Biodiversity is facing dramatic erosion and increasing pressure caused by anthropogenic activities intensifying in magnitude and extent. This leads to strong transformations impacting ecosystems globally, including changes in taxonomic diversity, as well as structure and functions at plant and ecosystem scales. These transformations need to be observed, assessed, and reported with dedicated monitoring programs in order to build efficient actions to mitigate or reverse them. Such monitoring programs need to rely on remote sensing (RS) data, as they potentially provide spatially explicit information from the Earth’s surface at regional to global scale, with regular revisit time, and collect information particularly relevant for the monitoring of vegetated surfaces. Such information is crucial to understanding how biodiversity responds to global environmental changes and directs human activity.
This Special Issue aims to publish original research that specifically addresses various aspects of biodiversity mapping and monitoring over space and time using remote sensing from local to global scales. We invite a wide range of contributions from methodological to applied and multidisciplinary research about the following (non-exclusive) topics:
Taxonomic, structural, and functional diversity mapping from RS data;
Species distribution modeling based on RS data;
Retrieving biophysical and biochemical variables from RS data and radiative transfer models;
Assessing and predicting ecosystem services from RS data;
Ecosystems health monitoring from RS data;
Reconstructing ecosystem trajectories over time from RS data;
Advanced machine learning techniques (deep learning, transfer learning, active learning) for biodiversity mapping based on RS data;
Fusion of multimodal images (optical/thermal/radar/lidar) to improve biodiversity mapping and monitoring.
Reviews covering one or more topics are welcome.
We encourage the authors to make their sample data and computational tools publicly available through online resources to ensure the reproducibility and transparency of all the experiments.
Dr. David Sheeren
Dr. Jean-Baptiste Féret
Dr. Laurence Hubert-Moy
Dr. Sophie Fabre
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