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Remote Sensing of Urban Forests and Landscape Ecology

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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 (1 January 2024) | Viewed by 9769

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Special Issue Editors

Dr. Ivan Pilaš

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Guest Editor

Division for Forest Ecology, Croatian Forest Research Institute, 10000 Zagreb, Croatia
Interests: remote sensing; geoinformatics; hydrology; ecological modeling; machine learning; habitat mapping; forest services and functions; soil science
Special Issues, Collections and Topics in MDPI journals

Dr. Mateo Gašparović

E-Mail Website
Guest Editor

Chair of Photogrammetry and Remote Sensing, Faculty of Geodesy, University of Zagreb, 10000 Zagreb, Croatia
Interests: computer vision; remote sensing; photogrammetry; lidar; unmanned aerial vehicles; geodesy; geographic information system; geoinformation; geoinformatics (GIS); 3D computer vision; satellite image analysis; mapping
Special Issues, Collections and Topics in MDPI journals

Dr. Damir Klobučar

E-Mail Website
Guest Editor

Croatian Forests Ltd., Ivana Meštrovića 28, 48000 Koprivnica, Croatia
Interests: remote sensing; forest inventory; socio-economic analysis; machine learning; geographic object based image analysis; neural networks

Special Issue Information

Dear Colleagues,

Urban forests provide critical ecosystem services to sustain human health and well-being and help to maintain the quality of environments in and around urban areas. Urban forests are characterized by a distinct biodiversity of introduced species and a variable structure of urban green fragments. Challenges that urban forests face are difficult growing conditions, insufficient resources for proper care, encroachment from development, and an incomplete public understanding of the benefits that they provide. In this sense, for their proper and timely management, a new set of tools and approaches is required.

Remote sensing has the capability to precisely detect structural and functional traits; however, it remains an unexploited technology with regard to these capabilities.

The main objective of this Special Issue is to provide a comprehensive overview of state-of-the-art remote sensing applications, aiming to enhance the management practices of urban forests. It will mainly focus on the high-precision mapping of urban forests and their relationship with the ecological functions and services that they provide in the urban environment. Remote sensing, multispectral, radar, and hyperspectral sources are very practical tools that can be used for the purpose of providing a means for mapping the structure and ecological functions of urban forests.

Suggested themes and article types for submissions:

Research related to improvements in the current high-precision mapping of urban forest structures up to the tree species level, using multi-source, multi-temporal, and multi-scale inputs such as very high-resolution imagery, LiDAR, UAS, and terrestrial scanners, together with the standard EO resources, such as Sentinel and Landsat.
Novel applications, including the mapping of allergenic tree species or specific ecosystem services such as the urban pollination potential, are highly encouraged.
Research based on remote sensing and external data sources related to a better quantification of the role of the urban forest in the improvement of urban, environmental and social conditions in terms of air pollution, urban flooding and heat reduction, biodiversity and social wellbeing.
Novel approaches to the analysis of urban landscape patterns and their interactions between ecosystems and ecological processes using a state-of-the-art inferential statistical analysis, spatio-temporal statistics, or predictive modeling (machine learning, deep learning).

Dr. Ivan Pilaš
Dr. Mateo Gašparović
Dr. Damir Klobučar
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

urban forests structure
ecological functions
machine/deep learning
air pollution
heat islands
urban flooding

Published Papers (5 papers)

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Research

21 pages, 27626 KiB

Open AccessArticle

Susceptibility Mapping of Unhealthy Trees in Jiuzhaigou Valley Biosphere Reserve

by Sheng Gao, Fulong Chen, Qin Wang, Pilong Shi, Wei Zhou and Meng Zhu

Remote Sens. 2023, 15(23), 5516; https://doi.org/10.3390/rs15235516 - 27 Nov 2023

Viewed by 642

Abstract

Jiuzhaigou Valley is recognized as both a world natural heritage site and a biosphere reserve. Conducting research on vegetation health within its scope can provide a demonstration role for sustainable development research. In this study, we proposed a technology integration approach that combined remote sensing intelligent identification and quantitative retrieval, and achieved vegetation health monitoring and susceptibility mapping of unhealthy trees. Leveraging WorldView-2 high-resolution satellite images, unhealthy trees were elaborately identified through the object-oriented classification method employing spectral and texture features, with F1 Score exceeding 75%. By applying fuzzy operations on indices related to leaf pigment and canopy architecture, we ultimately generated susceptibility maps of unhealthy trees on Sentinel-2 satellite images, with Area Under the Curve (AUC) exceeding 0.85. Our findings underscore that the vegetation health in Jiuzhaigou Valley is predominantly influenced by human activities and geological hazards. The forests of Jiuzhaigou Valley exhibit a certain degree of resilience to geological disasters, while human activities have been continuously exerting adverse effects on forest health in recent years, necessitating heightened attention. The methodology proposed in this study for mapping unhealthy trees susceptibility presents a cost-effective solution that can be readily applied for vegetation health monitoring and early warning in analogous biosphere reserves. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Forests and Landscape Ecology)

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18 pages, 11882 KiB

Open AccessEditor’s ChoiceArticle

Deep Learning-Based Detection of Urban Forest Cover Change along with Overall Urban Changes Using Very-High-Resolution Satellite Images

by Aisha Javed, Taeheon Kim, Changhui Lee, Jaehong Oh and Youkyung Han

Remote Sens. 2023, 15(17), 4285; https://doi.org/10.3390/rs15174285 - 31 Aug 2023

Cited by 1 | Viewed by 1863

Abstract

Urban forests globally face severe degradation due to human activities and natural disasters, making deforestation an urgent environmental challenge. Remote sensing technology and very-high-resolution (VHR) bitemporal satellite imagery enable change detection (CD) for monitoring forest changes. However, deep learning techniques for forest CD concatenate bitemporal images into a single input, limiting the extraction of informative deep features from individual raw images. Furthermore, they are developed for middle to low-resolution images focused on specific forests such as the Amazon or a single element in the urban environment. Therefore, in this study, we propose deep learning-based urban forest CD along with overall changes in the urban environment by using VHR bitemporal images. Two networks are used independently: DeepLabv3+ for generating binary forest cover masks, and a deeply supervised image fusion network (DSIFN) for the generation of a binary change mask. The results are concatenated for semantic CD focusing on forest cover changes. To carry out the experiments, full scene tests were performed using the VHR bitemporal imagery of three urban cities acquired via three different satellites. The findings reveal significant changes in forest covers alongside urban environmental changes. Based on the accuracy assessment, the networks used in the proposed study achieved the highest F1-score, kappa, IoU, and accuracy values compared with those using other techniques. This study contributes to monitoring the impacts of climate change, rapid urbanization, and natural disasters on urban environments especially urban forests, as well as relations between changes in urban environment and urban forests. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Forests and Landscape Ecology)

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20 pages, 4531 KiB

Open AccessArticle

Dielectric Fluctuation and Random Motion over Ground Model (DF-RMoG): An Unsupervised Three-Stage Method of Forest Height Estimation Considering Dielectric Property Changes

by Chang Liu, Qi Zhang, Linlin Ge, Samad M. E. Sepasgozar and Ziheng Sheng

Remote Sens. 2023, 15(7), 1877; https://doi.org/10.3390/rs15071877 - 31 Mar 2023

Viewed by 1058

Abstract

Polarimetric Synthetic Aperture Radar Interferometry (Pol-InSAR) based forest height estimation for ecosystem monitoring and management has been developing rapidly in recent years. Spaceborne Pol-InSAR systems with long temporal baselines of several days always lead to severe temporal decorrelation, which can cause a forest height overestimation error. However, most forest height estimation studies have not considered the change in dielectric property as a factor that may cause temporal decorrelation with a long temporal baseline. Therefore, it is necessary to propose a new method that considers dielectric fluctuations and random motions of scattering elements to compensate for the temporal decorrelation effect. The lack of ground truth for forest canopy also needs a solution. Unsupervised methods could be a solution because they do not require the use of true values of tree heights as the ground truth to calculate their estimation accuracies. This paper aims to present an unsupervised forest height estimation method called Dielectric Fluctuation and Random Motion over Ground (DF-RMoG) to improve accuracy by considering the dielectric fluctuations and random motions. Its performance is investigated using Advanced Land Observing Satellite (ALOS)-1 Pol-InSAR data acquired over a German forest site with temporal intervals of 46 and 92 days. The authors analyze the relationship between forest height and different parameters with DF-RMoG and conventional models. Compared with conventional models, the proposed DF-RMoG model significantly reduces the overestimation error due to temporal decorrelation in forest height estimation according to its lowest average forest height. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Forests and Landscape Ecology)

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24 pages, 37751 KiB

Open AccessArticle

Urban Tree Canopy Mapping Based on Double-Branch Convolutional Neural Network and Multi-Temporal High Spatial Resolution Satellite Imagery

by Shuaiqiang Chen, Meng Chen, Bingyu Zhao, Ting Mao, Jianjun Wu and Wenxuan Bao

Remote Sens. 2023, 15(3), 765; https://doi.org/10.3390/rs15030765 - 28 Jan 2023

Cited by 1 | Viewed by 2030

Abstract

Accurate knowledge of urban forest patterns contributes to well-managed urbanization, but accurate urban tree canopy mapping is still a challenging task because of the complexity of the urban structure. In this paper, a new method that combines double-branch U-NET with multi-temporal satellite images containing phenological information is introduced to accurately map urban tree canopies. Based on the constructed GF-2 image dataset, we developed a double-branch U-NET based on the feature fusion strategy using multi-temporal images to obtain an accuracy improvement with an IOU (intersection over union) of 2.3% and an F1-Score of 1.3% at the pixel level compared to the U-NET using mono-temporal images which performs best in existing studies for urban tree canopy mapping. We also found that the double-branch U-NET based on the feature fusion strategy has better accuracy than the early fusion strategy and decision fusion strategy in processing multi-temporal images for urban tree canopy mapping. We compared the impact of image combinations of different seasons on the urban tree canopy mapping task and found that the combination of summer and autumn images had the highest accuracy in the study area. Our research not only provides a high-precision urban tree canopy mapping method but also provides a direction to improve the accuracy both from the model structure and data potential when using deep learning for urban tree canopy mapping. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Forests and Landscape Ecology)

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17 pages, 7135 KiB

Open AccessArticle

Urban Tree Classification Based on Object-Oriented Approach and Random Forest Algorithm Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery

by Qian Guo, Jian Zhang, Shijie Guo, Zhangxi Ye, Hui Deng, Xiaolong Hou and Houxi Zhang

Remote Sens. 2022, 14(16), 3885; https://doi.org/10.3390/rs14163885 - 11 Aug 2022

Cited by 27 | Viewed by 3338

Abstract

Timely and accurate information on the spatial distribution of urban trees is critical for sustainable urban development, management and planning. Compared with satellite-based remote sensing, Unmanned Aerial Vehicle (UAV) remote sensing has a higher spatial and temporal resolution, which provides a new method for the accurate identification of urban trees. In this study, we aim to establish an efficient and practical method for urban tree identification by combining an object-oriented approach and a random forest algorithm using UAV multispectral images. Firstly, the image was segmented by a multi-scale segmentation algorithm based on the scale determined by the Estimation of Scale Parameter 2 (ESP2) tool and visual discrimination. Secondly, spectral features, index features, texture features and geometric features were combined to form schemes S1–S8, and S9, consisting of features selected by the recursive feature elimination (RFE) method. Finally, the classification of urban trees was performed based on the nine schemes using the random forest (RF), support vector machine (SVM) and k-nearest neighbor (KNN) classifiers, respectively. The results show that the RF classifier performs better than SVM and KNN, and the RF achieves the highest accuracy in S9, with an overall accuracy (OA) of 91.89% and a Kappa coefficient (Kappa) of 0.91. This study reveals that geometric features have a negative impact on classification, and the other three types have a positive impact. The feature importance ranking map shows that spectral features are the most important type of features, followed by index features, texture features and geometric features. Most tree species have a high classification accuracy, but the accuracy of Camphor and Cinnamomum Japonicum is much lower than that of other tree species, suggesting that the features selected in this study cannot accurately distinguish these two tree species, so it is necessary to add features such as height in the future to improve the accuracy. This study illustrates that the combination of an object-oriented approach and the RF classifier based on UAV multispectral images provides an efficient and powerful method for urban tree classification. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Forests and Landscape Ecology)

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