Forests constitute the primary component of terrestrial ecosystems and represent the largest reservoir of renewable resources, biological gene pools, and biomass energy on land. They play a crucial role in maintaining global biodiversity, regulating the climate, conserving water, protecting soil, and promoting human health. Currently, the increase in extreme weather events driven by climate change, coupled with deforestation and land-use changes caused by human activities, has exacerbated the degradation of forest vegetation and the loss of ecological functions.

Accurate measurement and dynamic monitoring of forest vegetation parameters - such as height, canopy area, biomass, and health status - are crucial for assessing and predicting forest growth conditions. These measurements are vital for promoting precise improvements in forest quality, ensuring sustainable forest development, mitigating climate change, and protecting the integrity and functionality of forest ecosystems.

This Research Topic aims to bring together the latest research and developments in the accurate measurement and dynamic monitoring of forest vegetation parameters. Topics of interest include, but are not limited to, the application of remote sensing technologies, the integration of multiple data sources, advances in data processing and machine learning, the development and application of forestry mathematical models, and case studies demonstrating practical applications. By highlighting these cutting-edge approaches, we seek to promote knowledge exchange and collaboration, develop more robust and accurate models, and ultimately contribute to more effective forest management and conservation strategies.

In this Research Topic, we welcome all article types published by Frontiers in Plant Science that explore accurate measurement and dynamic monitoring of forest vegetation parameters, particularly those focusing on:

• The application of remote sensing technologies to improve precision and efficiency in forest monitoring.
• Integration of multiple data sources and advanced data processing techniques.
• Developing forestry statistics models to predict forest dynamics.
• Mechanistic studies that elucidate the processes behind remote sensing measurements and model predictions.
• Research of Artificial Intelligence and Large Model in Precise Measurement of Forest Vegetation Parameters.
• Case studies demonstrating practical applications in forest management and conservation.

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