Special Issue "Remote Sensing for Mountain Ecosystems"

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

Deadline for manuscript submissions: 31 August 2021.

Special Issue Editors

Dr. Bogdan Andrei Mihai
E-Mail Website
Guest Editor
Department of Geomorphology-Pedology-Geomatics, Faculty of Geography, University of Bucharest, 050663 Bucharest, Romania
Interests: land use/land cover mapping; vegetation mapping; change detection; image classification; urban remote sensing; GIS; mapping and digital cartography
Dr. Mihai Nita
E-Mail Website
Guest Editor
Department of Forest Engineering, Universitatea Transilvania Brasov, 500036 Braşov, Romania
Interests: remote sensing; GIS; forest and water; forest management; machine learning
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Mountain environments represent a dynamic interface of the climate and environmental change, and are a permanent topic for researchers from all over the world. Remote sensing technology advances in terms of sensor resolution, algorithms for data processing, analysis, and product development have opened new directions in the current context of Earth Observation (EO). EO is an important tool to assess mountain environments, which are well known for their limited accessibility and feature diverse and dynamic ecosystems. This Special Issue proposed by Remote Sensing is an opportunity to publish and disseminate some of the up-to-date research results focused on the role of satellite and aerial imagery in the advanced evaluation and mapping of the mountain ecosystem changes at different scales, from local to regional and global levels. Some thematic aspects we propose include: the spatiotemporal modelling of mountain forest and alpine ecosystem disturbances under the impact of climate change and anthropogenic pressure, the quantitative mapping of the treeline ecotone and the recent transformation of montane vegetation zonation, land cover change and ecosystem dynamics mapping in mountain regions, the objective mapping and evaluation of the mountain depopulation impact over the local to regional ecosystem state, and natural hazard management. Authors are encouraged to test new techniques and methods such as big data processing for Earth Observation, machine learning, etc., and to enlarge the evaluation of the recent satellite sensors from different countries and spatial agencies in the context of mountain environmental analysis.   

Dr. Bogdan Andrei Mihai
Dr. Mihai Nita
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. 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 2400 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

  • Mountain treeline ecotone
  • Mountain ecosystem disturbances
  • Change detection
  • Earth Observation
  • Big data processing
  • Machine learning
  • Mapping
  • Spatiotemporal modeling

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Assessment of Sentinel-2 Images, Support Vector Machines and Change Detection Algorithms for Bark Beetle Outbreaks Mapping in the Tatra Mountains
Remote Sens. 2021, 13(16), 3314; https://doi.org/10.3390/rs13163314 - 21 Aug 2021
Viewed by 328
Abstract
Cambiophagous insects, fires and windthrow cause significant forest disturbances, generating ecological changes and economical losses. The bark beetle (Ips typographus L.), inhabiting coniferous forests and eliminating weakened trees, plays a key role in posing a threat to tree stands, which are dominated [...] Read more.
Cambiophagous insects, fires and windthrow cause significant forest disturbances, generating ecological changes and economical losses. The bark beetle (Ips typographus L.), inhabiting coniferous forests and eliminating weakened trees, plays a key role in posing a threat to tree stands, which are dominated by Norway spruce (Picea abies) and covers a large part of mountain areas, as well as the lowlands of Northern, Central and Eastern Europe. Due to the dynamics of the phenomena taking place, the EU recommends constant monitoring of forests in terms of large-area disturbances and factors affecting tree stands’ susceptibility to destruction. The right tools for this are multispectral satellite images, which regularly and free of charge provide up-to-date information on changes in the environment. The aim of this study was to develop a method of identifying disturbances of spruce stands, including the identification of bark beetle outbreaks. Sentinel 2 images from 2015–2018 were used for this purpose; the reference data were high-resolution aerial images, satellite WorldView 2, as well as field verification data. Support Vector Machines (SVM) distinguished six classes: deciduous forests, coniferous forests, grasslands, rocks, snags (dieback of standing trees) and cuts/windthrow. Remote sensing vegetation indices, Multivariate Alteration Detection (MAD), Multivariate Alteration Detection/Maximum Autocorrelation Factor (MAD/MAF), iteratively re-weighted Multivariate Alteration Detection (iMAD) and trained SVM signatures from another year, stacked band rasters allowed us to identify: (1) no changes; (2) dieback of standing trees; (3) logging or falling down of trees. The overall accuracy of the SVM classification oscillated between 97–99%; it was observed that in 2015–2018, as a result of the windthrow and bark beetle outbreaks and the consequences of those natural disturbances (e.g., sanitary cuts), approximately 62.5 km2 of coniferous stands (29%) died in the studied area of the Tatra Mountains. Full article
(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems)
Show Figures

Graphical abstract

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Forest habitat fragmentation in mountain protected areas using historical Corona KH-9 and Sentinel-2 satellite imagery
Authors: Bogdan Olariu; Marina Vîrghileanu; Bogdan-Andrei Mihai; Ionuț Săvulescu; Liviu Toma
Affiliation: Faculty of Geography, University of Bucharest, 050663 Bucharest, Romania
Abstract: Forest habitat fragmentation is one of the environmental global issues of concern, as a result of the forest management practices and socioeconomic drivers. Mountain protected areas situated near settlements can be extremely vulnerable to degradation and biodiversity loss. In this context, constant evaluation is still a challenge in order to achieve a general image of the environmental state of the protected area for a proper management. The purpose of ourstudy is to evaluate the evolution of the forest habitat in the last 40 years, focusing on Bucegi Natural Park, one of the most frequented protected areas in Romania, as relevant for highly human impacted areas. Our approach integrates historical panchromatic Corona KH-9 image from 1977 and a present-day Sentinel-2 multispectral data from 2020 in order to calculate a series of spatial metrics that reveal the changes in the pattern of the forest habitat and illustrate the forest habitat fragmentation density. The results show a growth of the forest surface, but also an increase of habitat fragmentation in areas where tourism was developed. The method can be of extensive use for environmental monitoring in protected areas management, understanding the environment history connected to the nowadays problems that are to be fixed under a rising human pressure.

Back to TopTop