#AITradingAffectsForex Automated forest inventory for forest land-use change detection refers to the use of advanced technologies, such as remote sensing, artificial intelligence (AI), and machine learning, to monitor and assess changes in forest landscapes over time. This process involves the systematic collection and analysis of data related to forest cover, species composition, biomass, and other ecological variables. The key components of automated forest inventory for land-use change detection are: 1. Remote Sensing Data: Satellite imagery, LiDAR (Light Detection and Ranging), and drone-based sensors are used to capture high-resolution spatial data about forest conditions. 2. Change Detection Algorithms: Machine learning and AI models analyze multi-temporal remote sensing data to detect and classify land-use changes, such as deforestation, afforestation, or forest degradation. 3. Data Processing and Analysis: The collected data is processed to identify trends in forest cover changes, which can inform management decisions, policy-making, and conservation efforts. 4. Automation: The integration of AI and machine learning enables the automation of data collection, processing, and analysis, reducing the need for manual intervention and enhancing the efficiency and scalability of monitoring efforts. The benefits of this approach include improved accuracy, faster detection of changes, and the ability to monitor large, remote areas more efficiently than traditional methods. It supports better forest management practices and aids in tracking the impacts of land-use changes on biodiversity and climate.