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Remote Sensing Applications in Urban Ecosystem Services

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 21819

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Special Issue Editors

Dr. Sharolyn Anderson

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Guest Editor

US National Park Service, Washington, DC, USA
Interests: remote sensing; ecosystem services; land use; geography; Landsat

Prof. Dr. Paul C. Sutton

E-Mail Website
Guest Editor

Department of Geography and the Environment, University of Denver, Denver, CO 80208, USA
Interests: sustainability; ecological economics; geospatial science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urban environments are an unusual concentration of human, built, social, and natural capital. Ecosystem services have been characterized as nature’s contribution to people that result from the interaction of social, human, built, and natural capital. Characterizing how well urban environments are achieving the Sustainable Development Goals is increasingly recognized as an important methodological challenge. There is increasing interest in developing Earth Observation (EO) methods for measuring indicators of the SDGs because of their uniformity of coverage, relative ease of acquisition, and relative objectivity. Characterizing, quantifying, valuing, and mapping spatiotemporal variability of urban ecosystem services can make significant contributions to charting a path to a sustainable and desirable future. This Special Issue serves as an outlet for articles covering but not limited to:

Spatiotemporal mapping of ecosystem services in urban environments;
Remote sensing of urban ecosystem services or their proxies;
Cross-disciplinary approaches that use remote sensing to characterize ecosystem services;
Valuation of urban ecosystem services;
Remote sensing and characterization of green and/or blue infrastructure in urban environments;
Modeling of urban metabolism which incorporates urban ecosystem services and remotely sensed inputs.

Dr. Sharolyn Anderson
Prof. Paul C. Sutton
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Urban Ecosystem Services
Earth Observation of SDGs
Urban Sustainability
Urban Metabolism

Published Papers (8 papers)

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Research

19 pages, 3234 KiB

Open AccessArticle

Nonlinear Effects of Landscape Patterns on Ecosystem Services at Multiple Scales Based on Gradient Boosting Decision Tree Models

by Cheng Li, Jie Zhao and Wei Hou

Remote Sens. 2023, 15(7), 1919; https://doi.org/10.3390/rs15071919 - 3 Apr 2023

Cited by 4 | Viewed by 1449

Abstract

Exploring the complex effects of landscape patterns on ecosystem services (ESs) has become increasingly important in offering scientific support for effective spatial planning and ecosystem management. However, there is a particular lack of research on the nonlinear effects of landscape patterns on ESs and scale dependence. Taking Huainan City (in China) as a case study, this study adopted the InVEST model to estimate four key ESs including carbon storage (CS), habitat quality (HQ), nitrogen export (NE), and water yield (WY). Then, we calculated the selected landscape metrics at multiple spatial scales. Furthermore, the gradient boosting decision tree (GBDT) model was developed to investigate the relative importance of landscape metrics in explaining ESs and their nonlinear interrelation. The results indicated that most of the selected landscape metrics were significantly correlated with ESs. The GBDT model, which can explore nonlinear relationships, performed better than the linear regression model in explaining the variations in ESs. The landscape-level metrics of the Shannon’s diversity index (SHDI) and the contagion index (CONTAG) and the class-level metrics of the aggregation index (AI) and edge density (ED) were the most important variables that influenced ESs. The landscape metrics affected ESs within a certain range, and the nonlinear effects varied with scale. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Urban Ecosystem Services)

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25 pages, 9073 KiB

Open AccessArticle

Evaluating Trade-Off and Synergies of Ecosystem Services Values of a Representative Resources-Based Urban Ecosystem: A Coupled Modeling Framework Applied to Panzhihua City, China

by Jianwen Zeng, Jipeng Xu, Wenyu Li, Xiaoai Dai, Jiayun Zhou, Yunfeng Shan, Junjun Zhang, Weile Li, Heng Lu, Yakang Ye, Li Xu, Shuneng Liang and Youlin Wang

Remote Sens. 2022, 14(20), 5282; https://doi.org/10.3390/rs14205282 - 21 Oct 2022

Cited by 10 | Viewed by 2494

Abstract

Following significant urban expansion, the ecological problems of resource-based cities are gradually exposed. It is of great significance to study the ecosystem services of resource-based cities to achieve their sustainable development goals and to alleviate the conflicts between environmental protection and the utilization of the surrounding resources. However, in the current research on resource-based cities, few scholars have combined multiple minerals and multiple ecosystem services to explore the impact of mineral resources on the ecosystem. In this study, based on the historical data spanning from 2002 to 2018, we used the CA–Markov model to project the land use of Panzhihua City to 2030. Based on future land use projection, we quantified four ecosystem services (ESs) variables, including water yield, carbon storage, habitat quality, and soil conservation, using the InVEST model from the perspective of land use evolution in Panzhihua City. In addition, we explored the trade-offs and synergies of different ecosystem services and the correlations between different mineral species and ecosystem services using Spearman’s correlation coefficient. Results showed the following: (1) During 2002–2018, water yield service, habitat quality service, and carbon storage service of Panzhihua City decreased year by year, and soil conservation service showed significant fluctuations; most of the low ESs areas were distributed in the central region of Panzhihua. On the contrary, most high ESs areas were located in the forest region. (2) The trade-offs and synergistic relationships among different ecosystem services showed significant spatial variations. There were synergistic relationships among ESs and weak trade-offs between water yield services, soil conservation, and habitat quality services. There was also significant spatial variability in the trade-offs and synergies among ecosystem services, with water production services showing “east trade-offs and west synergies” with soil conservation and habitat quality services, and most of the rest showing trade-offs in urban areas. (3) ESs in mining areas showed trade-offs in general, mainly between water production services and carbon storage services, with clay as the major negative factor of mineral species, and iron ore mines that have undergone ecological protection construction showed the lowest negative impact on ecology. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Urban Ecosystem Services)

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17 pages, 7550 KiB

Open AccessArticle

Urbanization Intensifies the Mismatch between the Supply and Demand of Regional Ecosystem Services: A Large-Scale Case of the Yangtze River Economic Belt in China

by Huayan Liu, Wenfa Xiao, Jianhua Zhu, Lixiong Zeng and Qi Li

Remote Sens. 2022, 14(20), 5147; https://doi.org/10.3390/rs14205147 - 14 Oct 2022

Cited by 7 | Viewed by 1711

Abstract

The process of rapid urbanization has been causing non-negligible disturbances to our ecosystems, which has aggravated the mismatch between ecosystem service (ES) supply and demand. A clear understanding of the relationship between the ES supply–demand mismatch and urbanization is crucial as it could have a lot of significance for implementing ecological compensation and conservation action. Although a large number of studies have explored this problem, previous studies have focused primarily on the spatial mismatching of the ESs, and only a few studies have considered the spatial relationship between the ES supply–demand mismatch and urbanization at the watershed scale. Taking the Yangtze River Economic Belt (YREB) as an example, this study quantitatively assesses the supply and demand of five ESs, including carbon sequestration, water retention, soil conservation, food production, and recreational opportunity. The bivariate Moran’s I method was used to analyze and visualize the spatial correlation between the ES supply–demand mismatch and urbanization. The results indicate that both the total supply and the total demand of the five ESs increased, while the increasing rate of total demand was higher than the total supply of the ESs; this resulted in a significant spatial mismatch between the supply and demand of the ESs from 2000 to 2020. There is also a negative spatial correlation between the ES supply–demand and urbanization, while the results of local spatial clustering have obvious spatial heterogeneity. The metropolis and its surrounding counties are mostly the ES supply and demand deficit area, but some surrounding counties have managed to transform a deficit into a surplus. These results indicate that urbanization has a certain interference on the mismatch of the ES supply and demand, and this interference is not irreversible. Moreover, this study provides a reliable reference for government management in the context of balancing urbanization and the ecosystem. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Urban Ecosystem Services)

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21 pages, 5019 KiB

Open AccessArticle

Distinguishing the Impacts of Rapid Urbanization on Ecosystem Service Trade-Offs and Synergies: A Case Study of Shenzhen, China

by Zhenhuan Liu, Ziyu Liu, Yi Zhou and Qiandu Huang

Remote Sens. 2022, 14(18), 4604; https://doi.org/10.3390/rs14184604 - 15 Sep 2022

Cited by 8 | Viewed by 2210

Abstract

Cities and urban areas are an important part of global sustainable development, and the health and well-being of urban residents are closely related to the quality, quantity, and diversity of urban ecosystem services. Although the rapid urbanization process has changed the structure and function of urban ecosystems, which is notably different from natural ecosystems, the affected ecosystem services and their interactions—the trade-off impact of urbanization intensity on ecosystem services—remain to be discussed. Using land use/land cover and impervious surface area remote sensing datasets, and InVEST and RUSLE-related ecosystem services models to evaluate seven typical ecosystem services in Shenzhen, this study explored the evolution of multiple ecosystem service trade-offs and synergies during the transition from a natural ecosystem to an urban ecosystem, and how they are affected by urbanization intensity through correlation analysis and a discrete time-step simulation model. The results show that: (1) from 1978 to 2018, in the process of ecosystem transformation, grain production dropped from 228,795 tons to 11,733 tons, fruit production peaked in 1990 at 271,508 tons, and service capacity of both showed obvious degradation. Conversely, the cultural service capacity was remarkably enhanced. (2) With the increase in urbanization level, the trade-off and synergy of ecosystem services gradually transition from linear to nonlinear. The rapid urbanization process drives the nonlinear degradation of ecosystem services and the nonlinear enhancement of synergy. (3) Over the past four decades, ecosystem service bundles within the same kilometer grid have shown a quadratic curve-like decrease with increasing impervious surface area, slowly in the early stages and faster in the later stages. This study concludes that urbanization intensity has a significant impact on ecosystem service trade-offs, which can provide support for the formulation of ecological protection and restoration strategies in territorial space based on ecosystem services. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Urban Ecosystem Services)

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21 pages, 13240 KiB

Open AccessArticle

Reading Greenness in Urban Areas: Possible Roles of Phenological Metrics from the Copernicus HR-VPP Dataset

by Enrico Borgogno-Mondino and Vanina Fissore

Remote Sens. 2022, 14(18), 4517; https://doi.org/10.3390/rs14184517 - 9 Sep 2022

Cited by 6 | Viewed by 1953

Abstract

Vegetation phenology is that branch of science that describes periodic plant life cycle events across the growing seasons. Remote sensing typically monitors these significant events by means of time series of vegetation indices, permitting to characterize vegetation dynamics. It is well known that vegetation in urban areas, i.e., green spaces in general, may benefit human health mainly by mitigating noise and air pollution, promoting physical or social activities, and improving mental health. Based on the influence that green space exposure seems to exert on Public Health and using a multidisciplinary approach, we mapped phenological behavior of urban green areas to explore yearly persistence of their potential favorable effect, such as heat reduction, air purification, noise mitigation, and promotion of physical/social activities and improvement of mental health. The study area corresponds to the municipality of Torino (about 800,000 inhabitants, NW, Italy). Renouncing to a rigorous at-species level phenological description, this work investigated macro-phenology of vegetated areas for the 2018, 2019 and 2020 years with reference to the new free and open Copernicus HR-VPP dataset. Vegetation type, deduced with reference to the 2019 BDTRE official technical map of the Piemonte Region, was considered and related to the correspondent macro-phenology using a limited number of metrics from the HR-VPP dataset. Investigation was aimed at exploring their capability of providing synthetic and easy-to-use information for urban planners. No validation was achieved about phenological metrics values (assuming their accuracy correspondent to the nominal one reported in the associated manuals). Nevertheless, a spatial validation was operated to investigate the capability of the dataset to properly recognize vegetated areas, thus providing correspondent metrics. Preliminary results showed a spatial inconsistency related to the HR-VPP dataset, that greatly overestimates (about 50%) vegetated areas in the city, assigning metric values to pixels that, if compared with technical maps, do not fall within vegetated areas. The work found out that, among HR-VPP metrics, LOS (Length Of Season) and SPROD (Seasonal Productivity) well characterized vegetation patches, making it possible to clearly read vegetation behavior, which can be effectively exploited to zone the city and make management of green areas and real estate considerations more effective. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Urban Ecosystem Services)

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24 pages, 38785 KiB

Open AccessArticle

Mitigating Effect of Urban Green Spaces on Surface Urban Heat Island during Summer Period on an Example of a Medium Size Town of Zvolen, Slovakia

by Veronika Murtinová, Igor Gallay and Branislav Olah

Remote Sens. 2022, 14(18), 4492; https://doi.org/10.3390/rs14184492 - 8 Sep 2022

Cited by 5 | Viewed by 3156

Abstract

Climate change affects the urban population’s health and quality of life. Urban green spaces (UGS) underpin several essential ecosystem services, amongst them climate regulation. Urban vegetation mitigates high temperatures and, thus, reduces the heat stress for urban residents. The study aimed to verify whether the Surface Urban Heat Island (SUHI) effect manifests itself even in a medium size town (Zvolen, Slovakia) surrounded by agricultural and forested landscape and to quantify the temperature mitigating effect of urban green spaces. Land surface temperature (LST) and SUHI distribution were derived from the Landsat data during the summer months of 2010–2021. To statistically prove the cooling effect of the urban vegetation, we tested (by one-way ANOVA) LST within three urban zones of the Zvolen municipality defined by the Copernicus imperviousness density data: (a) dense urban area (31–100% impervious surfaces), (b) discontinuous urban area (1–30% impervious surfaces), (c) urban green spaces (0% impervious surfaces), and the open land surrounding the town (0% impervious surfaces). The results showed a statistical difference in temperatures between all urban areas (all zones) and the open land. Moreover, the UGS temperature was statistically different compared to the other urban zones. The mean temperature difference through the years 2010–2021 between urban green spaces and the dense urban area was 3.5 °C, with a maximum of 4.9 °C and a minimum 1.7 °C in favor of the urban spaces. Moreover, the temperature of urban green spaces and open land varied during the studied summer period. The warmer the weather, the higher the difference, while at the end of August, on a notably colder day, there was no significant difference between them. The results confirmed that UGS are significantly cooler during hot days, and they can mitigate the local climate. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Urban Ecosystem Services)

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26 pages, 9706 KiB

Open AccessArticle

Identification of Coupling Relationship between Ecosystem Services and Urbanization for Supporting Ecological Management: A Case Study on Areas along the Yellow River of Henan Province

by Hejie Wei, Dong Xue, Junchang Huang, Mengxue Liu and Ling Li

Remote Sens. 2022, 14(9), 2277; https://doi.org/10.3390/rs14092277 - 9 May 2022

Cited by 17 | Viewed by 2767

Abstract

Urbanization has an important effect on ecosystem services (ESs) and identifying the relationship between urbanization and ESs can provide a decision-making reference for regional ecological protection and management. Taking the areas along the Yellow River of Henan Province (AYRHP) as a research area, a coupling system of ESs and urbanization is established in this study to reveal the coupling relationship between the two. ESs are estimated by using Carnegie–Ames–Stanford approach, revision universal soil loss equation, and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models. The urbanization level is evaluated from three dimensions, namely, population, economy, and land. The coupling coordination relationship between various ESs and urbanization in AYRHP is quantified from 2000 to 2018 on the county scale based on the coupling coordination degree (CCD) model. The lead–lag relationship between ESs and urbanization is identified by using the relative development degree model, and ecological management zoning is conducted. Results show that in the study period, net primary production (NPP), soil conservation, and food production are increased, whereas water yield is decreased. In the study period, population, economy, and land urbanization level are increasing, and the comprehensive urbanization level is increased by 51.63%. The total CCD between NPP, food production, and water yield and comprehensive urbanization is basic or moderate coordination, whereas that between soil conservation and comprehensive urbanization is moderate maladjustment. In the research period, the coupling coordination between NPP and food production and comprehensive urbanization is increasing; that between water yield and comprehensive urbanization is fluctuated; and that between soil conservation and comprehensive urbanization is decreasing. The result of the research into the relative development degree in 2018 showed that food production, water yield, and soil conservation lag behind the urbanization level in most regions and counties along the Yellow River of Henan Province. On the basis of the lead–lag relationship between different ESs and urbanization level, the AYRHP are divided into ecological reconstruction area, ecological and agricultural improvement area, and ecological conservation area. CCD and relative development degree models can be used to evaluate the coordination relationship between ESs and urbanization, which provides scientific support for regional ES management. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Urban Ecosystem Services)

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15 pages, 3065 KiB

Open AccessFeature PaperArticle

The Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape

by Mthembeni Mngadi, John Odindi and Onisimo Mutanga

Remote Sens. 2021, 13(21), 4281; https://doi.org/10.3390/rs13214281 - 25 Oct 2021

Cited by 18 | Viewed by 4462

Abstract

The transformation of the natural landscape into an impervious surface due to urbanization has often been considered an important driver of environmental change, affecting essential urban ecological processes and ecosystem services. Continuous forest degradation and deforestation due to urbanization have led to an increase in atmospheric carbon emissions, risks, and impacts associated with climate change within urban landscapes and beyond them. Hence, urban reforestation has become a reliable long-term alternative for carbon sink and climate change mitigation. However, there is an urgent need for spatially accurate and concise quantification of these forest carbon stocks in order to understand and effectively monitor the accumulation and progress on such ecosystem services. Hence, this study sought to examine the prospect of Sentinel-2 spectral data in quantifying carbon stock in a reforested urban landscape using the random forest ensemble. Results show that Sentinel-2 spectral data estimated reforested forest carbon stock to an RMSE between 0.378 and 0.466 t·ha⁻¹ and R² of 79.82 and 77.96% using calibration and validation datasets. Based on random forest variable selection and backward elimination approaches, the red-edge normalized difference vegetation index, enhanced vegetation index, modified simple ratio index, and normalized difference vegetation index were the best subset of predictor variables of carbon stock. These findings demonstrate the value and prospects of Sentinel-2 spectral data for predicting carbon stock in reforested urban landscapes. This information is critical for adopting informed management policies and plans for optimizing urban reforested landscapes carbon sequestration capacity and improving their climate change mitigation potential. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Urban Ecosystem Services)

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