About this Research Topic
Forest growth monitoring is now undergoing a pivotal evolution with diverse concepts spanning traditional in-situ measurement studies to advanced technique plot surveying applications. Phenotyping whole forests or individual stands with the construction of digital twin replicas will help users conduct evaluations of forest cultivation practices or genetic performance. Integration of multiple remote sensing data, versatile intelligent computer algorithms, and diverse terminal perceptual devices is the prevailing strategy to acquire forest phenotypic traits over its life cycle and construct digital twin avatars with growth or physiological properties mapping. Hence, development in the related domain of forest growth trajectories needs to employ a wide range of theory frameworks interpreting forest appearance variations and requires far richer techniques and methodologies stemming from multiple sources and disciplines for synergistically analyzing forest data.
Many studies have highlighted the challenges in developing and implementing phenotyping infrastructure and forest digitization, such as building customized high-throughput forest phenotyping platforms, enhancing the reliability and versatility of existing automatic algorithms, improving the digital tracking of tree morphogenesis variation throughout its life cycle, increasing the accuracy of forest growth attribute estimation from various remote sensing data, and boosting the virtual representation fidelity with actual phenotypic trait mapping. Therefore, it is crucial for research communities to conduct foundational works with heuristic and insightful perspectives to support the existing methodological framework and pursue advanced studies in the aforementioned topics. This can also help inform decision-making, policy-making, and public awareness of deforestation, health status, carbon sequestration, and biodiversity of forest ecosystems.
In this issue, we welcome studies pertaining to cross-disciplinary innovation or breakthroughs settled in forest phenotyping and digital twin construction, serving forest sustainable stewardship, environment-phenotype interaction analysis, and comprehensive digital archival conservation. Hence, the issue intentionally covers all aspects, from field measurements to landscape system surveying, along with providing rational inspiration and new techniques in a quickly advancing domain. Specific topics of interest include but are not limited to:
- Demonstration of methodologies for phenotyping whole forests or individual trees.
- Improvement of the accuracy of extracted forest phenotypic traits.
- Applications of a high-throughput phenotyping platform for forest monitoring.
- Forest-specialized software development with data visualization and intelligent data processing.
- deep learning frameworks for tree growth property assessment across multiple scales.
- Design of computer graphics to produce forest digital twin models from point clouds.
- Detection of tree phenotype variations under abiotic stress using computer-intelligent algorithms.
Many studies have highlighted the challenges in developing and implementing phenotyping infrastructure and forest digitization, such as building customized high-throughput forest phenotyping platforms, enhancing the reliability and versatility of existing automatic algorithms, improving the digital tracking of tree morphogenesis variation throughout its life cycle, increasing the accuracy of forest growth attribute estimation from various remote sensing data, and boosting the virtual representation fidelity with actual phenotypic trait mapping. Therefore, it is crucial for research communities to conduct foundational works with heuristic and insightful perspectives to support the existing methodological framework and pursue advanced studies in the aforementioned topics. This can also help inform decision-making, policy-making, and public awareness of deforestation, health status, carbon sequestration, and biodiversity of forest ecosystems.
In this issue, we welcome studies pertaining to cross-disciplinary innovation or breakthroughs settled in forest phenotyping and digital twin construction, serving forest sustainable stewardship, environment-phenotype interaction analysis, and comprehensive digital archival conservation. Hence, the issue intentionally covers all aspects, from field measurements to landscape system surveying, along with providing rational inspiration and new techniques in a quickly advancing domain. Specific topics of interest include but are not limited to:
- Demonstration of methodologies for phenotyping whole forests or individual trees.
- Improvement of the accuracy of extracted forest phenotypic traits.
- Applications of a high-throughput phenotyping platform for forest monitoring.
- Forest-specialized software development with data visualization and intelligent data processing.
- deep learning frameworks for tree growth property assessment across multiple scales.
- Design of computer graphics to produce forest digital twin models from point clouds.
- Detection of tree phenotype variations under abiotic stress using computer-intelligent algorithms.
Keywords: Forest, remote sensing, digital twin, artificial intelligence, phenotyping, LiDAR data, computer graphics
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
