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Remote Sensing of Urban Forests
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Remote Sensing of Urban Forests

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

Deadline for manuscript submissions: closed (22 June 2019) | Viewed by 45090

Special Issue Editor

Prof. Dr. Giovanni Sanesi

E-Mail Website
Guest Editor
Department of Agro-Environmental Sciences, University of Bari, Via Amendola, 165/A, 70126 Bari, Italy
Interests: urban forestry; urban ecology urban sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Through the provision of ecosystem services (ESS), urban forests and green infrastructures   provide multiple benefits for urban dwellers making cities more resilient to climate change by enhancing, for example, the degree of shading, evaporative cooling, rainwater interception and storage and filtration functions. To date, most of the available studies have considered one or more ESS provided by specific urban forest areas in cities and proposed remote sensing methods to quantify the amount of services in relation to their beneficiaries (i.e., citizens). Recent studies have attempted to assess the ESS provided by urban green spaces through the integration of social data with remotely sensed data, such as high- resolution satellite images and Laser Imaging Detection and Ranging (LiDAR) point-cloud. Given the mounting availability of satellite images from different sensors, there is a need to develop new research focusing on remote sensing applications for monitoring and assessing urban forest areas and associated ESS. The issues to be covered include:

  1. Comparison and evaluation of different remote sensing techniques for monitoring urban forests
  2. Analysis and assessment of urban forest areas and green infrastructures using remote sensing techniques, also through the development of new indicators of green amount
  3. Assessment of the ecosystem services provided by urban forest areas and green spaces
  4. Review articles covering one or more of these topics are also welcome
Prof. Giovanni Sanesi
Guest Editor

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

  • Ecosystem services
  • green infrastructure
  • LIDAR
  • urban forestry indicators

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Published Papers (7 papers)

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Editorial

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5 pages, 173 KiB  
Editorial
Remote Sensing of Urban Forests
by Giovanni Sanesi, Vincenzo Giannico, Mario Elia and Raffaele Lafortezza
Remote Sens. 2019, 11(20), 2383; https://doi.org/10.3390/rs11202383 - 15 Oct 2019
Cited by 6 | Viewed by 2817
Abstract
Urban forests and green infrastructures at large are of critical importance for contemporary cities as they provide a wide range of ecosystem services (ESS) that enhance the quality of life of urban dwellers. Remote sensing technologies have greatly contributed to assessing and mapping [...] Read more.
Urban forests and green infrastructures at large are of critical importance for contemporary cities as they provide a wide range of ecosystem services (ESS) that enhance the quality of life of urban dwellers. Remote sensing technologies have greatly contributed to assessing and mapping the spatial distribution of ESS in urban areas, although more research is needed given the availability of new sensors from multiple satellites and platforms and the particular characteristics of urban environments (e.g., high heterogeneity). This Special Issue hosts papers focusing on the temporal and spatial dynamics of urban forests with special attention given to the most recent remote sensing technologies as well as advanced methods for processing geospatial data and extracting meaningful information. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Forests)

Research

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18 pages, 6042 KiB  
Article
Urban Forest Growth and Gap Dynamics Detected by Yearly Repeated Airborne Light Detection and Ranging (LiDAR): A Case Study of Cheonan, South Korea
by Heejoon Choi, Youngkeun Song and Youngwoon Jang
Remote Sens. 2019, 11(13), 1551; https://doi.org/10.3390/rs11131551 - 29 Jun 2019
Cited by 10 | Viewed by 4171
Abstract
Understanding forest dynamics is important for assessing the health of urban forests, which experience various disturbances, both natural (e.g., treefall events) and artificial (e.g., making space for agricultural fields). Therefore, quantifying three-dimensional changes in canopies is a helpful way to manage and understand [...] Read more.
Understanding forest dynamics is important for assessing the health of urban forests, which experience various disturbances, both natural (e.g., treefall events) and artificial (e.g., making space for agricultural fields). Therefore, quantifying three-dimensional changes in canopies is a helpful way to manage and understand urban forests better. Multitemporal airborne light detection and ranging (LiDAR) datasets enable us to quantify the vertical and lateral growth of trees across a landscape scale. The goal of this study is to assess the annual changes in the 3-D structures of canopies and forest gaps in an urban forest using annual airborne LiDAR datasets for 2012–2015. The canopies were classified as high canopies and low canopies by a 5 m height threshold. Then, we generated pixel- and plot-level canopy height models and conducted change detection annually. The vertical growth rates and leaf area index showed consistent values year by year in both canopies, while the spatial distributions of the canopy and leaf area profile (e.g., leaf area density) showed inconsistent changes each year in both canopies. In total, high canopies expanded their foliage from 12 m height, while forest gap edge canopies (including low canopies) expanded their canopies from 5 m height. Annual change detection with LiDAR datasets might inform about both steady growth rates and different characteristics in the changes of vertical canopy structures for both high and low canopies in urban forests. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Forests)
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22 pages, 42084 KiB  
Article
A Hierarchical Urban Forest Index Using Street-Level Imagery and Deep Learning
by Philip Stubbings, Joe Peskett, Francisco Rowe and Dani Arribas-Bel
Remote Sens. 2019, 11(12), 1395; https://doi.org/10.3390/rs11121395 - 12 Jun 2019
Cited by 58 | Viewed by 8790
Abstract
We develop a method based on computer vision and a hierarchical multilevel model to derive an Urban Street Tree Vegetation Index which aims to quantify the amount of vegetation visible from the point of view of a pedestrian. Our approach unfolds in two [...] Read more.
We develop a method based on computer vision and a hierarchical multilevel model to derive an Urban Street Tree Vegetation Index which aims to quantify the amount of vegetation visible from the point of view of a pedestrian. Our approach unfolds in two steps. First, areas of vegetation are detected within street-level imagery using a state-of-the-art deep neural network model. Second, information is combined from several images to derive an aggregated indicator at the area level using a hierarchical multilevel model. The comparative performance of our proposed approach is demonstrated against a widely used image segmentation technique based on a pre-labelled dataset. The approach is deployed to a real-world scenario for the city of Cardiff, Wales, using Google Street View imagery. Based on more than 200,000 street-level images, an urban tree street-level indicator is derived to measure the spatial distribution of tree cover, accounting for the presence of obstructing objects present in images at the Lower Layer Super Output Area (LSOA) level, corresponding to the most commonly used administrative areas for policy-making in the United Kingdom. The results show a high degree of correspondence between our tree street-level score and aerial tree cover estimates. They also evidence more accurate estimates at a pedestrian perspective from our tree score by more appropriately capturing tree cover in areas with large burial, woodland, formal open and informal open spaces where shallow trees are abundant, in high density residential areas with backyard trees, and along street networks with high density of high trees. The proposed approach is scalable and automatable. It can be applied to cities across the world and provides robust estimates of urban trees to advance our understanding of the link between mental health, well-being, green space and air pollution. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Forests)
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19 pages, 7838 KiB  
Article
A Methodology to Monitor Urban Expansion and Green Space Change Using a Time Series of Multi-Sensor SPOT and Sentinel-2A Images
by Jinsong Deng, Yibo Huang, Binjie Chen, Cheng Tong, Pengbo Liu, Hongquan Wang and Yang Hong
Remote Sens. 2019, 11(10), 1230; https://doi.org/10.3390/rs11101230 - 23 May 2019
Cited by 42 | Viewed by 6774
Abstract
Monitoring urban expansion and greenspace change is an urgent need for planning and decision-making. This paper presents a methodology integrating Principal Component Analysis (PCA) and hybrid classifier to undertake this kind of work using a sequence of multi-sensor SPOT images (SPOT-2,3,5) and Sentinel-2A [...] Read more.
Monitoring urban expansion and greenspace change is an urgent need for planning and decision-making. This paper presents a methodology integrating Principal Component Analysis (PCA) and hybrid classifier to undertake this kind of work using a sequence of multi-sensor SPOT images (SPOT-2,3,5) and Sentinel-2A data from 1996 to 2016 in Hangzhou City, which is the central metropolis of the Yangtze River Delta in China. In this study, orthorectification was first applied on the SPOT and Sentinel-2A images to guarantee precise geometric correction which outperformed the conventional polynomial transformation method. After pre-processing, PCA and hybrid classifier were used together to enhance and extract change information. Accuracy assessment combining stratified random and user-defined plots sampling strategies was performed with 930 reference points. The results indicate reasonable high accuracies for four periods. It was further revealed that the proposed method yielded higher accuracy than that of the traditional post-classification comparison approach. On the whole, the developed methodology provides the effectiveness of monitoring urban expansion and green space change in this study, despite the existence of obvious confusions that resulted from compound factors. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Forests)
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17 pages, 2575 KiB  
Article
Spatial Accessibility of Urban Forests in the Pearl River Delta (PRD), China
by Rong Zhang, Jiquan Chen, Hogeun Park, Xuhui Zhou, Xuchao Yang, Peilei Fan, Changliang Shao and Zutao Ouyang
Remote Sens. 2019, 11(6), 667; https://doi.org/10.3390/rs11060667 - 19 Mar 2019
Cited by 4 | Viewed by 6060
Abstract
The Pearl River Delta (PRD) is one of the most important economic zones both in China and in the world. Its rapid economic development has been associated with many environmental problems such as the loss of forests in urban areas. We estimated the [...] Read more.
The Pearl River Delta (PRD) is one of the most important economic zones both in China and in the world. Its rapid economic development has been associated with many environmental problems such as the loss of forests in urban areas. We estimated the accessibility of forests in the PRD by quantifying spatial proximity and travel time. We found that distances from a large proportion of the points of interest (POIs) (~45%) and urban lands (~38%, where ~49 urban residents live) to the nearest forests were greater than 1000 m; suggesting a low spatial proximity to forests. Urban parks—important outdoor recreational areas—appeared to have insufficient forest coverage within their 1000 m buffer zones. When forest accessibility was measured by travel time under optimal modes of transport; it was less than 15 min for most urban lands (~95%), which accommodates 98% of the total urban population. More importantly; the travel time to the nearest forest was negatively correlated with gross domestic product density (GDPd), but not with population density (POPd). The GDPd and POPd; however; increased log-linearly with the Euclidean distance to the nearest forest. In addition to the low proximity to forests; there existed inequalities among urban residents who live in areas with different levels of GDPd and POPd. Future urban planning needs not only to increase the total coverage of urban forests; but also to improve their spatial evenness across the urban landscapes in the PRD. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Forests)
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16 pages, 3690 KiB  
Article
A New Algorithm for MLS-Based DBH Mensuration and Its Preliminary Validation in an Urban Boreal Forest: Aiming at One Cornerstone of Allometry-Based Forest Biometrics
by Yi Lin and Miao Jiang
Remote Sens. 2018, 10(5), 749; https://doi.org/10.3390/rs10050749 - 14 May 2018
Cited by 3 | Viewed by 3594
Abstract
This study aimed to improve one basic circle of allometry-based forest biometrics—diameter at breast height (DBH) mensuration. To address its common shortage of low efficiency in field measurement, this study attempted mobile laser scanning (MLS) as an efficient alternative and proposed a new [...] Read more.
This study aimed to improve one basic circle of allometry-based forest biometrics—diameter at breast height (DBH) mensuration. To address its common shortage of low efficiency in field measurement, this study attempted mobile laser scanning (MLS) as an efficient alternative and proposed a new MLS-based DBH mensuration algorithm to further exclude the effect of stem bending. That is, prior to the procedure of cone-based geometric modeling of a tree stem, an operation of Aligning the local stem axis series that is calculated by the Successive Cone-based Fitting of those continuously equi-height-layered laser points on the stem (ASCF) is appended. In the case of an urban boreal forest, tests showed that the proposed algorithm worked better (the coefficient of determination, R2 = 0.81 and root mean square error, RMSE = 52.1 mm) than the circle- (0.16 and 189.4 mm), cylinder- (0.77 and 58.7 mm), and cone-based (0.77 and 56.7 mm) geometric modeling algorithms. From a methodological viewpoint, the new ASCF algorithm was preliminarily validated for MLS-based tree DBH mensuration, with the “cornerstone-rebuilding” significance for allometry-based forest biometrics. With the development of MLS variants available for complex forest environments, this study will contribute fundamental implications for advancements in forestry. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Forests)
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Review

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20 pages, 1291 KiB  
Review
Remote Sensing in Urban Forestry: Recent Applications and Future Directions
by Xun Li, Wendy Y. Chen, Giovanni Sanesi and Raffaele Lafortezza
Remote Sens. 2019, 11(10), 1144; https://doi.org/10.3390/rs11101144 - 14 May 2019
Cited by 75 | Viewed by 11315
Abstract
Increasing recognition of the importance of urban forest ecosystem services calls for the sustainable management of urban forests, which requires timely and accurate information on the status, trends and interactions between socioeconomic and ecological processes pertaining to urban forests. In this regard, remote [...] Read more.
Increasing recognition of the importance of urban forest ecosystem services calls for the sustainable management of urban forests, which requires timely and accurate information on the status, trends and interactions between socioeconomic and ecological processes pertaining to urban forests. In this regard, remote sensing, especially with its recent advances in sensors and data processing methods, has emerged as a premier and useful observational and analytical tool. This study summarises recent remote sensing applications in urban forestry from the perspective of three distinctive themes: multi-source, multi-temporal and multi-scale inputs. It reviews how different sources of remotely sensed data offer a fast, replicable and scalable way to quantify urban forest dynamics at varying spatiotemporal scales on a case-by-case basis. Combined optical imagery and LiDAR data results as the most promising among multi-source inputs; in addition, future efforts should focus on enhancing data processing efficiency. For long-term multi-temporal inputs, in the event satellite imagery is the only available data source, future work should improve haze-/cloud-removal techniques for enhancing image quality. Current attention given to multi-scale inputs remains limited; hence, future studies should be more aware of scale effects and cautiously draw conclusions. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Forests)
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