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Review

The Trajectories, Trends, and Opportunities for Assessing Urban Ecosystem Services: A Systematic Review of Geospatial Methods

by
Muhammad Zaman-ul-Haq
1,
Zafeer Saqib
1,
Ambrina Kanwal
2,
Salman Naseer
3,
Muhammad Shafiq
4,*,
Nadia Akhtar
5,
Syed Atif Bokhari
6,
Azeem Irshad
7 and
Habib Hamam
8,9,10
1
GIS and Eco-Informatics Laboratory, Department of Environmental Science, Male Campus, International Islamic University, Islamabad 44000, Pakistan
2
Department of Computer Sciences, Bahria University, Islamabad 44000, Pakistan
3
Department of Information Technology, Gujranwala Campus, University of the Punjab, Gujranwala 52250, Pakistan
4
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
5
Department of Environmental Science, Female Campus, International Islamic University, Islamabad 44000, Pakistan
6
Department of Geography, Government College, Asghar Mall, Rawalpindi 46000, Pakistan
7
Department of Computer Science and Software Engineering, International Islamic University, Islamabad 44000, Pakistan
8
Faculty of Engineering, Université de Moncton, Moncton, NB E1A3E9, Canada
9
Spectrum of Knowledge Production & Skills Development, Sfax 3027, Tunisia
10
School of Electrical Engineering, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(3), 1471; https://doi.org/10.3390/su14031471
Submission received: 21 December 2021 / Revised: 22 January 2022 / Accepted: 24 January 2022 / Published: 27 January 2022
(This article belongs to the Special Issue Smart GIS and Geo-Technology in Sustainable and Crisis Land and Urban Management)
Browse Figures
Versions Notes

Abstract

:
Urban ecosystem services (UES) are indispensable for life. Stakeholders are improvising strategies for a more sustainable provisioning of UES. For this purpose and for identifying orientations towards geospatial data in UES studies, the “bibliometric analysis” technique was deployed. The inclinations facilitate assessments pertaining to spatio-temporal oscillations in the supply–demand equilibrium. The propensities are gaining recognition due to time and cost effectiveness. Besides this, Remote Sensing (RS) in conjunction with Geographic Information System (GIS), enables the conduct of synoptic and robust periodic evaluations. The study analyzes inclinations towards RS in contemporary research (2010–2020) focusing, particularly, on urban ecosystem services. It specifically focuses on methodological frameworks and major sources of remotely sensed data. Therefore, a total of 261 records of research articles were identified and retrieved. Subsequently, 79 articles were selected for further processing and content analysis. It transpired that approximately 30% of the selected publications deployed remotely sensed data for assessment purposes. The majority (96%) of such studies were conducted in economically developed and industrialized countries. However, the researchers from both developed and developing countries prefer open software and free data sources. Besides this, they prefer satellite-based optical sensors over image sensors such as TIR, SAR, or light sensors for acquiring data. The findings formulate that Land Use Land Cover (LULC)-based methodologies and inclinations for assessing regulating services are more frequently pursued. The findings revealed that enhanced research collaborations, access to data, and assessment gadgets are obligatory for capacity building in developing regions. Knowledge sharing and cost-effective access to RS and GIS based platforms are incumbent for ensuring urban environmental sustainability in developing economies.

1. Introduction

Ecosystem Services (ES) are the sum total of the functions, processes, and benefits that stem from ecological resources [1,2,3]. The reimbursements of natural capital (blue-green surfaces) are indispensable for the biosphere [4] and are classified into four distinct groups and various categories [5]. The provisioning of ES is symptomatically determined by the nature of biophysical infrastructure, its associated processes, and accompanying changes. Thus, they have spatio-temporal connotations, implications, and dimensions which have persistent bearings on the ecosystems and the services rendered by them [6]. Therefore, human beings are endeavoring to decipher the linkages between society and their environment from various perspectives [7]. The publication of Millennium Ecosystem Assessment (MEA) in 2005 proved to be a catalyst for launching an invigorative quest for monitoring the resilience of ecological resources [4].
Urban Ecosystem Services (UES) is an observational term used for assessing, evaluating, and portraying ecological infrastructure and its products in the urban areas. The information is collected for detecting spatio-temporal oscillations in the supply–demand budget of UES [8]. These estimations/information assist to determine whether the observed changes are the products of human interventions or nature-triggered modifications.
The reported demographic pressures [9], such as uncontrolled urbanization [10], access to technological gadgetry [11], and socio-economic changes [12] are embossing their indelible imprints on urban environment. Contrary to that, the required focus and actions are, still, far from the desired benchmarks in developing regions [13,14,15,16]. It entails informed assessments, coordinated efforts, and tangible measures for protecting urban ecological resources [17]. However, resource constraints thwart such proclivities which are vitally required for a resilient urban social life.
The evaluation of UES is carried out by deploying monetary and non-monetary based mechanisms. Bokhari et al. [8] opined that monetary-based tools and techniques are more suitable measures for documented economies. Whereas, non-monetary parameters seem to be a pragmatic option for transforming/developing economies, which are struggling with issues pertaining to undocumented financial transactions [4,13]. Recent advances in the domain of Remote Sensing (RS) offer time and cost-effective possibilities for assessing ES and their services [15,18]. The embedded features of Geographic Information System (GIS) and RS enable the conduct of synoptic, spatially connected, and periodic measurements in a synchronized manner. The information is utilized for interpreting man–environment interactions in a given urban setting [19].
Albeit, RS-based assessments are gaining recognition as a reliable option for modeling, mapping, and assessing ecosystems and their products [20,21,22]. Remotely sensed data is deployed for analyzing urban Land Use Land Cover (LULC) transformations [23,24,25], assessing urban heat island effect [26,27], and studying urban green species [28,29]. The data source is useful for detecting transitions in urban vegetative cover [30], water quality [31], and biomass estimation with the help of ALOS-2, PALSAR-2, and Sentinel-2A images [32]. The initiatives are obligatory for ensuring sustainable cities and societies as envisaged in (SDG 11) [33,34].
The scenario entails for assessing the potentials of RS for ensuring urban ecological integrity. Therefore, the present study is designed in order to demonstrate the role and potentialities of remote sensing for assessing UES. It analyzes inclinations towards RS in contemporary research (2010–2020) focusing on urban ecosystem services. For this purpose, a systematic review of literature was carried out. Hence, the present study focuses on the following dimensions: (i) to evaluate how researchers utilize RS data for assessing UES; (ii) to identify the dominant orientations regarding remotely sensed data sources; and (iii) to categorize the methodological constructs relied upon for evaluations.

2. Methodology

The methodological framework of the present study is based on the systematic review of literature. For this purpose, those research publications which utilized RS for assessing UES were identified through the Web of Science (WOS) (www.webofknowledge.com, accessed on 22 December 2020). The authors retrieved a total of 261 records of research articles against the search term “urban ecosystem services” published from January 2010 to December 2020. In retrospect and on a first perusal, it would appear that the reliance on the keyword “UES” has compromised the validity of findings by excluding studies of a similar nature but with different terminologies. However, the identification of publication through one keyword based on an “umbrella term” curtails subjectivity [35,36], prevents digressions [13], and minimizes the impacts of irrelevant (false-positive) results [37]. For this purpose, Atif et al. [4] has successfully deployed this very technique regarding their investigations pertaining to UES. However, the current investigation is inherently different as it exclusively assesses the reliance on remotely sensed data in contemporary UES studies.
It is pertinent to mention that only those records were selected which were published or accepted for publication in the English language. However, review papers were excluded from the count for ensuring objectivity. The information was processed by deploying a two-tier cascading arrangement. Firstly, those publications were identified which had the term “Urban Ecosystem Services” in the title, abstract, or keywords. Subsequently, in the second phase only those articles were selected which rely on remotely sensed data (Figure 1). Based on that mechanism, 79 records qualified for further processing and content analysis (Table A1).
The significant findings pertaining to: (i) the type of ES being focused; (ii) the nature of RS dataset(s)/the purpose of assessment; and (iii) methodology(s) deployed for investigation identified and portrayed through Dendrogram (Figure 11). The scheme is preferred for spotting similarities and differences among/between strategies employed for assessments by Booth et al. [38].

3. Results

3.1. The RS and UES Research

The findings transpire that 29.89% of the total retrieved records (n = 261) relied on RS data for assessing UES. Significant inter and intra-continental differences were observed regarding the use of RS (Figure 2 and Figure 3). The findings formulate that remotely sensed data-based research contributions from the European context (58.97%) are the highest. It transpired that the proportionate shares from the remaining continents were negligible as compared to their volume of urbanization (Figure 3). The majority of reviewed publications (96.15%) were planned in industrialized and developed regions, whereas the share from the developing hemisphere turned out to be quite miniscule (3.85%). It reflects the prevalent schism between the global “North and South” in this domain as well. The findings corroborate the notions rendered by Atif et al. [4] and Akhtar et al. [13], that such differences are linked to economic, scientific, and technological advancements.

3.2. Temporal Fluctuations in Tendencies

For assessing temporal fluctuations, the data (79 publications) were assessed. The available data were compartmentalized into two arbitrarily selected time intervals, i.e., 2010–2015 and 2016–2020. The largest number of publications appeared in the year 2016 (n = 14). The findings (Figure 4) depict that during the early phases of the first selected time interval (2010–2015), there was a growing tendency for RS. However, during the later phase (2016–2020), observable oscillations are evident in the proportionate share of different years, such as 2017 (15.19%), 2018 (16.46%), 2019(8.86%), to 2020 (13.92%). Atif et al. [4] also reported such tendencies based upon their investigation.

3.3. Remote Sensing Data Sources and UES

Research orientations regarding types of ES and Ecosystem Disservices (ED) were assessed (Figure 5). For the purpose of deciphering the linkages/interconnectedness between different ES services, the pair-based assessment technique was employed (Figure 6). The majority of the studies (n = 55) were adjudged to be cumulative assessments of four designated types of ES services. However, inclinations were observed to be more skewed towards regulating services as compared to cultural, provisioning, and supporting services.
Figure 7 portrays the frequency of data sources relied upon for data acquisition and Figure 8 illustrates their year-wise contribution. It transpired that most studies relied on no-cost/open data sources, such as Landsat family, MODIS, SPOT, etc., for assessing UES. Satellite-based optical sensors were preferred over image sensors, such as TIR, SAR, or light sensors for acquiring data. Open and free data resources, such as aerial ortho-photos and Google Earth imagery are mainly consulted for land use classification and data validation. The information was integrated with socio-economic and demographic data repositories for holistic appraisals. Subsequently, the evaluations were integrated with ecological inventories and portrayed in the form of urban atlas.

3.4. Methodological Frameworks and RS

UES are assessed by deploying multiple conceptual paradigms and approaches. The assessments (Figure 8) transpired that LULC-based technique was the preferred choice (59%) for assessing UES. It is followed by the Normalized Difference Vegetation Index (NDVI) amounting to 23%; Mapping Urban Tree (15%); Land Surface Temperature (LST) method (11%); the Green Canopy Cover technique (GCC) (6.3%); and Normalized Difference Water Index (NDWI)-based parameters (3.8%). Besides these methods, inVEST and mixed model-based approaches are gaining recognition as well. A growing propensity for innovative measures, such as Linear Iterative Clustering (LIC); Normalized Digital Surface Model (nDSM); Convolutional Neural Networks (CNN) etc., was also noticed (Figure 9 and Figure 10).
For the purpose, similarities, and dissimilarities in data sources, methods and types of UES relied upon for assessments were adjudged. Figure 11 reflects the similarities/dissimilarities in orientations towards UES, based on the cluster analysis technique. It portrays a cascading hierarchy of clusters (Figure 11). The Dendrogram construes the presence of two notable sets of clusters (A and B) having 0% similarity between them. The difference is attributable to the type of urban ecosystem services selected/relied upon for assessments in the selected publications. The intra group variations were observed more noticeably in group A as compared to group B. It formulates that the publications in group A were designed from divergent perspectives, scales, and scopes for assessments. However, the reliance on LULC technique for assessing UES was recognized as a common feature in both the observed groupings.

4. Discussion

The growing quantum of global urban population [39,40], the unprecedented expansions in urban areas [41], and associated ecological footprints are stressing people and their environment [13,42]. Stakeholders are improvising strategies for social, economic, and ecological resilience of urban life and infrastructure. For this purpose, the reliance on remotely sensed data for assessing, monitoring, and managing urban ecosystems and their services is gaining recognition [8,43]. The quantitative findings based on this investigation affirm that the reliance on RS and GIS is gaining recognition as tools for informed decision making. However, a significant decline in publications focusing on UES was noticed (Figure 4). The decline authenticates the reported observations that the propensities towards assessing UES are waning. Thus, the review of contemporary literature will serve as a barometer for assessing the causations and promoting scientific investigation regarding UES. The orientations are obligatory for realizing the objective of sustainability of urban environments.
The scrutiny of literature construed a delicate interplay between economic development and propensities towards environmental conservation. Whereas, whilst economic development encourages consumer culture, it simultaneously provides fiscal and technological support for ensuring environmental resilience. The findings (Figure 2.) surmise that the developed economies offer as more conducive playground for relying on RS in UES research. The spatial distribution of selected publications (Figure 2 and Figure 3) authenticates the notions about economic development and proclivities towards UES research [8,13,44]. The gap in publications (Figure 3) between “the developed North” and “the developing south” supports the dictum that an empty stomach does not afford the luxury of a choice.
An increasing proportion of Asian scientists was observed focusing on UES in their published studies. The study also affirms that the share of Asia is more in published UES research as compared to the contributions from North America during the selected time interval (2000–2020). The observations are contrary to the assessments rendered by [43,45], but authenticate the robustness of conclusions of de Araujo Barbosa et al. [43]. The plausible explanation is rooted in the paradigm of “economic growth” and its trickle-down effects. The reported and projected estimates about urban demography and concomitant urban-centric economic developments in Asia [14,16] are pushing for urban ecological sustainability. Consequently, researchers are focusing on and improvising measures for ensuring socio-economic and environmental integrity of urban areas. However, Europe was observed in the leading role according to the assessment criteria of this study. The early exposure to urban environmental degradation, access to advanced technologies, and urban-based economic development spurred “environmentalism” in Europe [46,47]. The European exposure to early industrialization and its aftermaths helped to acknowledge the significance of nature and its contribution [48]. These stimulators encouraged urban environmental sustainability. These concomitant dividends are pouring out in the form of scholastic initiatives and publications.
Critical findings (Figure 7 and Figure 8) transpired that the majority of studies relied on no-cost “open data sources”. Landsat, MODIS, and SPOT families were most frequently consulted for data acquisition. For this purpose, the majority of studies relied on the open and free data repositories, such as aerial ortho-photos and Google Earth imageries. The information was subsequently processed, analyzed, and portrayed through GIS environment. It is pertinent to mention here that the majority of these selected studies were carried out in economically and technologically developed regions (Figure 2 and Figure 3). On the contrary, the lack of resources for capacity building in developing regions retard the exposure, experience, and expertise of researchers; therefore, they rely less on these gadgets, i.e., RS and GIS. It surmises the hardships being faced by the academia and research fraternities in the less developed parts of the globe. Moreover, the restrictions to access/consult scholarly contributions due to “financial/monetary” constraints discourage their utilization in the fragile and resource-parched economies.
The assessments rendered that reliance on secondary data sources in the studies focusing on UES is a preferred choice among researchers. The observation corroborated the notion rendered by Tavares et al. [49], that RS facilitates data acquisitions for ecological studies. Reliance over RS ensures time and cost effectiveness. For this purpose, Landsat and Sentinel satellite families are frequently consulted (Figure 9 and Figure 10). This predisposition is an outcome of accessibility, availability, and cost factors [49,50]. Moreover, the data aggregation of Landsat and Sentinel families is possible and required for deciphering long-term oscillations in the natural environment [51], whereas high-resolution images, such as SPOT-5 [52,53], aerial and digital orthophotos [54,55,56], Worldview-2 [57,58], RapidEye imagery [59], GeoEye [60], IKONOS [60,61], and Quickbird [61,62,63] are considered as more reliable options for evaluating UES. However, the utilization of these repositories depends on the accessibility and availability of financial resources.
The findings formulate that regulating services are more focused on research, as compared to other categories of UES, while the content analysis revealed that the LULC evaluation based on RS data is a widely deployed mechanism for assessing UES [64,65,66,67]. The tendencies are gaining recognition due to objective, accurate, and user-friendly orientations of this mechanism. Recent advances in associated techniques (such as NDVI, LAI, LST, NDWI, and TCA) are providing more objectivity to LULC assessments [62,65,68,69,70]. The cluster analysis (Figure 11) corroborates the reported assertions of [8,49,71] that LULC assessments deftly infer about the fluctuations in UES. Growing exposure, experiences, and expertise are popularizing and giving impetus to the use of remotely sensed data in ecological studies [72,73]. However, growing restrictions on data acquisition [49] are posing newfound challenges for developing economies. The scenario entails for constructive support from developed to developing regions for global environmental security.
The observations underline the need for an out-of-the-box solution for this dichotomous situation. While the mechanics of the market economy operate on the premises of profit, the ecological health of the planet earth calls for cost-effective holistic interventions. It requires a sustainable solution with the support of developed economies and donors in the light of climate and justice debate. The cost-free dissemination of knowledge and sharing of experiences will symptomatically contribute towards capacity building. This stimulator will encourage the intended orientations towards urban green infrastructure in developing regions. Intellectual collaboration and funding are vitally required for informed assessments/decision making regarding urban ecological resources. A paradigm shift based on the principle of knowledge sharing will serve as a catalyst for transforming perceptions regarding the environment in such contextual settings.

5. Conclusions

Urbanization and modifications in urban areas are stressing the urban environment. Holistic appraisals of concomitant impacts on UES are imperative for informed decision making to ensure the resilience of urban life. The critical findings of this investigation authenticate the assertions that accessibility, availability, and cost of data and analysis are the crucial factors considered by the researchers. The findings of this review contemplate that the focus of contemporary studies regarding urban areas is drifting from interpretation to knowledge acquisition for informed decision making. For this purpose, reliance on RS and GIS is gaining recognition. The inclinations were observed more directed towards assessing regulating services, when compared to other categories of ES. Besides this, the LULC-based assessment technique is commonly deployed for evaluating UES. The assessments affirm that an overwhelmingly larger share of UES research comes from the developed world. Researchers from developed and developing countries prefer a no-cost or open data sources for investigating UES. Meanwhile, difficulties in data accessibility, the lack of availability of RS and GIS-based software, and the absence of capacity building measures in developing countries is impeding all research momentum. Consequently, the reliance on remotely sensed data is waning. It entails for “out of the box” measures for stimulating RS-based UES research in the transforming economies. Availability of open-ware tools, such as Google Earth Engine and cost-free access to databases, allied with other capacity building measures, are vitally required.

Author Contributions

Conceptualization, M.Z.-u.-H., Z.S., A.K., S.N. and M.S.; methodology, H.H., N.A., S.A.B. and A.I.; software, M.Z.-u.-H., Z.S., A.K., S.N. and M.S.; validation, H.H., N.A., S.A.B. and A.I.; formal analysis, M.Z.-u.-H., Z.S., A.K., S.N. and M.S.; investigation, H.H., N.A., S.A.B. and A.I.; resources and data curation, M.Z.-u.-H., Z.S., A.K., S.N. and M.S.; writing—original draft preparation, M.Z.-u.-H., Z.S., A.K., S.N. and M.S.; writing—review and editing, H.H., N.A., S.A.B. and A.I.; visualization, M.Z.-u.-H., Z.S., A.K., S.N. and M.S.; supervision, A.I. and H.H., project administration, A.I., M.S. and S.A.B. All authors have read and agreed to the published version of the manuscript.

Funding

The author thanks Natural Sciences and Engineering Research Council of Canada (NSERC) and New Brunswick Innovation Foundation (NBIF) for the financial support of the global project. These granting agencies did not contribute in the design of the study and collection, analysis, and interpretation of data.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. List of the articles used for systematic literature review.
Table A1. List of the articles used for systematic literature review.
Sr. No.Title JournalYearVolPage(s)IssueDOI
1Scale-Crossing Brokers and Network Governance of Urban Ecosystem Services: The Case of StockholmEcology and Society201015254
2Emergy-based evaluation of peri-urban ecosystem servicesEcological Complexity2011838–50110.1016/j.ecocom.2010.12.002
3The urban environmental indicator “Biotope Area Ratio”—An enhanced approach to assess and manage the urban ecosystem services using high resolution remote-sensingEcological Indicators20121393–103110.1016/j.ecolind.2011.05.016
4Urban ecosystem services: tree diversity and stability of tropospheric ozone removalEcol Appl201222349–360110.1890/11-0561.1
5A social-ecological assessment of vacant lots in New York CityLandscape and Urban Planning2013120218–233 10.1016/j.landurbplan.2013.05.003
6Urban ecosystem services assessment along a rural-urban gradient: A cross-analysis of European citiesEcological Indicators201329179–190 10.1016/j.ecolind.2012.12.022
7Urban vegetation classification: Benefits of multitemporal RapidEye satellite dataRemote Sensing of Environment201313666–75 10.1016/j.rse.2013.05.001
8Contribution of Ecosystem Services to Air Quality and Climate Change Mitigation Policies: The Case of Urban Forests in Barcelona, SpainAmbio201443466–479410.1007/s13280-014-0507-x
9Measuring urban ecosystem functions through ‘Technomass’—A novel indicator to assess urban metabolismEcological Indicators20144210–19 10.1016/j.ecolind.2014.02.035
10Mapping the diversity of regulating ecosystem services in European citiesGlobal Environmental Change201426119–129 10.1016/j.gloenvcha.2014.04.008
11Urban vegetation structure types as a methodological approach for identifying ecosystem services—Application to the analysis of micro-climatic effectsEcological Indicators20144258–72 10.1016/j.ecolind.2014.02.036
12Development of a concept for non-monetary assessment of urban ecosystem services at the site levelAmbio201443454–465410.1007/s13280-014-0502–2
13Quantification and monetary valuation of urban ecosystem services in Munich, GermanyZeitschrift Fur Wirtschaftsgeographie201559188–2003
14REVIEW: Quantifying urban ecosystem services based on high-resolution data of urban green space: an assessment for Rotterdam, the NetherlandsJournal of Applied Ecology2015521020–1032410.1111/1365-2664.12469
15The role of urban green infrastructure in mitigating land surface temperature in Bobo-Dioulasso, Burkina FasoEnvironment, Development and Sustainability201518373–392210.1007/s10668-015-9653-y
16Socio-ecological dynamics and inequality in Bogotá, Colombia’s public urban forests and their ecosystem servicesUrban Forestry & Urban Greening2015141040–1053410.1016/j.ufug.2015.09.011
17Intensity and spatial pattern of urban land changes in the megacities of Southeast AsiaLand Use Policy201548213–222 10.1016/j.landusepol.2015.05.017
18Understanding spatial patterns in the production of multiple urban ecosystem servicesEcosystem Services20151633–46 10.1016/j.ecoser.2015.08.007
19Contrasting values of cultural ecosystem services in urban areas: The case of park Montjuïc in BarcelonaEcosystem Services201512178–186 10.1016/j.ecoser.2014.11.016
20A comparison of the economic benefits of urban green spaces estimated with NDVI and with high-resolution land cover dataLandscape and Urban Planning2015133105–117 10.1016/j.landurbplan.2014.09.013
21Land sparing is crucial for urban ecosystem servicesFrontiers in Ecology and the Environment201513387–393710.1890/140286
22High-Resolution Mapping of Urban Surface Water Using ZY-3 Multi-Spectral ImageryRemote Sensing2015712,336–12,355910.3390/rs70912336
23A framework towards a composite indicator for urban ecosystem servicesEcological Indicators20166038–44 10.1016/j.ecolind.2015.05.035
24Appraisal of social-ecological innovation as an adaptive response by stakeholders to local conditions: Mapping stakeholder involvement in horticulture orientated green space managementUrban Forestry & Urban Greening20161886–94 10.1016/j.ufug.2016.05.010
25Estimating stormwater runoff for community gardens in New York CityUrban Ecosystems201620129–139110.1007/s11252-016-0575-8
26The impact of land use/land cover scale on modelling urban ecosystem servicesLandscape Ecology2016311509–1522710.1007/s10980-015-0337-7
27Exploring local consequences of two land-use alternatives for the supply of urban ecosystem services in Stockholm year 2050Ecological Indicators201670615–629 10.1016/j.ecolind.2016.02.062
28The value of urban ecosystem services in New York City: A spatially explicit multicriteria analysis of landscape scale valuation scenariosEnvironmental Science & Policy20166257–68 10.1016/j.envsci.2016.04.012
29Carbon sequestration through urban ecosystem services: A case study from FinlandSci Total Environ2016563–564623–632 10.1016/j.scitotenv.2016.03.168
30Mapping transition potential with stakeholder- and policy-driven scenarios in Rotterdam CityEcological Indicators201670630–643 10.1016/j.ecolind.2016.02.028
31Balancing demand and supply of multiple urban ecosystem services on different spatial scalesEcosystem Services20162218–31 10.1016/j.ecoser.2016.09.008
32Sustainable drainage system site assessment method using urban ecosystem servicesUrban Ecosystems201620293–307210.1007/s11252-016-0593-6
33Green Roof Cost-Benefit Analysis: Special Emphasis on Scenic BenefitsJournal of Benefit-Cost Analysis20167488–522310.1017/bca.2016.18
34Deriving and Evaluating City-Wide Vegetation Heights from a TanDEM-X DEMRemote Sensing201689401110.3390/rs8110940
35Modeling above-ground carbon storage: a remote sensing approach to derive individual tree species information in urban settingsUrban Ecosystems20162097–111110.1007/s11252-016-0585-6
36Spatially explicit urban green indicators for characterizing vegetation cover and public green space proximity: a case study on Brussels, BelgiumInternational Journal of Digital Earth201610798–813810.1080/17538947.2016.1252434
37Analysing scale, quality and diversity of green infrastructure and the provision of Urban Ecosystem Services: A case from Mexico CityEcosystem Services201723127–137 10.1016/j.ecoser.2016.12.004
38Improving models of urban greenspace: from vegetation surface cover to volumetric survey, using waveform laser scanningMethods in Ecology and Evolution201781443–14521110.1111/2041-210x.12794
39Projecting the CO2 and Climatic Change Effects on the Net Primary Productivity of the Urban Ecosystems in Phoenix, AZ in the 21st Century under Multiple RCP (Representative Concentration Pathway) ScenariosSustainability201791366810.3390/su9081366
40Capturing the value of green space in urban parks in a sustainable urban planning and design context: pros and cons of hedonic pricingEcology and Society20172213210.5751/es-09365-220221
41Assessing mismatches in ecosystem services proficiency across the urban fabric of Porto (Portugal): The influence of structural and socioeconomic variablesEcosystem Services20172382–93 10.1016/j.ecoser.2016.11.015
42A GIS-based mapping methodology of urban green roof ecosystem services applied to a Central European cityUrban Forestry & Urban Greening20172254–63 10.1016/j.ufug.2017.01.001
43Mapping and Monitoring Urban Ecosystem Services Using Multitemporal High-Resolution Satellite DataIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing201710669–680210.1109/jstars.2016.2586582
44The potential of green infrastructure application in urban runoff control for land use planning: A preliminary evaluation from a southern Italy case studyEcosystem Services201726345–354 10.1016/j.ecoser.2017.04.015
45Analysing urban green space accessibility and quality: A GIS-based model as spatial decision support for urban ecosystem services in BrusselsEcosystem Services201728328–340 10.1016/j.ecoser.2017.10.016
46Temporal Changes in Ecosystem Services in European Cities in the Continental Biogeographical Region in the Period from 1990–2012Sustainability20179665410.3390/su9040665
47Ecological landscape regulation approaches in Xilingol, Inner Mongolia: an urban ecosystem services perspectiveInternational Journal of Sustainable Development and World Ecology201724401–407510.1080/13504509.2016.1273263
48Urban ecosystem services—assessment of potential at the different spatial scale: an example of poznanEkonomia I Srodowisko-Economics and Environment20171207–22560
49A Needs-Driven, Multi-Objective Approach to Allocate Urban Ecosystem Services from 10,000 TreesSustainability201810151210.3390/su10124488
50A Differentiated Spatial Assessment of Urban Ecosystem Services Based on Land Use Data in Halle, GermanyLand20187101310.3390/land7030101
51Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep LearningWater201810585510.3390/w10050585
52Assessing ecosystem services of bornova’s green infrastructure, izmir (turkey)Fresenius Environmental Bulletin2018273530–35415A
53Exploring temporal dynamics of urban ecosystem services in Latin America: The case of Bogota (Colombia) and Santiago (Chile)Ecological Indicators2018851068–1080 10.1016/j.ecolind.2017.11.062
54Assessing how green space types affect ecosystem services delivery in Porto, PortugalLandscape and Urban Planning2018170195–208 10.1016/j.landurbplan.2017.10.007
55Mapping and Quantifying Variations in Ecosystem Services of Urban Green Spaces: A Test Case of Carbon Sequestration at the District Scale for Seoul, Korea (1975–2015)International Review for Spatial Planning and Sustainable Development20186110–120310.14246/irspsd.6.3_110
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57Within-Class and Neighborhood Effects on the Relationship between Composite Urban Classes and Surface TemperatureSustainability201810645310.3390/su10030645
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Figure 1. Flow chart for review of literature.
Figure 1. Flow chart for review of literature.
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Figure 2. The spatial distribution of reviewed studies.
Figure 2. The spatial distribution of reviewed studies.
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Figure 3. Continent-wise apportionment of reviewed studies.
Figure 3. Continent-wise apportionment of reviewed studies.
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Figure 4. Temporal distribution of reviewed publications (2010–2020).
Figure 4. Temporal distribution of reviewed publications (2010–2020).
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Figure 5. Types of UES and reviewed publications during 2010–2020.
Figure 5. Types of UES and reviewed publications during 2010–2020.
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Figure 6. Pair based interconnectedness among UES.
Figure 6. Pair based interconnectedness among UES.
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Figure 7. The frequency of data sources relied upon for assessments.
Figure 7. The frequency of data sources relied upon for assessments.
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Figure 8. The year-wise contribution of data sources.
Figure 8. The year-wise contribution of data sources.
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Figure 9. The frequency of methodological frameworks deployed for assessments.
Figure 9. The frequency of methodological frameworks deployed for assessments.
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Figure 10. The year-wise contribution of methodological frameworks deployed for assessments.
Figure 10. The year-wise contribution of methodological frameworks deployed for assessments.
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Figure 11. The similarities and differences among methodologies (Group A and Group B) and Cluster analysis technique.
Figure 11. The similarities and differences among methodologies (Group A and Group B) and Cluster analysis technique.
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Zaman-ul-Haq, M.; Saqib, Z.; Kanwal, A.; Naseer, S.; Shafiq, M.; Akhtar, N.; Bokhari, S.A.; Irshad, A.; Hamam, H. The Trajectories, Trends, and Opportunities for Assessing Urban Ecosystem Services: A Systematic Review of Geospatial Methods. Sustainability 2022, 14, 1471. https://doi.org/10.3390/su14031471

AMA Style

Zaman-ul-Haq M, Saqib Z, Kanwal A, Naseer S, Shafiq M, Akhtar N, Bokhari SA, Irshad A, Hamam H. The Trajectories, Trends, and Opportunities for Assessing Urban Ecosystem Services: A Systematic Review of Geospatial Methods. Sustainability. 2022; 14(3):1471. https://doi.org/10.3390/su14031471

Chicago/Turabian Style

Zaman-ul-Haq, Muhammad, Zafeer Saqib, Ambrina Kanwal, Salman Naseer, Muhammad Shafiq, Nadia Akhtar, Syed Atif Bokhari, Azeem Irshad, and Habib Hamam. 2022. "The Trajectories, Trends, and Opportunities for Assessing Urban Ecosystem Services: A Systematic Review of Geospatial Methods" Sustainability 14, no. 3: 1471. https://doi.org/10.3390/su14031471

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