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Remote Sensing of Ecosystem Diversity

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 23089

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Special Issue Editors

Dr. Francesco Petruzzellis

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Department of Life Sciences, University of Trieste, via L. Giorgieri 10, 34127 Trieste, Italy
Interests: functional ecology; invasive plants; plant diversity; plant functional traits; plant physiology; tree mortality and forest decline; urban trees
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Dr. Enrico Tordoni

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Department of Life Sciences, University of Trieste, via L. Giorgieri 10, 34127 Trieste, Italy
Interests: biodiversity; ecological informatics; functional ecology; spatial ecology; species diversity
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Mr. Daniele Da Re

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Georges Lemaître Center for Earth and Climate Research, Earth and Life Institute, UCLouvain, Place Louis Pasteur 3, 1348 Louvain-la-Neuve, Belgium
Interests: biogeography; biological impacts of climate change; geoinformatics; invasive alien species; spatial ecology; spatial epidemiology

Prof. Dr. Giovanni Bacaro

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Department of Life Sciences, University of Trieste, via L. Giorgieri 10, 34127 Trieste, Italy
Interests: ecological modelling; ecosystem functioning; ecosystem services; invasive alien species; plant diversity; spatial ecology; statistics
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Prof. Dr. Duccio Rocchini

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Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, 40126 Bologna, Italy
Interests: biodiversity estimate; computational ecology; ecological informatics; remote sensing; spatial ecology
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Special Issue Information

Dear Colleagues,

Remotely sensed Earth observations (RS/EO) is a key instrument for monitoring ecosystem functioning and diversity. The recent advances in sensor technology (i.e. fine spatial resolution, broad coverage and high revisit frequency) make its prompt application in highly heterogeneous ecosystems to the point that the use of RS/EO has been increasingly considered as an elective choice of many scientists across the world to measure some facets of biodiversity.

Current satellite/airborne multispectral imagery coupled with newly developed and future hyperspectral missions (e.g., EnMAP - Environmental Mapping and Analysis Program; PRISMA - Precursore IperSpettrale della Missione Applicativa), together with high spatial resolution Radar sensor (e.g., Sentinel-1), open new opportunities for the analysis of ecosystems at a broad scale. Indeed, new analytical approaches relying on big data, ecoinformatics, cloud-based computing and spatio-temporal statistics have allowed significant improvement in the modelling, mapping, and detection of biological and ecological changes. Novel intriguing questions and issues could be derived by integrating remote sensing and field data acquisition and analysis. In this Special Issue dedicated to “Ecosystem Diversity”, we are calling for innovative, integrative and multidisciplinary contributions covering multiple facets of ecosystem diversity (including terrestrial floral and/or faunal components), from spectral to taxonomic, functional and phylogenetic features over different spatial scales (from ecosystems to habitats, communities and populations).

We would like to invite the whole community of ecologists, biologists and remote sensing scientists to submit articles about recent research with respect to the following topics:

Relationships between plant trait diversity and multispectral/hyperspectral/radar data
Relationships between spectral diversity and functional and phylogenetic diversity of terrestrial floral and/or faunal components of ecosystems
Ecosystem diversity (spectral heterogeneity) and its variation across different spatial (habitats, communities, and populations) and temporal scales
Ecosystem monitoring in space and time
Novel methodological approaches (e.g., open-source platforms, R based packages for remote data analysis) in ecosystem diversity monitoring

Authors having ideas for potential Review articles can contact the Editors to discuss the suitability of the topic.

Dr. Francesco Petruzzellis
Dr. Enrico Tordoni
Mr. Daniele Da Re
Prof. Dr. Giovanni Bacaro
Prof. Dr. Duccio Rocchini
Guest Editors

Manuscript Submission Information

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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

conservation
functional diversity
ecosystem structure
hyperspectral analysis
multispectral analysis
ecosystem monitoring
novel methodological approaches
open source
phylogenetic diversity
radar data
spatio-temporal patterns
spectral diversity
trait diversity
vegetation

Published Papers (7 papers)

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19 pages, 3648 KiB

Open AccessArticle

Spatial Characterisation of Vegetation Diversity in Groundwater-Dependent Ecosystems Using In-Situ and Sentinel-2 MSI Satellite Data

by Kudzai Shaun Mpakairi, Timothy Dube, Farai Dondofema and Tatenda Dalu

Remote Sens. 2022, 14(13), 2995; https://doi.org/10.3390/rs14132995 - 23 Jun 2022

Cited by 9 | Viewed by 2243

Abstract

Groundwater-Dependent Ecosystems (GDEs) are under threat from groundwater over-abstraction, which significantly impacts their conservation and sustainable management. Although the socio-economic significance of GDEs is understood, their ecosystem services and ecological significance (e.g., biodiversity hotspots) in arid environments remains understudied. Therefore, under the United Nations Sustainable Development Goal (SDG) 15, characterizing or identifying biodiversity hotspots in GDEs improves their management and conservation. In this study, we present the first attempt towards the spatial characterization of vegetation diversity in GDEs within the Khakea-Bray Transboundary Aquifer. Following the Spectral Variation Hypothesis (SVH), we used multispectral remotely sensed data (i.e., Sentinel-2 MSI) to characterize the vegetation diversity. This involved the use of the Rao’s Q to measure spectral diversity from several measures of spectral variation and validating the Rao’s Q using field-measured data on vegetation diversity (i.e., effective number of species). We observed that the Rao’s Q has the potential of spatially characterizing vegetation diversity of GDEs in the Khakea-Bray Transboundary Aquifer. Specifically, we discovered that the Rao’s Q was related to field-measured vegetation diversity (R² = 0.61 and p = 0.00), and the coefficient of variation (CV) was the best measure to derive the Rao’s Q. Vegetation diversity was also used as a proxy for identifying priority conservation areas and biodiversity hotspots. Vegetation diversity was more concentrated around natural pans and along roads, fence lines, and rivers. In addition, vegetation diversity was observed to decrease with an increasing distance (>35 m) from natural pans and simulated an inverse piosphere (i.e., minimal utilization around the natural water pans). We provide baseline information necessary for identifying priority conservation areas within the Khakea-Bray Transboundary Aquifer. Furthermore, this work provides a pathway for resource managers to achieve SDG 15 as well as national and regional Aichi biodiversity targets. Full article

(This article belongs to the Special Issue Remote Sensing of Ecosystem Diversity)

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18 pages, 2406 KiB

Open AccessArticle

Combining Passive Acoustics and Environmental Data for Scaling Up Ecosystem Monitoring: A Test on Coral Reef Fishes

by Simon Elise, François Guilhaumon, Gérard Mou-Tham, Isabel Urbina-Barreto, Laurent Vigliola, Michel Kulbicki and J. Henrich Bruggemann

Remote Sens. 2022, 14(10), 2394; https://doi.org/10.3390/rs14102394 - 16 May 2022

Cited by 4 | Viewed by 2384

Abstract

Ecological surveys of coral reefs mostly rely on visual data collected by human observers. Although new monitoring tools are emerging, their specific advantages should be identified to optimise their simultaneous use. Based on the goodness-of-fit of linear models, we compared the potential of passive acoustics and environmental data for predicting the structure of coral reef fish assemblages in different environmental and biogeographic settings. Both data types complemented each other. Globally, the acoustic data showed relatively low added value in predicting fish assemblage structures. The predictions were best for the distribution of fish abundance among functional entities (i.e., proxies for fish functional groups, grouping species that share similar eco-morphological traits), for the simplest functional entities (i.e., combining two eco-morphological traits), and when considering diet and the level in the water column of the species. Our study demonstrates that Passive Acoustic Monitoring (PAM) improves fish assemblage assessment when used in tandem with environmental data compared to using environmental data alone. Such combinations can help with responding to the current conservation challenge by improving our surveying capacities at increased spatial and temporal scales, facilitating the identification and monitoring of priority management areas. Full article

(This article belongs to the Special Issue Remote Sensing of Ecosystem Diversity)

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21 pages, 71870 KiB

Open AccessArticle

Mapping Plant Diversity Based on Combined SENTINEL-1/2 Data—Opportunities for Subtropical Mountainous Forests

by Qichi Yang, Lihui Wang, Jinliang Huang, Lijie Lu, Yang Li, Yun Du and Feng Ling

Remote Sens. 2022, 14(3), 492; https://doi.org/10.3390/rs14030492 - 20 Jan 2022

Cited by 7 | Viewed by 3973

Abstract

Plant diversity is an important parameter in maintaining forest ecosystem services, functions and stability. Timely and accurate monitoring and evaluation of large-area wall-to-wall maps on plant diversity and its spatial heterogeneity are crucial for the conservation and management of forest resources. However, traditional botanical field surveys designed to estimate plant diversity are usually limited in their spatiotemporal resolutions. Using Sentinel-1 (S-1) and Sentinel-2 (S-2) data at high spatiotemporal scales, combined with and referenced to botanical field surveys, may be the best choice to provide accurate plant diversity distribution information over a large area. In this paper, we predicted and mapped plant diversity in a subtropical forest using 24 months of freely and openly available S-1 and S-2 images (10 m × 10 m) data over a large study area (15,290 km²). A total of 448 quadrats (10 m × 10 m) of forestry field surveys were captured in a subtropical evergreen-deciduous broad-leaved mixed forest to validate a machine learning algorithm. The objective was to link the fine Sentinel spectral and radar data to several ground-truthing plant diversity indices in the forests. The results showed that: (1) The Simpson and Shannon-Wiener diversity indices were the best predicted indices using random forest regression, with ȓ² of around 0.65; (2) The use of S-1 radar data can enhance the accuracy of the predicted heterogeneity indices in the forests by approximately 0.2; (3) As for the mapping of Simpson and Shannon-Wiener, the overall accuracy was 67.4% and 64.2% respectively, while the texture diversity’s overall accuracy was merely 56.8%; (4) From the evaluation and prediction map information, the Simpson, Shannon-Wiener and texture diversity values (and its confidence interval values) indicate spatial heterogeneity in pixel level. The large-area forest plant diversity indices maps add spatially explicit information to the ground-truthing data. Based on the results, we conclude that using the time-series of S-1 and S-2 radar and spectral characteristics, when coupled with limited ground-truthing data, can provide reasonable assessments of plant spatial heterogeneity and diversity across wide areas. It could also help promote forest ecosystem and resource conservation activities in the forestry sector. Full article

(This article belongs to the Special Issue Remote Sensing of Ecosystem Diversity)

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23 pages, 8215 KiB

Open AccessArticle

Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery

by Hafiz Ali Imran, Damiano Gianelle, Michele Scotton, Duccio Rocchini, Michele Dalponte, Stefano Macolino, Karolina Sakowska, Cristina Pornaro and Loris Vescovo

Remote Sens. 2021, 13(14), 2649; https://doi.org/10.3390/rs13142649 - 06 Jul 2021

Cited by 17 | Viewed by 3573

Abstract

Plant biodiversity is an important feature of grassland ecosystems, as it is related to the provision of many ecosystem services crucial for the human economy and well-being. Given the importance of grasslands, research has been carried out in recent years on the potential to monitor them with novel remote sensing techniques. In this study, the optical diversity (also called spectral diversity) approach was adopted to check the potential of using high-resolution hyperspectral images to estimate α-diversity in grassland ecosystems. In 2018 and 2019, grassland species composition was surveyed and canopy hyperspectral data were acquired at two grassland sites: Monte Bondone (IT-MBo; species-rich semi-natural grasslands) and an experimental farm of the University of Padova, Legnaro, Padua, Italy (IT-PD; artificially established grassland plots with a species-poor mixture). The relationship between biodiversity (species richness, Shannon’s, species evenness, and Simpson’s indices) and optical diversity metrics (coefficient of variation-CV and standard deviation-SD) was not consistent across the investigated grassland plant communities. Species richness could be estimated by optical diversity metrics with an R = 0.87 at the IT-PD species-poor site. In the more complex and species-rich grasslands at IT-MBo, the estimation of biodiversity indices was more difficult and the optical diversity metrics failed to estimate biodiversity as accurately as in IT-PD probably due to the higher number of species and the strong canopy spatial heterogeneity. Therefore, the results of the study confirmed the ability of spectral proxies to detect grassland α-diversity in man-made grassland ecosystems but highlighted the limitations of the spectral diversity approach to estimate biodiversity when natural grasslands are observed. Nevertheless, at IT-MBo, the optical diversity metric SD calculated from post-processed hyperspectral images and transformed spectra showed, in the red part of the spectrum, a significant correlation (up to R = 0.56, p = 0.004) with biodiversity indices. Spatial resampling highlighted that for the IT-PD sward the optimal optical pixel size was 1 cm, while for the IT-MBo natural grassland it was 1 mm. The random pixel extraction did not improve the performance of the optical diversity metrics at both study sites. Further research is needed to fully understand the links between α-diversity and spectral and biochemical heterogeneity in complex heterogeneous ecosystems, and to assess whether the optical diversity approach can be adopted at the spatial scale to detect β-diversity. Such insights will provide more robust information on the mechanisms linking grassland diversity and optical heterogeneity. Full article

(This article belongs to the Special Issue Remote Sensing of Ecosystem Diversity)

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17 pages, 2496 KiB

Open AccessArticle

Measuring Alpha and Beta Diversity by Field and Remote-Sensing Data: A Challenge for Coastal Dunes Biodiversity Monitoring

by Flavio Marzialetti, Silvia Cascone, Ludovico Frate, Mirko Di Febbraro, Alicia Teresa Rosario Acosta and Maria Laura Carranza

Remote Sens. 2021, 13(10), 1928; https://doi.org/10.3390/rs13101928 - 15 May 2021

Cited by 15 | Viewed by 4412

Abstract

Combining field collected and remotely sensed (RS) data represents one of the most promising approaches for an extensive and up-to-date ecosystem assessment. We investigated the potential of the so called spectral variability hypothesis (SVH) in linking field-collected and remote-sensed data in Mediterranean coastal dunes and explored if spectral diversity provides reliable information to monitor floristic diversity, as well as the consistency of such information in altered ecosystems due to plant invasions. We analyzed alpha diversity and beta diversity, integrating floristic field and Remote-Sensing PlanetScope data in the Tyrrhenian coast (Central Italy). We explored the relationship among alpha field diversity (species richness, Shannon index, inverse Simpson index) and spectral variability (distance from the spectral centroid index) through linear regressions. For beta diversity, we implemented a distance decay model (DDM) relating field pairwise (Jaccard similarities index, Bray–Curtis similarities index) and spectral pairwise (Euclidean distance) measures. We observed a positive relationship between alpha diversity and spectral heterogeneity with richness reporting the higher R score. As for DDM, we found a significant relationship between Bray–Curtis floristic similarity and Euclidean spectral distance. We provided a first assessment of the relationship between floristic and spectral RS diversity in Mediterranean coastal dune habitats (i.e., natural or invaded). SVH provided evidence about the potential of RS for estimating diversity in complex and dynamic landscapes. Full article

(This article belongs to the Special Issue Remote Sensing of Ecosystem Diversity)

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16 pages, 3097 KiB

Open AccessTechnical Note

Use of Remote Sensing Techniques to Estimate Plant Diversity within Ecological Networks: A Worked Example

by Francesco Liccari, Maurizia Sigura and Giovanni Bacaro

Remote Sens. 2022, 14(19), 4933; https://doi.org/10.3390/rs14194933 - 02 Oct 2022

Cited by 4 | Viewed by 2374

Abstract

As there is an urgent need to protect rapidly declining global diversity, it is important to identify methods to quickly estimate the diversity and heterogeneity of a region and effectively implement monitoring and conservation plans. The combination of remotely sensed and field-collected data, under the paradigm of the Spectral Variation Hypothesis (SVH), represents one of the most promising approaches to boost large-scale and reliable biodiversity monitoring practices. Here, the potential of SVH to capture information on plant diversity at a fine scale in an ecological network (EN) embedded in a complex landscape has been tested using two new and promising methodological approaches: the first estimates α and β spectral diversity and the latter ecosystem spectral heterogeneity expressed as Rao’s Quadratic heterogeneity measure (Rao’s Q). Both approaches are available thanks to two brand-new R packages: “biodivMapR” and “rasterdiv”. Our aims were to investigate if spectral diversity and heterogeneity provide reliable information to assess and monitor over time floristic diversity maintained in an EN selected as an example and located in northeast Italy. We analyzed and compared spectral and taxonomic α and β diversities and spectral and landscape heterogeneity, based on field-based plant data collection and remotely sensed data from Sentinel-2A, using different statistical approaches. We observed a positive relationship between taxonomic and spectral diversity and also between spectral heterogeneity, landscape heterogeneity, and the amount of alien species in relation to the native ones, reaching a value of R² = 0.36 and R² = 0.43, respectively. Our results confirmed the effectiveness of estimating and mapping α and β spectral diversity and ecosystem spectral heterogeneity using remotely sensed images. Moreover, we highlighted that spectral diversity values become more effective to identify biodiversity-rich areas, representing the most important diversity hotspots to be preserved. Finally, the spectral heterogeneity index in anthropogenic landscapes could be a powerful method to identify those areas most at risk of biological invasion. Full article

(This article belongs to the Special Issue Remote Sensing of Ecosystem Diversity)

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16 pages, 1541 KiB

Open AccessTechnical Note

Using Digital Photography to Track Understory Phenology in Mediterranean Cork Oak Woodlands

by Catarina Jorge, João M. N. Silva, Joana Boavida-Portugal, Cristina Soares and Sofia Cerasoli

Remote Sens. 2021, 13(4), 776; https://doi.org/10.3390/rs13040776 - 20 Feb 2021

Cited by 3 | Viewed by 2152

Abstract

Monitoring vegetation is extremely relevant in the context of climate change, and digital repeat photography is a method that has gained momentum due to a low cost–benefit ratio. This work aims to demonstrate the possibility of using digital cameras instead of field spectroradiometers (FS) to track understory vegetation phenology in Mediterranean cork oak woodlands. A commercial camera was used to take monthly photographs that were processed with the Phenopix package to extract green chromatic coordinates (GCC). GCC showed good agreement with the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) obtained with FS data. The herbaceous layer displayed a very good fit between GCC and NDVI (coefficient of determination, represented by r² = 0.89). On the contrary, the GCC of shrubs (Cistus salviifolius and Ulex airensis) showed a better fit with NDWI (r² = 0.78 and 0.55, respectively) than with NDVI (r² = 0.60 and 0.30). Models show that grouping shrub species together improves the predictive results obtained with ulex but not with cistus. Concerning the relationship with climatic factors, all vegetation types showed a response to rainfall and temperature. Grasses and cistus showed similar responses to meteorological drivers, particularly mean maximum temperature (r = −0.66 and −0.63, respectively). The use of digital repeat photography to track vegetation phenology was found to be very suitable for understory vegetation with the exception of one shrub species. Thus, this method proves to have the potential to monitor a wide spectrum of understory vegetation at a much lower cost than FS. Full article

(This article belongs to the Special Issue Remote Sensing of Ecosystem Diversity)

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