Conference Agenda

In the context of climate change UK forests are increasingly susceptible to drought, leading to reduced productivity and increased mortality, thus reducing the carbon sink. 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. Here, Sitka spruce clones are exposed to an experimental drought and monitored over eight weeks. The spruce express stress pigments and lose water content as the drought progresses The stress response differs 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.

I will present some of my work on developing methodologies to detect, quantify and better understand climatic and structural forest stress in the United Kingdom with satellite remote sensing data. In particular, I will describe the utility of the time series analysis of vegetation indexes for stress detection, with a focus on effective models for removing the systematic noise and phenological signals in the data. Additionally, I will describe experiments with regression models for predicting the health of a region of forest in a given year, using climate, geographical and contextual information as predictors.

Quantitative assessment of forest carbon sequestration capacity is of great significance for maintaining sustainable forest development, improving resource utilization efficiency, and mitigating environmental degradation. This research takes Pu'er City, Yunnan Province as the research area to explore the potential of using LiDAR biomass index (LBI) for precise carbon sequestration measurement of Simao pine species. Firstly, airborne laser scanning (ALS) data of the research area in 2018 and 2023 were obtained, and accurate matching of multiple data periods was achieved. Secondly, based on the 2018 ALS data, we selected the measured individual trees of different diameter classes to calibrate the individual tree level biomass model based on the LBI. Thirdly, the individual tree segmentation of ALS data in 2018 and 2023 were completed using the same segmentation algorithm, respectively. The AGB_LBI model was applied to the segmented laser point cloud data of the two periods to realize biomass estimation. To verify the accuracy of the model, we obtained a certain amount of individual trees with precise positions and forest sample plots from the flight area of the two periods airborne LiDAR. The reference biomass was calculated using the existing allometric equation, and then used to evaluate the applicability accuracy of the 2018 biomass estimation model for the sample plots obtained in 2018 and 2023, respectively. The results indicate that high-precision AGB_LBI model (R²=0.83, RMSE=15.68 kg) was constructed using 57 individual trees, and high accuracy was obtained when using the model to calculate the biomass of the same year's sample plots (R²=0.78, RMSE=26.49 t/ha). Meanwhile, when using this model to calculate the biomass of the sample plots obtained in 2023, R² of 0.72 and RMSE of 33.11 t/ha were obtained. Therefore, LBI has great potential for measuring carbon sinks in forest stands or even on a larger scale.

Quantitative ground validation of satellite remote sensing reflectance data is of great research significance to determine whether the data can support quantitative remote sensing applications, especially accurate extraction of forest parameters in complex terrain forest areas. In this paper, Pu'er City, Yunnan Province was selected as the research area to explore the method of using airborne hyperspectral reflectance data based on the Bidirectional Reflectance Distribution Function (BRDF) model to validate satellite reflectance data. Firstly, the airborne hyperspectral image acquired on December 13, 2020 (local imaging time: 13:47) was radiometrically calibrated, atmospherically corrected, and geometrically corrected to obtain the airborne hyperspectral reflectance image. Secondly, the GF-6 satellite image on December 14, 2020 (local imaging time: 12:26) was radiometrically calibrated, atmospherically corrected and geometrically corrected to obtain the surface reflectance image. Thirdly, based on the spectral response function of both images, the narrow band reflectance of the airborne hyperspectral image was converted to the broad band reflectance of the GF-6 satellite image. Finally, the BRDF model was used to model the airborne reflectance image (corresponding to the GF-6 bands), and the surface reflectance at specific time (i.e., specific solar-observation geometry) was generated based on this. This paper compared two validation methods, direct use of airborne data to validate satellite data and reconstruction of airborne data at different times (i.e., local time 10:30, 11:30, 12:26, 13:30, 14:30, 15:30) to validate satellite data. The results showed that the reconstructed airborne reflectance data based on the BRDF model (corresponding to the satellite imaging time of 12:26) were more effective in validating satellite reflectance images in complex terrain forest areas.

Validation of high spatial-temporal resolution gross primary production (GPP) is not only important in monitoring vegetation, but also crucial in calibrating remote sensing models for vegetation productivity. Flux tower measurements provide an effective way to validate GPP derived from satellite observations. Studies have demonstrated that footprint variations can affect the feasibility to match flux tower GPP with satellite GPP in any type of ecosystem due to spatial heterogeneity within the flux tower footprint. Besides, the average daily GPP could not fully reflect the photosynthesis of the satellite overpassing time as GPP varied a lot in a day due to the changes of solar radiation and meteorological condition. But few studies focused on the spatial and temporal scale to validate the high resolution GPP. In this condition, optimizing the strategy to validate high resolution GPP could help to improve their agreement between high resolution satellite GPP and flux tower GPP in photosynthesis estimates.

In this study, based on the flux tower measurement from Puer site (101°5′24″E, 22°24′59″N) in Yunnan Province and Baotianman site (111°56′10″ E, 33°29′59″ N) in Henan Province in China, an optimized method to validate Sentinel-2 GPP was proposed. Firstly, high resolution GPP was estimated by using a Light Use Efficiency (LUE) model and Sentinel-2 images in 2019 and 2020. Then the Footprint Source Area Model (FSAM) was adopted to obtain the real time and near real time (1 min, 5min, 10min, 30 min, 1 h, 2 h, 3h, 4 h) footprint when the Sentinel-2 overpassing time. And the weighted GPP in the footprint was used to validate the Sentinel-2 GPP. Results of this study demonstrated that better linear relationships could be achieved between the satellite derived GPP and ground observed GPP when taking into account the footprint of flux data. And better correlation could be observed between Sentinel-2 GPP and flux tower GPP derived in 30min~2h of the satellite overpassing time. Results of this study may provide some new theory for the validation of satellite derived GPP with high resolution.

The forest fuel load influences the spreading rate and fire intensity during a forest fire. However, the mechanism of environmental filtering and dispersal limitation that affects the spatial distribution of the forest fuel load remains unclear. In this study, live (tree, herbaceous, and shrub) and dead fuel loads (litter and humus) were estimated based on the plot investigation results of four typical stands (Pinus massoniana, Platycladus orientalis, Ficus microcarpa, and Cinnamomum camphora) in Chongqing, China. The results demonstrated that the tree, shrub, herbaceous, litter, and humus fuel loads of the four typical stands were 66.92–118.54 Mg/ha, 2.93–4.04 Mg/ha, 0.77–1.01 Mg/ha, 0.90–1.39 Mg/ha, and 1.49-1.98 Mg/ha, respectively. The forest fuel load varied significantly among the different stands. The Mantel test revealed that the forest fuel load had significantly positive correlations with the geospatial distance and stand environment but no significant correlation with the topographic factor. Additionally, the redundancy analysis demonstrated that the stand factors, canopy density and average canopy height, and the topographic factor, altitude, had significant impacts on the forest fuel load. The variance partitioning analysis revealed that the spatial heterogeneity of the forest fuel load was mainly attributed to the co-variation of environmental and spatial factors (29.55%). Moreover, the geospatial distance was a dominant independent factor for the fuel distribution (14.66%), followed by the stand environment (9.51%), and topographic factor (0.35%). In summary, the spatial distribution of the forest fuel load was dependent on niche-based and random processes, and dispersal limitation was the dominant factor.

Land-use change is the second largest source of carbon emissions and directly affects the balance and structural function of carbon storage in terrestrial ecosystems. Analyzing the driving mechanisms of regional land-use change on carbon storage and exploring a sustainable land-use approach is of great practical significance for guiding future urban planning and development. In this study, we used Beijing as an example and based on Landsat MSS, TM/ETM and Landsat 8 land-use/cover data, we applied the PLUS-InVEST model to analyze the correlation between the growth of various land types and multiple driving factors using the random forest classification algorithm. We also established a multi-sensor remote sensing and non-remote sensing data-driven system to analyze the characteristics and driving mechanisms of land-use change in Beijing from 2000 to 2020. Based on this, we predicted the spatial pattern of land-use and the spatiotemporal differences in carbon storage in Beijing in 2030 under natural evolution and ecological protection scenarios, providing theoretical support for optimizing land-use structure and achieving carbon neutrality in Beijing in the future. The results show that annual average temperature is the largest driving factors affecting arable land expansion, respectively. DEM and distance from municipal government are key driving factors for forest expansion, while population density is the main driving factor for construction land expansion. Under the natural evolution scenario, by 2030, forest, grassland, and water area will increase by 161.59 km², 142.23 km², and 100.06 km² respectively, with a carbon storage of 2.03×10⁸ t. Under the ecological protection scenario, forest, grassland, and water area will increase by 168.11 km², 148.85 km², and 56.13 km² respectively, with a carbon storage of 2.10×10⁸ t.