New Insights into Ecosystem Monitoring Using Geospatial Techniques

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

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• 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