Special Issue "Monitoring of Status and Disturbances of Bio- and Geodiversity, Their Traits and Interactions Using Remote Sensing"

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

Deadline for manuscript submissions: 1 December 2021.

Special Issue Editors

Prof. Dr. Andrew Skidmore
E-Mail Website
Guest Editor
Faculty for Geo-Information Science and Earth Observation (ITC), University of Twente, 7500AA Enschede, The Netherlands
Interests: spatial ecology; fragmentation; climate change; hyperspectral remote sensing; image processing; geo-information techniques
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Prof. Dr. Michael Vohland
E-Mail Website
Guest Editor
Geoinformatics and Remote Sensing, Institute for Geography, Leipzig University, Johannisallee 19a, D-04103 Leipzig, Germany
Interests: remote sensing (hyper- and multispectral, thermal); portable vis-NIR and FTIR spectroscopy; digital soil mapping; vegetation mapping (agriculture, forestry); multivariate data analysis; spatial modeling (SVAT, hydrology)
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Prof. Dr. Natascha Oppelt
E-Mail Website
Guest Editor
Christian-Albrechts-University Kiel (CAU), Department for Geography, Remote Sensing & Environmental Modelling, 24118 Kiel, Germany
Interests: remote sensing of deep and shallow water; monitoring of shallow benthis coverage; coupling of earth observation data and modelling approaches; time series nalysis and sensor fusion
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Dr. Jan Bumberger
E-Mail Website
Guest Editor
Department of Monitoring and Exploration Technologies, Helmholtz Centre for Environmental Research (UFZ), Permoserstr.15, D-04318 Leipzig, Germany
Interests: scalable sensor network technologies; calibration and validation of remote sensing data; high frequency electromagnetic and optical spectral monitoring; data management; signal processing of multiparametric and cross-domain data; data-driven information extraction
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In the face of current global changes there are two common approaches to the conservation of biodiversity: On the one hand, there is the direct conservation of species and populations, and, on the other, the conservation of habitats. In order to improve our understanding of the status, existing disturbances, and the resilience of biodiversity for all levels of organisms, it is imperative not only to monitor biodiversity itself, but also to monitor geodiversity in particular and the interactions of organisms with each other at different scales. The subject matter of this issue is, therefore, the “Monitoring of Status and Disturbances of Abiotic and Biotic Traits/Diversity and/or their Interactions using Remote Sensing”.

We thus focus on manuscripts that deal with remote sensing approaches to monitor the status and disturbances of the essential characteristics of biodiversity—the phylo-diversity, taxonomic diversity, structural diversity, functional diversity, and trait diversity on different levels of organismic organization from the molecular and gene level, to individuals, populations, communities, ecosystems, and landscapes.

A crucial insight from ecology is that biodiversity and geodiversity are intrinsically linked and that feedback processes play a crucial role in the resilience of both biodiversity and the entire ecosystem. For this reason, this Special Issue also focuses on recording the status and disturbances of abiotic diversity as well as the interactions between biotic and abiotic diversity using remote sensing procedures.

As a third component, we highlight human-induced disturbances of biotic and abiotic properties. Therefore, we will demonstrate how ecosystems change abiotic and biotic traits that are influenced by humans.

The Special Issue will look at the following topics:

  • The monitoring of status, stress or disturbances of biotic-abiotic traits/diversity, and/or their interactions using remote sensing (RS)
  • RS sensors on different platforms (close-range, air-, and spaceborne) for monitoring biotic, abiotic trait/diversity, and their interactions
  • Approaches to monitoring biotic diversity/traits using RS (phylo-diversity, taxonomic diversity, structural diversity, functional diversity, trait diversity)
  • Approaches to monitoring abiotic traits/diversity (geodiversity) using RS
  • Use of plants/ the vegetation/ plant communities as sensors or bio-indicators for abiotic status, stress or disturbances.
  • Approaches to monitoring changes of abiotic and/or biotic traits that are influenced by humans like land-use intensity, urbanization, and further human drivers
  • The monitoring of essential biodiversity variables (EBV) using RS
  • The monitoring of essential climate and geo-variables (GEO Essential – GEO EV) using RS
  • The monitoring of interactions of EBV/ECV/GEO-Essential using RS

Privat. Doz. Dr. habil. Angela Lausch
Prof. Dr. Andrew Skidmore
Prof. Dr. Michael Vohland
Prof. Dr. Natascha Oppelt
Dr. Jan Bumberger
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

  • Biotic diversity / biodiversity
  • Phylo-diversity
  • Taxonomic diversity
  • Structural diversity
  • Functional diversity
  • Abiotic, biotic, human traits
  • Abiotic diversity
  • Geodiversity
  • Biotic, abiotic, and humans interactions

Published Papers (4 papers)

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Research

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Article
Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables
Remote Sens. 2019, 11(24), 2934; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11242934 - 07 Dec 2019
Cited by 2 | Viewed by 1264
Abstract
Soil total nitrogen (STN) is an important indicator of soil quality and plays a key role in global nitrogen cycling. Accurate prediction of STN content is essential for the sustainable use of soil resources. Synthetic aperture radar (SAR) provides a promising source of [...] Read more.
Soil total nitrogen (STN) is an important indicator of soil quality and plays a key role in global nitrogen cycling. Accurate prediction of STN content is essential for the sustainable use of soil resources. Synthetic aperture radar (SAR) provides a promising source of data for soil monitoring because of its all-weather, all-day monitoring, but it has rarely been used for STN mapping. In this study, we explored the potential of multi-temporal Sentinel-1 data to predict STN by evaluating and comparing the performance of boosted regression trees (BRTs), random forest (RF), and support vector machine (SVM) models in STN mapping in the middle reaches of the Heihe River Basin in northwestern China. Fifteen predictor variables were used to construct models, including land use/land cover, multi-source remote sensing-derived variables, and topographic and climatic variables. We evaluated the prediction accuracy of the models based on a cross-validation procedure. Results showed that tree-based models (RF and BRT) outperformed SVM. Compared to the model that only used optical data, the addition of multi-temporal Sentinel-1A data using the BRT method improved the root mean square error (RMSE) and the mean absolute error (MAE) by 17.2% and 17.4%, respectively. Furthermore, the combination of all predictor variables using the BRT model had the best predictive performance, explaining 57% of the variation in STN, with the highest R2 (0.57) value and the lowest RMSE (0.24) and MAE (0.18) values. Remote sensing variables were the most important environmental variables for STN mapping, with 59% and 50% relative importance in the RF and BRT models, respectively. Our results show the potential of using multi-temporal Sentinel-1 data to predict STN, broadening the data source for future digital soil mapping. In addition, we propose that the SVM, RF, and BRT models should be calibrated and evaluated to obtain the best results for STN content mapping in similar landscapes. Full article
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Article
Spectral Diversity Metrics for Detecting Oil Pollution Effects on Biodiversity in the Niger Delta
Remote Sens. 2019, 11(22), 2662; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11222662 - 14 Nov 2019
Cited by 1 | Viewed by 1041
Abstract
Biodiversity monitoring in the Niger delta has become pertinent in view of the incessant spillages from oil production activities and the socio-economic impact of these spillages on the inhabitants who depend on the resources for their livelihood. Conventional methods of post-impact assessments are [...] Read more.
Biodiversity monitoring in the Niger delta has become pertinent in view of the incessant spillages from oil production activities and the socio-economic impact of these spillages on the inhabitants who depend on the resources for their livelihood. Conventional methods of post-impact assessments are expensive, time consuming, and cause damage to the environment, as they often require the removal of affected samples/specimens for laboratory analysis. Remote sensing offers the opportunity to track biodiversity changes from space while using the spectral variability hypothesis (SVH). The SVH proposes that the species diversity of a sampled area is linearly correlated with the variability of spectral reflectance of the area. Several authors have tested the SVH on various land cover types and spatial scales; however, the present study evaluated the validity of the SVH against the backdrop of oil pollution impact on biodiversity while using vascular plant species as surrogates. Species richness and diversity indices were computed from vegetation data collected from polluted and non-polluted transects. Spectral metrics that were derived from Sentinel 2 bands and broadband vegetation indices (BVIs) using various algorithms, including averages, spread, dimension reduction, and so on, were assessed for their ability to estimate vascular plants species richness and diversity. The results showed significant differences in vegetation characteristics of polluted and control transects (H = 76.05, p-value = <0.05 for abundance and H = 170.03, p-value < 0.05 for richness). Spectral diversity metrics correlated negatively with species data on polluted transects and positively on control transects. The metrics computed using Sentinel 2A bands and vegetation indices proved to be sensitive to changes in vegetation characteristics following oil pollution. The most robust relationship was observed between the metrics and indices on control transects, whereas the weakest relationships were observed on polluted transects. Index-wise, the Simpson’s diversity index regressed better with spectral metrics (R2 > 0.5), whereas the Chao-1 richness index regressed the least (R2 < 0.5). The strength of the relationship resulted in successfully estimating species richness and diversity values of investigated transects, thereby enhancing biodiversity monitoring over time and space. Full article
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Review

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Review
Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces
Remote Sens. 2020, 12(22), 3690; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223690 - 10 Nov 2020
Cited by 2 | Viewed by 1791
Abstract
The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to [...] Read more.
The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring. Full article
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Review
Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics
Remote Sens. 2019, 11(20), 2356; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11202356 - 11 Oct 2019
Cited by 19 | Viewed by 4178
Abstract
In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. [...] Read more.
In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. Despite the great importance of geodiversity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geodiversity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geodiversity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geodiversity characteristics can be recorded. The paper provides an overview of those geotraits. Full article
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