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Selected Papers from the 2nd International Electronic Conference on Remote...
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Selected Papers from the 2nd International Electronic Conference on Remote Sensing (ECRS-2)

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 January 2019) | Viewed by 10626

Special Issue Editor

Dr. Ioannis Gitas

E-Mail Website
Guest Editor
Lab of Forest Management and Remote Sensing, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: forest fires; land-use/land-cover mapping; pre-fire planning and post-fire assessment; remote sensing; GIS; forest management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 2nd International Electronic Conference on Remote Sensing (ECRS-2) was successfully held, from 22 March to 05 April, 2018 (https://ecrs-2.sciforum.net/), verifying the great interest of the remote sensing community on this Conference Series. The e-conference was hosted on sciforum.net, an online platform developed by MDPI for scholarly exchange and collaboration.

During the event, a large number of excellent contributions covering key areas of opportunity and challenge in remote sensing sciences was presented. More specifically, the following areas were covered:

  • New Platforms and Sensors
  • Big Data Handling
  • New Image Analysis Approaches
  • Applications
  • Product Validation
  • Operational Applications and Services
  • Open Source Data, Code, Algorithm

This Special Issue welcomes selected papers from ECRS-2 that promote and advance the exciting and rapidly changing field of remote sensing and which, therefore, contribute towards outlining the role of Earth observation in monitoring the environment for a sustainable future.

Submitted contributions will be subjected to peer review and—upon acceptance—will be published with the aim of rapidly and widely disseminating research results, developments, and applications.

It should be noted that submitted manuscripts should have at least 30% additional, new, and unpublished material compared to the ECRS-2 published paper. In addition, that papers that will be published in this e-conference-related Special Issue will receive a 20% discount on the APCs.

Looking forward to receiving your contributions.

Prof. Ioannis Gitas
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.

Published Papers (2 papers)

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Research

17 pages, 5576 KiB  
Article
Predictive Modeling of Future Forest Cover Change Patterns in Southern Belize
by Carly Voight, Karla Hernandez-Aguilar, Christina Garcia and Said Gutierrez
Remote Sens. 2019, 11(7), 823; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11070823 - 05 Apr 2019
Cited by 28 | Viewed by 5802
Abstract
Tropical forests and the biodiversity they contain are declining at an alarming rate throughout the world. Although southern Belize is generally recognized as a highly forested landscape, it is becoming increasingly threatened by unsustainable agricultural practices. Deforestation data allow forest managers to efficiently [...] Read more.
Tropical forests and the biodiversity they contain are declining at an alarming rate throughout the world. Although southern Belize is generally recognized as a highly forested landscape, it is becoming increasingly threatened by unsustainable agricultural practices. Deforestation data allow forest managers to efficiently allocate resources and inform decisions for proper conservation and management. This study utilized satellite imagery to analyze recent forest cover and deforestation in southern Belize to model vulnerability and identify the areas that are the most susceptible to future forest loss. A forest cover change analysis was conducted in Google Earth Engine using a supervised classification of Landsat 8 imagery with ground-truthed land cover points as training data. A multi-layer perceptron neural network model was performed to predict the potential spatial patterns and magnitude of forest loss based on the regional drivers of deforestation. The assessment indicates that the agricultural frontier will continue to expand into recently untouched forests, predicting a decrease from 75.0% mature forest cover in 2016 to 71.9% in 2026. This study represents the most up-to-date assessment of forest cover and the first vulnerability and prediction assessment in southern Belize with immediate applications in conservation planning, monitoring, and management. Full article
(This article belongs to the Special Issue Selected Papers from the 2nd International Electronic Conference on Remote Sensing (ECRS-2))
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21 pages, 6376 KiB  
Article
An Automated Model to Classify Barrier Island Geomorphology Using Lidar Data and Change Analysis (1998–2014)
by Joanne N. Halls, Maria A. Frishman and Andrea D. Hawkes
Remote Sens. 2018, 10(7), 1109; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10071109 - 12 Jul 2018
Cited by 16 | Viewed by 4100
Abstract
Previous research has documented the usefulness of Lidar data to derive a variety of topographic products (e.g., DEM, DTM, canopy and forest structure, and urban infrastructure). Lidar has been used to map coastal environments and geomorphology; however, there is no comprehensive model to [...] Read more.
Previous research has documented the usefulness of Lidar data to derive a variety of topographic products (e.g., DEM, DTM, canopy and forest structure, and urban infrastructure). Lidar has been used to map coastal environments and geomorphology; however, there is no comprehensive model to derive coastal geomorphology. Therefore, the purpose of this project was to build on existing research and develop an automated modeling approach to classify coastal geomorphology across barrier islands. The model was developed and tested at four sites in North Carolina including two undeveloped and two developed islands. Barrier island geomorphology is shaped by natural coastal processes, such as storms and longshore sediment transport, as well as human influences, such as beach nourishment and urban development. The model was developed to classify ten geomorphic features over four time-steps from 1998 to 2014. Model results were compared to compute change through time and derived the rate and direction of feature movement. Tropical storms and hurricanes had the most influence in geomorphic change and movement. On the developed islands, there was less influence of storms due to the inability of features to move because of coastal infrastructure. From 2005 to 2010, beach nourishment was the dominant influence on developed beaches because this activity ameliorated the natural tendency for an island to erode. Understanding how natural and anthropogenic processes influence barrier island geomorphology is critical to predicting an island’s future response to changing environmental factors such as sea-level rise. The development of an automated model enables it to be replicated in other locations where policy makers and coastal managers may use this information to make development and conservation decisions. Full article
(This article belongs to the Special Issue Selected Papers from the 2nd International Electronic Conference on Remote Sensing (ECRS-2))
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