Conference Agenda

The 3nd Year Progress of CEFO project are:

1、 System integration, multi-source LiDAR data acquisition and application

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.

2、The joint use of Chinese and European satellites and data process of Chinese Terrestrial Ecosystem Carbon Monitoring Satellite

We developed a cloud free remote sensing image composition algorithm that accounts for forest phenology, along with a technology for aggregating multiple land cover products. The resulting process allowed for high-precision mapping of forest cover remote sensing in the Pu'er area, resulting in the development of 30 m resolution 2000/2010/2020 Pu'er forest cover products. Based on the cloud free images, the vegetation coverage of Pu'er City was estimated and the forest cover mapping was conducted using Sentinel-2 and GF-6 Data, field survey data, airborne data and terrain auxiliary data. To achieve measurement of forest height and terrain, the potential of GF-7 LiDAR and stereo image was evaluated. The validation test was conducted in Pu'er City, and encouraging results were obtained. To preliminarily evaluate the parameter estimation ability of waveform LiDAR data for complex forest conditions, 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.

3、 Forest disturbance, stress, diseases, drought and flux monitoring

Our study employed the long-term time series Landsat 8 images spanning the period of 2015 to 2020, and utilized the continuous change detection and classification algorithm to detect forest changes in the Pu'er region. The accuracy of the algorithm was evaluated by means of visual interpretation of high-resolution images and forest inventory data, yielding an overall accuracy of over 88%. Results show that the loss of forest cover is primarily caused by urbanization, cash crop plantations, and regular harvesting of fast-growing plantations. Furthermore, we have proposed methods for assessing forest stress with satellite remote sensing. This work shows a methodology to detect, quantify and better understand forest stress produced by climatic and structural variations using time-series of satellite imagery in the United Kingdom. Time-series analysis of vegetation indexes are detrended to eliminate systematic noise and historical trends, to elaborate models of phenological cycles of the vegetation at pixel level. These data-based models are used to detect differences in new image acquisitions that are compared to climatic and structural variations. Once climatic effects like drought or temperature variations are integrated, the anomalies are used as a proxy for pathogen activity in a forest area. Understanding how species and genotypes respond to drought can inform the transition to more drought tolerant forests in the future. Remote sensing provides tools to non-destructively monitor plant health at multiple spatial scales. Therefore, Sitka spruce clones were exposed to an experimental drought and monitored over eight weeks. The spruce expressed stress pigments and lost water content as the drought progressed. The stress response differed between clones suggesting intraspecific drought tolerance detectable by remote sensing. This work can inform future spruce breeding programmes and contribute to national forest health monitoring. Based on observation data of Puwen Forest flux Tower, the daily net ecosystem carbon exchange (NEE), evapotranspiration (ET) and canopy greenness index (GI) were calculated. We found that the tropical evergreen broad-leaved forest was a carbon sink in February, March and April. GI、photosynthetically active radiation and air temperature in April was the highest in three months, but carbon sink became weaken compared with that in March. Maybe drought in April reduced gross primary productivity more than ecosystem respiration.

4、 Forest gap identification and aboveground biomass calculation based on 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 that established using the TLS data, R² of 0.61 and RMSE of 27.04 kg was obtained. For the model of ALS data, R² of 0.83 and RMSE of 15.68 kg was obtained. In addition, the CHM was derived from the point cloud data of UAV LiDAR and the fixed threshold method was used to identify forest gaps in CHM. The reference data from visual interpretation of images was used for accuracy assessment of forest gap identification. The overall accuracy of the fixed threshold method was 92%, and the spatial distribution of the gap was aggregation. Forest gap information from UAV LiDAR can be used for the accuracy assessment and validation for the forest gap derived from GF-7 satellite imagery for large area.

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. In the past 3 years of Dragon 5, joint research results have been achieved in the following four 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.