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Urban Landscapes and Global Environmental Challenges: Monitoring and Modelling Using Remote Sensing

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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 (28 February 2021) | Viewed by 35218

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Dr. Salman Qureshi

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Department of Geography (Landscape Ecology), Humboldt University of Berlin, Germany
Interests: urban ecosystem services; spatial pattern recognition and LULC change modelling; socio-ecological complexities; urbanization and sustainability; environmental cognition and landscape perception
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urban landscapes are the everyday environment for the majority of the global population that lives in urban areas. The continuous growth in the number and size of urban areas along with an increasing demand on resources and energy pose great challenges for ensuring human welfare in cities while preventing an increasing loss of biodiversity. An integrated approach by remote sensing techniques and systems thinking helps to address the complex issues related to overall functioning of urban landscapes and how they lead to global challenges. The resilience of urban systems to sustain the pressure of unprecedented urban growth is yet an open area of research, where the integrated modelling of all these urban system indicators and phenomenon need in-depth investigation. Urban (ecological) systems modelling is a rapidly developing field, but remains rather diffuse across a wide range of international journals, including disciplines devoted to the spatial sciences, as well as ecology, forestry, agriculture, environmental management, geography, global change, etc. The Special Issue aims to bridge the knowledge gap between urbanisation, global environmental changes, demand creation and provisioning of services in urban regions on the one hand and schemes of urban governance and planning on the other. Nevertheless, the indicator-based approach to modelling urban ecosystems will be the highlight of this Special Issue. We are seeking the submission of papers from urban ecological and related environmental studies, as well as more technical articles including topics such as spatial data infrastructure, OBIA, VHR data modelling, health studies, risk modelling, big data approaches and machine learning techniques – to understand the patterns and processes of urban landscapes. We are particularly interested in special and unusual (new) ways of thinking about and processing remote sensing data at different scales.

Dr. Salman Qureshi
Guest Editor

Manuscript Submission Information

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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.

Keywords

Urban ecology and planning
Big data modelling
Object based remote sensing
VHR and airborne data
Geostatistical techniques
Socio-ecological complexities
Climate change adaptation

Published Papers (10 papers)

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Research

22 pages, 10141 KiB

Open AccessArticle

A New Integrated Approach for Municipal Landfill Siting Based on Urban Physical Growth Prediction: A Case Study Mashhad Metropolis in Iran

by Salman Qureshi, Saman Nadizadeh Shorabeh, Najmeh Neysani Samany, Foad Minaei, Mehdi Homaee, Fatemeh Nickravesh, Mohammad Karimi Firozjaei and Jamal Jokar Arsanjani

Remote Sens. 2021, 13(5), 949; https://doi.org/10.3390/rs13050949 - 03 Mar 2021

Cited by 26 | Viewed by 3085

Abstract

Due to irregular and uncontrolled expansion of cities in developing countries, currently operational landfill sites cannot be used in the long-term, as people will be living in proximity to these sites and be exposed to unhygienic circumstances. Hence, this study aims at proposing an integrated approach for determining suitable locations for landfills while considering their physical expansion. The proposed approach utilizes the fuzzy analytical hierarchy process (FAHP) to weigh the sets of identified landfill location criteria. Furthermore, the weighted linear combination (WLC) approach was applied for the elicitation of the proper primary locations. Finally, the support vector machine (SVM) and cellular automation-based Markov chain method were used to predict urban growth. To demonstrate the applicability of the developed approach, it was applied to a case study, namely the city of Mashhad in Iran, where suitable sites for landfills were identified considering the urban growth in different geographical directions for this city by 2048. The proposed approach could be of use for policymakers, urban planners, and other decision-makers to minimize uncertainty arising from long-term resource allocation. Full article

(This article belongs to the Special Issue Urban Landscapes and Global Environmental Challenges: Monitoring and Modelling Using Remote Sensing)

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18 pages, 4783 KiB

Open AccessArticle

Land Use Classification of VHR Images for Mapping Small-Sized Abandoned Citrus Plots by Using Spectral and Textural Information

by Sergio Morell-Monzó, María-Teresa Sebastiá-Frasquet and Javier Estornell

Remote Sens. 2021, 13(4), 681; https://doi.org/10.3390/rs13040681 - 13 Feb 2021

Cited by 14 | Viewed by 3647

Abstract

Agricultural land abandonment is an increasing problem in Europe. The Comunitat Valenciana Region (Spain) is one of the most important citrus producers in Europe suffering this problem. This region characterizes by small sized citrus plots and high spatial fragmentation which makes necessary to use Very High-Resolution images to detect abandoned plots. In this paper spectral and Gray Level Co-Occurrence Matrix (GLCM)-based textural information derived from the Normalized Difference Vegetation Index (NDVI) are used to map abandoned citrus plots in Oliva municipality (eastern Spain). The proposed methodology is based on three general steps: (a) extraction of spectral and textural features from the image, (b) pixel-based classification of the image using the Random Forest algorithm, and (c) assignment of a single value per plot by majority voting. The best results were obtained when extracting the texture features with a 9 × 9 window size and the Random Forest model showed convergence around 100 decision trees. Cross-validation of the model showed an overall accuracy of the pixel-based classification of 87% and an overall accuracy of the plot-based classification of 95%. All the variables used are statistically significant for the classification, however the most important were contrast, dissimilarity, NIR band (720 nm), and blue band (620 nm). According to our results, 31% of the plots classified as citrus in Oliva by current methodology are abandoned. This is very important to avoid overestimating crop yield calculations by public administrations. The model was applied successfully outside the main study area (Oliva municipality); with a slightly lower accuracy (92%). This research provides a new approach to map small agricultural plots, especially to detect land abandonment in woody evergreen crops that have been little studied until now. Full article

(This article belongs to the Special Issue Urban Landscapes and Global Environmental Challenges: Monitoring and Modelling Using Remote Sensing)

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14 pages, 6365 KiB

Open AccessArticle

On the Spatial Patterns of Urban Thermal Conditions Using Indoor and Outdoor Temperatures

by Sadroddin Alavipanah, Dagmar Haase, Mohsen Makki, Mir Muhammad Nizamani and Salman Qureshi

Remote Sens. 2021, 13(4), 640; https://doi.org/10.3390/rs13040640 - 10 Feb 2021

Cited by 4 | Viewed by 1906

Abstract

The changing climate has introduced new and unique challenges and threats to humans and their environment. Urban dwellers in particular have suffered from increased levels of heat stress, and the situation is predicted to continue to worsen in the future. Attention toward urban climate change adaptation has increased more than ever before, but previous studies have focused on indoor and outdoor temperature patterns separately. The objective of this research is to assess the indoor and outdoor temperature patterns of different urban settlements. Remote sensing data, together with air temperature data collected with temperature data loggers, were used to analyze land surface temperature (outdoor temperature) and air temperature (indoor temperature). A hot and cold spot analysis was performed to identify the statistically significant clusters of high and low temperature data. The results showed a distinct temperature pattern across different residential units. Districts with dense urban settlements show a warmer outdoor temperature than do more sparsely developed districts. Dense urban settlements show cooler indoor temperatures during the day and night, while newly built districts show cooler outdoor temperatures during the warm season. Understanding indoor and outdoor temperature patterns simultaneously could help to better identify districts that are vulnerable to heat stress in each city. Recognizing vulnerable districts could minimize the impact of heat stress on inhabitants. Full article

(This article belongs to the Special Issue Urban Landscapes and Global Environmental Challenges: Monitoring and Modelling Using Remote Sensing)

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23 pages, 48160 KiB

Open AccessArticle

Urban Riverway Extraction from High-Resolution SAR Image Based on Blocking Segmentation and Discontinuity Connection

by Yu Li, Yun Yang and Quanhua Zhao

Remote Sens. 2020, 12(24), 4014; https://doi.org/10.3390/rs12244014 - 08 Dec 2020

Cited by 2 | Viewed by 1859

Abstract

An urban riverway extraction method is proposed for high-resolution synthetic aperture radar (SAR) images. First, the original image is partitioned into overlapping sub-image blocks, in which the sub-image blocks that do not cover riverways are regarded as background. Sub-image blocks covering riverways are then filtered using the iterative adaptive speckle reduction anisotropic diffusion (SRAD) that introduces the relative signal-to-noise ratio (RSNR). The filtered images are segmented quickly by the Sauvola algorithm, and the false riverway fragments are removed by the area and aspect ratio of the connected component in the segmentation results. Using the minimum convex hull of each riverway segment as the connection object, the seeds are automatically determined by the difference between adjacent pyramid layers, and the sub-image block riverway extraction result is used as the bottom layer. The discontinuity connection between river segments is achieved by multi-layer region growth. Finally, the processed sub-image blocks are stitched to get the riverway extraction results for the entire image. To verify the applicability and usefulness of the proposed approach, high-resolution SAR imagery obtained by the Gaofen-3 (GF-3) satellite was used in the assessment. The qualitative and quantitative evaluations of the experimental results show that the proposed method can effectively and completely extract complex urban riverways from high-resolution SAR images. Full article

(This article belongs to the Special Issue Urban Landscapes and Global Environmental Challenges: Monitoring and Modelling Using Remote Sensing)

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16 pages, 1278 KiB

Open AccessArticle

Using SPOT Data and FRAGSTAS to Analyze the Relationship between Plant Diversity and Green Space Landscape Patterns in the Tropical Coastal City of Zhanjiang, China

by Xia-Lan Cheng, Mir Muhammad Nizamani, C.Y. Jim, Kelly Balfour, Liang-Jun Da, Salman Qureshi, Zhi-Xin Zhu and Hua-Feng Wang

Remote Sens. 2020, 12(21), 3477; https://doi.org/10.3390/rs12213477 - 22 Oct 2020

Cited by 16 | Viewed by 3100

Abstract

Urban green spaces provide a host of ecosystem services, the quantity and structure of which play an important role in human well-being. Rapid urbanization may modify urban green spaces, having various effects on plant diversity. Tropical coastal cities have urbanized rapidly in recent decades, but few studies have been conducted with a focus on their green spaces. We studied the responses of cultivated and spontaneous plants, both key components of urban flora, to the landscape structure of urban green spaces and possible social drivers. We analyzed existing relationships between plant diversity indices, urban green space landscape metrics (using Systeme Probatoire d’Observation de la Terre (SPOT) data,), and social factors, including the type, population density, construction age, and GPS coordinates of each Urban Functional Unit, or UFU. We found that UFUs with more green space patches had higher cultivated and spontaneous species richness than those with fewer green space patches. Spontaneous species richness decreased when green space patches became fragmented, and it increased when green space patches were more connected (e.g., via land bridges). Conversely, cultivated species richness increased with green space patch fragmentation. The phylogenetic diversity of both cultivated and spontaneous plants were weakly associated with green space structure, which was strongly driven by land use. Old UFUs and those with larger populations had more green space patches overall, although they tended to be small and fragmented. Green space patch density was found to increase as the UFU age increased. From the viewpoint of knowledge transfer, understanding the effects and drivers of landscape patterns of urban green spaces could inform the development of improved policies and management of urban green space areas. Full article

(This article belongs to the Special Issue Urban Landscapes and Global Environmental Challenges: Monitoring and Modelling Using Remote Sensing)

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24 pages, 7867 KiB

Open AccessArticle

A Remotely Sensed Assessment of Surface Ecological Change over the Gomishan Wetland, Iran

by Salman Qureshi, Seyed Kazem Alavipanah, Maria Konyushkova, Naeim Mijani, Solmaz Fathololomi, Mohammad Karimi Firozjaei, Mehdi Homaee, Saeid Hamzeh and Ata Abdollahi Kakroodi

Remote Sens. 2020, 12(18), 2989; https://doi.org/10.3390/rs12182989 - 14 Sep 2020

Cited by 53 | Viewed by 3869

Abstract

Due to the excessive use of natural resources in the contemporary world, the importance of ecological and environmental condition modeling has increased. Wetlands and cities represent the natural and artificial strategic areas that affect ecosystem conditions. Changes in the ecological conditions of these areas have a great impact on the conditions of the global ecosystem. Therefore, modeling spatiotemporal variations of the ecological conditions in these areas is critical. This study was aimed at comparing degrees of variation among surface ecological conditions due to natural and unnatural factors. Consequently, the surface ecological conditions of Gomishan city and Gomishan wetland in Iran were modeled for a period of 30 years, and the spatiotemporal variations were evaluated and compared with each other. To this end, 20 Landsat 5, 7, and 8, and 432 Moderate Resolution Imaging Spectroradiometer (MODIS), monthly land surface temperature (LST) (MOD11C3) and normalized difference vegetation index (NDVI) (MOD13C3) products were utilized. The surface ecological conditions were modeled according to the Remote Sensing-based Ecological Index (RSEI), and the spatiotemporal variation of the RSEI values in the study area (Gomishan city, Gomishan wetland) were evaluated and compared with each other. According to MODIS products, the mean of the LST and NDVI variance values for the study area (Gomishan city, Gomishan wetland) were obtained to be 6.5 °C (2.1, 12.1) and 0.009 (0.005, 0.013), respectively. The highest LST and NDVI temporal variations were found for Gomishan wetland near the Caspian Sea. According to Landsat images, Gomishan wetland and Gomishan city have the highest and lowest temporal variations in surface biophysical characteristics, respectively. The mean RSEI for the study area (Gomishan city, Gomishan wetland) was 0.43 (0.65, 0.29), respectively. Additionally, the mean Coefficient of Variation (CV) of RSEI for the study area (Gomishan city, Gomishan wetland) was 0.10 (0.88, 0.51), respectively. The surface ecological conditions of Gomishan city were worse than those of the Gomishan wetland at all dates. Temporal variations in the surface ecological conditions of Gomishan wetland were greater than those of the study area and Gomishan city. These results can provide useful and effective information for environmental planning and decision-making to improve ecological conditions, protect the environment, and support sustainable ecosystem development. Full article

(This article belongs to the Special Issue Urban Landscapes and Global Environmental Challenges: Monitoring and Modelling Using Remote Sensing)

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24 pages, 10327 KiB

Open AccessArticle

Evaluating the Spectral Indices Efficiency to Quantify Daytime Surface Anthropogenic Heat Island Intensity: An Intercontinental Methodology

by Mohammad Karimi Firozjaei, Solmaz Fathololoumi, Naeim Mijani, Majid Kiavarz, Salman Qureshi, Mehdi Homaee and Seyed Kazem Alavipanah

Remote Sens. 2020, 12(17), 2854; https://doi.org/10.3390/rs12172854 - 02 Sep 2020

Cited by 18 | Viewed by 2982

Abstract

The surface anthropogenic heat island (SAHI) phenomenon is one of the most important environmental concerns in urban areas. SAHIs play a significant role in quality of urban life. Hence, the quantification of SAHI intensity (SAHII) is of great importance. The impervious surface cover (ISC) can well reflect the degree and extent of anthropogenic activities in an area. Various actual ISC (AISC) datasets are available for different regions of the world. However, the temporal and spatial coverage of available and accessible AISC datasets is limited. This study was aimed to evaluate the spectral indices efficiency to daytime SAHII (DSAHII) quantification. Consequently, 14 cities including Budapest, Bucharest, Ciechanow, Hamburg, Lyon, Madrid, Porto, and Rome in Europe and Dallas, Seattle, Minneapolis, Los Angeles, Chicago, and Phoenix in the USA, were selected. A set of 91 Landsat 8 images, the Landsat provisional surface temperature product, the High Resolution Imperviousness Layer (HRIL), and the National Land Cover Database (NLCD) imperviousness data were used as the AISC datasets for the selected cities. The spectral index-based ISC (SIISC) and land surface temperature (LST) were modelled from the Landsat 8 images. Then, a linear least square model (LLSM) obtained from the LST-AISC feature space was applied to quantify the actual SAHII of the selected cities. Finally, the SAHII of the selected cities was modelled based on the LST-SIISC feature space-derived LLSM. Finally, the values of the coefficient of determination (R²) and the root mean square error (RMSE) between the actual and modelled SAHII were calculated to evaluate and compare the performance of different spectral indices in SAHII quantification. The performance of the spectral indices used in the built LST-SIISC feature space for SAHII quantification differed. The index-based built-up index (IBI) (R² = 0.98, RMSE = 0.34 °C) and albedo (0.76, 1.39 °C) performed the best and worst performance in SAHII quantification, respectively. Our results indicate that the LST-SIISC feature space is very useful and effective for SAHII quantification. The advantages of the spectral indices used in SAHII quantification include (1) synchronization with the recording of thermal data, (2) simplicity, (3) low cost, (4) accessibility under different spatial and temporal conditions, and (5) scalability. Full article

(This article belongs to the Special Issue Urban Landscapes and Global Environmental Challenges: Monitoring and Modelling Using Remote Sensing)

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19 pages, 8020 KiB

Open AccessArticle

Spatiotemporal Variation of Surface Urban Heat Islands in Relation to Land Cover Composition and Configuration: A Multi-Scale Case Study of Xi’an, China

by Linlin Lu, Qihao Weng, Da Xiao, Huadong Guo, Qingting Li and Wenhua Hui

Remote Sens. 2020, 12(17), 2713; https://doi.org/10.3390/rs12172713 - 21 Aug 2020

Cited by 59 | Viewed by 5305

Abstract

Urban heat islands (UHI) can lead to multiple adverse impacts, including increased air pollution, morbidity, and energy consumption. The association between UHI effects and land cover characteristics has been extensively studied but is insufficiently understood in inland cities due to their unique urban environments. This study sought to investigate the spatiotemporal variations of the thermal environment and their relationships with land cover composition and configuration in Xi’an, the largest city in northwestern China. Land cover maps were classified and land surface temperature (LST) was estimated using Landsat imagery in six time periods from 1995 to 2020. The variations of surface heat island were captured using multi-temporal LST data and a surface urban heat island intensity (SUHII) indicator. The relationship between land cover features and land surface temperature was analyzed through multi-resolution grids and correlation analysis. The results showed that mean SUHII in the study area increased from 0.683 °C in 1995 to 2.759 °C in 2020. The densities of impervious surfaces had a stronger impact on LST than green space, with Pearson’s correlation coefficient r ranging from 0.59 to 0.97. The correlation between normalized difference impervious surface index and LST was enhanced with the enlargement of the grid cell size. The correlations between normalized difference vegetation index and LST reached maxima and stabilized at grid cell sizes of 210 and 240 m. Increasing the total area and aggregation level of urban green space alleviated the negative impacts of UHI in the study area. Our results also highlight the necessity of multi-scale analysis for examining the relationships between landscape configuration metrics and LST. These findings improved our understanding of the spatiotemporal variation of the surface urban heat island effect and its relationship with land cover features in a major inland city of China. Full article

(This article belongs to the Special Issue Urban Landscapes and Global Environmental Challenges: Monitoring and Modelling Using Remote Sensing)

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19 pages, 2810 KiB

Open AccessArticle

Modeling the Effect of Green Roof Systems and Photovoltaic Panels for Building Energy Savings to Mitigate Climate Change

by Yuanfan Zheng and Qihao Weng

Remote Sens. 2020, 12(15), 2402; https://doi.org/10.3390/rs12152402 - 27 Jul 2020

Cited by 28 | Viewed by 5507

Abstract

Green roofs and rooftop solar photovoltaic (PV) systems are two popular mitigation strategies to reduce the net building energy demand and ease urban heat island (UHI) effect. This research tested the potential mitigation effects of green roofs and solar photovoltaic (PV) systems on increased buildings energy demand caused by climate change in Los Angeles County, California, USA. The mitigation effects were assessed based on selected buildings that were predicted to be more vulnerable to climate change. EnergyPlus software was used to simulate hourly building energy consumption with the proper settings of PV-green roofs. All buildings with green roofs showed positive energy savings with regard to total energy and electricity. The savings caused by green roofs were positively correlated with three key parameters: Leaf Area Index (LAI), soil depth, and irrigation saturation percentage. Moreover, the majority of the electricity-saving benefits from green roofs were found in the Heating, Ventilation, and Cooling (HVAC) systems. In addition, this study found that green roofs have different energy-saving abilities on different types of buildings with different technologies, which has received little attention in previous studies. Full article

(This article belongs to the Special Issue Urban Landscapes and Global Environmental Challenges: Monitoring and Modelling Using Remote Sensing)

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20 pages, 4856 KiB

Open AccessArticle

An Integrated Approach to Study Spatial Patterns and Drivers of Land Cover Within Urban Functional Units: A Multi-City Comparative Study in China

by Hua-Feng Wang, Xia-Lan Cheng, Mir Muhammad Nizamani, Kelly Balfour, Liangjun Da, Zhi-Xin Zhu and Salman Qureshi

Remote Sens. 2020, 12(14), 2201; https://doi.org/10.3390/rs12142201 - 09 Jul 2020

Cited by 13 | Viewed by 2781

Abstract

Understanding the factors that drive green space composition and richness in heterogeneous urban landscapes is critical for maintaining important ecosystem services and biodiversity. Few studies have been conducted on urban greening and plant diversity at the urban functional unit (UFU) level, although a handful of studies have explored the drivers of greening percentage and its relationships with plant richness in tropical cities. In this study, we conducted field surveys, compiled census and remote sensing data, and performed spatial analyses to investigate the interrelationship between greening percentages, plant diversity, and the socioeconomic variables of different primary and secondary UFUs in the cities of Beijing, Zhanjiang, and Haikou in China. We found that these relationships did not differ significantly between primary and secondary UFUs, and that Parks represented the largest areas of forested land, grassland, and water bodies across all three cities. Moreover, the greening percentages of all UFUs across these three cities were positively correlated with both socioeconomic variables and plant species richness. These relationships can be utilized to guide future green space planning within urban ecosystems. Full article

(This article belongs to the Special Issue Urban Landscapes and Global Environmental Challenges: Monitoring and Modelling Using Remote Sensing)

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