Conference Agenda

The CEFO project is a transnational collaborative effort involving four teams from UK and China, aiming to advance forestry research through remote sensing technology. This work emphasizes the development and application of advanced remote sensing techniques, including time-series data processing, satellite LiDAR, and radiative transfer modelling. These methods are intended for forest parameter extraction, change detection, yield assessment, and carbon sequestration measurement. The UK-China collaboration integrates diverse data sources, including LiDAR and satellite imagery, to enhance the understanding and management of forests, contributing to both research and practical forestry applications. The main outputs include:

1. System integration, multi-source LiDAR data acquisition and forest inventory

We have designed and integrated a novel airborne system, which integrates commercial waveform LiDAR, thermal, CCD camera and hyperspectral sensors into a common platform system (CAF-LiTCHy). Based on this system, the airborne data of Pu’er research area was collected. And then, the data obtained from each sensor were processed and provided a foundation for further data analysis. Furthermore, we have completed the forest inventory using a combination of point clouds generated by airborne LiDAR, drone overflight and mobile Laser scanning surveys. This method combines growth models with LiDAR point clouds analysis and the Sub-Compartment Database of the public state and the National Forest Inventory maps for the private forests. Overflights with drones and mobile Laser scanning have been used in plots across the country to validate the estimates with R² ranging between 0.7-0.9 for broadleaves and above 0.95 in conifers. Time-series of LiDAR surveys and drone data have also been used to validate growth in time as estimated by the yield models. Some plots have been covered with GeoSLAM and the point clouds have been analysed to produce estimates of DBH and stem profiles.

1. Forest Cover Mapping using Sentinel-2 and GF-6 Data, Pu’er Study area, China

The research area focuses on the surroundings of Pu'er City in Yunnan Province, using Sentinel-2 images analysed through the Google Earth Engine (GEE) platform to extract the spectral features, texture features, and terrain features, combined with field survey data, airborne remote sensing data, and terrain data. A classification data set containing the optimal features was obtained by feature screening. The object-oriented and pixel-based classification methods were used to respectively carry out random forest classification. The results show that the classification accuracy of the object-oriented classification method is higher than that of the pixel-based classification method, with an overall classification accuracy of 88.21% and the Kappa coefficient of 0.865. This was followed by a comparison and verification of classification results. The classification results are compared at pixel level with other published land cover products (including the Dynamic World, ESA, ESRI) to analyze their area consistency and spatial consistency. Accuracy evaluation of all products was carried out combining Pu'er airborne hyperspectral data and LULC ground truth data. Finally, the product inconsistency factors were analysed to improve the quality of classified products.

1. Assessing Forest Disturbance and Stress Using Satellite Remote Sensing Techniques

Amid climate change, both China and UK forests face increasing vulnerability to drought, impacting their role as carbon sinks. In previous studies, Sitka spruce clones showed stress-related changes during controlled drought, suggesting potential for identifying drought resistance via remote sensing. This research informs breeding strategies and forest health monitoring. Recent efforts concentrate on upscaling methods to identify water stress using innovative satellite techniques. The Dragon 5 European young scientist expands on this by investigating pathogen-impacted trees' spectral response in a controlled experiment. This study presents an approach to understand forest stress in the UK due to climate and structural changes using satellite imagery time-series. By analyzing vegetation index time-series and removing noise, models depict vegetation cycles at a per-pixel level, identifying deviations linked to climate and structural changes, which serve as indicators of potential stress factors or pathogen presence. In China, long-term Landsat 8 images from 2015 to 2020 were used to detect forest changes in the Pu'er region with over 88% accuracy, attributing forest cover loss mainly to urbanization and plantation activities. Proposed methods evaluate forest stress by quantifying climatic and structural variations, aiding in understanding species responses to drought. Sitka spruce clones exhibited stress-related changes under controlled drought conditions, guiding breeding strategies and forest health monitoring.

1. Aboveground biomass estimation using multi-source LiDAR

The LiDAR biomass index (LBI) was applied to pinus khasys species in Pu’er city. Terrestrial laser scanning data and airborne laser scanning data was collected on the field sample plots and used for accurate estimation of forest aboveground biomass from individual tree level to stand level. For the model established from the TLS data, R² of 0.61 and RMSE of 27.04 kg was obtained. Through suitability compensating the laser point clouds of each tree detected from airborne LiDAR, and use a small number of trees measured in the field for model calibration, robust and highly accurate results were obtained (R² = 0.83 and RMSE = 15.68 kg). To preliminarily evaluate the AGB estimation ability of waveform LiDAR data, we conducted data collection of Pu'er ALS data and screening, preprocessing, and parameter extraction of TECIS waveform data. The preliminary research results showed that when the SNR was greater than 15, in the consistency results between ALS and TECIS data for two-track data, the R² was greater than 0.6 and the RMSE was lower than 3.7 m.

As the largest terrestrial ecosystem in China, as well as the sources of many major rivers and key areas of water and soil conservation, grassland plays an irreplaceable role in ensuring national scale ecological security and promoting ecological civilization construction. However, grassland ecosystem in China has been greatly degrading caused by climate change, overgrazing and other human activities. Therefore, monitoring and assessment of grassland degradation have become an extremely urgent work. In Dragon 5 project 59313, we did some scientific studies based on the geomatics methods on remotely sensed data from both European and Chinese side and other geospatial databases. Joint research results have been achieved in the following five aspects:

(1) Types of grassland identification: Integrating the advantages of Sentinel-1 and Sentinel-2 active-passive synergistic observation, takes the typical grassland of Zhenglan Banner in Inner Mongolia grassland, China as the study area, and innovates the method of grassland types classification by applying the object-oriented techniques, which improves the accuracy and refinement of grassland type classification.

(2) High temporal and spatial estimation of grass yield: Based on the Carnegie–Ames–Stanford approach (CASA) model, integrating the advantages of the high spatial resolution of GaoFen-6 wide-field-of-view data and the high temporal resolution of MODIS NDVI data, we propose a reasonable expression method for the optimal temperature of the model. The applicability of the NPP conversion method to estimation of grass yield in different grassland types is then analyzed in Zhanglan Banner.

(3) Identification of shrub-encroached grassland: In order to explore the application potential of remote sensing technology in the recognition of spatial distribution of shrub-encroached grassland, combing the domestic multi-source remote sensing data GF-2, GF-3 and GF-6 to study the remote sensing technology in identification of shrub-encroached grassland at different scales from the perspective of classification identification and quantitative extraction respectively by using random forest algorithm and scrub cover estimation model.

(4) Global grassland degradation detection and assessment: We quantitatively explored global grassland degradation trends from 2000 to 2020 by coupling vegetation growth and its response to climate change. Furthermore, the driving factors behind these trends were analyzed, especially in hotspots.

(5) Estimation of Grassland Utilization Intensity based on the Generated High-Resolution Daily Land Surface Reflectance Dataset: Based on a single remote sensing data source, there are limitations in obtaining time-continuous data, which affects the accurate monitoring of grassland distribution, utilization patterns, and intensity. The study proposed a method to construct a daily scale dataset based on HLS data and GF-6 WFV data and constructed a grassland utilization intensity index based on time series data to estimate the utilization intensity.