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New Insights into Ecosystem Monitoring Using Geospatial Techniques

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 32143

Special Issue Editors

Operational Centre for Environmental Monitoring Crisis, Emergency and Damage Control - Institute for Environmental Protection and Research (ISPRA), Rome, Italy
Interests: natural resource management; conservation; environmental impact assessment; geographic information system; plant ecology; spatial analysis; climate change; remote sensing
Hémera Centro de Observación de la Tierra, Escuela de Agronomía, Universidad Mayor, Santiago, Chile
Interests: agricultural drought; vegetation dynamics; land cover; soil moisture; crop water status; plant and vegetation phenology; crop water use efficiency; machine learning
Department of Geography, University of Toronto Mississauga, Mississauga, ON, Canada
Interests: vegetation remote sensing; image classification; unmanned aerial vehicles; multispectral and hyperspectral imaging; radiative transfer modelling; integration of multi-source data; grassland ecology
Special Issues, Collections and Topics in MDPI journals
U.S. Geological Survey St. Petersburg Center for Coastal and Marine Science, St. Petersburg, FL, USA
Interests: coastal wetlands; geomorphology; barrier islands; estuaries; coastal hazards; sea level rise; tempestology; shoreline evolution; habitat change; ecosystems modeling; remote sensing; geospatial analysis
Environmental Hydraulics Institute “IH Cantabria”, University of Cantabria, ES-39011 Santander, Spain
Interests: copernicus; GIS, habitat mapping; land use and cover change; landscape ecology; remote sensing; scenarios; spatial modeling
Special Issues, Collections and Topics in MDPI journals
Operational Centre for Environmental Monitoring Crisis, Emergency and Damage Control - Institute for Environmental Protection and Research (ISPRA), 00144 Rome, Italy
Interests: optical multispectral and hyperspectral image processing; environment remote sensing; spatial ecology; time series analysis; wildfires; vegetation dynamics and phenology; water color; coastal areas; spatial epidemiology; classification; big data
Institut Universitaire Européen de la Mer (IUEM), Université de Brest (UBO), 29238 Brest, France
Interests: remote sensing of environment; wetlands; land cover/land use dynamics; image classification and mapping; sensor fusion; natural risk of coastal areas
Special Issues, Collections and Topics in MDPI journals
East China Normal University, Cina, Shanghai, Putuo, Zhongshan N Rd, No. 3663, MBA of East China Normal University
Interests: ecosystem ecology; global biogeochemical model; carbon-nitrogen interactions; traceability analysis; vegetation canopy monitoring; earth observation analysis
Institute of Botany, Plant Science and Biodiversity Center, Slovak Academy of Sciences, Bratislava, Slovakia
Interests: remote sensing in vegetation science; exploring of Natura 2000 habitats by segmentation of Sentinel-2 satellite images; classification and monitoring of riparian vegetation; identification and monitoring of invasive species by remote sensing techniques; development of the NaturaSat software; impact of alien trees planting on forest diversity; fragmentation and homogenization of forest vegetation
Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum - University of Bologna, Bologna, Italy
Interests: biogeography; plant and vegetation ecology; biodiversity; global change ecology; remote sensing for environmental science and vegetation; nature conservation
Department of Biodiversity and Conservation -Institute for Environmental Protection and Research (ISPRA).
Interests: biogeography; macroecology; biodiversity conservation; spatial and temporal changes in vegetation and habitats; earth observation analysis for vegetation science

Special Issue Information

Dear Colleagues,

Recent results from Earth Observation System analysis through the use of remote and/or proximal sensing techniques are some of the most important assets that will bring us new challenges in life science knowledge. The continuous views of our planet, supplied by satellites and drones equipped by optical high-resolution and spectral sensors (e.g., multispectral and hyperspectral), provide data for geospatial modeling and useful tools for decision-makers for monitoring ecosystem changes.

Earth observation is an essential complementary component to in situ observations and experiments designed to monitor habitat and ecosystem changes across a range of spatial and temporal scales. The huge data services provided by several earth observation programs, such as Copernicus and the Nasa Biodiversity and Ecological Forecasting, supported by incoming expert systems powerful techniques, such as machine learning and artificial intelligence, have pushed the research community to explore new methods and approaches for monitoring ecosystems around the world.

In recent years, natural and biological science researchers have stored many plots recorded in digital databases (i.e., BioCASe, the Global Biodiversity Information Facility—GBIF, Digitized Biocollections—iDigBio and EVA/sPlot databases), with the aim of studying the species and their ecological niches functioning in different ecosystems in most biomes around the world.

Global observations and assessments are key to monitor and understand the ecosystems changes and related drivers allowing them to finally conserve biodiversity in space and time. Remote and proximal sensing observations supported by analytical approaches, like machine learning and deep learning, have demonstrated the capacity for global monitoring in several scientific fields. Hence now the particular focus must be paid on setting the use of Earth Observations products and their complementarity with in-situ data, for global biodiversity monitoring and assessment.

 

To better understand the challenges and opportunities in integrating remote and proximal sensing image datasets with biological worldwide databases, this Special Issue invites contributions on:

  • Use of multispectral/hyperspectral imaging from remote or proximal sensing, for tackling ecosystem and biodiversity challenges;
  • Direct comparisons of EO products with in-situ data survey plots (i.e., biological databases);
  • Assessment of the added value of EO products to models for ecosystem monitoring;
  • Innovative applications of UAVs in ecosystem monitoring;
  • Operational examples of how EO processing chains can support habitat monitoring;
  • Case studies of remote or proximal sensing in the monitoring of different habitat types;
  • New sensors, algorithms, and applications for ecosystem/habitat mapping;
  • Requirements of national conservation action plans towards innovative spatial data products for ecosystem conservation status monitoring;
  • Machine learning and deep learning approaches for ecosystem monitoring and detection;
  • Synergies among different EO platforms (UAV-borne and spaceborne) for habitat and ecosystem monitoring;
  • Expert system techniques for detection, mapping, and assessment of habitat types their distributions and/or conservation, at different spatial and temporal scales;
  • Remote and proximal sensing for habitats detection, from coastal areas to mountain ecosystems, from large, homogenous, and spatially continuous units to highly fragmented, heterogeneous and spatially discontinuous landscapes (e.g., mosaics).

Dr. Emiliano Agrillo
Dr. Francisco Zambrano Bigiarini
Dr. Bing Lu
Dr. Kathryn E.L. Smith
Dr. Jose Manuel Álvarez-Martínez
Dr. Federico Filipponi
Dr. Simona Niculescu
Dr. Jianyang Xia
Dr. Maria Sibikova
Dr. Nicola Alessi
Dr. Laura Casella
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 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

  • Conservation status
  • Data interoperability
  • Ecological models
  • Ecological Niche
  • Ecosystem functions
  • Ecosystem modeling
  • Ecosystem monitoring
  • Essential biodiversity variables
  • Global change
  • Habitat-Vegetation-environment relationships
  • Hierarchical Classification Models
  • Hyperspectral imagery
  • Machine and deep learning
  • Multispectral imagery
  • Proximal sensing
  • Random Forest
  • Remote sensing
  • Remote sensing of drought from the ecosystem perspective
  • Spectral-temporal metrics
  • Sustainable Forest Management
  • Unmanned aerial vehicles

Published Papers (10 papers)

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Editorial

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7 pages, 230 KiB  
Editorial
Editorial for Special Issue: “New Insights into Ecosystem Monitoring Using Geospatial Techniques”
by Emiliano Agrillo, Nicola Alessi, Jose Manuel Álvarez-Martínez, Laura Casella, Federico Filipponi, Bing Lu, Simona Niculescu, Mária Šibíková and Kathryn E. L. Smith
Remote Sens. 2022, 14(10), 2346; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102346 - 12 May 2022
Cited by 2 | Viewed by 1252
Abstract
Recent global-scale environmental issues from climate change to biodiversity loss are generating an intense social pressure on the scientific community [...] Full article
(This article belongs to the Special Issue New Insights into Ecosystem Monitoring Using Geospatial Techniques)

Research

Jump to: Editorial

22 pages, 2907 KiB  
Article
From Forest Dynamics to Wetland Siltation in Mountainous Landscapes: A RS-Based Framework for Enhancing Erosion Control
by Gonzalo Hernández-Romero, Jose Manuel Álvarez-Martínez, Ignacio Pérez-Silos, Ana Silió-Calzada, David R. Vieites and Jose Barquín
Remote Sens. 2022, 14(8), 1864; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14081864 - 13 Apr 2022
Cited by 9 | Viewed by 2512
Abstract
Human activities have caused a significant change in the function and services that ecosystems have provided to society since historical times. In mountainous landscapes, the regulation of services such as water quality or erosion control has been impacted by land use and land [...] Read more.
Human activities have caused a significant change in the function and services that ecosystems have provided to society since historical times. In mountainous landscapes, the regulation of services such as water quality or erosion control has been impacted by land use and land cover (LULC) changes, especially the loss and fragmentation of forest patches. In this work, we develop a Remote Sensing (RS)-based modelling approach to identify areas for the implementation of nature-based solutions (NBS) (i.e., natural forest conservation and restoration) that allow reducing the vulnerability of aquatic ecosystems to siltation in mountainous regions. We used time series Landsat 5TM, 7ETM+, 8OLI and Sentinel 2A/2B MSI (S2) imagery to map forest dynamics and wetland distribution in Picos de Europa National Park (Cantabrian Mountains, northern Spain). We fed RS-based models with detailed in situ information based on photo-interpretation and fieldwork completed from 2017 to 2021. We estimated a forest cover increase rate of 2 ha/year comparing current and past LULC maps against external validation data. We applied this forest gain to a scenario generator model to derive a 30-year future LULC map that defines the potential forest extent for the study area in 2049. We then modelled the distribution of wetlands to identify the areas with the greatest potential for moisture accumulation. We used an S2 mosaic and topography-derived data such as the slope and topographic wetness index (TWI), which indicate terrain water accumulation. Overall accuracy scores reached values of 86% for LULC classification and 61% for wetland mapping. At the same time, we obtained the potential erosion using the NetMap software to identify potential sediment production, transport and deposition areas. Finally, forest dynamics, wetland distribution and potential erosion were combined in a multi-criteria analysis aiming to reduce the amount of sediment reaching selected wetlands. We achieved this by identifying the most suitable locations for the conservation and restoration of natural forests on slopes and in riparian areas, which may reduce the risk of soil erosion and maximise sediment filtering, respectively. The results show a network pattern for forest management that would allow for controlling erosion effects across space and time at three levels: one, by reducing the load that originates upslope in the absence of forest cover; two, by intersecting runoff at watercourses related to sediment transport; and three, by a lack of former barriers, by trapping erosion near to the receiving wetland systems, main river axes and contributing streams. In conclusion, the proposed methodology, which could be transferred to other mountain regions, allows to optimise investment for erosion prevention and wetland conservation by using only very specific areas of the landscape for habitat management (e.g., for NBS implementation). Full article
(This article belongs to the Special Issue New Insights into Ecosystem Monitoring Using Geospatial Techniques)
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16 pages, 3440 KiB  
Article
Assessing the Impacts of Species Composition on the Accuracy of Mapping Chlorophyll Content in Heterogeneous Ecosystems
by Bing Lu and Yuhong He
Remote Sens. 2021, 13(22), 4671; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224671 - 19 Nov 2021
Cited by 1 | Viewed by 1722
Abstract
Chlorophyll is an essential vegetation pigment influencing plant photosynthesis rate and growth conditions. Remote sensing images have been widely used for mapping vegetation chlorophyll content in different ecosystems (e.g., farmlands, forests, grasslands, and wetlands) for evaluating vegetation growth status and productivity of these [...] Read more.
Chlorophyll is an essential vegetation pigment influencing plant photosynthesis rate and growth conditions. Remote sensing images have been widely used for mapping vegetation chlorophyll content in different ecosystems (e.g., farmlands, forests, grasslands, and wetlands) for evaluating vegetation growth status and productivity of these ecosystems. Compared to farmlands and forests that are more homogeneous in terms of species composition, grasslands and wetlands are more heterogeneous with highly mixed species (e.g., various grass, forb, and shrub species). Different species contribute differently to the ecosystem services, thus, monitoring species-specific chlorophyll content is critical for better understanding their growth status, evaluating ecosystem functions, and supporting ecosystem management (e.g., control invasive species). However, previous studies in mapping chlorophyll content in heterogeneous ecosystems have rarely estimated species-specific chlorophyll content, which was partially due to the limited spatial resolution of remote sensing images commonly used in the past few decades for recognizing different species. In addition, many previous studies have used one universal model built with data of all species for mapping chlorophyll of the entire study area, which did not fully consider the impacts of species composition on the accuracy of chlorophyll estimation (i.e., establishing species-specific chlorophyll estimation models may generate higher accuracy). In this study, helicopter-acquired high-spatial resolution hyperspectral images were acquired for species classification and species-specific chlorophyll content estimation. Four estimation models, including a universal linear regression (LR) model (i.e., built with data of all species), species-specific LR models (i.e., built with data of each species, respectively), a universal random forest regression (RFR) model, and species-specific RFR models, were compared to determine their performance in mapping chlorophyll and to evaluate the impacts of species composition. The results show that species-specific models performed better than the universal models, especially for species with fewer samples in the dataset. The best performed species-specific models were then used to generate species-specific chlorophyll content maps using the species classification results. Impacts of species composition on the retrieval of chlorophyll content were further assessed to support future chlorophyll mapping in heterogeneous ecosystems and ecosystem management. Full article
(This article belongs to the Special Issue New Insights into Ecosystem Monitoring Using Geospatial Techniques)
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23 pages, 3257 KiB  
Article
Mapping and Monitoring of Land Cover/Land Use (LCLU) Changes in the Crozon Peninsula (Brittany, France) from 2007 to 2018 by Machine Learning Algorithms (Support Vector Machine, Random Forest, and Convolutional Neural Network) and by Post-classification Comparison (PCC)
by Guanyao Xie and Simona Niculescu
Remote Sens. 2021, 13(19), 3899; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193899 - 29 Sep 2021
Cited by 25 | Viewed by 3925
Abstract
Land cover/land use (LCLU) is currently a very important topic, especially for coastal areas that connect the land and the coast and tend to change frequently. LCLU plays a crucial role in land and territory planning and management tasks. This study aims to [...] Read more.
Land cover/land use (LCLU) is currently a very important topic, especially for coastal areas that connect the land and the coast and tend to change frequently. LCLU plays a crucial role in land and territory planning and management tasks. This study aims to complement information on the types and rates of LCLU multiannual changes with the distributions, rates, and consequences of these changes in the Crozon Peninsula, a highly fragmented coastal area. To evaluate the multiannual change detection (CD) capabilities using high-resolution (HR) satellite imagery, we implemented three remote sensing algorithms: a support vector machine (SVM), a random forest (RF) combined with geographic object-based image analysis techniques (GEOBIA), and a convolutional neural network (CNN), with SPOT 5 and Sentinel 2 data from 2007 and 2018. Accurate and timely CD is the most important aspect of this process. Although all algorithms were indicated as efficient in our study, with accuracy indices between 70% and 90%, the CNN had significantly higher accuracy than the SVM and RF, up to 90%. The inclusion of the CNN significantly improved the classification performance (5–10% increase in the overall accuracy) compared with the SVM and RF classifiers applied in our study. The CNN eliminated some of the confusion that characterizes a coastal area. Through the study of CD results by post-classification comparison (PCC), multiple changes in LCLU could be observed between 2007 and 2018: both the cultivated and non-vegetated areas increased, accompanied by high deforestation, which could be explained by the high rate of urbanization in the peninsula. Full article
(This article belongs to the Special Issue New Insights into Ecosystem Monitoring Using Geospatial Techniques)
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27 pages, 4803 KiB  
Article
Satellite-Derived Barrier Response and Recovery Following Natural and Anthropogenic Perturbations, Northern Chandeleur Islands, Louisiana
by Julie C. Bernier, Jennifer L. Miselis and Nathaniel G. Plant
Remote Sens. 2021, 13(18), 3779; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183779 - 21 Sep 2021
Cited by 4 | Viewed by 2249
Abstract
The magnitude and frequency of storm events, relative sea-level rise (RSLR), sediment supply, and anthropogenic alterations drive the morphologic evolution of barrier island systems, although the relative importance of any one driver will vary with the spatial and temporal scales considered. To explore [...] Read more.
The magnitude and frequency of storm events, relative sea-level rise (RSLR), sediment supply, and anthropogenic alterations drive the morphologic evolution of barrier island systems, although the relative importance of any one driver will vary with the spatial and temporal scales considered. To explore the relative contributions of storms and human alterations to sediment supply on decadal changes in barrier landscapes, we applied Otsu’s thresholding method to multiple satellite-derived spectral indices for coastal land-cover classification and analyzed Landsat satellite imagery to quantify changes to the northern Chandeleur Islands barrier system since 1984. This high temporal-resolution dataset shows decadal-scale land-cover oscillations related to storm–recovery cycles, suggesting that shorter and (or) less resolved time series are biased toward storm impacts and may significantly overpredict land-loss rates and the timing of barrier morphologic state changes. We demonstrate that, historically, vegetation extent and persistence were the dominant controls on alongshore-variable landscape response and recovery following storms, and are even more important than human-mediated sediment input. As a result of extensive vegetation losses over the past few decades, however, the northern Chandeleur Islands are transitioning to a new morphologic state in which the landscape is dominated by intertidal environments, indicating reduced resilience to future storms and possibly rapid transitions in morphologic state with increasing rates of RSLR. Full article
(This article belongs to the Special Issue New Insights into Ecosystem Monitoring Using Geospatial Techniques)
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19 pages, 10548 KiB  
Article
NaturaSat—A Software Tool for Identification, Monitoring and Evaluation of Habitats by Remote Sensing Techniques
by Karol Mikula, Mária Šibíková, Martin Ambroz, Michal Kollár, Aneta A. Ožvat, Jozef Urbán, Ivan Jarolímek and Jozef Šibík
Remote Sens. 2021, 13(17), 3381; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173381 - 26 Aug 2021
Cited by 7 | Viewed by 3209
Abstract
The NaturaSat software integrates various image processing techniques together with vegetation data, into one multipurpose tool that is designed for performing facilities for all requirements of habitat exploration, all in one place. It provides direct access to multispectral Sentinel-2 data provided by the [...] Read more.
The NaturaSat software integrates various image processing techniques together with vegetation data, into one multipurpose tool that is designed for performing facilities for all requirements of habitat exploration, all in one place. It provides direct access to multispectral Sentinel-2 data provided by the European Space Agency. It supports using these data with various vegetation databases, in a user-friendly environment, for, e.g., vegetation scientists, fieldwork experts, and nature conservationists. The presented study introduces the NaturaSat software, describes new powerful tools, such as the semi-automatic and automatic segmentation methods, and natural numerical networks, together with validated examples comparing field surveys and software outputs. The software is robust enough for field work researchers and stakeholders to accurately extract target units’ borders, even on the habitat level. The deep learning algorithm, developed for habitat classification within the NaturaSat software, can also be used in various research tasks or in nature conservation practices, such as identifying ecosystem services and conservation value. The exact maps of the habitats obtained within the project can improve many further vegetation and landscape ecology studies. Full article
(This article belongs to the Special Issue New Insights into Ecosystem Monitoring Using Geospatial Techniques)
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19 pages, 2364 KiB  
Article
Coastal Wetland Shoreline Change Monitoring: A Comparison of Shorelines from High-Resolution WorldView Satellite Imagery, Aerial Imagery, and Field Surveys
by Kathryn E. L. Smith, Joseph F. Terrano, Jonathan L. Pitchford and Michael J. Archer
Remote Sens. 2021, 13(15), 3030; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13153030 - 02 Aug 2021
Cited by 17 | Viewed by 3865
Abstract
Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques [...] Read more.
Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into shoreline monitoring. Geospatial shoreline data created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020 were compared to contemporaneous field-surveyed Global Position System (GPS) data. WV-derived shorelines were found to have a mean difference of 2 ± 0.08 m of GPS data, but accuracy decreased at high-wave energy shorelines that were unvegetated, bordered by sandy beach or semi-submergent sand bars. Shoreline change rates calculated from WV imagery were comparable to those calculated from GPS surveys and geospatial data derived from aerial remote sensing but tended to overestimate shoreline erosion at highly erosive locations (greater than 2 m yr−1). High-resolution satellite imagery can increase the spatial scale-range of shoreline change monitoring, provide rapid response to estimate impacts of coastal erosion, and reduce cost of labor-intensive practices. Full article
(This article belongs to the Special Issue New Insights into Ecosystem Monitoring Using Geospatial Techniques)
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28 pages, 5988 KiB  
Article
Spatiotemporal Modeling of Coniferous Forests Dynamics along the Southern Edge of Their Range in the Central Russian Plain
by Tatiana Chernenkova, Ivan Kotlov, Nadezhda Belyaeva and Elena Suslova
Remote Sens. 2021, 13(10), 1886; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101886 - 11 May 2021
Cited by 2 | Viewed by 2163
Abstract
Forests with predominance of Norway spruce (Picea abies (L.) H. Karst.) and Scots pine (Pinus sylvestris L.) within the hemiboreal zone are considered as secondary communities formed under long-term human activity (logging, plowing, fires and silviculture). This study raises the question—how [...] Read more.
Forests with predominance of Norway spruce (Picea abies (L.) H. Karst.) and Scots pine (Pinus sylvestris L.) within the hemiboreal zone are considered as secondary communities formed under long-term human activity (logging, plowing, fires and silviculture). This study raises the question—how stable is current state of coniferous forests on the southern border of their natural distribution in the center of Eastern Europe using the example of the Moscow region (MR)? The object of the study are spruce and pine forests in different periods of Soviet and post-Soviet history within the Moscow Region (MR). The current proportion of spruce forests is 21.7%, and the proportion of pine forests is 18.5% from total forest area according to our estimates. The direction and rate of forest succession were analyzed based on current composition of populations of the main forest-forming species (spruce, pine, birch, aspen, oak, linden, and ash) based on ground-based research materials collected in 2006–2019. This allowed to develop the dynamic model (DM) of forest communities with the participation of Norway spruce and Scots pine for several decades. Assessment of the spatial distribution of coniferous communities is based on field data and spatial modeling using remote sensing data—Landsat 8 mosaic for 2020. In parallel, a retrospective model (RM) of the spatial-temporal organization of spruce and pine forests for a 30-year period was developed using two Landsat 5 mosaics. For this, nine different algorithms were tested and the best one for this task was found—random forest. Geobotanical relevés were used as a training sample combined with the 2006–2012 mosaic; the obtained spectral signatures were used for modeling based on the 1984–1990 mosaic. Thus, two multi-temporal spatial models of coniferous formations have been developed. Detailed analysis of the structure of spruce and pine forests based on field data made it possible to track trends of successional dynamics for the first time, considering the origin of communities and the ecological conditions of habitats. As a result, ideas about the viability of spruce and pine cenopopulations in different types of communities were formulated, which made possible to develop a dynamic model (DM) of changes in forest communities for future. Comparison of the areas and nature of changes in the spatial structure of coniferous formations made possible to develop the RM. Comparison of two different-time models of succession dynamics (DM and RM) makes possible to correct the main trends in the transformation of coniferous forests of natural and artificial origin under the existing regime of forestry. A set of features was identified that indicates risk factors for coniferous forests in the region. A further decrease of the spruce and pine plantations and increase of the spruce-small-leaved and deciduous formations are expected in the study area. The proportion of pine-spruce forests does not exceed 3% of the area and can be considered as the most vulnerable type of forest. Full article
(This article belongs to the Special Issue New Insights into Ecosystem Monitoring Using Geospatial Techniques)
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28 pages, 8289 KiB  
Article
Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping
by Emiliano Agrillo, Federico Filipponi, Alice Pezzarossa, Laura Casella, Daniela Smiraglia, Arianna Orasi, Fabio Attorre and Andrea Taramelli
Remote Sens. 2021, 13(7), 1231; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071231 - 24 Mar 2021
Cited by 18 | Viewed by 6242
Abstract
In the light of the “Biological Diversity” concept, habitats are cardinal pieces for biodiversity quantitative estimation at a local and global scale. In Europe EUNIS (European Nature Information System) is a system tool for habitat identification and assessment. Earth Observation (EO) data, which [...] Read more.
In the light of the “Biological Diversity” concept, habitats are cardinal pieces for biodiversity quantitative estimation at a local and global scale. In Europe EUNIS (European Nature Information System) is a system tool for habitat identification and assessment. Earth Observation (EO) data, which are acquired by satellite sensors, offer new opportunities for environmental sciences and they are revolutionizing the methodologies applied. These are providing unprecedented insights for habitat monitoring and for evaluating the Sustainable Development Goals (SDGs) indicators. This paper shows the results of a novel approach for a spatially explicit habitat mapping in Italy at a national scale, using a supervised machine learning model (SMLM), through the combination of vegetation plot database (as response variable), and both spectral and environmental predictors. The procedure integrates forest habitat data in Italy from the European Vegetation Archive (EVA), with Sentinel-2 imagery processing (vegetation indices time series, spectral indices, and single bands spectral signals) and environmental data variables (i.e., climatic and topographic), to parameterize a Random Forests (RF) classifier. The obtained results classify 24 forest habitats according to the EUNIS III level: 12 broadleaved deciduous (T1), 4 broadleaved evergreen (T2) and eight needleleaved forest habitats (T3), and achieved an overall accuracy of 87% at the EUNIS II level classes (T1, T2, T3), and an overall accuracy of 76.14% at the EUNIS III level. The highest overall accuracy value was obtained for the broadleaved evergreen forest equal to 91%, followed by 76% and 68% for needleleaved and broadleaved deciduous habitat forests, respectively. The results of the proposed methodology open the way to increase the EUNIS habitat categories to be mapped together with their geographical extent, and to test different semi-supervised machine learning algorithms and ensemble modelling methods. Full article
(This article belongs to the Special Issue New Insights into Ecosystem Monitoring Using Geospatial Techniques)
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14 pages, 3447 KiB  
Article
Surface Tradeoffs and Elevational Shifts at the Largest Italian Glacier: A Thirty-Years Time Series of Remotely-Sensed Images
by Nicola Alessi, Camilla Wellstein, Duccio Rocchini, Gabriele Midolo, Klaus Oeggl and Stefan Zerbe
Remote Sens. 2021, 13(1), 134; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13010134 - 03 Jan 2021
Cited by 5 | Viewed by 3057
Abstract
Biodiversity loss occurring in mountain ecosystems calls for integrative approaches to improve monitoring processes in the face of human-induced changes. With a combination of vegetation and remotely-sensed time series data, we quantitatively identify the responses of land-cover types and their associated vegetation between [...] Read more.
Biodiversity loss occurring in mountain ecosystems calls for integrative approaches to improve monitoring processes in the face of human-induced changes. With a combination of vegetation and remotely-sensed time series data, we quantitatively identify the responses of land-cover types and their associated vegetation between 1987 and 2016. Fuzzy clustering of 11 Landsat images was used to identify main land-cover types. Vegetation belts corresponding to such land-cover types were identified by using species indicator analysis performed on 80 vegetation plots. A post-classification evaluation of trends, magnitude, and elevational shifts was done using fuzzy membership values as a proxy of the occupied surfaces by land-cover types. Our findings show that forests and scrublands expanded upward as much as the glacier retreated, i.e., by 24% and 23% since 1987, respectively. While lower alpine grassland shifted upward, the upper alpine grassland lost 10% of its originally occupied surface showing no elevational shift. Moreover, an increase of suitable sites for the expansion of the subnival vegetation belt has been observed, due to the increasing availability of new ice-free areas. The consistent findings suggest a general expansion of forest and scrubland to the detriment of alpine grasslands, which in turn are shifting upwards or declining in area. In conclusion, alpine grasslands need urgent and appropriate monitoring processes ranging from the species to the landscape level that integrates remotely-sensed and field data. Full article
(This article belongs to the Special Issue New Insights into Ecosystem Monitoring Using Geospatial Techniques)
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