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Forests
Special Issues
Geospatial Monitoring of Urban Green Space
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Geospatial Monitoring of Urban Green Space

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Urban Forestry".

Deadline for manuscript submissions: 29 July 2024 | Viewed by 8720

Special Issue Editors

Dr. Dino Dobrinic

E-Mail Website
Guest Editor
Faculty of Geodesy, Chair of Geoinformatics, 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. 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. Ivan Pilaš

E-Mail Website
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

Special Issue Information

Dear Colleagues,

A green infrastructure (GI) is a network of natural and semi-natural areas, features and green spaces in rural and urban areas that collectively provide society with a sustainable and healthy living environment. In Europe, more than two-thirds of the population live in urban areas, and it is a highly urbanized continent with a slow but steady degradation of urban green vegetation.

Satellite remote sensing technology is key data source for mapping such environments but is insufficient for fully understanding them. GI also comprise vertical structures, such as green terraces and balconies that are not detectable by the perpendicular satellite imagery which is often used to monitor GI.

The main objective of this Special Issue is to collect research from different perspectives, and to establish an innovative, multidimensional system for monitoring urban green infrastructure. This Special Issue will integrate the latest means of data collection (multispectral satellite imagery, improved and calibrated with high-resolution terrestrial and airborne multispectral sources) and advanced spatial analysis to improve the decision-support system for better management of urban GI. Review papers on this topic are also welcome.

Authors are encouraged to submit articles on, but not limited to, the following subjects:

  • Deep learning methods using remote sensing data;
  • Multitemporal and multi-sensor data fusion and classification of green urban areas;
  • Time-series image analysis;
  • Monitoring of urban green spaces;
  • SAR-based features;
  • Optical-based features;
  • Usage of the analysis-ready image collections and cloud computing services;
  • Geospatial data analysis of urban green spaces;
  • Automatic mapping of urban green spaces;
  • Geospatial analysis of the distribution of urban green spaces.

Dr. Dino Dobrinic
Dr. Mateo Gašparović 
Dr. Ivan Pilaš
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. Forests is an international peer-reviewed open access monthly 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 2600 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 green spaces
  • remote sensing
  • deep learning
  • classification
  • geospatial techniques
  • monitoring
  • green infrastructure
  • SAR-based features
  • optical-based features

Published Papers (4 papers)

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Research

17 pages, 12045 KiB  
Article
Mapping of Allergenic Tree Species in Highly Urbanized Area Using PlanetScope Imagery—A Case Study of Zagreb, Croatia
by Mateo Gašparović, Dino Dobrinić and Ivan Pilaš
Forests 2023, 14(6), 1193; https://doi.org/10.3390/f14061193 - 09 Jun 2023
Cited by 3 | Viewed by 4221
Abstract
Mapping and identifying allergenic tree species in densely urbanized regions is vital for understanding their distribution and prevalence. However, accurately detecting individual allergenic tree species in urban green spaces remains challenging due to their smaller site and patchiness. To overcome these issues, PlanetScope [...] Read more.
Mapping and identifying allergenic tree species in densely urbanized regions is vital for understanding their distribution and prevalence. However, accurately detecting individual allergenic tree species in urban green spaces remains challenging due to their smaller site and patchiness. To overcome these issues, PlanetScope (PS) satellite imagery offers significant benefits compared with moderate or high-resolution RS imagery due to its daily temporal resolution and 3 m spatial resolution. Therefore, the primary objectives of this research were to: assess the feasibility of mapping allergenic tree species in the highly urbanized area using high-resolution PS imagery; evaluate and compare the performance of the most important machine learning and feature selection methods for accurate detection of individual allergenic tree species. The research incorporated three classification scenarios based on ground truth data: The first scenario (CS1) used single-date PS imagery with vegetation indices (VI), while the second and third scenarios (CS2 and CS3) used multitemporal PS imagery with VI, and GLCM and VI, respectively. The study demonstrated the feasibility of using multitemporal eight-band PlanetScope imagery to detect allergenic tree species, with the XGB method outperforming others with an overall accuracy of 73.13% in CS3. However, the classification accuracy varied between the scenarios and species, revealing limitations including the inherent heterogeneity of urban green spaces. Future research should integrate high-resolution satellite imagery with aerial photography or LiDAR data along with deep learning methods. This approach has the potential to classify dominant tree species in highly complex urban environments with increased accuracy, which is essential for urban planning and public health. Full article
(This article belongs to the Special Issue Geospatial Monitoring of Urban Green Space)
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23 pages, 16419 KiB  
Article
Biomass Estimation of Urban Forests Using LiDAR and High-Resolution Aerial Imagery in Athens–Clarke County, GA
by Katrina Ariel Henn and Alicia Peduzzi
Forests 2023, 14(5), 1064; https://doi.org/10.3390/f14051064 - 22 May 2023
Cited by 1 | Viewed by 1233
Abstract
The benefits and services of urban forests are becoming increasingly well documented, with carbon storage being the main focus of attention. Recent efforts in urban remote sensing have incorporated additional data such as LiDAR data but have been limited to sections of an [...] Read more.
The benefits and services of urban forests are becoming increasingly well documented, with carbon storage being the main focus of attention. Recent efforts in urban remote sensing have incorporated additional data such as LiDAR data but have been limited to sections of an urban area or only certain species. Existing models are not generalizable to remaining unmeasured urban trees. To make a generalizable individual urban tree model, we used metrics from NAIP aerial imagery and NOAA and USGS LiDAR data for 2013 and 2019, and two crown-level urban tree biomass models were developed. We ran a LASSO regression, which selected the best variables for the biomass model, followed by a 10-fold cross-validation. The 2013 model had an adjusted R2 value of 0.85 and an RMSE of 1797 kg, whereas the 2019 model had an adjusted R2 value of 0.87 and an RMSE of 1444 kg. The 2019 model was then applied to the rest of the unsampled trees to estimate the total biomass and total carbon stored for all the trees in the county. Recommendations include changes to ground inventory techniques to adapt to the current methods and limitations of remote sensing biomass estimation. Full article
(This article belongs to the Special Issue Geospatial Monitoring of Urban Green Space)
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19 pages, 3364 KiB  
Article
Recreation Potential Assessment at Tamarix Forest Reserves: A Method Based on Multicriteria Evaluation Approach and Landscape Metrics
by Mahmoud Bayat, Pete Bettinger, Sahar Heidari Masteali, Seyedeh Kosar Hamidi, Hafiz Umair Masood Awan and Azam Abolhasani
Forests 2023, 14(4), 705; https://doi.org/10.3390/f14040705 - 30 Mar 2023
Viewed by 1465
Abstract
The purpose of this study was to develop new methods to describe outdoor recreation potential based on landscape indicators and systemic multicriteria evolution in the Tamarix forest reserves of Varamin city, a part of Iranian–Turanian forests of the Tehran province in Iran. First, [...] Read more.
The purpose of this study was to develop new methods to describe outdoor recreation potential based on landscape indicators and systemic multicriteria evolution in the Tamarix forest reserves of Varamin city, a part of Iranian–Turanian forests of the Tehran province in Iran. First, in conducting a multicriteria evaluation, ecological factors that included slope, aspect, elevation, vegetation density, precipitation, temperature, and soil texture were mapped, classified, and coded according to the degree of desirability for outdoor recreation. All these maps were then intersected and the final map of recreational potential for three regions of the forest reserves was prepared. Results showed that the Shokrabad region had more recreation potential than the other two regions (Fakhrabad and Dolatabad) in terms of the sum of ecological factors potentially affecting tourism potential. Second, in conducting a landscape-based method, six of the most important indicators of the landscape that are effective in outdoor recreational potential were developed for each region. The combination of these landscape features determined the value of a place for recreational activities from a landscape perspective. The results showed that a large part of the Shokrabad region and a smaller number of places in the Fakhrabad and Dolatabad regions have high outdoor recreational potential. The area suitable for recreation in the output of the multicriteria evaluation method turned out to be greater than the area suggested by the landscape method, as more factors were examined in the multicriteria evaluation method. Of the set investigated, the topography and soil factors played an important role in the evaluation. Full article
(This article belongs to the Special Issue Geospatial Monitoring of Urban Green Space)
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18 pages, 6680 KiB  
Article
Vegetation Extraction from Airborne Laser Scanning Data of Urban Plots Based on Point Cloud Neighborhood Features
by Jianpeng Zhang, Jinliang Wang, Weifeng Ma, Yuncheng Deng, Jiya Pan and Jie Li
Forests 2023, 14(4), 691; https://doi.org/10.3390/f14040691 - 28 Mar 2023
Cited by 1 | Viewed by 1081
Abstract
This study proposes an accurate vegetation extraction method used for airborne laser scanning data of an urban plot based on point cloud neighborhood features to overcome the deficiencies in the current research on the precise extraction of vegetation in urban plots. First, the [...] Read more.
This study proposes an accurate vegetation extraction method used for airborne laser scanning data of an urban plot based on point cloud neighborhood features to overcome the deficiencies in the current research on the precise extraction of vegetation in urban plots. First, the plane features in the R-neighborhood are combined with Euclidean distance clustering to extract the building point cloud accurately, and the rough vegetation point cloud is extracted using the discrete features in the R-neighborhood. Then, under the building point cloud constraints, combined with the Euclidean distance clustering method, the remaining building boundary points in the rough vegetation point cloud are removed. Finally, based on the vegetation point cloud after removing the building boundary point cloud, points within a specific radius r are extracted from the vegetation point cloud in the original data, and a complete urban plot vegetation extraction result is obtained. Two urban plots of airborne laser scanning data are selected to calculate the point cloud plane features and discrete features with R = 0.6 m and accurately extract the vegetation point cloud from the urban point cloud data. The visual effect and accuracy analysis results of vegetation extraction are compared under four different radius ranges of r = 0.5 m, r = 1 m, r = 1.5 m and r = 2 m. The best vegetation extraction results of the two plots are obtained for r = 1 m. The recall and precision are obtained as 92.19% and 98.74% for plot 1 and 94.30% and 98.73% for plot 2, respectively. Full article
(This article belongs to the Special Issue Geospatial Monitoring of Urban Green Space)
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