Remote Sensing Models of Forest Structure, Composition, and Function
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".
Deadline for manuscript submissions: closed (20 May 2022) | Viewed by 15272
Special Issue Editors
Interests: forest modeling; remote sensing; photogrammetry; 3-D modeling of vegetation; individual-based models; stochastic modeling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In this Special Issue, we provide a platform for a collegial exchange of ideas and a broad discussion on the role of remote sensing in next-generation models of forests. Empirical, process, and/or stochastic models have long been used to model forest structure, composition, and/or function, yet a number of vegetation properties and processes remain challenging to simulate. These include detailed spatiotemporal patterns of canopy competition, leaf pigment concentrations, photosynthetic rates, phenological cycles, CO2, CH4, and BVOC emissions, mortality, regeneration, decomposition, canopy radiative transfer, and the spread of fire, pests, and pathogens. These and many other dynamics are linked to forest genetics through long-timescale phylogeographic processes and short-timescale adaptive gene expression. Current individual-based models of forests are poised to grow in geometric/structural and biochemical realism, necessitating efficient model approximations and/or statistical emulators for global modeling. Recent advances in deep learning for remote sensing provide a new opportunity to develop new spatiotemporal models. Machine learning models based on remote sensing may capture forest properties and processes, and can be embedded in existing models, ideally, without prohibitive parameterization requirements. These and other approaches such as radiative transfer model emulation and/or inversion may be applied to a variety of remote sensing data sources, including passive optical multi-spectral, hyperspectral, and high-resolution structure-from-motion (SfM) data, and active LiDAR, SAR, and GNSS-R data, as well as PhenoCam near-sensing observations, FLUXNET tower measurements, and the TRY database for mapping traits to species locations. We especially invite papers demonstrating state-of-the-art techniques for learning models of forest properties and processes directly from these and other data sources in order to address shortcomings in the representation of forests in existing models of the terrestrial biosphere.
Dr. Nikolay Strigul
Dr. Adam Erickson
Guest Editors
Manuscript Submission Information
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Keywords
- Data-driven vegetation models
- Remote sensing of vegetation
- Machine/deep learning
- Convolutional neural networks
- Generative adversarial networks
- Radiative transfer model inversion
- Near sensing of vegetation
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