shapefile. This new shapefile was joined to the shapefile, where the flowers were mapped using the orthomosaic image using the Join Field tool.8 In total, 25578 flowers were vectorised (Fig. 1C). Between the UAV flight and the field validation (10 days), no management (e.g., mowing) was applied.

After vectorisation and field validation, two well-known methods (Kernel and Point Density) were applied to identify Dandelion density, which served as a proxy to map Pollination ES (Fig. 2). This process has been applied in previous works [25]. Kernel density assessment was conducted according to Silverman [26] and also described by Pereira et al. [25].(1) Density=1(radius)2∑i=1n[3π.popi(1−(distiradius))]

where: i = 1,…,n are the input points. Only include points in the sum if they are within the radius distance of the (x,y) location. popi is the population field value of point i, which is an optional parameter, and disti is the distance between point i and the (x,y) location.9 As Bandwidth, we applied the standard distance method10 [25]. The Bandwidth was calculated according to the formula:(2) SearchRadius=0.9×min(SD,1ln(2)*Dm)×n−0.2

“where: Dm is the (weighted) median distance from the (weighted) mean centre. n is the number of points if no population field is used, or if a population field is supplied, n is the sum of the population field values, and SD is the standard distance”7 [25].

Here, the Standard distance was calculated using the unweighted distance using the following formula:(3) SD=∑i=1n(xi−X¯)2n+∑i=1n(yi−Y¯)2n+∑i=1n(zi−Z¯)2n

where: “xi, yi and zi are the coordinates for feature i, X¯, Y¯ and Z¯ represent the mean centre for the features, and n is equal to the total number of features.”11 [25].

Before applying the Kernel Density method, the area units were established as ``square m2'', output cell values as ``densities'', and the method as ``planar''. Since the area of interest does not have a quadrangular shape, we established the ``lawn shapefile'' as an input barrier feature as applied elsewhere [25]. The point density method calculates the “magnitude-per-unit area from point features that fall within a neighbourhood around each cell12”. As a neighbourhood, we selected a ``circle'' with a ratio of 20 m, and as unit type, we selected ``maps'' and as area units ``square meters'' [25]. The final map's resolution was 1 cm, and the densities were established as square meters. The map's legend was created following the stretch method and the stretch type as the minimum and maximum values. There are several differences between the two methods applied. Kernel Density “spreads the known quantity of the population for each point out from the point location. The resulting surfaces surrounding each point in kernel density are based on a quadratic formula with the highest value at the centre of the surface (the point location) and tapering to zero at the search radius distance,'' and Point Density establishes “a neighbourhood is specified that calculates the density of the population around each output cell13” [25].

The density maps produced by Kernel and Point Density maps are shown in Fig. 4. The highest Pollination ES potential was identified in the southern and partially on the northeast parts of the lawn. The density was, on average, very high, as shown in Table 1. The results obtained using both methods were similar. Most values were located in the low values (positive Kurtosis), indicating that most of the plot area has a low density due to bare soil or areas covered by trees (Fig. 2). Although the statistical results are similar, as observed in previous works [25], Kernel Density produced better maps with smother surfaces than the Point Density method. This may also related to a specific data distribution. Further work is needed to understand in which circumstances point density can be a better method.Fig. 4 Dandelions density using A) Kernal and B) Point density methods.

Fig. 4

Table 1 Kernel and point density descriptive statistics.

Table 1	Kernel density (m2)	Point density (m2)	
Mean	19 812 820 449.84	19 894 616 365.22	
Median	5 267 451 392	4 192 839 168	
Standard Deviation	32 586 811 609.06	33 848 071 705.97	
Minimum	0	0	
Maximum	234 383 785 984	268 341 706 752	
Skewness	2.50	2.58	
Kurtosis	9.79	10.49	

Method limitation and implications

Applying UAV to map Pollination ES potential is novel. It has the advantage that it is a non-destructive method and can be applied without affecting the environment (e.g., trampling or flower collection). However, the method has several limitations that need to be highlighted. Little disturbance was done during the field, and we verified some areas where the Dandelions were vectorised incorrectly. However, this occurred mainly near the pathways with bare soil (under a green background (e.g., grass). Dandelions identification is easier), limited to specific lawn areas. Therefore, an effort was made to avoid stepping on grass-covered areas. Although no management was applied in the studied lawn, during the interval between the vectorisation and field validation, some flowers near the pathways or under the trees may have been caught or trampled by animals or people. This may have caused some errors at the time of validation. Nevertheless, this was unavoidable because the time between vectorisation and field validation was needed. Vectorisation is very intensive work and needs to be conducted carefully. Despite the challenges, when applying the method, we encourage doing field validation, especially if the areas are accessible and small.

Many points were vectorised, which required a considerable amount of time. Kernel and Point density mapping was fast because we used hardware with high processing capacity (Table S2). Nevertheless, additional challenges may occur if we assess larger areas since 1) the time for image acquisition will be higher. The drone used in this study has a maximum flight time between 37 and 43 min using one battery (Table S1). However, this time can be reduced in strong wind conditions, and 2) the time needed for flower vectorisation and field validation will be higher. This may pose some challenges when applying this method in big plots.

Another critical issue is that we could not map small Dandelions (smaller than 1.59 cm). Therefore, although the resolution applied was very high, higher-resolution images should be created whenever possible. However, this can also increase data processing time. Furthermore, in this work, we only considered Dandelions. Other flowers need to be considered in future studies. Nevertheless, Dandelions were the most abundant species in the studied lawn. In other areas, it will be important to consider mapping species that can be identified using high-resolution UAV imagery.

This work established the methodology for assessing Pollination ES potential using high-resolution images. The advantages of using a UAV are that the monitoring can be more frequent, and the method is encouraged to be established at different times during the Summer and Spring seasons. It is essential to know if the potential for Pollination ES changes over time. The results obtained can also help connect vegetation status and soil properties. Grass flowering is related to vegetation properties and soil quality [27,28]. The development of this method in urban lawns and other environments can be beneficial in understanding the potential for Pollination ES and the degradation status. Lawns that have reduced flowers and high bare soil cover may undergo a degradation process. Monitoring lawns will help to understand their status and if restoration practices are needed to ensure the continued supply of Pollination and other regulating ES. This information is critical for local authorities aiming to have UGS with good ecological quality, which is essential to improving wellbeing in urban areas. The mapping exercise is relevant for providing spatial information to managers, planners, and decision-makers and supporting evidence-based governance. As observed in previous studies, mapping ES in urban environments is essential for decision-making [[29], [30], [31]].

Ethics statements

The work did not involve human beings.

The work did not involve animals.

The work did not involve data collected from social media platforms.

CRediT authorship contribution statement

Paulo Pereira: Conceptualization, Methodology, Investigation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Project administration. Marius Kalinauskas: Methodology, Visualization, Writing – review & editing. Luis Valenca Pinto: Methodology, Visualization, Writing – review & editing. Egle Baltranaite: Writing – review & editing. Damia Barcelo: Writing – review & editing. Wenwu Zhao: Writing – review & editing. Miguel Inacio: Methodology, Investigation, Resources, Writing – review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix Supplementary materials

Image, application 1

Data availability

Data will be made available on request.

Acknowledgements

This work was supported by the project Monetary valuation of soil ecosystem services and creation of initiatives to invest in soil health: setting a framework for the inclusion of soil health in business and in the policy making process (InBestSoil) (10.13039/501100007601 Horizon Europe ) Grant agreement ID: 101091099 .

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.mex.2024.102943.

1 https://cices.eu/resources/.

2 https://enterprise.dji.com/mavic-3-m/specs.

3 https://earth.google.com/web/.

4 https://www.bhphotovideo.com/lit_files/911800.pdf.

5 https://enterprise-insights.dji.com/blog/ground-sample-distance.

6 https://enterprise.dji.com/dji-terra.

7 https://pro.arcgis.com/en/pro-app/latest/tool-reference/conversion/kml-to-layer.htm.

8 https://pro.arcgis.com/en/pro-app/latest/tool-reference/data-management/join-field.htm.

9 https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/how-kernel-density-works.htm.

10 https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/standard-distance.htm.

11 https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/how-kernel-density-works.htm.

12 https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/an-overview-of-the-density-tools.htm.

13 https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/differences-between-point-line-and-kernel-density.htm.
==== Refs
References

1 Paltseva A.A. Cheng Z. Egendorf S.P. Groffman P.M. Remediation of an urban garden with elevated levels of soil contamination Sci. Total Environ. 722 2020 137965 10.1016/j.scitotenv.2020.137965
2 Theodorou P. Herbst S.C. Kahnt B. Landaverde-González P. Baltz L.M. Osterman J. Paxton R.J. Urban fragmentation leads to lower floral diversity, with knock-on impacts on bee biodiversity Sci. Rep. 10 2020 21756 10.1038/s41598-020-78736-x 33303909
3 O'Riordan R. Davies J. Stevens C. Quinton J. The effects of sealing on urban soil carbon and nutrients Soil 7 2021 661 675 10.5194/soil-7-661-2021
4 Kowe P. Mutanga O. Dube T. Advancements in the remote sensing of landscape pattern of urban green spaces and vegetation fragmentation Int. J. Remote Sens. 42 2021 3797 3832 10.1080/01431161.2021.1881185
5 Watson C.J. Carignan-Guillemette L. Turcotte C. Maire V. Proulx R. Ecological and economic benefits of low-intensity urban lawn management J. Appl. Ecol. 57 2019 436 446 10.1111/1365-2664.13542
6 Meftaul I.Md. Venkateswarlu K. Dharmarajan R. Annamalai A. Megharaj M Pesticides in the urban environment: a potential threat that knocks at the door Sci. Total Environ. 771 2020 134612 10.1016/j.scitotenv.2019.134612
7 Foti L. Barot S. Gignoux J. Grimaldi M. Lata J.C. Lerch T.Z. Nold F. Nunan N. Raynaud X. Abbadie L. Dubs F. Topsoil characteristics of forests and lawns along an urban-rural gradient in the Paris region (France) Soil Use Manag. 37 2021 749 761 10.1111/sum.12640
8 Paudel S. States S.L. Urban green spaces and sustainability: Exploring the ecosystem services and disservices of grassy lawns versus floral meadows Urban For. Urban Green. 84 2023 127932 10.1016/j.ufug.2023.127932
9 Francoeur X.W. Dagenais D. Paquette A. Dupras J. Messier C. Complexifying the urban lawn improves heat mitigation and arthropod biodiversity Urban For. Urban Green. 60 2021 127007 10.1016/j.ufug.2021.127007
10 Phillips C.L. Wang R. Mattox C. Trammell T.L.E. Young J. Kowalewski A. High soil carbon sequestration rates persist several decades in turfgrass systems: a meta-analysis Sci. Total Environ. 858 2023 159974 10.1016/j.scitotenv.2022.159974
11 Guimarães Porto R. Fernandes de Almeida R. Cruz-Neto O. Tabarelli M. Felipe Viana B. Peres C.A. Valentina Lopes A. Pollination ecosystem services: a comprehensive review of economic values, research funding and policy actions Food Secur. 12 2020 1425 1442 10.1007/s12571-020-01043-w
12 Lerman S.B. Contosta A.R. Milam J. Bang C. To mow or to mow less: lawn mowing frequency affects bee abundance and diversity in suburban yards Biol. Conserv. 221 2018 160 174 10.1016/j.biocon.2018.01.025
13 Baldock K.C.R. Opportunities and threats for pollinator conservation in global towns and cities Curr. Opin. Insect Sci. 38 2020 63 71 10.1016/j.cois.2020.01.006 32126514
14 Rahami E. Barghjelveh S. Dong P. A review of diversity of bees, the attractiveness of host plants and the effects of landscape variables on bees in urban gardens Agric. Food Secur. 11 2022 6 10.1186/s40066-021-00353-2
15 Ignatieva M. Haase D. Dushkova D. Haase A. Lawns in cities: from a globalised urban green space phenomenon to sustainable nature-based solutions Land 9 2020 73 10.3390/land9030073
16 Fan M. Zhang X. Song H. Zhang Y. Dandelion (Taraxacum Genus): a review of chemical constituents and pharmacological effects Molecules 28 2022 5022 10.3390/molecules28135022
17 Wignall V.R. Balfour N.J. Gandy S. Ratnieks F.L.W. Food for flower-visiting insects: appreciating common native wild flowering plants People Nat. 5 2023 1072 1081 10.1002/pan3.10475
18 Cloutier S. Mendes P. Cimon-Morin J. Pellerin S. Fournier V. Poulin M. Assessing the contribution of lawns and semi-natural meadows to bee, wasp, and flower fly communities across different landscapes Urban Ecosyst. 2024 10.1007/s11252-024-01516-2
19 Pulighe G. Fava F. Lupia F. Insights and opportunities from mapping ecosystem services of urban green spaces and potentials in planning Ecosyst. Serv. 22 2016 1 10 10.1016/j.ecoser.2016.09.004
20 Larondelle N. Haase D. Kabisch N. Mapping the diversity of regulating ecosystem services in European cities Glob. Environ. Change 26 2014 119 129 10.1016/j.gloenvcha.2014.04.008
21 Schulp C.J.E. Lautenbach S. Verburg P.H. Quantifying and mapping ecosystem services: demand and supply of pollination in the European Union Ecol. Indic. 36 2014 131 141 10.1016/j.ecolind.2013.07.014
22 Perennes M. Diekötter D. Groß J. Burkhard B. A hierarchical framework for mapping pollination ecosystem service potential at the local scale Ecol. Model. 444 2021 109484 10.1016/j.ecolmodel.2021.109484
23 Herrera C.M. Flower traits, habitat, and phylogeny as predictors of pollinator service: a plant community perspective Ecol. Monogr. 90 2019 e01402 10.1002/ecm.1402
24 Taylor R.J. Populational variation and biosystematic interpretations in weedy dandelions Bull. Torrey Bot. Club 114 1987 109 120
25 Pereira P. Kalinauskas M. Pinto L.V. Barcelo D. Zhao W. Inacio M. A simple method for mapping winter recreational fishing ecosystem services supply in lakes. A contribution to mapping freshwater ecosystem services Methods X 12 2024 102764 10.1016/j.mex.2024.102764
26 Silverman B.W. Density Estimation for Statistics and Data Analysis 1986 Chapman & Hall London 10.1007/978-1-4899-3324-9
27 Goulnik J. Plantureux P. Théry M. Baude M. Delattre M. van Reeth C. Villerd J. Michelot-Antalik A. Floral trait functional diversity is related to soil characteristics and positively influences pollination function in semi-natural grasslands Agric. Ecosyst. Environ. 301 2020 107033 10.1016/j.agee.2020.107033
28 Hackney B. Rodham C. Dyce G. Piltz J. Pasture legumes differ in herbage production and quality throughout spring, impacting their potential role in fodder conservation and animal production Grass Forage Sci. 76 2021 116 133 10.1111/gfs.12525
29 Vorstius A.C. Spray C. A comparison of ecosystem services mapping tools for their potential to support planning and decision-making on a local scale Ecosyst. Serv. 15 2015 75 83 10.1016/j.ecoser.2015.07.007
30 Stępniewska M. Ecosystem service mapping and assessment as a support for policy and decision making CLEAN Soil Water 44 2016 1414 1422 10.1002/clen.201500777
31 Cortinovis C. Geneletti D. A performance-based planning approach integrating supply and demand of urban ecosystem services Landsc. Urban Plan. 201 2020 103842 10.1016/j.landurbplan.2020.103842
