#AITradingAffectsForex Automated forest inventory for forest multi-criteria decision analysis (MCDA) integrates advanced technologies, such as remote sensing, machine learning, and automated data collection systems, to support decision-making in forest management. The goal is to evaluate and prioritize multiple forest management objectives, including biodiversity conservation, timber production, carbon sequestration, and recreational value, among others. Key components of the process include: 1. Data Collection: Remote sensing technologies (e.g., satellite imagery, LiDAR) and automated systems (e.g., drones, sensors) are used to gather detailed forest data, such as tree species, density, biomass, and canopy cover. 2. Data Processing: The collected data is processed using automated algorithms, including machine learning models, to extract relevant information on forest structure, composition, and environmental variables. 3. Criteria Definition: MCDA frameworks are developed to define multiple criteria or objectives (e.g., biodiversity, carbon storage, wood production) that must be considered in forest management decisions. 4. Evaluation: AI and statistical models analyze how different forest management actions will affect each criterion. These models assess trade-offs between conflicting objectives, such as maximizing timber yield while minimizing biodiversity loss