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Remote Sensing
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Urban Forest Detection with Remote Sensing
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Urban Forest Detection with Remote Sensing

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

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 14484

Special Issue Editor

Dr. Ryan Jensen

E-Mail Website
Guest Editor
Department of Geography, Brigham Young University, Provo, UT, USA
Interests: geographic information system; urbanism; analysis; models; land cover; imagery; OBIA

Special Issue Information

Dear Colleagues,

Urban areas are humanity’s principal habitat. Indeed, over half of all people live in urban areas, and virtually all nations are becoming more urbanized (United Nations, 2018). Our continued study and understanding of urban areas and their complex characteristics is important in order to help improve urban conditions. Urban forests are an important characteristic of urban areas, and they have many significant benefits. These benefits include filtering the air and water, ameliorating summer temperatures and helping conserve energy, and providing animal habitats. Urban forests come in many different forms—urban parks, street trees, yard trees, landscaped boulevards, wetlands, and many others. In many urban areas, urban forests form the green infrastructure on which urban residents depend on for their link with nature. Our ability to measure, estimate, map, and model urban forests and their characteristics using remote sensing data and techniques should provide contributions to elected and appointed officials, as they seek to make information and science-based urban forest policy decisions. This Special Issue seeks innovative and original studies that use remote sensing techniques and datasets to study urban forests and their many characteristics in various urban settings.

Dr. Ryan Jensen
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

  • Urban forest remote sensing
  • Urban forest benefits
  • Multispectral urban remote sensing
  • Urban forest biomass
  • Hyperspectral urban remote sensing
  • Urban forest leaf area
  • sUAS (drone) urban remote sensing
  • Urban remote sensing scale
  • Urban forest carbon dynamics
  • Urban forest productivity
  • Urban tree species
  • Urban forest policy

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

14 pages, 5495 KiB  
Article
Improved Classification of Urban Trees Using a Widespread Multi-Temporal Aerial Image Dataset
by Daniel S. W. Katz, Stuart A. Batterman and Shannon J. Brines
Remote Sens. 2020, 12(15), 2475; https://doi.org/10.3390/rs12152475 - 1 Aug 2020
Cited by 19 | Viewed by 5390
Abstract
Urban tree identification is often limited by the accessibility of remote sensing imagery but has not yet been attempted with the multi-temporal commercial aerial photography that is now widely available. In this study, trees in Detroit, Michigan, USA are identified using eight high [...] Read more.
Urban tree identification is often limited by the accessibility of remote sensing imagery but has not yet been attempted with the multi-temporal commercial aerial photography that is now widely available. In this study, trees in Detroit, Michigan, USA are identified using eight high resolution red, green, and blue (RGB) aerial images from a commercial vendor and publicly available LiDAR data. Classifications based on these data were compared with classifications based on World View 2 satellite imagery, which is commonly used for this task but also more expensive. An object-based classification approach was used whereby tree canopies were segmented using LiDAR, and a street tree database was used for generating training and testing datasets. Overall accuracy using multi-temporal aerial images and LiDAR was 70%, which was higher than the accuracy achieved with World View 2 imagery and LiDAR (63%). When all data were used, classification accuracy increased to 74%. Taxa identified with high accuracy included Acer platanoides and Gleditsia, and taxa that were identified with good accuracy included Acer, Platanus, Quercus, and Tilia. Our results show that this large catalogue of multi-temporal aerial images can be leveraged for urban tree identification. While classification accuracy rates vary between taxa, the approach demonstrated can have practical value for socially or ecologically important taxa. Full article
(This article belongs to the Special Issue Urban Forest Detection with Remote Sensing)
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20 pages, 7268 KiB  
Article
Detecting Long-Term Urban Forest Cover Change and Impacts of Natural Disasters Using High-Resolution Aerial Images and LiDAR Data
by Raoul Blackman and Fei Yuan
Remote Sens. 2020, 12(11), 1820; https://doi.org/10.3390/rs12111820 - 4 Jun 2020
Cited by 20 | Viewed by 5213
Abstract
Urban forests provide ecosystem services; tree canopy cover is the basic quantification of ecosystem services. Ground assessment of the urban forest is limited; with continued refinement, remote sensing can become an essential tool for analyzing the urban forest. This study addresses three research [...] Read more.
Urban forests provide ecosystem services; tree canopy cover is the basic quantification of ecosystem services. Ground assessment of the urban forest is limited; with continued refinement, remote sensing can become an essential tool for analyzing the urban forest. This study addresses three research questions that are essential for urban forest management using remote sensing: (1) Can object-based image analysis (OBIA) and non-image classification methods (such as random point-based evaluation) accurately determine urban canopy coverage using high-spatial-resolution aerial images? (2) Is it possible to assess the impact of natural disturbances in addition to other factors (such as urban development) on urban canopy changes in the classification map created by OBIA? (3) How can we use Light Detection and Ranging (LiDAR) data and technology to extract urban canopy metrics accurately and effectively? The urban forest canopy area and location within the City of St Peter, Minnesota (MN) boundary between 1938 and 2019 were defined using both OBIA and random-point-based methods with high-spatial-resolution aerial images. Impacts of natural disasters, such as the 1998 tornado and tree diseases, on the urban canopy cover area, were examined. Finally, LiDAR data was used to determine the height, density, crown area, diameter, and volume of the urban forest canopy. Both OBIA and random-point methods gave accurate results of canopy coverages. The OBIA is relatively more time-consuming and requires specialist knowledge, whereas the random-point-based method only shows the total coverage of the classes without locational information. Canopy change caused by tornado was discernible in the canopy OBIA-based classification maps while the change due to diseases was undetectable. To accurately exact urban canopy metrics besides tree locations, dense LiDAR point cloud data collected at the leaf-on season as well as algorithms or software developed specifically for urban forest analysis using LiDAR data are needed. Full article
(This article belongs to the Special Issue Urban Forest Detection with Remote Sensing)
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19 pages, 2696 KiB  
Article
Producing Urban Aerobiological Risk Map for Cupressaceae Family in the SW Iberian Peninsula from LiDAR Technology
by Raúl Pecero-Casimiro, Santiago Fernández-Rodríguez, Rafael Tormo-Molina, Inmaculada Silva-Palacios, Ángela Gonzalo-Garijo, Alejandro Monroy-Colín, Juan Francisco Coloma and José María Maya-Manzano
Remote Sens. 2020, 12(10), 1562; https://doi.org/10.3390/rs12101562 - 14 May 2020
Cited by 13 | Viewed by 3431
Abstract
Given the rise in the global population and the consequently high levels of pollution, urban green areas, such as those that include plants in the Cupressaceae family, are suitable to reduce the pollution levels, improving the air quality. However, some species with ornamental [...] Read more.
Given the rise in the global population and the consequently high levels of pollution, urban green areas, such as those that include plants in the Cupressaceae family, are suitable to reduce the pollution levels, improving the air quality. However, some species with ornamental value are also very allergenic species whose planting should be regulated and their pollen production reduced by suitable pruning. The Aerobiological Index to create Risk maps for Ornamental Trees (AIROT), in its previous version, already included parameters that other indexes did not consider, such as the width of the streets, the height of buildings and the geographical characteristics of cities. It can be considered by working with LiDAR (Light Detection and Ranging) data from five urban areas, which were used to create the DEM and DSM (digital elevation and surface models) needed to create one of the parameters. Pollen production is proposed as a parameter (α) based on characteristics and uses in the forms of hedges or trees that will be incorporated into the index. It will allow the comparison of different species for the evaluation of the pruning effect when aerobiological risks are established. The maps for some species of Cupressaceae (Cupressus arizonica, Cupressus macrocarpa, Cupressus sempervirens, Cupressocyparis leylandii and Platycladus orientalis) generated in a GIS (geographic information system) from the study of several functions of Kriging, have been used in cities to identify aerobiological risks in areas of tourist and gastronomic interest. Thus, allergy patients can make decisions about the places to visit depending on the levels of risk near those areas. The AIROT index provides valuable information for allergy patients, tourists, urban planning councillors and restaurant owners in order to structure the vegetation, as well as planning tourism according to the surrounding environmental risks and reducing the aerobiological risk of certain areas. Full article
(This article belongs to the Special Issue Urban Forest Detection with Remote Sensing)
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