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Remote Sensing
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Remote Sensing of Biodiversity Monitoring
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Remote Sensing of Biodiversity Monitoring

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

Deadline for manuscript submissions: closed (15 November 2021) | Viewed by 12377

Special Issue Editors

Dr. João F. Gonçalves

E-Mail Website
Guest Editor
InBIO – Research Network in Biodiversity and Evolutionary Biology/CIBIO, Research Center in Biodiversity and Genetic Resources, University of Porto, Porto, Portugal
Interests: global change; biodiversity monitoring; species distribution modelling; remote sensing/Earth observation; ecoinformatics
Special Issues, Collections and Topics in MDPI journals
Dr. João P. Honrado

E-Mail Website
Guest Editor
1. Assoc. Prof., CIBIO/InBIO—Research Centre in Biodiversity and Genetic Resources, Universidade do Porto, Campus de Vairão, Rua Padre Armando Quintas, 7, 4485-661 Vairão, Portugal
2. Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Rua Campo Alegre s/n, 4169-007 Porto, Portugal
Interests: environmental management; remote sensing; ecosystem services; ecological modeling; predictive ecology
Special Issues, Collections and Topics in MDPI journals
Dr. Adrián Regos Sanz

E-Mail Website
Guest Editor
University of Santiago de Compostela & InBIO—Research Network in Biodiversity and Evolutionary Biology/CIBIO—Research Center in Biodiversity and Genetic Resources—University of Porto
Interests: conservation biogeography; conservation biology; remote sensing; landscape ecology; predictive ecology; fire ecology; spatial conservation prioritization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Monitoring fast changes and long-term trends in biodiversity driven by widespread environmental alterations in the Anthropocene is a critical international endeavor increasingly supported by remotely-sensed Earth observations (RS/EO)—even more so if we consider the need to track the progress of global conservation initiatives and policy such as the CBD’s Aichi Targets, the UN’s Sustainable Development Goals or the EU’s Habitat Directive. In a wider sense, RS/EO is critical to monitor biodiversity changing face, which includes species range shifts, community reassembly, and biological invasions up to changes in ecosystem and landscape functioning. Ultimately, this will allow assessing how these changes affect the vulnerability of life on Earth and the benefits people extract from nature.

Despite the inherent complexity and multidimensional nature of the Biodiversity concept, entailing several hierarchical levels (from genes to biomes), distinct facets (structure, composition and function), and several spatial scales (from local to global), the current “Golden Age” of RS/EO, with increasingly diverse platforms and enhanced spectral, spatial, and temporal coverage, enables assessing these dimensions and their scalar inter-connections. From airborne to satellite platforms, RS/EO together with field-surveys and innovative techniques related to bioacoustics, sensor networks, camera trapping, radiotracking or environmental metagenomics enable monitoring several dimensions of biodiversity in unprecedented and novel ways. This has been made possible also due to advances in modeling approaches (correlative, mechanistic, process-based), ecoinformatics, cloud-based computing, time series analysis, and spatial statistics allowing the modeling, mapping, and detection of biological and ecological change.

In this Special Issue dedicated to “Biodiversity Monitoring”, we are calling for innovative, integrative and multidisciplinary contributions covering biodiversity’s multiple dimensions in the terrestrial, freshwater, and marine domains which combine RS/EO with multiple biodiversity observation data-streams (e.g., from field surveillance time series to citizen-science programs or metabarcoding), to better understand the drivers and improve the monitoring of biodiversity spatiotemporal change.

Dr. João F. Gonçalves
Prof. João P. Honrado
Dr. Adrián Regos Sanz
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 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.

Keywords

  • Biodiversity change
  • Biodiversity monitoring from space
  • Biodiversity observation time-series
  • Community reassembly
  • Ecosystem functioning
  • Global environmental change
  • Land use change
  • Landscape fragmentation
  • Multiscalar and multitemporal Earth observation
  • Species range shifts

Published Papers (3 papers)

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Research

22 pages, 2912 KiB  
Article
Combining Citizen Science Data and Satellite Descriptors of Ecosystem Functioning to Monitor the Abundance of a Migratory Bird during the Non-Breeding Season
by Francisco S. Moreira, Adrián Regos, João F. Gonçalves, Tiago M. Rodrigues, André Verde, Marc Pagès, José A. Pérez, Bruno Meunier, Jean-Pierre Lepetit, João P. Honrado and David Gonçalves
Remote Sens. 2022, 14(3), 463; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030463 - 19 Jan 2022
Cited by 3 | Viewed by 3088
Abstract
Migratory birds are particularly exposed to habitat changes in their breeding and non-breeding grounds. Remote sensing technologies offer an excellent opportunity to monitor species’ habitats from space at unprecedented spatiotemporal scales. We analyzed if remotely sensed ecosystem functioning attributes (EFAs) adequately predict the [...] Read more.
Migratory birds are particularly exposed to habitat changes in their breeding and non-breeding grounds. Remote sensing technologies offer an excellent opportunity to monitor species’ habitats from space at unprecedented spatiotemporal scales. We analyzed if remotely sensed ecosystem functioning attributes (EFAs) adequately predict the spatiotemporal variation of the Woodcock’s (Scolopax rusticola) relative abundance in southwest Europe, during autumn migration and wintering periods. We used data gathered from Woodcock monitoring through citizen science (N = 355,654 hunting trips) between 2009 and 2018. We computed a comprehensive set of EFAs on a weekly basis from three MODIS satellite products: enhanced vegetation index (EVI), tasseled cap transformation (TCT), and land surface temperature (LST). We developed generalized linear mixed models to explore the predictive power of EFAs on Woodcock’s abundance during the non-breeding season. Results showed that Woodcock abundance is correlated with spatiotemporal dynamics in primary productivity (measured through the EVI), water cycle dynamics (wetness component of TCT), and surface energy balance (LST) in both periods. Our findings underline the potential of combining citizen science and remote sensing data to monitor migratory birds throughout their life cycles—an issue of critical importance to ensure adequate habitat management in the non-breeding areas. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity Monitoring)
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16 pages, 8754 KiB  
Article
Detecting the Spatial Variability of Seagrass Meadows and Their Consequences on Associated Macrofauna Benthic Activity Using Novel Drone Technology
by Subhash Chand and Barbara Bollard
Remote Sens. 2022, 14(1), 160; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010160 - 30 Dec 2021
Cited by 3 | Viewed by 3391
Abstract
Seagrass meadows are undergoing significant decline locally and globally from human and climatic impacts. Seagrass decline also impacts seagrass-dependent macrofauna benthic activity, interrupts their vital linkage with adjacent habitats, and creates broader degradation through the ecosystem. Seagrass variability (gain and loss) is a [...] Read more.
Seagrass meadows are undergoing significant decline locally and globally from human and climatic impacts. Seagrass decline also impacts seagrass-dependent macrofauna benthic activity, interrupts their vital linkage with adjacent habitats, and creates broader degradation through the ecosystem. Seagrass variability (gain and loss) is a driver of marine species diversity. Still, our understanding of macrofauna benthic activity distribution and their response to seagrass variability from remotely sensed drone imagery is limited. Hence, it is critical to develop fine-scale seasonal change detection techniques appropriate to the scale of variability that will apply to dynamic marine environments. Therefore, this research tested the performance of the VIS and VIS+NIR sensors from proximal low altitude remotely piloted aircraft system (RPAS) to detect fine-scale seasonal seagrass variability using spectral indices and a supervised machine learning classification technique. Furthermore, this research also attempted to identify and quantify macrofauna benthic activity from their feeding burrows and their response to seagrass variability. The results from VIS (visible spectrum) and VIS+NIR (visible and near-infrared spectrum) sensors produced a 90–98% classification accuracy. This accuracy established that the spectral indices were fundamental in this study to identify and classify seagrass density. The other important finding revealed that seagrass-associated macrofauna benthic activity showed increased or decreased abundance and distribution with seasonal seagrass variability from drone high spatial resolution orthomosaics. These results are important for seagrass conservation because managers can quickly detect fine-scale seasonal changes and take mitigation actions before the decline of this keystone species affects the entire ecosystem. Moreover, proximal low-altitude, remotely sensed time-series seasonal data provided valuable contributions for documenting spatial ecological seasonal change in this dynamic marine environment. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity Monitoring)
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14 pages, 3506 KiB  
Article
Model-Assisted Bird Monitoring Based on Remotely Sensed Ecosystem Functioning and Atlas Data
by Adrián Regos, Pablo Gómez-Rodríguez, Salvador Arenas-Castro, Luis Tapia, María Vidal and Jesús Domínguez
Remote Sens. 2020, 12(16), 2549; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12162549 - 07 Aug 2020
Cited by 12 | Viewed by 4765
Abstract
Urgent action needs to be taken to halt global biodiversity crisis. To be effective in the implementation of such action, managers and policy-makers need updated information on the status and trends of biodiversity. Here, we test the ability of remotely sensed ecosystem functioning [...] Read more.
Urgent action needs to be taken to halt global biodiversity crisis. To be effective in the implementation of such action, managers and policy-makers need updated information on the status and trends of biodiversity. Here, we test the ability of remotely sensed ecosystem functioning attributes (EFAs) to predict the distribution of 73 bird species with different life-history traits. We run ensemble species distribution models (SDMs) trained with bird atlas data and 12 EFAs describing different dimensions of carbon cycle and surface energy balance. Our ensemble SDMs—exclusively based on EFAs—hold a high predictive capacity across 71 target species (up to 0.94 and 0.79 of Area Under the ROC curve and true skill statistic (TSS)). Our results showed the life-history traits did not significantly affect SDM performance. Overall, minimum Enhanced Vegetation Index (EVI) and maximum Albedo values (descriptors of primary productivity and energy balance) were the most important predictors across our bird community. Our approach leverages the existing atlas data and provides an alternative method to monitor inter-annual bird habitat dynamics from space in the absence of long-term biodiversity monitoring schemes. This study illustrates the great potential that satellite remote sensing can contribute to the Aichi Biodiversity Targets and to the Essential Biodiversity Variables framework (EBV class “Species distribution”). Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity Monitoring)
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