Research shows machine learning enhances forest inventory data in mesic forests, suggesting new insights into canopy reflectance.
The use of satellite images and machine learning in addition to in-situ data in national forest inventories enables covering large areas and significantly reduces costs. However, such combined inventories provide modelled stand properties, the relationships between which are not well understood. An approach to investigating linear and non-linear relationships between forest inventory parameters is proposed. It is applied to a study of the Multi-Source National Forest Inventory (MS-NFI) stand properties for the case of mesic forests. The relationships between MS-NFI parameters and stand reflectance in the visible, Red Edge, near infrared, and short-wave infrared spectral regions were investigated for the Sentinel-2 satellite sensor. Linear models of canopy reflectance as a function of forest stand and elevation properties were developed. These models allowed to assess the comparative influence of MS-NFI parameters on stand reflectance as well as the monthly dynamics of this influence during the season (May-August 2019). Linear relationships between forest inventory parameters were investigated using a correlation matrix. Generalised additive models were used to investigate non-linear pairwise relationships between forest inventory parameters. The proposed approach can be applied to assess the impact of stand features obtained from conventional ground-based forest inventory on forest canopy reflectance.
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Kavats et al. (2025) studied this question.
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