Conference Agenda

The China-ESA Forest Observation (CEFO-2) project focuses on temperate and subtropical forests in China, and forest of temperate forests in United Kingdom. Recognizing the critical role of forests in carbon sequestration and the current limitations in quantifying carbon stocks and predicting trends, the project aims to conduct national-scale dynamic forest resource surveys by integrating ground-based and satellite remote sensing technologies. Over the 1^st year, CEFO-2 has achieved following results: (1) integration of multi-platform LiDAR and high resolution optical imageries for forest parameter extraction, and (2) forest dynamics classification and prediction.

In terms of multi-source data integration for forest parameter extraction, CEFO-2 utilized multi-platform LiDAR (terrestrial, airborne, spaceborne) and optical imagery to estimate key forest parameters—height, carbon stocks, volume, and DBH (diameter at breast height). Motivated by the limitations of legacy inventory systems such as the Sub-Compartment Database, the initiative addressed emerging forestry challenges including climate resilience, species diversity, and precision silviculture. A canopy detection algorithm was applied to delineate individual trees and generate detailed Treelists including height, crown dimensions, and spatial coordinates. Species-specific allometric models, developed using a combination of the global Tallo database and permanent sample plots in the UK, were used to estimate DBH from LiDAR-derived tree metrics. Results showed an exciting accuracy for coniferous species such as Douglas fir and Scots pine yielded high model accuracy (R² > 0.85), while broadleaved species showed greater structural variability, necessitating more nuanced modelling approaches. Volume estimates were calculated using species-calibrated equations, enabling accurate prediction of merchantable timber volume across the district. Complementary satellite-based species classification using PlanetScope imagery and Random Forest algorithms achieved over 92% accuracy by leveraging seasonal spectral variation. Integration of ALS, TLS, and satellite data facilitated the assignment of species attributes to individual trees, enabling the creation of a spatially coherent forest inventory. For spaceborne LiDAR, CEFO-2 acquired and processed spaceborne waveform LiDAR datasets of the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS) from three representative temperate forest sites in northern China (Genhe site, Mengjiagang site, and Saihanba site) and across the UK. Using TECIS data, we extracted critical forest structural parameters such as vegetation height and carbon stocks.

In terms of forest dynamics classification and prediction, CEFO-2 firstly addressed the shortcomings of existing algorithms in disturbance type identification by employing a novel monitoring method based on Temporal Extended Convolutional Neural Network (TempCNN). This model employs 1D CNN to thoroughly analyze temporal dynamic features and spectral information from time-series remote sensing data. The classification result of Mengjiagang site indicated that the TempCNN can precisely classify the anthropogenic disturbance types including thinning, clear-cutting, canopy opening, selective canopy thinning, growth-enhancing silviculture, ecological thinning, forest closure and natural regeneration with the overall classification accuracy of 75%. The TempCNN is expected to provide a reliable tool for forest degradation attribution analysis. Secondly, this project focused on the improvement of forest management in the context of natural regrowth and managed plantation contexts. Work explored the integration of Digital Twin (DT) frameworks with artificial intelligence for improved forest management, particularly in natural regrowth and managed plantation contexts. A modular DT approach using remote sensing data, neural networks, and reinforcement learning was used to optimise forest operations such as thinning. A key contribution is the use of a custom reinforcement learning agent to simulate decision-making in silvicultural scenarios, demonstrating improved performance over traditional benchmarks. Complementing this, Physics-Informed Neural Networks (PINNs), incorporated biological growth models, specifically the Chapman-Richards equation, into the learning process to predict forest growth using sparse and noisy LiDAR data. This biologically grounded architecture significantly outperformed conventional data-driven models in robustness and generalisation, showing strong potential for use in DT systems. Together, these projects illustrated the viability of combining domain-specific ecological knowledge with machine learning to enhance forest simulation and prediction. Both highlight that embedding biological or procedural understanding into AI systems could improve interpretability and reliability. This interdisciplinary approach supported the UK's movement toward precision forestry practices, offering scalable digital tools for planning, carbon accounting, and climate resilience.

This year’s progress demonstrated that multi-source remote sensing integration enables spatially explicit forest inventory as Tree Lists and dynamic management strategies. Future efforts will strengthen the China-Europe scientific collaboration network, developed methods for monitor changes, improving species classification and the upscaling to estimates at a national level by engaging both Chinese and British datasets. The project will to facilitate policy applications of research outcomes, while cultivating interdisciplinary young researchers through project participation.