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Special Issue "Remote Sensing for Habitat Mapping"

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

Deadline for manuscript submissions: 31 January 2021

Special Issue Editors

Guest Editor
Dr. Cristina Tarantino

Institute of Atmospheric Pollution Research (IIA), National Research Council of Italy (CNR), c/o Interateneo Physics Department University of Bari, Via Amendola 173, 70126 Bari, Italy
Website | E-Mail
Interests: remote sensing; classification; land cover/land use mapping; habitat mapping; change detection; invasive species monitoring; time series analysis; GIS environments
Guest Editor
Dr. Maria Adamo

Institute of Atmospheric Pollution Research (IIA), National Research Council of Italy (CNR), c/o Interateneo Physics Department University of Bari, Via Amendola 173, 70126 Bari, Italy
Website | E-Mail
Interests: optical remote sensing; land cover/land use mapping; habitat mapping; time series analysis; oil spill monitoring; wind fields retrieval from SAR
Guest Editor
Dr. Valeria Tomaselli

Institute of Biosciences and BioResources (IBBR), National Research Council of Italy (CNR), Via Amendola 165/A, 70126 Bari, Italy
Website | E-Mail
Interests: plant community diversity; vegetation mapping; habitat mapping; habitat taxonomies; LCCS taxonomy; expert knowledge; plant phenology

Special Issue Information

Dear Colleagues,

The mapping of natural and semi-natural habitats is increasingly required in environmental policies, as well as in spatial planning, land management, and the designation of protected areas. Habitats are effective indicators of biodiversity and their periodic and consistent monitoring, in terms of extent, status, and changes can provide an effective tool for policy makers engaged in the conservation plans. This is in accordance with the GEO strategies planned for 2016–2025 period and the attainment of SDG 15 for preserving biodiversity and ecosystem sustainability.

Remote sensing data and techniques offer significant opportunities for long-term habitats monitoring because of the availability of a large amount of multi-temporal data from past and current spaceborne missions with continuity provided by planned future missions. Routinely, mapping can be generated and intra-annual and inter-annual changes quantified providing synoptic spatial views of expansive landscapes and regions from the integration of remote sensed (RS) data with in situ and ancillary data.

Due to the great relevance and interest in this theme, there are a great deal of questions to be answered concerning, for example, the best methods and standards to use in acquiring and processing data, habitat classification terms and systems, as well as the reliability of the maps produced depending on the scale adopted, this Special Issue is inviting manuscripts on the following topics:

  • RS data and techniques for identification, mapping, and assessment of different habitat types, their conditions and/or conservation, at different spatial and temporal scales;
  • Remote sensing and habitats characterization for different marine and terrestrial environments, from coastal areas to mountain regions, from large, homogenous, and spatially continuous units to highly fragmented, heterogeneous and spatially discontinuous landscapes (e.g., mosaics);
  • Satellite time series analysis for long-term habitat mapping;
  • Habitat change maps from RS data;
  • Integration of RS data with in situ data and expert knowledge;
  • Habitat taxonomies and semantics in a framework of integration of RS data and in situ data;
  • Indicators from RS data for the habitat modeling.

Dr. Cristina Tarantino
Dr. Maria Adamo
Dr. Valeria Tomaselli
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 1800 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
  • Time series analysis 
  • Habitat mapping 
  • Habitat modeling 
  • In situ data
  • Land cover/Land Use (LC/LU)
  • LC/LU and habitat Taxonomies
  • Change detection
  • Open Access 
  • Multiple scales

Published Papers (1 paper)

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Research

Open AccessArticle Analysis of Using Dense Image Matching Techniques to Study the Process of Secondary Succession in Non-Forest Natura 2000 Habitats
Remote Sens. 2019, 11(8), 893; https://doi.org/10.3390/rs11080893
Received: 2 March 2019 / Revised: 9 April 2019 / Accepted: 9 April 2019 / Published: 12 April 2019
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Abstract
Secondary succession is considered a threat to non-forest Natura 2000 habitats. Currently available data and techniques such as airborne laser scanning (ALS) data processing can be used to study this process. Thanks to these techniques, information about the spatial extent and the height [...] Read more.
Secondary succession is considered a threat to non-forest Natura 2000 habitats. Currently available data and techniques such as airborne laser scanning (ALS) data processing can be used to study this process. Thanks to these techniques, information about the spatial extent and the height of research objects—trees and shrubs—can be obtained. However, only archival aerial photographs can be used to conduct analyses of the stage of succession process that took place in the 1960s or 1970s. On their basis, the extent of trees and shrubs can be determined using photointerpretation, but height information requires stereoscopic measurements. State-of-the-art dense image matching (DIM) algorithms provide the ability to automate this process and create digital surface models (DSMs) that are much more detailed than ones obtained using image matching techniques developed a dozen years ago. This research was part of the HabitARS project on the Ostoja Olsztyńsko-Mirowska Natura 2000 protected site (PLH240015). The source data included archival aerial photographs (analogue and digital) acquired from various phenological periods from 1971–2015, ALS data from 2016, and data from botanical campaigns. First, using the DIM algorithms, point clouds were generated and converted to DSMs. Heights interpolated from the DSMs were compared with stereoscopic measurements (1971–2012) and ALS data (2016). Then, the effectiveness of tree and shrub detection was analysed, considering the relationship between the date and the parameters of aerial images acquisition and DIM effects. The results showed that DIM can be used successfully in tree and shrub detection and monitoring, but the source images must meet certain conditions related to their quality. Based on the extensive material analysed, the detection of small trees and shrubs in aerial photographs must have a scale greater than 1:13,000 or a 25 cm GSD (Ground Sample Distance) at most, an image acquisition date from June–September (the period of full foliage in Poland), and good radiometric quality. Full article
(This article belongs to the Special Issue Remote Sensing for Habitat Mapping)
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