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Special Issue "Upscaling and Downscaling Modelling and/or Identification of Relevant Scales and Thresholds for Environmental Impacts in Ecology by Remote Sensing"

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

Deadline for manuscript submissions: 1 March 2020

Special Issue Editors

Guest Editor
Privat. Doz. Dr. habil. Angela Lausch

Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research (UFZ), Permoserstr.15, D-04318 Leipzig, Germany
Website | E-Mail
Interests: Remote sensing; scaling approaches; linked open data; semantic web; data science approaches; spectral abiotic and biotic traits; spectral trait and trait variation concepts; spatial-temporal process-pattern interactions; vegetation; biodiversity ecosystem health; land-use intensity using RS approaches; essential biodiversity variables (EBV)
Guest Editor
Dr. Carsten Neumann

Remote Sensing Section 1.4, Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Telegrafenberg A17, D-14473 Potsdam, Germany
Website | E-Mail
Interests: Remote Sensing, Scaling Approaches, Habitat Management and Monitoring, Habitat and Environmental Gradient Modelling, Biodiversity Indicators, Processes in Open Landscapes, Machine Learning for Habitat Classification, Genetic Algorithms for Plant Species Mapping, Vegetation Ecology, Imaging and Field Spectroscopy, Spectral Plant Trait Modelling
Guest Editor
Dr. Reinhard Klenke

Department Conservation Biology, Helmholtz Centre for Environmental Research – UFZ, Permoserstr.15, D-04318 Leipzig, Germany
Website | E-Mail
Phone: +49-172-3020894
Interests: Population Ecology, Community Ecology, Landscape Ecology, Biodiversity Conservation, Scaling Approaches, Scale Effects, Scale Matching, Scale Transition, Monitoring, Life History Traits, Spectral Traits, Light Pollution, Multiple Anthropogenic Drivers, Essential Biodiversity Variables (EBV), Human-Wildlife Conflicts, Spatial Statistics, Proxies, Uncertainty
Guest Editor
Ms. Uta Ködel

Department Monitoring & Exploration Technologies, Helmholtz Centre for Environmental Research – UFZ, Permoserstr.15, D-04318 Leipzig, Germany
Website | E-Mail
Interests: Remote Sensing, Scaling Approaches, novel monitoring approaches, interlinkage of processes in the subsurface, geophysical monitoring, FTIR, gas geochemistry, electrochemical and elektrokinetical processes, geostatistics, proxies, uncertainties

Special Issue Information

Dear colleagues,

The complex heterogeneity of ecological processes, disturbances and anthropogenic activities at various spatial, temporal and directional scales affect both biotic and abiotic traits, structures, processes and essential ecosystem functions. Research on the effects of the scale-dependency of processes, structures and functions is still in its infancy. In fact, when only certain spatial, temporal or directional scales are considered, this can inevitably lead to a misunderstanding of the current status or disturbances in biodiversity or the ecosystem.

RS represent cost-effective and comprehensive methods enabling repetition and the recording of continuous abiotic and biotic diversity and trait information in space and over time. There are numerous kinds of sensors that differ in terms of their sensor characteristics such as radiometric, spatial, spectral, temporal and directional resolution. Hence, procedures, methods and models are required that enable the use of robust and comparable multi-sensor and multi-temporal RS information and data products in conjunction with ecosystem and biodiversity models.

A large part of our ecological understanding is derived from in-situ measurement data on the point, site or regional scale. The foodprint of these measurements is often very small compared to the model scales. However, the monitoring data are absolutely essential in the calibration, validation and process understanding of RS data. Even though some progress has been made over recent years, numerous scientific issues still remain concerning the up- and downscaling approaches and the interlinking and transferability of local observation/monitoring data on regional and national scales.

Faced with the sensitivity of ecosystem diversity to natural and anthropogenic impacts, the complex heterogeneity within the “smaller” scales and the different drivers and processes in the ecosystem at different scales, it is essential to develop flexible and robust methods to identify relevant scales (spatial, temporal, directional) and threshold values for impacts and stressors. In addition, the consideration and evaluation of uncertainty of both monitoring and RS data provides additional information for assessment.

The following Special Issue focuses on upscaling and downscaling modelling and/or identification of relevant scales and thresholds for environmental impacts in ecology by remote sensing. The following topics are looked at in the Special Issue:

  • Upscaling and downscaling modelling approaches by RS
  • Approaches to validate upscaling and downscaling modelling approaches
  • Multisensor image fusion by RS
  • Multitemporal image analysis by RS
  • Linkage in-situ measurements with close-range, air-and spaceborne RS applications
  • Multigrid Methods and Hierarchical Segmentation Algorithms and other methods
  • Machine learning and deep learning approaches for upscaling and downscaling modelling by RS
  • Derivation from the overall effect of processes and functions beyond scales with RS?
  • Scale effects on ecosystem processes
  • Identification of relevant spatial scales and thresholds for environmental impacts and disturbances
  • The influence of stability and concreteness of spatial patterns on earth system processes and dynamics
  • Methods to close gaps between top-down and bottom-up approaches
  • Evaluation of uncertainty estimation in RS scaling approaches
  • Automatic detection of functional/data gaps and anomalies in RS
  • Development of visual methods to explore and analyse multi-parameter data as functions of space and time
  • Data Science techniques for a scalable ecological modelling approach by integration of RS (Linked Open Data, Semantic Web, and others)

Priv. Doz. Dr. habil. Angela Lausch
Dr. Carsten Neumann
Dr. Reinhard Klenke
Ms. Uta Ködel
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 1800 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

  • Scale effects
  • Scaling
  • Scale domain
  • Scale threshold
  • Mechanism analyses of scale effects In
  • Upscaling and downscaling modelling
  • Linkage in-situ, close-range and air-spaceborne RS
  • Multisensor image fusion
  • Multitemporal image analysis
  • Pattern delineation
  • Image sharpening
  • Spatial, spectral, temporal clustering
  • Data cap and anomaly detection
  • Uncertainty
  • Model driven monitoring for validation

Published Papers (1 paper)

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Research

Open AccessArticle Multispectral Approach for Identifying Invasive Plant Species Based on Flowering Phenology Characteristics
Remote Sens. 2019, 11(8), 953; https://doi.org/10.3390/rs11080953
Received: 10 March 2019 / Revised: 14 April 2019 / Accepted: 16 April 2019 / Published: 21 April 2019
PDF Full-text (3801 KB) | HTML Full-text | XML Full-text
Abstract
Invasive plant species (IPS) are the second biggest threat to biodiversity after habitat loss. Since the spatial extent of IPS is essential for managing the invaded ecosystem, the current study aims at identifying and mapping the aggressive IPS of Acacia salicina and Acacia [...] Read more.
Invasive plant species (IPS) are the second biggest threat to biodiversity after habitat loss. Since the spatial extent of IPS is essential for managing the invaded ecosystem, the current study aims at identifying and mapping the aggressive IPS of Acacia salicina and Acacia saligna, to understand better the key factors influencing their distribution in the coastal plain of Israel. This goal was achieved by integrating airborne-derived hyperspectral imaging and multispectral earth observation for creating species distribution maps. Hyperspectral data, in conjunction with high spatial resolution species distribution maps, were used to train the multispectral images at the species level. We incorporated a series of statistical models to classify the IPS location and to recognize their distribution and density. We took advantage of the phenological flowering stages of Acacia trees, as obtained by the multispectral images, for the support vector machine classification procedure. The classification yielded an overall Kappa coefficient accuracy of 0.89. We studied the effect of various environmental and human factors on IPS density by using a random forest machine learning model, to understand the mechanisms underlying successful invasions, and to assess where IPS have a higher likelihood of occurring. This algorithm revealed that the high density of Acacia most closely related to elevation, temperature pattern, and distances from rivers, settlements, and roads. Our results demonstrate how the integration of remote-sensing data with different data sources can assist in determining IPS proliferation and provide detailed geographic information for conservation and management efforts to prevent their future spread. Full article
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