Special Issue "Application of Remote Sensing and GIS in Environmental Studies"

A special issue of Environments (ISSN 2076-3298).

Deadline for manuscript submissions: closed (30 April 2019)

Special Issue Editors

Guest Editor
Dr. Dimitris Poursanidis

Foundation for Research and Technology, Institute of Applied and Computational Mathematics, Remote Sensing Lab, N. Plastira 100, Heraklion, Crete, Greece
Website | E-Mail
Phone: 0030 2810 391774
Interests: coastal habitat mapping, satellite bathymetry, spatial ecology, LULC, biodiversity monitoring
Guest Editor
Dr. Kostas Poirazidis

Department of Environmental Technology, Technological Educational Institute of Ionian Islands, Zakinthos, Greece
Website | E-Mail
Interests: management of protected areas; species distribution modelling; landscape structure analysis and relations with biodiversity; ecological modeling

Special Issue Information

Dear Colleagues,

The plethora of geospatial data and tools for use in different domains and aspects of environmental research has led to multidiscipline applications on land and sea, driven both from the needs of scientific research and also from the private sector. This Special Issue invites application using Remote Sensing and GIS in real world case studies for an effective analysis at different scales. Applications in spatial planning, species and habitats conservation, urban biodiversity, use of historical aerial images for change detections at inaccessible areas, ecosystem management from all biomes, ecosystem services mapping and valuation are welcome. Application results and discussions, especially those targeting contributions to exploring research framework linked with societal needs, are highly welcome. Papers selected for this Special Issue are subjected to a rigorous peer review procedure with the aim of rapid and wide dissemination of research results, developments, and applications in Remote Sensing and GIS in Environmental Studies.

Dr. Dimitris Poursanidis
Dr. Kostas Poirazidis
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 papers will be 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. Environments 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 300 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

  • Environmental Management
  • Real World Application
  • Natural Resources Management
  • Remote Sensing for Protected Areas
  • GIS and Cityscapes

Published Papers (4 papers)

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Research

Open AccessArticle
Detection of Vegetation Cover Change in Renewable Energy Development Zones of Southern California Using MODIS NDVI Time Series Analysis, 2000 to 2018
Environments 2019, 6(4), 40; https://doi.org/10.3390/environments6040040
Received: 3 February 2019 / Revised: 12 March 2019 / Accepted: 15 March 2019 / Published: 28 March 2019
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Abstract
New solar energy facilities on public lands in the deserts of southern California are being monitored long-term to detect environmental impacts. For this purpose, we have developed a framework for detecting changes in vegetation cover region-wide using greenness index data sets from the [...] Read more.
New solar energy facilities on public lands in the deserts of southern California are being monitored long-term to detect environmental impacts. For this purpose, we have developed a framework for detecting changes in vegetation cover region-wide using greenness index data sets from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor. This study focused on three sites, Joshua Tree National Park (JOTR), Mojave National Preserve (MOJA), and a proximal group of solar energy Development Focus Areas (DFAs). Three MODIS vegetation indices (VIs), the normalized difference (NDVI), enhanced (EVI), and soil-adjusted (SAVI), all at 250-m spatial resolution, were evaluated using the Breaks for Additive Season and Trend (BFAST) methodology to estimate significant time series shifts (“breakpoints”) in green vegetation cover, from February 2000 to May 2018. The sample cross-correlation function with local precipitation records and comparison with timing of wildfires near the study sites for breakpoint density (proportion of area with a breakpoint) showed that NDVI had the strongest response and hence greatest sensitivity to these major disturbances compared to EVI and SAVI, supporting its use over the other VIs for subsequent analysis. Time series of NDVI breakpoint change densities for individual solar energy DFAs did not have a consistent vegetation response following construction. Bootstrap-derived 95% confidence intervals show that the DFAs have significantly larger kurtosis and standard deviation in positive NDVI breakpoint distribution than protected National Park System (NPS) sites, but no significant difference appeared in the negative distribution among all sites. The inconsistent postconstruction NDVI signal and the large number of detected breakpoints across all three sites suggested that the largest shifts in greenness are tied to seasonal and total annual precipitation amounts. Further results indicated that existing site-specific conditions are the main control on vegetation response, mostly driven by the history of human disturbances in DFAs. Although the results do not support persistent breakpoints in solar energy DFAs, future work should seek to establish links between statistical significance and physical significance through ground-based studies to provide a more robust interpretation. Full article
(This article belongs to the Special Issue Application of Remote Sensing and GIS in Environmental Studies)
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Open AccessArticle
A Statistical and Spatial Analysis of Portuguese Forest Fires in Summer 2016 Considering Landsat 8 and Sentinel 2A Data
Environments 2019, 6(3), 36; https://doi.org/10.3390/environments6030036
Received: 29 January 2019 / Revised: 6 March 2019 / Accepted: 12 March 2019 / Published: 16 March 2019
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Abstract
Forest areas in Portugal are often affected by fires. The objective of this work was to analyze the most fire-affected areas in Portugal in the summer of 2016 for two municipalities considering data from Landsat 8 OLI and Sentinel 2A MSI (prefire and [...] Read more.
Forest areas in Portugal are often affected by fires. The objective of this work was to analyze the most fire-affected areas in Portugal in the summer of 2016 for two municipalities considering data from Landsat 8 OLI and Sentinel 2A MSI (prefire and postfire data). Different remote sensed data-derived indices, such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR), could be used to identify burnt areas and estimate the burn severity. In this work, NDVI was used to evaluate the area burned, and NBR was used to estimate the burn severity. The results showed that the NDVI decreased considerably after the fire event (2017 images), indicating a substantial decrease in the photosynthesis activity in these areas. The results also indicate that the NDVI differences (dNDVI) assumes the highest values in the burned areas. The results achieved for both sensors regarding the area burned presented differences from the field data no higher than 13.3% (for Sentinel 2A, less than 7.8%). We conclude that the area burned estimated using the Sentinel 2A data is more accurate, which can be justified by the higher spatial resolution of this data. Full article
(This article belongs to the Special Issue Application of Remote Sensing and GIS in Environmental Studies)
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Open AccessArticle
Land-Use Change Detection with Convolutional Neural Network Methods
Environments 2019, 6(2), 25; https://doi.org/10.3390/environments6020025
Received: 4 January 2019 / Revised: 7 February 2019 / Accepted: 19 February 2019 / Published: 24 February 2019
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Abstract
Convolutional neural networks (CNN) have been used increasingly in several land-use classification tasks, but there is a need to further investigate its potential. This study aims to evaluate the performance of CNN methods for land classification and to identify land-use (LU) change. Eight [...] Read more.
Convolutional neural networks (CNN) have been used increasingly in several land-use classification tasks, but there is a need to further investigate its potential. This study aims to evaluate the performance of CNN methods for land classification and to identify land-use (LU) change. Eight transferred CNN-based models were fully evaluated on remote sensing data for LU scene classification using three pre-trained CNN models AlexNet, GoogLeNet, and VGGNet. The classification accuracy of all the models ranges from 95% to 98% with the best-performed method the transferred CNN model combined with support vector machine (SVM) as feature classifier (CNN-SVM). The transferred CNN-SVM model was then applied to orthophotos of the northeastern Cloverdale as part of the City of Surrey, Canada from 2004 to 2017 to perform LU classification and LU change analysis. Two sources of datasets were used to train the CNN–SVM model to solve a practical issue with the limited data. The obtained results indicated that residential areas were expanding by creating higher density, while green areas and low-density residential areas were decreasing over the years, which accurately indicates the trend of LU change in the community of Cloverdale study area. Full article
(This article belongs to the Special Issue Application of Remote Sensing and GIS in Environmental Studies)
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Open AccessArticle
Land Use/Land Cover (LULC) Using Landsat Data Series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco
Environments 2018, 5(12), 131; https://doi.org/10.3390/environments5120131
Received: 17 October 2018 / Revised: 29 November 2018 / Accepted: 3 December 2018 / Published: 5 December 2018
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Abstract
The study of land use/land cover (LULC) has become an increasingly important stage in the development of forest ecosystems strategies. Hence, the main goal of this study was to describe the vegetation change of Azrou Forest in the Middle Atlas, Morocco, between 1987 [...] Read more.
The study of land use/land cover (LULC) has become an increasingly important stage in the development of forest ecosystems strategies. Hence, the main goal of this study was to describe the vegetation change of Azrou Forest in the Middle Atlas, Morocco, between 1987 and 2017. To achieve this, a set of Landsat images, including one Multispectral Scanner (MSS) scene from 1987; one Enhanced Thematic Mapper Plus (ETM+) scene from 2000; two Thematic Mapper (TM) scenes from 1995 and 2011; and one Landsat 8 Operational Land Imager (OLI) scene from 2017; were acquired and processed. Ground-based survey data and the normalized difference vegetation index (NDVI) were used to identify and to improve the discrimination between LULC categories. Then, the maximum likelihood (ML) classification method was applied was applied, in order to produce land cover maps for each year. Three classes were considered by the classification of NDVI value: low-density vegetation; moderate-density vegetation, and high-density vegetation. Our study achieved classification accuracies of 66.8% (1987), 99.9% (1995), 99.8% (2000), 99.9% (2011), and 99.9% (2017). The results from the Landsat-based image analysis show that the area of low-density vegetation was decreased from 27.4% to 2.1% over the past 30 years. While, in 2017, the class of high-density vegetation was increased to 64.6% of the total area of study area. The results of this study show that the total forest cover remained stable. The present study highlights the importance of the image classification algorithms combined with NDVI index for better understanding the changes that have occurred in this forest. Therefore, the findings of this study could assist planners and decision-makers to guide, in a good manner, the sustainable land development of areas with similar backgrounds. Full article
(This article belongs to the Special Issue Application of Remote Sensing and GIS in Environmental Studies)
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