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

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• 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