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Urban Green Spaces: Understanding Their Form, Structure and Composition Using Remote Sensing

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

Deadline for manuscript submissions: 10 July 2024 | Viewed by 5780

Special Issue Editors

Prof. Dr. John O. Odindi

E-Mail Website
Guest Editor
Discipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Scottsville, Pietermaritzburg 3209, South Africa
Interests: adoption of remotely sensed datasets in understanding urban land use; land covers; urban green spaces; urban ecosystem services; urban heat islands; climate change and urban transformation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhiming Zhang

E-Mail Website
Guest Editor
School of Ecology and Environmental Science, Yunnan University, Kunming 650091, China
Interests: urban land-cover mapping; vegetation remote sensing; sustainable forest management; urban heat island and planning; environmental degradation; biodiversity mapping
Prof. Dr. Frieke M.B. Van Coillie

E-Mail Website
Guest Editor
Department of Environment, Remote Sensing | Spatial Analysis Lab (REMOSA), Ghent University, 9000 Ghent, Belgium
Interests: geophysical image processing; vegetation mapping and trait retrieval; image fusion and classification; environmental change monitoring; urban remote sensing

Special Issue Information

Dear Colleagues,

Urbanization is typified by spatial and temporal transformation arising from the conversion of natural greenery to impervious and built-up surfaces. These conversions affect ecosystem functioning at local, regional and global scales and compromise their ability to effectively provide their respective goods and services. Furthermore, the conversions are known to be a major driver of environmental change associated with, among others, natural landscape fragmentation and related adverse effects, deterioration in environmental quality, biodiversity loss, and changes in the micro- and macro-climate. Hence, the establishment, restoration and preservation of urban green infrastructure is increasingly becoming a popular approach to dealing with adversities associated with urbanization processes. In this regard, understanding urban spatio-temporal ecological and natural patterns is critical for the management of urban physical, ecological and social processes. Specifically, determining past, present and future patterns and drivers is critical for urban environmental management, urban spatial planning, optimal and sustainable use of urban landscapes and climate change mitigation, among others.

Recently, remote sensing approaches have become indispensable in understanding the implications of urbanization for local natural landscapes and ecological integrity. Hence, this Special Issue solicits articles adopting remotely sensed datasets and approaches in understanding the form, structure and composition of urban greenery. These may cover the use of multi- and hyperspectral data sets and thermal datasets acquired using aerial and satellite platforms. Articles may include, but are not limited to, the following topics:

  • Urban green spaces
  • Urban tree-related cool islands
  • Urban forests
  • Urban trees
  • Urban carbon sequestration
  • Transformation in urban greenery
  • Urban reforestation
  • Urban micro-climate related to urban greenery

Prof. Dr. John Odindi
Prof. Dr. Zhiming Zhang
Prof. Dr. Frieke M.B. Van Coillie
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.

Keywords

  • remote sensing
  • urban green spaces
  • urban forests
  • urban cool islands
  • urban natural ecosystems
  • urban trees

Published Papers (3 papers)

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Research

17 pages, 4398 KiB  
Article
Multi-Sensor Classification Framework of Urban Vegetation for Improving Ecological Services Management
by Arti Tiwari, Oz Kira, Julius Bamah, Hagar Boneh and Arnon Karnieli
Remote Sens. 2024, 16(6), 1110; https://doi.org/10.3390/rs16061110 - 21 Mar 2024
Viewed by 666
Abstract
Recent climatic changes have profoundly impacted the urban microclimate, exposing city dwellers to harsh living conditions. One effective approach to mitigating these events involves incorporating more green infrastructure into the cityscape. The ecological services provided by urban vegetation play a crucial role in [...] Read more.
Recent climatic changes have profoundly impacted the urban microclimate, exposing city dwellers to harsh living conditions. One effective approach to mitigating these events involves incorporating more green infrastructure into the cityscape. The ecological services provided by urban vegetation play a crucial role in enhancing the sustainability and livability of cities. However, monitoring urban vegetation and accurately estimating its status pose challenges due to the heterogeneous nature of the urban environment. In response to this, the current study proposes utilizing a remote sensing-based classification framework to enhance data availability, thereby improving practices related to urban vegetation management. The aim of the current research is to explore the spatial pattern of vegetation and enhance the classification of tree species within diverse and complex urban environments. This study combines various remote sensing observations to enhance classification capabilities. High-resolution colored rectified aerial photographs, LiDAR-derived products, and hyperspectral data are merged and analyzed using advanced classifier methods, specifically partial least squares-discriminant analysis (PLS-DA) and object-based image analysis (OBIA). The OBIA method demonstrates an impressive overall accuracy of 95.30%, while the PLS-DA model excels with a remarkable overall accuracy of 100%. The findings validate the efficacy of incorporating OBIA, aerial photographs, LiDAR, and hyperspectral data in improving tree species classification and mapping within the context of PLS-DA. This classification framework holds significant potential for enhancing management practices and tools, thereby optimizing the ecological services provided by urban vegetation and fostering the development of sustainable cities. Full article
(This article belongs to the Special Issue Urban Green Spaces: Understanding Their Form, Structure and Composition Using Remote Sensing)
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19 pages, 4986 KiB  
Article
A Deep Learning Network for Individual Tree Segmentation in UAV Images with a Coupled CSPNet and Attention Mechanism
by Lujin Lv, Xuejian Li, Fangjie Mao, Lv Zhou, Jie Xuan, Yinyin Zhao, Jiacong Yu, Meixuan Song, Lei Huang and Huaqiang Du
Remote Sens. 2023, 15(18), 4420; https://doi.org/10.3390/rs15184420 - 08 Sep 2023
Cited by 2 | Viewed by 1493
Abstract
Accurate individual tree detection by unmanned aerial vehicles (UAVs) is a critical technique for smart forest management and serves as the foundation for evaluating ecological functions. Existing object detection and segmentation methods, on the other hand, have reduced accuracy when detecting and segmenting [...] Read more.
Accurate individual tree detection by unmanned aerial vehicles (UAVs) is a critical technique for smart forest management and serves as the foundation for evaluating ecological functions. Existing object detection and segmentation methods, on the other hand, have reduced accuracy when detecting and segmenting individual trees in complicated urban forest landscapes, as well as poor mask segmentation quality. This study proposes a novel Mask-CSP-attention-coupled network (MCAN) based on the Mask R-CNN algorithm. MCAN uses the Cross Stage Partial Net (CSPNet) framework with the Sigmoid Linear Unit (SiLU) activation function in the backbone network to form a new Cross Stage Partial Residual Net (CSPResNet) and employs a convolutional block attention module (CBAM) mechanism to the feature pyramid network (FPN) for feature fusion and multiscale segmentation to further improve the feature extraction ability of the model, enhance its detail information detection ability, and improve its individual tree detection accuracy. In this study, aerial photography of the study area was conducted by UAVs, and the acquired images were used to produce a dataset for training and validation. The method was compared with the Mask Region-based Convolutional Neural Network (Mask R-CNN), Faster Region-based Convolutional Neural Network (Faster R-CNN), and You Only Look Once v5 (YOLOv5) on the test set. In addition, four scenes—namely, a dense forest distribution, building forest intersection, street trees, and active plaza vegetation—were set up, and the improved segmentation network was used to perform individual tree segmentation on these scenes to test the large-scale segmentation ability of the model. MCAN’s average precision (AP) value for individual tree identification is 92.40%, which is 3.7%, 3.84%, and 12.53% better than that of Mask R-CNN, Faster R-CNN, and YOLOv5, respectively. In comparison to Mask R-CNN, the segmentation AP value is 97.70%, an increase of 8.9%. The segmentation network’s precision for the four scenes in multi-scene segmentation ranges from 95.55% to 92.33%, showing that the proposed network performs high-precision segmentation in many contexts. Full article
(This article belongs to the Special Issue Urban Green Spaces: Understanding Their Form, Structure and Composition Using Remote Sensing)
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23 pages, 36003 KiB  
Article
Assessing the Contributions of Urban Green Space Indices and Spatial Structure in Mitigating Urban Thermal Environment
by Yu Zhang, Yuchen Wang, Nan Ding and Xiaoyan Yang
Remote Sens. 2023, 15(9), 2414; https://doi.org/10.3390/rs15092414 - 05 May 2023
Cited by 4 | Viewed by 2540
Abstract
Urban green space takes a dominant role in alleviating the urban heat island (UHI) effect. Most investigations into the effects of cooling factors from urban green spaces on the UHI have evaluated the correlation between each factor and land surface temperature (LST) separately, [...] Read more.
Urban green space takes a dominant role in alleviating the urban heat island (UHI) effect. Most investigations into the effects of cooling factors from urban green spaces on the UHI have evaluated the correlation between each factor and land surface temperature (LST) separately, and the contribution weights of various typical cooling factors in mitigating the thermal environment have rarely been analyzed. For this research, three periods of Landsat 8 data captured between 2014 and 2018 of Xuzhou during the summer and autumn seasons were selected along with corresponding meteorological and flux measurements. The mono-window method was employed to retrieve LST. Based on the characteristics of the vegetation and spatial features of the green space, eight factors related to green space were selected and computed, consisting of three indices that measure vegetation and five metrics that evaluate landscape patterns: vegetation density (VD), evapotranspiration (ET), green space shading degree (GSSD), patch area ratio (PLAND), largest patch index (LPI), patch natural connectivity (COHESION), patch aggregation (AI), and patch mean shape index distribution (SHPAE_MN). Linear regression and bivariate spatial autocorrelation analyses between each green space factor and LST showed that there were significant negative linear and spatial correlations between all factors and LST, which proved that the eight factors were all cooling factors. In addition, LST was strongly correlated with all factors (|r| > 0.5) except for SHPAE_MN, which was moderately correlated (0.3 < |r| < 0.5). Based on this, two principal components were extracted by applying principal component analysis with all standardized green space factors as the original variables. To determine the contribution weight of each green space factor in mitigating the urban heat island (UHI) effect, we multiplied the influence coefficient matrix of the initial variables with the standardized multiple linear regression coefficients between the two principal component variables and LST. The final results indicated that the vegetation indices of green space contribute more to the alleviation of the UHI than its landscape pattern metrics, and the contribution weights are ranked as VD ≥ ET > GSSD > PLAND ≈ LPI > COHESION > AI > SHAPE_MN. Our study suggests that increasing vegetation density is preferred in urban planning to mitigate urban thermal environment, and increasing broadleaf forests with high evapotranspiration and shade levels in urban greening is also an effective way to reduce ambient temperature. For urban green space planning, a priority is to multiply the regional green space proportion or the area of largest patches. Second, improving the connectivity or aggregation among patches of green space can enhance their ability to cool the surrounding environment. Altering the green space spatial shape is likely the least significant factor to consider. Full article
(This article belongs to the Special Issue Urban Green Spaces: Understanding Their Form, Structure and Composition Using Remote Sensing)
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