Special Issue "Remote Sensing for Biodiversity & Conservation in Mountain and Polar Regions"

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

Deadline for manuscript submissions: closed (31 December 2020).

Special Issue Editor

Dr. Katarzyna Ostapowicz
Website
Guest Editor
1. Institute of Geography and Spatial Management, Jagiellonian University, Gronostajowa 7, 30-387 Krakow, Poland
2. Department of Environmental Science, Policy & Management, University of California, Berkeley, 326 Mulford Hall, Berkeley, CA 94720, USA
Interests: remote sensing; big EO data; multi-scale and multi-dimensional spatial analysis; programming and modelling; machine learning; land system science; biodiversity, conservation; mountainous and polar regions

Special Issue Information

Dear Colleagues,

Mountain regions are storehouses of global biodiversity but also with polar areas demonstrate the ability of life to succeed in extreme conditions. Their diversity leads to exceptional flora and fauna and the unique cultural variety of people, making mountains and polar regions particularly important spots for conservation efforts which require among others detail and continuous monitoring and assessment. Moreover, due to its exceptional nature, climate, and sensitivity to climate and land use changes, the high-altitude and high-latitude regions are a warning system as far as climate, and land use changes are concerned.

Remote sensing provides a unique perspective on what is happening on the Earth, therefore, plays an important role in biodiversity and conservation monitoring and assessment.

Earth observation needs for ecosystems in the mountain and polar regions constantly changing but still include accurate and continuous biodiversity monitoring and assessment using both in situ and remote sensing observations to observe and evaluate ecological change and better understand coupled natural and human systems.

This Special Issue is aiming to cover the most recent advances in techniques and algorithms to process remotely sensed information for ecology and conservation in the high-altitude and high-latitude regions. Welcomed contributions include:

  • developments in Earth observation Data Cubes use,
  • improvements in data fusion techniques,
  • novel use of high to very-high resolution Earth observation data,
  • advances in cloud-based computing.

Dr. Katarzyna Ostapowicz
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 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

  • Remote sensing
  • Earth observation
  • Mountains
  • Polar regions
  • Biodiversity
  • Conservation

Published Papers (2 papers)

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Research

Open AccessArticle
Multi-Site and Multi-Year Remote Records of Operative Temperatures with Biomimetic Loggers Reveal Spatio-Temporal Variability in Mountain Lizard Activity and Persistence Proxy Estimates
Remote Sens. 2020, 12(18), 2908; https://doi.org/10.3390/rs12182908 - 08 Sep 2020
Abstract
Commonly, when studies deal with the effects of climate change on biodiversity, mean value is used more than other parameters. However, climate change also leads to greater temperature variability, and many papers have demonstrated its importance in the implementation of biodiversity response strategies. [...] Read more.
Commonly, when studies deal with the effects of climate change on biodiversity, mean value is used more than other parameters. However, climate change also leads to greater temperature variability, and many papers have demonstrated its importance in the implementation of biodiversity response strategies. We studied the spatio-temporal variability of activity time and persistence index, calculated from operative temperatures measured at three sites over three years, for a mountain endemic species. Temperatures were recorded with biomimetic loggers, an original remote sensing technology, which has the same advantages as these tools but is suitable for recording biological organisms data. Among the 42 tests conducted, 71% were significant for spatial variability and 28% for temporal variability. The differences in daily activity times and in persistence indices demonstrated the effects of the micro-habitat, habitat, slope, altitude, hydrography, and year. These observations have highlighted the great variability existence in the environmental temperatures experienced by lizard populations. Thus, our study underlines the importance to implement multi-year and multi-site studies to quantify the variability and produce more representative results. These studies can be facilitated by the use of biomimetic loggers, for which a user guide is provided in the last part of this paper. Full article
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Open AccessArticle
Multi-Temporal Sentinel-2 Data in Classification of Mountain Vegetation
Remote Sens. 2020, 12(17), 2696; https://doi.org/10.3390/rs12172696 - 20 Aug 2020
Cited by 2
Abstract
The electromagnetic spectrum registered via satellite remote sensing methods became a popular data source that can enrich traditional methods of vegetation monitoring. The European Space Agency Sentinel-2 mission, thanks to its spatial (10–20 m) and spectral resolution (12 spectral bands registered in visible-, [...] Read more.
The electromagnetic spectrum registered via satellite remote sensing methods became a popular data source that can enrich traditional methods of vegetation monitoring. The European Space Agency Sentinel-2 mission, thanks to its spatial (10–20 m) and spectral resolution (12 spectral bands registered in visible-, near-, and mid-infrared spectrum) and primarily its short revisit time (5 days), helps to provide reliable and accurate material for the identification of mountain vegetation. Using the support vector machines (SVM) algorithm and reference data (botanical map of non-forest vegetation, field survey data, and high spatial resolution images) it was possible to classify eight vegetation types of Giant Mountains: bogs and fens, deciduous shrub vegetation, forests, grasslands, heathlands, subalpine tall forbs, subalpine dwarf pine scrubs, and rock and scree vegetation. Additional variables such as principal component analysis (PCA) bands and selected vegetation indices were included in the best classified dataset. The results of the iterative classification, repeated 100 times, were assessed as approximately 80% median overall accuracy (OA) based on multi-temporal datasets composed of images acquired through the vegetation growing season (from late spring to early autumn 2018), better than using a single-date scene (70%–72% OA). Additional variables did not significantly improve the results, showing the importance of spectral and temporal information themselves. Our study confirms the possibility of fully available data for the identification of mountain vegetation for management purposes and protection within national parks. Full article
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