Conference Agenda

This study investigated the use of C- and X-band SAR data for estimation of forest changes (height, biomass and biomass change) in a boreal forest in Sweden. Field plot data (10 m plots) from 2016 and 2021, and lidar data were used as references. Plots with substantial decreases of biomass (due to clear-cuts and thinning) could be detected using Radarsat-2 normalized backscatter images (C-band) while the use of interferometry (InSAR) of TanDEM-X images allowed both accurate biomass and biomass change estimation, and mapping of smaller forest height changes (increase). We conclude that both C- and X-band SAR (Radarsat-2 and TanDEM-X) are useful for estimation of forest decline, while the use of TanDEM-X InSAR provides added value in terms of height information and therefore more accurate estimates of biomass and biomass change.

With the aggravation of global warming, the outbreaks of forest pests happen more frequently and damage huge amounts of forests. Detecting and removing trunk-boring infestations from the forest at an early stage (green attack) is important to avoid spreading. By using remote sensing techniques, forest mortality can be efficiently detected and mapped; however, achieving early detection of the infestation is still challenging because the spectral changes are subtle.

This study assessed the detectability of the green attacks by the European spruce bark beetle (Ips typographus, L.) using multispectral drone images. The scientific questions and objectives are (1) testing whether the infestations showed detectable vulnerability before attacks, (2) quantifying the detectability of the attacked trees with different duration of infestations, (3) testing the detection performance using a single vegetation index (VI) in comparison with machine learning models with multiple variables, and testing their performance when applied on untrained areas, and (4) testing the performance of the MR_DSWI2 index we proposed in a previous study.

The study used multispectral drone images covering 24 plots from 6 forest stands in southern Sweden, acquired in May (before attacks), June (green attack), August (green and yellow attack), and October 2021 (red attack). Weekly field inventory was conducted on 997 spruce trees and the starting weeks of the infestations were recorded for 208 attacked trees. Drone images of individual-tree crowns were segmented using marker-controlled watershed segmentation, and 10 VIs [5] were calculated for every single tree. Trees with the same duration of infestation were grouped for the analysis. Random Forest Classification (RF) and linear discriminant analysis (LDA) models were built using (1) all bands, (2) all VIs, and (3) the four bands used for MR_DSWI2. Three LDA models were also built using MR_DSWI2, NDRE2, and NGRDI indices, respectively. To test the potential overfitting and test the applicability of the models on mapping untrained areas, two ways of separating training and testing data were used and compared. Method A trained the model using trees from 5 stands and tested on the trees from the remaining stand. This design tested the performance of the models in untrained areas, and overfitted models would yield low accuracy. Method B did not separate trees from different stands, but randomly assigned 90% of all trees for training and the other 10% for testing. Method B is commonly used for separating training and testing dataset, but it could not show the potential performance when applied to untrained areas. When comparing the results from Method A and B, a potential overfitting could be exhibited. The classification accuracy was presented using the Kappa Coefficient.

The results and conclusions are: (1) When using models with more dimensions and higher complexity (i.e., the RF models), the detection had high accuracy in the trained areas but low accuracy in untrained areas. Overfitting was more prominent with those models, and Method B, i.e., testing models with data from the same areas as the training data, could not indicate the applicability outside the study area. Thus, testing the model performance using Method A was proposed and results were further discussed. (2) When testing on untrained areas (Method A, Figure 1), no model successfully classified healthy and attacked trees before the attacks. We conclude that the attacked trees showed no vulnerability before the attacks. (3) When testing on untrained areas (Method A, Figure 1), no model successfully separated healthy and attacked trees when infested for less than 5 weeks. The green attacks during 1-5 weeks of infestation were almost undetectable. During 10–13 weeks of infestation, the detectability increased, with 0.67–0.75 of the median kappa coefficients using the best classification model (LDA by MR_DWSI2, Figure 1). Trees infested for 19–22 weeks (red attack) showed high detectability. (4) Among all classification models, using LDA on the MR_DWSI2 index achieved the highest and most stable accuracy at various infestation stages, followed by LDA using the four bands and LDA on NGRDI.

【Objective】Using the five bands (P/L/S/C/X) of quad-pol SAR data acquired by the High-resolution Airborne System, this study analyzed the response law and sensitivity of different band signals to forest stem volume. Moreover, the capability of estimating stem volume based on both single-frequency and multi-frequency PolSAR data was evaluated. 【Method】Combined with field measurements and airborne LiDAR data, forest stem volume was scaled-up to the entire study area, and a total of 196 samples were obtained through stratified sampling. Based on the samples, the forest stem volume estimation capability of multi-band polarimetric SAR was evaluated. The Water Cloud Model was utilized to analyze the response law of SAR backscattered intensity to stem volume across different bands, and the dynamic range and saturation point were quantified. Furthermore, multiple polarimetric decomposition components were extracted and their sensitivity to stem volume was analyzed using correlation coefficients. On this basis, random forest and support vector regression algorithms were used to perform feature selection and regression modeling. 【Result】The Water Cloud Model analysis revealed that the dynamic range of the longer wavelength (P/L) is higher compared to the shorter wavelength (S/C/X). In particular, the saturation point for the P band exceeds 160 m³/ha, whereas it does not surpass 100 m³/ha for the other bands.The correlation analysis results indicated that the correlation between the P band, L/S bands, and C/X bands and stem volume decreases in order, with values above 0.6, between 0.3-0.4, and below 0.3, respectively. When estimating stem volume using a single band, the accuracy of the P band was 73.79%, while other bands did not exceed 60%. When using multi-band joint estimation, the combination of L/S and P bands improved the estimation accuracy by approximately 2% compared to using the P band alone. The contribution of adding the C/X band to the accuracy improvement was minimal. The best estimation result was obtained by combining all the bands, achieving an accuracy of 77.25%. 【Conclusion】Taking into account factors such as signal dynamic range, saturation point and correlation, the P band is the most sensitive to forest stem volume, which is significantly better than the other bands. The L/S bands are second in sensitivity, while the C/X bands are the least sensitive. When estimating forest stem volume using PolSAR data, the P band should be the first choice. Additionally, when using multi-band joint estimation, the combination of P and L/S bands should be preferred.

Cold-temperate Forest (CTF) is a vital carbon sink and source for the world economy but has been significantly disturbed by intensified human activities and climate change such as deforestation, fires, pests, diseases, etc., resulting in a significant degradation trend, which has an impact on the regional and global carbon budget process and its assessment. However, the pattern of forest disturbance in CTF in northern China is not well known. In this paper, Genhe forest area, a typical CTF region located in Inner Mongolia Autonomous Region, Northeast China (about 2.001×10⁴ km²), was selected as the study area. Based on the Landsat historical archived data on the Google Earth Engine (GEE) platform, we used the Continuous Change Detection and Classification (CCDC) algorithm incorporating spectral indices and seasonal characteristics to detect forest disturbances in nearly 30 years. First, we created six (2 seasons × 3 indices) interannual time-series seasonal vegetation index datasets to map the forest coverage using the between-class variance algorithm (OTSU). Second, we improved the CCDC algorithm incorporating with vegetation index and seasonal characteristics to extract the disturbance information. Finally, we evaluated how the disruption relates to the climate and human activity. The results showed that the disturbance map produced by using summer (June-August) imagery and the EVI vegetation index had the highest overall accuracy of 84.15%. Forests have been disturbed to the extent of 12.65% (2137.31km²) over the last 30 years, and the disturbed area generally showed a trend toward reduction, especially after commercial logging activities were banned in 2015. But there was an unusual increase in the disturbed area in 2002 and 2003 due to large fires. Monitoring of potential widespread forest disturbance due to extreme drought and fire events in the context of climate change should be strengthened in the future, and preventive and salvage measures should be taken in a timely manner. Our results demonstrates that CTF disturbance can be robustly mapped using CCDC algorithm based on Landsat time-series seasonal imagery in areas with complex meteorological conditions and spatial heterogeneity, which is essential for understanding forest change processes.

The multi-temporal polarimetric SAR data provides the difference of scattering characteristics in time dimension for scene observation, hence it could reflect the time-variant characteristics of the same scene. Based on this advantage, classification is one of the important applications of multi-temporal polarimetric SAR data. However, the features of time and polarization dimension used for classification basically are from the data at each certain time, which lack the interpretation of the scattering variant characteristics between multi-temporal data. To solve the problem, based on the specific data representation models for multi temporal polarimetric SAR data, this paper extracts new time-variant scattering features, including the change type as well as the change direction of scattering, which the previous static temporal/polarimetric features cannot provide.

Time series Radarsat-2 data is used for experiments. It includes 8 Fully PolSAR images from April 14,2009 to September 29,2009. The data interval of each scene is 24 days. The image size is 5300*3100 pixels. It contains 22 categories. Based on the difference model T_m=T_i-r_mT_j（T_i,T_j are polarization coherence matrices of two times polarimetric SAR data, and r_m is the smallest eigenvalue of T_i^-1T_j, it can make sure that T_m is always a positive semidefinite Hermitian matrix）, we extract a series of time-variant scattering features, using the components of H/A/α decomposition and Freeman decomposition to classify. Through analysis the classification results of transformer classifier, compared with traditional static features, using the proposed time-variant scattering features to classify can effectively improve the classification accuracy.

As a kind of active microwave remote sensing, SAR (Synthetic Aperture Radar) is very suitable for the resolution of different features with its all-weather and all-day characteristics. In order to improve the resolution of different features and make in-depth analysis and research on different feature types, it is necessary to use the multi-band and multi-polarization information of SAR. The transmission characteristics and the backscattering characteristics of target echoes are different for SAR of different bands, and the fusion of SAR images of different bands can better integrate the information of SAR images of different bands.

In this paper, the experimental data were selected from the airborne data acquired by two Xinzhou 60 remote sensing aircraft modified by the Air and Space Academy of Chinese Academy of Sciences under the support of the Chinese Aeronautic Remote Sensing System (CARSS) construction project. Fully PolSAR data including C and S bands contains five types such as paddy fields, forested lands, dry lands, artificial buildings and water. And it is classified using scattering features such as H/A/α and Freeman decomposition components. In order to make full use of the advantages of multi-band, a multi-band fully PolSAR image fusion method based on wavelet transform is also proposed in the paper, which takes advantage of the wavelet transform for multi-resolution fusion and combines the variation of scattering features of different feature types by different bands, and is applied to airborne C-band and S-band SAR images to realize the classification of different types of features in the same area, which can effectively improve the classification effect and increase the classification accuracy.

Polarized synthetic aperture radar (PolSAR) can obtain rich feature information by receiving electromagnetic waves from different features at different polarization combinations, and is widely used in fields such as feature classification. The coherence of multi-temporal polarization SAR data is a useful supplement to polarization SAR data, which contains information that is not available in single phase polarization data. This paper aims to introduce temporal coherence analysis into dual polarization data information and coherently process SAR images acquired at different times in the same region, so as to effectively combine the information of both time dimension and polarization dimension and improve the accuracy of crop classification of Sentinel data. In order to fully extract the changes of feature in the time dimension, this paper obtain the distinction and connection of each category in temporal features by using the multi-temporal feature information of SBAS. Based on multi-temporal polarization SAR coherence optimization, Sentinel-1 data is used for experimental analysis and verification. Data were collected from the time series dual-polarization data of Yucatan Lake area from April to September 2019. The effects of different polarization states on the characteristics of crop species is explored and the classification effects of the optimized values of coherence features is analyzed.

With the BIOMASS mission, developed by the European Space Agency, P-band polarimetric interferometric synthetic aperture radar (PolInSAR) is expected to provide a fresh perspective on the estimation of above-ground biomass in global forests. However, in sparse boreal forest areas, the strong penetration of P-band interferometric SAR (InSAR) can lead to a loss of forest canopy information, resulting in a biased estimation of forest AGB. In contrast, X-band InSAR signals are sensitive to forest canopy information. By combining the two type approaches, forest parameters such as height, density, and AGB can be effectively extracted. Furthermore, there are a number of X-band InSAR satellite systems which is expected to be launched or already in operation, such as Tandem-X. The data from those systems has great potential to collaborate with BIOMASS data to estimate forest AGB. In summary, a novel forest AGB estimation algorithm based on dual-frequency InSAR data has been proposed for accurate estimation of above-ground biomass in boreal forests. This approach utilizes the difference in penetration ability of P-band and X-band InSAR in forest areas to extract forest height without bias. Based on this, a multi-dimensional feature set has been constructed, including direct information such as forest height and density, as well as indirect information such as backscatter intensity, polarization features, and image texture, to achieve the estimation of forest AGB. To verify the method, experiments were conducted based on airborne P-band PolInSAR data and spaceborne X-band InSAR data.

Synthetic aperture radar tomography (TomoSAR) is a three-dimensional imaging technique developed on the basis of multi-baseline SAR interferometry (MB-InSAR). In forest scenarios, TomoSAR can achieve vertical characterization of forest scatterers, which is an effective method for extracting structural parameters such as forest height and above-ground biomass (AGB). However, due to factors such as baseline errors and changing atmospheric conditions, phase errors between MB-InSAR data cause severe sidelobe effects or even complete defocusing in tomography imaging.

In the study, we compared the calibration methods based on polynomial fitting (PF) and entropy minimization (EM), as well as proposed a novel approach combine the two methods. All three methods are tested based on airborne P-band MB-InSAR data, and the accuracy of the forest height extracted based on proposed method is verified based on airborne light detection and ranging (LiDAR) data. he results indicate that the proposed approach outperforms the other two methods in term of tomography imaging and can accurately determine forest height with an accuracy of over 80%.

Grassland is an important component of terrestrial ecosystems, but due to human activities and natural changes, the productivity and ecological service capacity of grassland ecosystems have declined. Ecological environmental problems such as land desertification and grassland degradation have become hot topics of global concern. Therefore, timely and accurate monitoring of changes in grassland type distribution, vegetation utilization, and intensity is of irreplaceable importance for protecting the ecological environment. High- and medium-resolution optical imagery is the most commonly used data source for grassland remote sensing monitoring. However, due to limitations in data acquisition capabilities, it is not possible to obtain time-continuous data using a single data source, which affects the precise monitoring of grassland distribution, utilization, and intensity. With the increasing availability of different high- and medium-resolution remote sensing data, the fusion of multiple data sources to generate high spatiotemporal resolution data for grassland monitoring has been widely applied. However, there is currently limited research on grassland monitoring that considers the fusion of China's high-resolution data with other medium- to high-resolution data without introducing low spatial resolution data information. To address this issue, this study aims to use a combination of China's satellite products and other high- to medium-resolution optical remote sensing data to generate daily high spatiotemporal resolution surface reflectance data for grassland monitoring. This study was conducted in a 50×50 km study area in Hulunbuir grassland. First, based on the coordinated satellite products Landsat, Sentinel-2 (HLS) data, and GF-6 WFV image data, the spectral reflectance conversion equation between the two was constructed using the least squares method to transform the reflectance of GF-6 WFV. Second, based on the acquired real image data set, the local linear interpolation method was used to fill in the missing images to generate a daily data set. The Savitzky-Golay spatial filtering algorithm was used to smooth and denoise the time series data set to construct the daily high spatiotemporal resolution surface reflectance data set (HLSG). Finally, based on the time series vegetation index information, the grassland utilization intensity index was proposed. The study showed that time series reconstruction based on GF-6 WFV and HLS data can solve the problems of data missing, quality, and accuracy, thereby improving the reliability and accuracy of medium spatial resolution time series data. Moreover, the grassland utilization intensity estimation method based on the daily NDVI time series data set can well reflect the differences in grassland utilization intensity and has important significance for monitoring the utilization status of grasslands.

Vegetation is an important material foundation for the ecological function of grassland ecosystem, and the long-term changes in vegetation status can provide important information reference for the study on evolution laws of various ecosystems. Therefore, based on the normalized vegetation index product (NDVI, MOD13A2) of long time series (2000-2020) MODIS data, an improved directional pixel binary model was used to construct the annual vegetation coverage dataset of the Beijing-Tianjin Sandstorm Source Area (BTSSA) from 2001 to 2021. Research methods such as Sen-Mann Kendall time series trend significance test and spatial statistical analysis were used to analyze the spatiotemporal changes in vegetation coverage in the region over the past 20 years. The results showed that in the past 20 years, 50.94% of the study area had significantly decreased vegetation coverage, while less than 2% had significantly increased vegetation coverage. This indicates that due to urban development and climate change, the vegetation situation in the BTSSA has caused some damage to a certain extent. These research results, combined with climate and underlying surface change data, can provide important information support for subsequent research on grassland ecosystem.

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.

Using the soil evapotranspiration model based on the improved Priestley Taylor (PT) method, combined with fAPAR data and surface reflectance (albedo) data from the MODIS, monthly soil evapotranspiration with 5 km spatial resolution in typical agricultural areas in China and Europe from 2017 to 2021 was estimated. Moreover, the analysis of the spatial and temporal variation characteristics of evapotranspiration were carried out. The model result was validated using field data obtained from farming observation stations in North China, and RMSE and R² were calculated. It showed that the soil evapotranspiration estimation model achieved good results in the farmland area, where the RMSE was around 1 mm and R² was around 0.8, indicating that the model can well simulate the spatial and temporal variation of soil evapotranspiration in the farmland and is suitable for further analysis of key evapotranspiration variables on spatial and temporal bases covering typical farming areas.

The easy access to the high-resolution satellite data at 10-to-30-meter resolution makes the agricultural remote sensing technology develop even faster. Under the support of the Dragon program, the sentinel series satellite in Europe and GF series satellite in China are providing the data options for agricultural monitoring as well as enhancing the capability of agricultural monitoring in general. Because of the diversified cultivation patterns in China, there are existing the big fields with one crop type and the small fields with the mosaic of various crop types. This fact is limited the application of satellite data in agricultural monitoring in China, therefore, the users have to make a compromise between the spatial resolution and the size of study area. In general, it had better use even higher resolution satellite image for crop monitoring in order to adapt to the crop cultivation situation in China. This project has made the great progress since the inception of this project. Two types of study areas were selected. The first one is with big fields and good at the development of modern agriculture that is comparable with the European agricultural farms. Another one is the typic northern Chine fields with the conventional agricultural development that is challenging for the agricultural monitoring with remote sensing data. The crop types in the study areas are winter wheat, crop, rice, and vegetable, representing the irrigation agriculture and rain fed agriculture in northern China. The project used the Chinese and European satellite data and the third partner satellite data to retrieve the crop growth and crop management information at field level in order to provide timely information to improve agricultural management. Through this joint project and the heavy involvement of young scientists from Europe and China, the satellite data finely processing and information retrieval algorithm is being exchanged and it is expected to bring a step forwards to support agricultural monitoring at fine scale.

Agriculture is the largest water user worldwide and irrigation water management is facing important challenges in sustainable development of food production and water use. Improving irrigation water efficiency is a must in our changing world and requires extensive, comprehensive and accurate tools (physically based). Satellite data, as largely recognized, may play an important role in supporting data for agricultural models, especially to determine crop water needs or phenological crop status. While using satellite data to support agriculture may seem intuitive and straightforward, there is a strong need for accuracy in retrieving agricultural model parameters and state variables especially when the object is high resolution for precise agriculture, a key approach to food production and irrigation water management. In this respect the present DRAGON 5 project, thanks to ESA and the Ministry of Science and Technology (MOST), focuses on the exploitation of visible, thermal and microwave satellite data for operative agriculture.

The Chinese and Italian research groups since many years use satellite data for soil moisture assessment and precise agriculture modelling on several test sites in China and Italy, as well as in other places of the world, characterized by different crop cover and heterogeneity, different climates, irrigation practices. Indeed, satellite data together with field data and soil water balance models contribute to the accuracy needed in precision agriculture. In the past two years, the project work examined data from case studies in China, Italy, Africa and global scale.

In China, over agricultural fields in Shiyang River Basin (northwestern China) the present work supports the development of tools for crop type characterization, evapotranspiration estimation and irrigation water need: 1) Early-Season Crop Identification Using a Deep Learning Algorithm and Time-Series Sentinel-2 (S2) Data in Shiyang River Basin in China Timely and accurate crop identification and mapping are of great significance for crop yield estimation, disaster warning, and food security. Early-season crop identification places higher demands on the quality and mining of time-series information than post-season mapping. 2) A data-driven high spatial resolution model to estimate biomass accumulation and crop yield using S2 and other satellite data was developed and applied in the Shiyang River Basin in northwestern China. For highly heterogeneous desert-oasis agroecosystem characterized by dominant crops, i.e., spring wheat, maize, sunflower, and melon, the developed model relies on three major innovations: i) the identification of start/end of the growing season of crops is done using NDVI from the S2 MSI (Multi-Spectral Instrument) in combination with limited local phenological information; ii) ETMonitor ET at 1km resolution was downscaled to 10m resolution to monitor crop water stress indicator in the biomass/yield model; iii) the air temperature stress indicator in the biomass/yield model was mapped after characterizing the thermal contrast and heterogeneity of the desert-oasis system.

Taking the Sahel as an example, we investigated the impacts of land use/land cover change (LULC) and climate variability on the water balance components in 1990-2020 in three typical basins in the Sahel (Senegal, Niger rivers and Lake Chad) by using satellite-observation-based evapotranspiration derived from our model ETMonitor and ESA CCI soil moisture. The outcomes give useful hydrological insights into water and land management, emphasizing the crucial role of water recycling. This study has been published in Journal of Hydrology: Regional Studies and will be presented as a poster by a young scientist at the Dragon 5 symposium.

Soil moisture (SM) derived from microwave remote sensing is very useful, although the spatial resolution is not favorable for agricultural water use monitoring in farmland scale. The topography influences the emitted brightness temperature observed by a satellite microwave radiometer, leading to uncertainties in SM retrieval. A new methodology using the first brightness Stokes parameter observed by the Soil Moisture and Ocean Salinity (SMOS) was proposed to improve SM retrieval under complex topographic conditions. This work has been published in IEEE JSTARS and will be presented as a poster by a young scientist at the Dragon 5 symposium.

In Italy irrigated fields within the domain of irrigation consortia have been used as test area for SM and irrigation water demand estimates using satellite data and pixel-wise water-energy balance model (FEST-EWB) for different soil types and land cover heterogeneity.

Satellite data were used by FEST-EWB model: 1) for control model state variable (LST) and relative SM over large areas pixel-wise computed by the FEST-EWB model, solving the energy and water balances (Corbari-Mancini, 2014); ii) for definition of input parameter maps (e.g., leaf area Index, vegetational fraction cover).

The first approach analyses different scheme of soil water energy balance equations in consideration of remote sensing data crop or arboreal land cover heterogeneity comparing simulated energy, mass fluxes and relative surface temperature with fluxes observed at ground station and surface temperature from satellite. Using this approach a crop trees total evapotranspiration modelled with the water-energy balance scheme FEST-EWB seems to be slightly affected by the spatial resolution. For this reason, in the crop trees field the two-source modelling approach of the water and energy FEST-EWB seems to better explain the evapotranspiration from the vegetated pixel and soil components. Indeed, in the specific case study where LST are not different between trees and grass covering the interrow, similar values of latent heat are computed using both two-source and one-source energy water balance models.

Pixelwise land surface temperature computed by the hydrologic model have been compared with Satellite LST (Sentinel 3, Landsat 7, 8) showing the possibility to quantitative control pixel wise soil water balance model with the satellite data on large extension.

The second approach uses a coupled vegetation growth model with soil water and energy balance FEST-EWB-SAFY showing consistent estimates of LAI against satellite image information. This is also confirmed by modelled crop yields on the entire irrigation season respect to the observed yields for tomatoes and maize crop.

The project results obtained for the different case studies strengthen the idea that a synergic use of satellite data in water and energy balance models is a robust approach for irrigation engineer controlling crop water use of large irrigation district at high spatial resolution.

Predictions of agroecosystem processes as well as the hydrological and biogeochemical cycles in response to climate change and human interventions are needed both at continental level and at management-relevant scales. Obtaining such information is challenging since a multitude of essential variables need to be monitored to evaluate all relevant processes and to generate an agricultural digital twin. Natural hydrological and biogeochemical processes are additionally altered by anthropogenic drivers. The research community has to face this scientific challenge by a comprehensive consideration of multi-compartment interactions and scale-dependent relationships to enable the prediction of the response of agricultural systems to changing environmental conditions. Especially the current role of agriculture as a carbon source needs to be critically evaluated and strategies developed to transform farming systems into sinks for carbon.

To this end, in the fifth phase of the Dragon Cooperation (Dragon 5), we propose a project (No. 59197) to carry out agroecosystem health diagnosis and investigate agricultural processes based on various in situ and earth observation data, allowing to conserve, protect and improve the efficiency in the use of natural resources to facilitate sustainable agriculture development. At the mid-term of the Dragon 5, this paper summarizes individual steps of our project to gain knowledge about full agroecosystem states and processes by remote sensing, exemplarily for regions in Europe and China, in order to present our understanding of linking agroecosystem monitoring with carbon farming through multi-source remote sensing observations. The current study provides remote sensing approaches to identify crops such as object extraction based on SAR observations and individual plant detection by UAV; to monitor crop biophysical parameters such as leaf area index and biomass; to record hydrological states such as soil moisture, evapotranspiration, drought stress; as well as to finally provide a carbon budget (e.g., soil organic carbon content, gross primary productivity and net primary productivity) for agricultural fields. This can be seen as a workflow scheme of combining essential variables in the agricultural domain to meet the multiple challenges for providing a basis for mitigation measures, if they are at the continental level for policy advisory or at the local level to inform directly involved farmers to support sustainable agriculture development.

The work conducted in these three years of activity aims to make a quantitative use of remote sensing information in agriculture and to develop products targeted at optimizing the production and allowing a more sustainable agriculture (e.g. optimization in the use of fertilizers and pesticides). The project has been concentrated on the following themes: retrieval of biophysical variables of vegetation, estimation of bare soil properties, prediction of crop yield and monitoring pest and diseases. The project included three sites, Maccarese farm in Central Italy, some site in Central East-Africa, a site in the middle East and farms in the Quzhou district in China city of Handan, in the south of Hebei Province.

For vegetation physical and chemical parameters, taking advantage of the PRISMA and ENMAP EO data, the work is aiming to use the potential oh hyperspectral data in deriving biophysical parameter related to equivalent water thickness (EWT). In particular, we aim to retrieve EWT by using a constrain minimization procedure applying the Beer-Lambert law in the 940-1100nm range to derive the optically leaf active water layer in cm. The EWT retrieved with the minimization procedure are compared with the one retrieved by using the hybrid approach (i.e. radiative simulation and Machine Learning Regression). Test site are in central Italy, and in the far East.

For topsoil characterization, the purpose of this activity was to investigate the suitability of PRISMA and Sentinel-2 images for the retrieval of topsoil properties such as Soil Organic Matter, and nutrients like Nitrogen, Phosphorus, Potassium and pH in croplands. Procedure is based on different Machine Learning (ML) algorithms and spectra pre-treatment. Results in the study area located in the north-eastern China near the prefecture-level city of Handan, in the south of Hebei Province, revealed better accuracies in retrieving topsoil properties obtained by PRISMA data instead to the Sentinel-2 data.

For crop yield prediction, we generated a 30-m Chinese winter wheat yield dataset (ChinaWheatYield30m) for major winter wheat-producing provinces in China for the period 2016–2021 with a semi-mechanistic model (hierarchical linear model, HLM). The yield prediction model was built by considering the wheat growth status and climatic factors. It can estimate wheat yield with excellent accuracy and low cost using a combination of satellite observations and regional meteorological information (i.e., Landsat 8, Sentinel-2 and ERA5 data from the Google Earth Engine (GEE) platform). The results were validated by using in situ measurements and census statistics and indicated a stable performance of the HLM model based on calibration datasets across China, with r of 0.81** and nRMSE of 12.59 %. With regards to validation, the ChinaWheatYield30m dataset was highly consistent with in situ measurement data and census data, indicated by r (nRMSE) of 0.72** (15.34 %) and 0.73** (19.41 %). With its high spatial resolution and accuracy, the ChinaWheatYield30m is a valuable dataset that can support numerous applications, including crop production modeling and regional climate evaluation.

For what concerns crop threats, the core of the system aiming at detecting yellow rust outbreaks in maize and wheat crops, will be built on PRISMA satellite. Several VIs (NDVI, SIPI, PRI, PSRI, MSR) computed by using hyperspectral images will be used to implement a Diseases Infection Index. The DI will be classified into four classes including healthy (DI≤5%), slight infection (5<DI≤20%), moderate infection (20<DI≤50%), and severe infection (DI>50%). It should be underlined that the algorithms proposed by Guo et al. (2021) have been developed based on hyperspectral images acquired by drones. The impact of spatial resolution on the capability to detect yellow rust in crops will be one of the results of the activity.

In addition, we have established the remote sensing-based risk assessment methods for agricultural pests, specifically for grasshopper and desert locust. For grasshopper, we took the two steppe types of Xilingol (the Inner Mongolia Autonomous Region of China) as the research object and coupled them with the MaxEnt and multisource remote sensing data to establish a remote sensing monitoring model for grasshopper potential habitat. The results demonstrated that the most suitable and moderately suitable areas were distributed mainly in the southern part of the meadow steppe and the eastern and southern parts of the typical steppe. We also found that the soil temperature in the egg stage, the vegetation type, the soil type, and the precipitation amount in the nymph stage were significant factors both in the meadow and typical steppes. For desert locust presence risk forecasting, we have proposed a dynamic prediction method of desert locust presence risk at Somalia-Ethiopia-Kenya. Monthly prediction experiments from February to December 2020 were conducted, extracting high, medium and low risk areas of desert locust occurrence in the study area. Results demonstrated that the overall accuracy was 77.46%, and the model enables daily dynamic forecasting of desert locust risk up to 16 days in advance, providing early warning and decision support for preventive ground control measures for the desert locust.

In summary, as of the third year of the Dragon 5, the project's execution progress is consistent with the schedule, and most of the activities have achieved good results. Additionally, some scholars in the project team are conducting scientific research using the data obtained through the cooperation between the two sides.

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Forests play a critical role in the Earth's ecosystem and strongly impact the environment. Under the threat of global climate change, remote sensing techniques can provide information for a better understanding of the forest ecosystems, early detection of forest diseases, and both rapid and continuous monitoring of forest disasters. This project concerns the topic of ecosystems and spans the subtopics estimation of forest quality parameters and forest and grassland disaster monitoring. The aim is to study and explore the application of multi-source remote sensing technology in forest parameter extraction and forest disaster monitoring using data fusion of satellite images, drone-based laser scanning and drone-based hyperspectral images. The research contents include tree species classification, forest parameters estimation, and forest disturbance detection.

1. Work performed

(1) Satellite image data

We applied for satellite images through ESA and MOST of China, including RADARSAT-2 (2020 and 2021), WorldView-3 (June 2021), Sentinel-1/2 (from 2018 to 2022), and Gaofen-1/2/6 (from 2020 to 2022). These data cover several study areas including Gaofeng, Weihai, Fushun, Lu'an, Wangyedian, Genhe and Pu'er in China and Remningstorp in Sweden.

(2) Field investigation data

For different research contents, field investigations were carried out in Gaofeng, Fushun, Lu'an, Genhe, Pu'er and Remningstorp. The details are as follows:

l The forest information of the sample plots in Gaofeng and Genhe in China was updated in 2021 and 2022.

l Spectral information from healthy and pine nematode-infested forests at different stages of the Fushun and Lu'an study areas in China was collected in 2021.

l Forest tree species types, forest changes and disturbance information of Pu'er study area in China were collected in 2023. The occurrence status and geographical distribution of Simao pine bollworm pests and diseases were recorded.

l The forest information of the sample plots in Remningstorp, Sweden was updated in 2019 and 2021. Controlled experiments were conducted for bark beetle infestation in 2021 and 2023.

(3) Technical progress

l Tree species classification. We proposed four pixel-based deep learning tree species classification models using drone-based hyperspectral data: an improved prototype network (IPrNet), a CBAM-P-Net model of the prototype network combined with an attention mechanism, a Proto-MaxUp+CBAM-P-Net model of the CBAM-P-Net combined with a data enhancement strategy, and SCL-P-Net introducing contrast supervised learning. We evaluated and screened low-cost and efficient UAV optical image acquisition solutions for individual tree species identification，and developed an instance segmentation algorithm, ACE R-CNN, for individual-tree species identification using UAV LiDAR and RGB images. The performance of these models was demonstrated in the Gaofeng study area. A tree species classification method based on multi-temporal Sentinel-2 data was developed and the performance was verified at Remningstorp.

l Forest parameters extraction. We proposed a method for extracting crown parameters considering inter-tree competition using terrestrial close-range observation data with missing canopy information. We proposed a mean-shift individual-tree crown segmentation algorithm based on canopy attributes using UAV oblique photography data, and developed an individual-tree biomass estimation model fusing multidimensional features. A three-level stratified feature screening method fusing airborne hyperspectral and LiDAR data was innovated to construct regional AGB estimation models for different tree species, which has good performance in the Gaofeng study area. A high spatial resolution tree height extraction method combining ZY-3 stereo images and DEM was proposed, and a forest AGB estimation model using Sentinel-2 data and tree height data was developed to obtain accurate forest AGB maps in the Wangyedian study area. We proposed a quantitative method for thinning and clear-cutting phase height for detecting silvicultural treatment using the phase-height data from time-series TanDEM-X. In addtion, we investigated the use of interferometry (InSAR) of TanDEM-X images for estimation of forest changes (height, biomass and biomass change), and mapped smaller forest height changes (increase) in a boreal forest in Sweden.

l Forest disturbance detection. For Bursaphelenchus xylophilus, we analyzed the spectral characteristics of two tree species (Pinus tabulaeformis and Pinus koraiensis) in the study areas of Weihai and Fushun during different infection stages. Sensitive bands were selected and a detection model was constructed to identify the infection stages of Bursaphelenchus xylophilus. A conifer information extraction index (NDFI) based on time-series Landsat images was constructed to assist remote sensing monitoring of pine wood nematode disease. For European spruce bark beetles (Ips typographus [L.]) infestation, methods of early detecting infestations were proposed using drone-based multispectral images. We investigated how early the infestation can be detected after an attack. We also compared the machine-learning- and vegetation-index-based methods for the early detection of bark beetle infestations, and found the machine-learning-based methods had overfitting issues with low transferability for the untrained areas. For forest disturbance, a CCDC disturbance detection algorithm incorporating spectral indices and seasonal features was proposed to robustly map forest disturbances over the past 30 years in the Genhe study area.

(4) Collaborative Research

l One visiting PhD student from BFU to SLU from 2022 to 2023.

l Co-supervising 1 PhD student.

l One joint research paper published in Ecological Indicators. One joint research paper under view by IEEE Transactions on Geoscience and Remote Sensing. Two conference papers were published in IGARSS 2022, and one joint conference paper was accepted by IGARSS 2023.

2. Future Plans

(1) The research contents

l For tree species classification, we will explore deep learning models for individual-tree and stand-scale tree species classification using WorldView-3 and Sentinel-2 imagery.

l For tree forest parameters, we will explore crown extraction methods combining satellite imagery and LiDAR, and monitor regional biomass dynamics using Sentinel-1 data under multi-factors disturbance.

l For forest insect damage detection, we will study early identification methods of Bursaphelenchus xylophilus and Ips typographus [L.] based on multispectral and hyperspectral images from UAVs. The improved CCDC algorithm will be used to further explore the spatial and temporal distribution patterns of forest disturbance in China.

(2) Cooperation plan:

l Co-research on Cooperation project between China and Europe in Earth Observation on forest monitoring technology and demonstration applications.

l Co-publishing 1~2 research papers.

Co-organizing an international summer school on forest parameters and deforestation mapping using remote sensing data.

The airborne multi-dimensional SAR flight experiment in the forest area of Genhe district was organized in 2021, and the PolSAR dataset of P, L, S, C and X bands, P-band TomoSAR dataset and dual antenna InSAR dataset of C band were obtained. Based on this dataset, we analyzed and evaluated the performance of PolSAR data of 5 bands and different band combinations in estimating forest volume.

In the case of PolSAR data radiometric calibration method development, using space-borne GF-3 data and airborne UAVSAR data, we proposed the cross-co-polarization radio coefficient, which can be used to obtain the truth value of polarization scattering of any distributed targets. The calibration method can effectively reduce the constraints on targets of existing methods. In case of terrain radiometric correction (RTC), one RTC method for PolSAR based on RPC model has been proposed, which reduces the technical threshold for geometric and radiometric correction of PolSAR. In addition, a RTC method suitable for supervised classification of PolSAR was proposed, which can improve the accuracy of forest type classification by about 20%.

A series of land cover classification method studies were carried out using spaceborne Radarsat-2 and airborne UAVSAR time-series polarimetric SAR data. We constructed the time-polarization features, reflected the degree of feature variance, and constructed the foundation for effective feature selection; proposed the polarimetric and time dimension feature selection algorithms IESSM and SSV, designed a classifier based on Transformer, and reduced the feature redundancy and enhanced the adaptability of the classifier; extracted the time-variant scattering features based on the time-series polarimetric SAR data characterization model, enhanced the expression ability of scattering variation, and improved the classification accuracy.

In terms of InSAR, the multi-layer model suitable for short wavelength InSAR is innovated, and the retrieval accuracy of forest height is improved by realizing that the observation calculation and theoretical model of InSAR coherence obey the same assumption. Moreover, the algorithm for jointly measuring forest height using P/X dual-frequency InSAR has been proposed, which effectively improves the extraction accuracy of DTM/DSM/CHM in forest areas.

Most current TomoSAR methods use a local means value of the sample covariance matrix, which may get the poorly refined spectrum, and lose some detailed information. In addition, the spectrum will inevitably produce sidelobe effects. To address the above issues, a non-local means method is applied to identify neighboring pixels with high similarity to the target pixel, thereby comprehensively reflecting its feature information. Moreover, G-Pisarenko method is introduced in TomoSAR to reduce the spurious interference signal. These two methods have been respectively verified that the feasibility and effectiveness with BioSAR 2008 L-band data and AfriSAR 2016 P-band datasets, respectively. In addition, we propose a TomoSAR algorithm based on atomic norm minimization(ANM) to solve the scatterer location error caused by elevation discretization in traditional TomoSAR methods. The performance of the algorithm has been verified by TerraSAR-X data. TomoSAR baseline correction and phase compensation methods for multi baseline interferometric SAR assisted by DEM have been developed, improving the imaging quality of TomoSAR over forested areas. Additionally, we have developed a multi feature collaborative forest biomass estimation method based on TomoSAR profile fitting, which has achieved high accuracy (>90%) in tropical rainforest regions.

PolTomoSAR techniques have been developed to characterize tropical forests at P band using adaptive parametric signal processing approaches, and over temperate forests at L band using a minimal number of images. The benefits of a synergistic use of the different modes of the upcoming BIOMASS missions have been evaluated by computing the ultimate performance limits of this mission for different forest characteristics, and according to various temporal scenarios. The gain provided by external sources of information, such as GEDI, has been evaluated by coupling this estimation with Bayesian principles. The case of estimation techniques using models of forest vertical profiles that differ from actual ones has been investigated. Some of the developed techniques will contribute to the BIOMASS processing group of methods Multi-Mission Algorithm and Analysis Platform (MAAP). Some work also been done regarding the definition of BOMASS level 3 product processing chain, i.e. Forest Height, Above Ground Biomass and Forest Disturbance.

Times series of Sentinel 1 measurements were used for mapping deforestation at large scale (see https://www.tropisco.org/ )and new techniques, based on Bayesian processing are being developed.

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.