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Urban/Coastal Vegetation Change and Their Impacts on Metropolitan Territories

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (15 July 2021) | Viewed by 25345

Special Issue Editors


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Guest Editor
Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Information, Systems & Modelling, University of Technology Sydney, CB11.06.106, Building 11, 81 Broadway, Ultimo NSW 2007 (PO Box 123), Australia
Interests: remote sensing and image processing; GIS and complex modelling; soft computing techniques in natural hazards; environmental and natural resources applications
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Infrastructure Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
Interests: geomatics; GIS; GEOBIA; Earth Observation; urban green; bush-fire; environmental modelling; change detection; spatial statistics; machine learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: biophysical remote sensing; terrestrial ecohydrology; land surface phenology; carbon and water fluxes; geostationary and low earth observations; time series analyses; climate change impacts; vegetation health and ecosystem resilience; ecological forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today’s world is experiencing exponential growth in urban residential population and continuous change in urban/coastal vegetation. In particular, in recent decades, megacities of the world are facing the loss of urban and coastal vegetation due to developmental activities and the environmental processes due to climate change. Geospatial technology has played a key role in mapping, monitoring, and recommending urban and coastal vegetation abundance for policy implications. Monitoring and mapping of urban and coastal vegetation at multiple scales (e.g., micro and mesoscales) is possible due to the availability of various resolution Earth observation data sets generated from different platforms (i.e., space-borne, airborne, UAV, ground-based sensors). This Special Issue aims to collate recent advances in geospatial technology-based methods applied to urban and coastal vegetation mapping and characterization. Manuscripts can be related to any aspects of geospatial technology used for ecosystem science-based applications of monitoring urban and coastal vegetation. Of special interest are those manuscripts with novel approaches to vegetation abundance and changes in metropolitan areas. The novelty of these methods could be a combination of statistical theory, machine learning, and data analytics amongst many others.

The topics of interest include, but are not limited to the following:

  • Multi-level statistical methods in quality assessment of vegetation mapping
  • Emerging Geospatial technologies for vegetation mapping
  • Uncertainty and error quantification in vegetation characterization
  • Comparison of existing methods for vegetation mapping and characterization
  • Up-scaling/down-scaling of vegetation mapping and change of support approaches
  • Development of spatial tools (analytical/interface) to report vegetation risk in coastal environments
  • Ecosystem science-based applications of monitoring coastal and urban vegetation
  • Novel monitoring techniques to quantify vegetation changes over time
  • Optimization of monitoring/sampling programs for vegetation mapping, assessment, and characterization
  • Holistic and integrated approaches for large scale vegetation characterization (the use of proxy variables)
  • Literature reviews and meta-analysis of existing methods
  • Data analytics aspect of Earth observation
  • Laser scanning technologies for vegetation identification
  • Deep-leaning in image classification and vegetation characterization
  • Novel machine learning techniques in vegetation characterization
  • Novel pixel-based and object-based image analysis

Prof. Dr. Biswajeet Pradhan
Dr. Jagannath Aryal
Prof. Dr. Alfredo R. Huete
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.

Published Papers (3 papers)

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Research

27 pages, 9098 KiB  
Article
Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN)
by Shirisa Timilsina, Jagannath Aryal and Jamie B. Kirkpatrick
Remote Sens. 2020, 12(18), 3017; https://doi.org/10.3390/rs12183017 - 16 Sep 2020
Cited by 48 | Viewed by 8395
Abstract
Urban trees provide social, economic, environmental and ecosystem services benefits that improve the liveability of cities and contribute to individual and community wellbeing. There is thus a need for effective mapping, monitoring and maintenance of urban trees. Remote sensing technologies can effectively map [...] Read more.
Urban trees provide social, economic, environmental and ecosystem services benefits that improve the liveability of cities and contribute to individual and community wellbeing. There is thus a need for effective mapping, monitoring and maintenance of urban trees. Remote sensing technologies can effectively map and monitor urban tree coverage and changes over time as an efficient and low-cost alternative to field-based measurements, which are time consuming and costly. Automatic extraction of urban land cover features with high accuracy is a challenging task, and it demands object based artificial intelligence workflows for efficiency and thematic accuracy. The aim of this research is to effectively map urban tree cover changes and model the relationship of such changes with socioeconomic variables. The object-based convolutional neural network (CNN) method is illustrated by mapping urban tree cover changes between 2005 and 2015/16 using satellite, Google Earth imageries and Light Detection and Ranging (LiDAR) datasets. The training sample for CNN model was generated by Object Based Image Analysis (OBIA) using thresholds in a Canopy Height Model (CHM) and the Normalised Difference Vegetation Index (NDVI). The tree heatmap produced from the CNN model was further refined using OBIA. Tree cover loss, gain and persistence was extracted, and multiple regression analysis was applied to model the relationship with socioeconomic variables. The overall accuracy and kappa coefficient of tree cover extraction was 96% and 0.77 for 2005 images and 98% and 0.93 for 2015/16 images, indicating that the object-based CNN technique can be effectively implemented for urban tree coverage mapping and monitoring. There was a decline in tree coverage in all suburbs. Mean parcel size and median household income were significantly related to tree cover loss (R2 = 58.5%). Tree cover gain and persistence had positive relationship with tertiary education, parcel size and ownership change (gain: R2 = 67.8% and persistence: R2 = 75.3%). The research findings demonstrated that remote sensing data with intelligent processing can contribute to the development of policy input for management of tree coverage in cities. Full article
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25 pages, 7934 KiB  
Article
Spatio-Temporal Analysis of Oil Spill Impact and Recovery Pattern of Coastal Vegetation and Wetland Using Multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models
by Abdul-Lateef Balogun, Shamsudeen Temitope Yekeen, Biswajeet Pradhan and Omar F. Althuwaynee
Remote Sens. 2020, 12(7), 1225; https://doi.org/10.3390/rs12071225 - 10 Apr 2020
Cited by 45 | Viewed by 7197
Abstract
Oil spills are a global phenomenon with impacts that cut across socio-economic, health, and environmental dimensions of the coastal ecosystem. However, comprehensive assessment of oil spill impacts and selection of appropriate remediation approaches have been restricted due to reliance on laboratory experiments which [...] Read more.
Oil spills are a global phenomenon with impacts that cut across socio-economic, health, and environmental dimensions of the coastal ecosystem. However, comprehensive assessment of oil spill impacts and selection of appropriate remediation approaches have been restricted due to reliance on laboratory experiments which offer limited area coverage and classification accuracy. Thus, this study utilizes multispectral Landsat 8-OLI remote sensing imagery and machine learning models to assess the impacts of oil spills on coastal vegetation and wetland and monitor the recovery pattern of polluted vegetation and wetland in a coastal city. The spatial extent of polluted areas was also precisely quantified for effective management of the coastal ecosystem. Using Johor, a coastal city in Malaysia as a case study, a total of 49 oil spill (ground truth) locations, 54 non-oil-spill locations and Landsat 8-OLI data were utilized for the study. The ground truth points were divided into 70% training and 30% validation parts for the classification of polluted vegetation and wetland. Sixteen different indices that have been used to monitor vegetation and wetland stress in literature were adopted for impact and recovery analysis. To eliminate similarities in spectral appearance of oil-spill-affected vegetation, wetland and other elements like burnt and dead vegetation, Support Vector Machine (SVM) and Random Forest (RF) machine learning models were used for the classification of polluted and nonpolluted vegetation and wetlands. Model optimization was performed using a random search method to improve the models’ performance, and accuracy assessments confirmed the effectiveness of the two machine learning models to identify, classify and quantify the area extent of oil pollution on coastal vegetation and wetland. Considering the harmonic mean (F1), overall accuracy (OA), User’s accuracy (UA), and producers’ accuracy (PA), both models have high accuracies. However, the RF outperformed the SVM with F1, OA, PA and UA values of 95.32%, 96.80%, 98.82% and 95.11%, respectively, while the SVM recorded accuracy values of F1 (80.83%), OA (92.87%), PA (95.18%) and UA (93.81%), respectively, highlighting 1205.98 hectares of polluted vegetation and 1205.98 hectares of polluted wetland. Analysis of the vegetation indices revealed that spilled oil had a significant impact on the vegetation and wetland, although steady recovery was observed between 2015-2018. This study concludes that Chlorophyll Vegetation Index, Modified Difference Water Index, Normalized Difference Vegetation Index and Green Chlorophyll Index vegetation indices are more sensitive for impact and recovery assessment of both vegetation and wetland, in addition to Modified Normalized Difference Vegetation Index for wetlands. Thus, remote sensing and Machine Learning models are essential tools capable of providing accurate information for coastal oil spill impact assessment and recovery analysis for appropriate remediation initiatives. Full article
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20 pages, 16734 KiB  
Article
Mapping of Post-Wildfire Burned Area Using a Hybrid Algorithm and Satellite Data: The Case of the Camp Fire Wildfire in California, USA
by Mutiara Syifa, Mahdi Panahi and Chang-Wook Lee
Remote Sens. 2020, 12(4), 623; https://doi.org/10.3390/rs12040623 - 13 Feb 2020
Cited by 37 | Viewed by 7915
Abstract
On November 8, 2018, a devastating wildfire, known as the Camp Fire wildfire, was reported in Butte County, California, USA. Approximately 88 fatalities ensued, and 18,804 structures were damaged by the wildfire. As a response to this destructive wildfire, this study generated a [...] Read more.
On November 8, 2018, a devastating wildfire, known as the Camp Fire wildfire, was reported in Butte County, California, USA. Approximately 88 fatalities ensued, and 18,804 structures were damaged by the wildfire. As a response to this destructive wildfire, this study generated a pre- and post-wildfire maps to provide basic data for evacuation and mitigation planning. This study used Landsat-8 and Sentinel-2 imagery to map the pre- and post-wildfire conditions. A support vector machine (SVM) optimized by the imperialist competitive algorithm (ICA) hybrid model was compared with the non-optimized SVM algorithm for classification of the pre- and post-wildfire map. The SVM–ICA produced a better accuracy (overall accuracies of 83.8% and 83.6% for pre- and post-wildfire using Landsat-8 respectively; 90.8% and 91.8% for pre- and post-wildfire using Sentinel-2 respectively), compared to SVM without optimization (overall accuracies of 80.0% and 78.9% for pre- and post-wildfire using Landsat-8 respectively; 83.3% and 84.8% for pre- and post-wildfire using Sentinel-2 respectively. In total, eight pre- and post-wildfire burned area maps were generated; these can be used to assess the area affected by the Camp Fire wildfire as well as for wildfire mitigation planning in the future. Full article
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