Dear Colleagues,

Forest growth is the keystone ecological process that determines forest structure and function. A forest site is characterized as an interaction of various environmental factors, and site productivity is a quantitative estimate of the potential of a site to produce woods and biomass. Modeling forest growth and site productivity has been an intrinsic part of forestry research for decades, as they contribute to the development of effective forest management plans. An increasing body of literature has shown that the influences of biotic and abiotic factors (climate, stand dynamics, natural disturbances, management practices, etc.) on forest growth and site productivity are substantial, and their action on forest growth is compounded nonlinearly, generating indirect and tipping-point processes. Climate change has already caused a remarkable change in growth, mortality, and site productivity, altering the range of species distributions. Growth and site productivity models developed with the integration of all the interacting factors, including climate, provide high prediction accuracy. Among potential data sources available for growth and site productivity modeling, LiDAR data can be the most accurate, and can be acquired with reasonable cost. Since LiDAR allows for the 3D modeling of individual trees and stands, time-series matrices derived from LiDAR images can be used for growth and site productivity modeling, regardless of the forest types (monospecific or mixed-species; even-aged or uneven-aged). Advances in LiDAR systems alone or in combination with other sensors may be useful in reducing problems associated with the 3D characterization of mixed forests that are structurally more complex and have higher productivity and more stability against climate change than monospecific forests. Models developed with LiDAR data acquired from mixed forests will become more useful to manage these forests.

This Special Issue aims to compile original research articles focusing on the state-of-the-art studies on forest growth and site productivity responses to multiple interacting factors. Researchers may apply various modeling techniques, ranging from parametric to nonparametric techniques and from simpler to complex ones using LiDAR data alone or in combination with ground-based measurements. Critical reviews on the advancement of forest growth and site productivity modeling, as well as those on the validation of conventional growth models against independent data, are suitable for submission. Review articles covering overviews of state-of-the-art growth data acquisition techniques, data processing, forest growth models and their applications are also welcome. Studies based on empirical- and process-based growth modeling using retrospective environmental and dendrometric databases, including data acquired from LiDAR, UAV, dendrochronology, etc., will be considered. This Issue will contribute to the advancement of knowledge on forest growth and yield, helping researchers globally to better understand the patterns of forest growth and site productivity conditions under the influence of various interacting factors. This Special Issue will improve our capacity to understand complex growth and site productivity models, which will provide valuable support in the development of silvicultural strategies and forest management plans under the climate change context. We kindly invite you to read the Special Issue "Advances in Forest Growth and Site Productivity Modeling" at https://www.mdpi.com/journal/forests/special_issues/Forest_Growth

Dr. Ram P. Sharma
Dr. Xiangdong Lei
Guest Editors

Manuscript Submission Information

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• tree growth models
• stand growth models
• dominant height growth models
• growth trends
• climate-sensitive growth models
• site index
• site productivity index
• competition index
• growth series database
• empirical growth models
• processed-based growth models
• LiDAR time-series data
• species mixture effects
• dendrochronology
• modeling forest biomass
• carbon dynamics