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Urban Ecophysiology: A Remote Sensing Perspective
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Urban Ecophysiology: A Remote Sensing Perspective

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 17608

Special Issue Editors

Dr. Peng Fu

E-Mail Website
Guest Editor
School of Plant, Environmental and Soil Sciences, Louisiana State University AgCenter, Louisiana State University, Baton Rouge, LA 70803, USA
Interests: remote sensing; plant physiology; urban climate; soil science; machine learning; digital agriculture; ecology
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaonan Tai

E-Mail Website
Guest Editor
New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: ecohydrology; forest mortality and disturbance; hydrologic and ecosystem modeling; remote sensing; GIS
Prof. Dr. Xiang Zhang

E-Mail Website
Guest Editor
National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences (Wuhan), Lumo Road 388, Hongshan District, Wuhan, China
Interests: drought disaster monitoring and analysis; geospatial sensor web theory, methods and applications; smart city technology; sustainable development goals (SDGs)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cities have been considered a herald for future environmental and climate change, as they are experiencing changes such as higher levels of light at night, higher carbon dioxide emissions, greater levels of pollution, increased ambient temperatures, enhanced nutrient deposition, and altered precipitation patterns. In the meantime, more than 50% of the global population already lives in cities, and that number is projected to be ~70% by 2050. As the urban environment continues growing and encroaching into natural environments, it is necessary to understand how urban environmental and climatic changes impact plant physiological functions that are critical to the provision of ecosystem services. These topics are also important for understanding and managing the fluxes of heat, water, carbon, and nutrients that can help cities develop livable, sustainable, and resilient plans to adapt to future global climate change.

While plants are an integral part of most nature-based solutions to environmental and societal challenges, studies in eco-physiological functions are limited to individual plants. As remotely sensed images at both high spatial and temporal resolutions are available, there is a chance to scale up our understanding from leaf to individual plants and to the landscape level. Thus, in this Special Issue, we seek contributions leveraging remote sensing and/or other types of datasets and techniques that can help elucidate changes in the plant eco-physiological functions associated with various environmental alterations in cities. These topics can include, but are not limited to:

  • Urban green space and its function;
  • Urban plant phenology and productivity;
  • Light pollution/impacts on vegetation;
  • Urban extreme climates such as drought and heat waves on plants;
  • Plant evapotranspiration;
  • Plant diversity and invasive species in cities;
  • The relationship between building environment and vegetation structure;
  • Carbon, nutrient, and water fluxes using eddy covariance and remote sensing;
  • Airborne/satellite solar-induced fluorescence for characterizing urban vegetation.

Dr. Peng Fu
Dr. Xiaonan Tai
Prof. Dr. Xiang Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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.

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Published Papers (5 papers)

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Research

20 pages, 6562 KiB  
Article
Exploring the Relationship between the Eco-Environmental Quality and Urbanization by Utilizing Sentinel and Landsat Data: A Case Study of the Yellow River Basin
by Xiaolei Wang, Shiru Zhang, Xue Zhao, Shouhai Shi and Lei Xu
Remote Sens. 2023, 15(3), 743; https://doi.org/10.3390/rs15030743 - 27 Jan 2023
Cited by 19 | Viewed by 3050
Abstract
Yellow River Basin urban agglomeration (YRBU) is the main carrier of regional socio-economic development in the Yellow River Basin, and its eco-environmental quality, urbanization, and coupling coordination degree are facing higher demands. It is of great significance for the development of YRBU to [...] Read more.
Yellow River Basin urban agglomeration (YRBU) is the main carrier of regional socio-economic development in the Yellow River Basin, and its eco-environmental quality, urbanization, and coupling coordination degree are facing higher demands. It is of great significance for the development of YRBU to understand the interactive coupling relationship between the eco-environment and urbanization development from the multi-scale perspective. This research intended to understand the spatio-temporal characteristics of eco-environmental quality, urbanization, and coupling coordination degree in the study area from 2013 to 2021. We proposed an Adjusted Remote Sensing Ecological Index (A-RSEI), integrated Sentinel-2A, Landsat 8, and other remote sensing data to evaluate the eco-environmental quality of the study area, from 2013 to 2021. Coupled coordination degree (CCD) model was used to obtain the CCD between eco-environmental quality and urbanization. In addition, spatio-temporal and multi-scale analysis was carried out from the perspectives of urban agglomeration, municipal, county, and pixel scales. Combined with spatial autocorrelation analysis and Tapio decoupling model, the CCD was further explored. The results show that the proposed A-RSEI model is more suitable for monitoring the eco-environmental quality of the Yellow River Basin. The coupling coordination degree of eco-environment and urbanization in most regions of the study area are rising in a relatively green development trend. The multi-scale analysis among eco-environmental quality, urbanization, and CCD can not only indicate the impact of the central city on its surrounding areas but also help to describe the details of CCD combined with the terrain. The comprehensive discrimination of urban agglomeration and county scale is helpful to express the relationship between urbanization and eco-environmental quality centered on a certain city. The results can provide scientific support for eco-environment protection and high-quality development of the Yellow River Basin. Full article
(This article belongs to the Special Issue Urban Ecophysiology: A Remote Sensing Perspective)
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25 pages, 4876 KiB  
Article
Ecosystem Service Function Supply–Demand Evaluation of Urban Functional Green Space Based on Multi-Source Data Fusion
by Yingqi Wang, Huiping Huang, Guang Yang and Wei Chen
Remote Sens. 2023, 15(1), 118; https://doi.org/10.3390/rs15010118 - 26 Dec 2022
Cited by 16 | Viewed by 3090
Abstract
With the rapid development of urbanization, it is an important goal for urban green space (UGS) to meet the needs of residents’ production and life using a supply–demand evaluation method of ecosystem service (ES). However, most studies have considered all functional types of [...] Read more.
With the rapid development of urbanization, it is an important goal for urban green space (UGS) to meet the needs of residents’ production and life using a supply–demand evaluation method of ecosystem service (ES). However, most studies have considered all functional types of UGS as the supply side, or rely solely on a single supply or demand side to conduct ecosystem service function (ESF) evaluation, resulting in less accurate and targeted research findings. As a result, a novel methodological framework for matching each ESF with corresponding functional types of UGS, and considering both supply and demand sides have been required. Firstly, the object-oriented approach combining support vector machine (SVM) and normalized difference vegetation index (NDVI) was used to automatically identify UGS, and integrated Point of Interest (POI), urban built-up area, road land, parcel, and socio-economic data to classify six functional types of UGS using the near-convex-hull. Next, matching the functional types of UGS with five ESFs, both supply and demand status were evaluated using the carbon sequestration and release analysis, Gaussian two-step floating catchment area, and spatial equilibrium degree methods. This method was demonstrated in Beijing, China. The results show: (1) the ES supply–demand situation provided by each functional type of UGS is different in five ESFs; (2) considering both supply and demand is more intuitive to see whether the city’s demand for UGS has been met. Our results provide a new perspective for evaluating the contribution of UGS and have practical implications for UGS planning. Full article
(This article belongs to the Special Issue Urban Ecophysiology: A Remote Sensing Perspective)
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19 pages, 13257 KiB  
Article
Study on the Landscape Space of Typical Mining Areas in Xuzhou City from 2000 to 2020 and Optimization Strategies for Carbon Sink Enhancement
by Shi Qiu, Qiang Yu, Teng Niu, Minzhe Fang, Hongqiong Guo, Hongjun Liu and Song Li
Remote Sens. 2022, 14(17), 4185; https://doi.org/10.3390/rs14174185 - 25 Aug 2022
Cited by 27 | Viewed by 3341
Abstract
The continuous extraction of mining resources has led to the destruction of landscape space, which has had a great impact on the human living environment and pristine ecosystems. Optimizing the ecological spatial networks of mining areas can restore and enhance the damaged ecological [...] Read more.
The continuous extraction of mining resources has led to the destruction of landscape space, which has had a great impact on the human living environment and pristine ecosystems. Optimizing the ecological spatial networks of mining areas can restore and enhance the damaged ecological environment. However, there are few cases of ecological spatial network optimization in mining areas, and there are still some shortcomings. Therefore, in this study, we propose an ecological spatial network theory and a synergistic enhancement of ecological functions and carbon sink optimization model (SEEC) for urban restoration in mining areas, emphasizing the functional and carbon sink nature of ecological sources. We selected a typical mining area in Xuzhou City as the study area, explored the changes in the nature and function of the ecological spatial network from 2000 to 2020, and selected the ecological spatial network in the mining area of Xuzhou City in 2020 as the optimization study case, adding 27 ecological stepping stones and 72 ecological corridors. Through the comparison of robustness before and after optimization, we found that the optimized ecological spatial network has a stronger stability and ecological restoration ability. This study provides strategies and methods for ecological restoration projects in national mining cities and also provides references and lessons for ecological restoration in other mining areas in the future. Full article
(This article belongs to the Special Issue Urban Ecophysiology: A Remote Sensing Perspective)
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23 pages, 9511 KiB  
Article
Deep Learning-Based 500 m Spatio-Temporally Continuous Air Temperature Generation by Fusing Multi-Source Data
by Xiang Zhang, Tailai Huang, Aminjon Gulakhmadov, Yu Song, Xihui Gu, Jiangyuan Zeng, Shuzhe Huang, Won-Ho Nam, Nengcheng Chen and Dev Niyogi
Remote Sens. 2022, 14(15), 3536; https://doi.org/10.3390/rs14153536 - 23 Jul 2022
Cited by 13 | Viewed by 3040
Abstract
The all-weather high-resolution air temperature data is crucial for understanding the urban thermal conditions with their spatio-temporal characteristics, driving factors, socio-economic and environmental consequences. In this study, we developed a novel 5-layer Deep Belief Network (DBN) deep learning model to fuse multi-source data [...] Read more.
The all-weather high-resolution air temperature data is crucial for understanding the urban thermal conditions with their spatio-temporal characteristics, driving factors, socio-economic and environmental consequences. In this study, we developed a novel 5-layer Deep Belief Network (DBN) deep learning model to fuse multi-source data and then generated air temperature data with 3H characteristics: High resolution, High spatio-temporal continuity (spatially seamless and temporally continuous), and High accuracy simultaneously. The DBN model was developed and applied for two different urban regions: Wuhan Metropolitan Area (WMA) in China, and Austin, Texas, USA. The model has a excellent ability to fit the complex nonlinear relationship between temperature and different predictive variables. After various adjustments to the model structure and different combinations of input variables, the daily 500-m air temperature in Wuhan Metropolitan Area (WMA) was initially generated by fusing remote sensing, reanalysis and in situ measurement products. The ten-fold cross-validation results indicated that the DBN model achieved promising results with the RMSE of 1.086 °C, MAE of 0.839 °C, and R2 of 0.986. Compared with conventional data fusion algorithms, the DBN model also exhibited better performance. In addition, the detailed evaluation of the model on spatial and temporal scales proved the advantages of using DBN model to generate 3H temperature data. The spatial transferability of the model was tested by conducting a validation experiment for Austin, USA. In general, the results and fine-scale analyses show that the employed framework is effective to generate 3H temperature, which is valuable for urban climate and urban heat island research. Full article
(This article belongs to the Special Issue Urban Ecophysiology: A Remote Sensing Perspective)
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22 pages, 4094 KiB  
Article
Spatial Structure of an Urban Park System Based on Fractal Theory: A Case Study of Fuzhou, China
by Meizi You, Chenghe Guan and Riwen Lai
Remote Sens. 2022, 14(9), 2144; https://doi.org/10.3390/rs14092144 - 29 Apr 2022
Cited by 13 | Viewed by 3649
Abstract
The rationality and efficiency of the spatial structure of an urban park system are critical in building a livable urban environment. Fractal theory is currently treated as the frontier theory for exploring the law of complex systems; however, it has rarely been applied [...] Read more.
The rationality and efficiency of the spatial structure of an urban park system are critical in building a livable urban environment. Fractal theory is currently treated as the frontier theory for exploring the law of complex systems; however, it has rarely been applied to urban park systems. This study applied the aggregation, grid and correlation dimension models of fractal theory in Fuzhou, China. The spatial structure and driving factors of the urban park system were analyzed and an innovative model was proposed. The evidence shows that the spatial structure of the park system has fractal characteristics, although self-organization and optimization have not yet been fully formed, revealing a multi-core nesting pattern. Moreover, the core is cluster of four popular parks with weakening adsorption, and the emerging Baima River Park is located at the geometric center, which is likely to be further developed. The system structure is primarily driven by geographical conditions, planning policies, and transportation networks. Against this backdrop, an innovative model for the park system was proposed. The central park has heterogeneity and synergistic development, relying on the kinds of flow which can lead to the formation of a park city, a variation of a garden city. At the regional scale, relying on the geographical lines, the formation of a regional park zone could be realized. These findings provide new perspectives to reveal the spatial structure of urban park systems. The information derived can assist policy makers and planners in formulating more scientific plans, and may contribute to building a balanced and efficient urban park system. Full article
(This article belongs to the Special Issue Urban Ecophysiology: A Remote Sensing Perspective)
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