The objective of this research is to test and evaluate hyperspectral and lidar data to derive information on tree species dominance and above ground biomass in the Białowieża Forest in Poland. This forest is threatened by climate change, fire, bark beetles attacks, and logging, with changes in species composition and dominance. In this conservation valuable area, the monitoring of forest resources is thus critical.

Results indicate that vegetation indices from hyperspectral data can support species dominance detection: using a Classification and Regression Trees algorithm the three main plot types (dominated by Deciduous, Spruce, and Pines species) were classified with an Overall Accuracy > 0.9. The accuracy decreased when a ‘Mixed’ group was added to account for very heterogeneous plots, and plots dominated by Spruce were not correctly detected. Hyperspectral vegetation indices were also used to estimate the level of species dominance in the forest plots, using a Multivariate Multiple Linear Regression model; the obtained accuracy varied according to groups, being higher for Deciduous (R² = 0.87), compared to Pines (R² = 0.61), and to Spruce-dominated plots (R² = 0.37).

Lidar data were employed to estimate above ground biomass, using an exponential regression model; overall the R² resulted equal to 0.66 but ranged from 0.57 to 0.78 when considering subgroups according to species dominance; the addition of hyperspectral vegetation indices improved the result only for Pines.

The illustrated methods provide a reliable description of important forest characteristics and simplify resource monitoring, supporting local authorities to address the challenges imposed by climate change and other forest threats.

这项研究的目的是测试和评估高光谱和激光雷达数据，以获取有关波兰比亚沃维耶阿森林中树种优势度和地上生物量的信息。该森林受到气候变化，火灾，树皮甲虫袭击和伐木的威胁，物种组成和优势地位也发生变化。因此，在这个具有保护价值的地区，森林资源的监测至关重要。

结果表明，来自高光谱数据的植被指数可以支持物种优势检测：使用分类和回归树算法，对三种主要样地类型（以落叶，云杉和松树种为主）进行了分类，总体精度> 0.9。当添加“混合”组以说明非常异构的图时，准确性降低，并且未正确检测到以云杉为主的图。利用多元多元线性回归模型，高光谱植被指数还被用于估算林地中物种的优势水平。所获得的准确度根据组而有所不同 ，与松木（R ²  = 0.61）和云杉为主的样地（R ²）相比，落叶（R ² = 0.87）更高。 = 0.37）。

利用指数回归模型，利用激光雷达数据估算地上生物量；总体而言，根据物种优势考虑亚组时，R ^2的结果等于0.66，但范围为0.57至0.78；高光谱植被指数的添加仅对Pines改善了结果。

所示方法对重要的森林特征提供了可靠的描述，并简化了资源监控，支持地方当局应对气候变化和其他森林威胁带来的挑战。