Conference Agenda

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 are tree species classification, forest parameter estimation, and forest insect damage detection.

1. Work performed

We applied for satellite images through ESA and Ministry of Science and Technology of the China. These included images acquired from RADARSAT-2, WorldView-3, Sentinel-1/2, and Gaofen series. These data cover several study areas in China and Sweden, including Gaofeng, Yantai, Fushun, Lu'an, Wangyedian, Genhe and Pu'er in China and Remningstorp in Sweden. Field investigations were carried out in Gaofeng, Fushun, Lu'an and Remningstorp. In these study ares, drone-based multispectral and hyperspectral images and laser scanning data were also acquired and studied.

(1) Satellite images

The satellite images acquired for the study areas in China are:

RADARSAT-2, one image covering Fushun and one image covering Qingyuan.

Sentinel-1, time-series images from 2019 to 2021, covering Gaofeng and Wangyedian.

Sentinel-2, time-series cloud-free images from 2019 to 2021, covering Gaofeng, Lu'an, Wangyedian, Genhe, and Pu'er.

Gaofen-1/2/6, 295 images from 2021 to 2022, covering Gaofen, Genhe and Pu'er.

The satellite images acquired in Remningstorp, Sweden are:

WorldView-3, one SWIR image (June 2021).

Sentinel-2, time-series cloud-free images from 2018 to 2021.

RADARSAT-2, one image each in 2020 and 2021.

Pleiades, one image (29 Apr 2021).

(2) Field investigations data

Field investigation of forest parameters was conducted in Gaofeng, China. The inventory recorded diameter at breast height, tree height, under branch height, and the coordinates of the plots.

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

The forest information of the sample plots in Remningstorp was updated. A controlled experiment was conducted for bark beetle infestation, and the infestation symptoms were recorded.

(3) Technical progress

Tree species classification. We proposed three deep learning 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, and a Proto-MaxUp+CBAM-P-Net model of the CBAM-P-Net combined with a data enhancement strategy. We developed ACE R-CNN, an attention mechanism, edge detection and region-based instance segmentation algorithm, to accurately identify individual-tree species 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 compared with the classification using mono-temporal data. The performance was verified at Remningstorp.

Forest parameters extraction. 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, which has good performance in the Gaofeng study area. We proposed a method to automatically extract high-resolution tree height products by combining ZY-3 stereo images and DEM. We developed a forest aboveground biomass estimation model using Sentinel-2 data and tree height data, which obtained accurate forest aboveground biomass maps in the Wangyedian study area. The potential of using PolSAR data acquired at C- and X-band was investigated to estimate forest aboveground biomass at a test site in southern Sweden. The polarization decomposition method was used to RADARSAT-2 and TerraSAR-X data for estimating forest aboveground biomass.We also investigated the potential of time-series TanDEM-X for monitoring forest growth by using the phase height data. We proposed methods to quantify the effects of thinning and clear-cuts on the phase height and apply the methods on detecting silvicultural treatment.

Detection of forest biotic disturbance. About D. tabulaeformis, we proposed a spectral-spatial classification framework combining drone-based hyperspectral images and RGB images to identify damaged tree crowns. 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. 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 sensitivity of Sentinel-2 and WorldView-3 SWIR images in detecting early-stage infestations.

(4) Collaborative Research

Co-supervising 1 PhD student.

One joint research paper (accepted for publication in Ecological Indicators), one manuscript, and one published conference paper (IGARSS 2022).

2. Future Plans

(1) Data acquisition

We would like to apply for TanDEM-X and WorldView-3 images covering the study areas of Gaofeng, Genhe, and Pu'er. We plan to obtain drone-based multispectral and hyperspectral imagery covering Remningstorp. We will continue the controlled experiment and field observation of bark beetle infestations.

(2) The research contents

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.

For tree forest parameters, we will explore crown extraction methods combining satellite imagery and LiDAR, and monitor regional biomass dynamics using Sentinel-1 SAR data. We will develop a method of detecting forest biomass change using RADARSAT-2 imagery.

For forest insect damage detection, we will explore the early identification method of Bursaphelenchus xylophilus using the acquired hyperspectral data. We will study early identification methods of Ips typographus [L.] based on multispectral and hyperspectral images from UAVs.

(3) Cooperation plan:

Co-supervising 1~2 PhD students.

Co-publishing 2~3 research papers.

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

Monitoring the status and dynamics of forest is a major issue in the frame of current climate change analysis, as carbon stock variations for the biosphere represent the major source of uncertainties within the global carbon cycle. Synthetic Aperture Radar (SAR) is an active remote sensing device, able to image the reflectivity of wide environments from space, in a systematic way, independently of weather or light conditions. The penetration of electromagnetic waves into vegetated media makes SAR a unique tool for forest 3-D remote sensing applications. Series of SAR acquisitions are used in this study according to two different processing modes.

The first one uses the coherent information contained in a set of SAR images, acquired with a Polarimetric diversity (PolSAR), Interferometric, i.e. spatial, diversity (InSAR), or Tomographic, i.e. multi-baseline InSAR, diversity (TomoSAR), 3-D imaging purposes. Some very significant results have been found regarding the characterization (forest height, underlying grund topography, and Above Ground Biomass) of tropical forests measured at P band, and participate to the preparation of the upcoming ESA BIOMASS mission. Estimated quantities were further analyzed by comparison with other sources of information, such as the GEDI spaceborne lidar acquisitions , or by evaluating the geophysical properties of the retrieved topographic indicators. At higher frequency bands, the difference between the correlation time of radar echoes measured over forests and the revisit time of a spaceborne SAR platform does not allow to apply classical repeat pass imaging techniques. This working group experienced different approaches able to cope with this serous limitation, and based on the model-based analysis of single-pass InSAR pairs on the one side, and on the reconstruction of classical repeat-pass information from a time series of InSAR pairs. Both approaches led to very promising results at L and X bands.

The second processing mode used in this project is related to the incoherent analysis of time series of SAR images, in order to detect changes an relate them to specific properties of forest. In particular the Tropisco service, providing operational deforestation maps derived from Sentinel 1

data, has been launched recently Details may be found at the following links

https://www.spaceclimateobservatory.org/tropisco-amazonia
https://www.spaceclimateobservatory.org/tropisco-south-east-asia

This joint project combines the use of field, airborne and drone remote sensing, and spectroradiometer data to validate and calibrate innovative new satellite sensors from CNAS, ESA and NASA for forest inventory, assessment and health monitoring applications in China and the UK.

1.Vegetation parameter distribution using airborne LiDAR data - UK

Work with airborne LiDAR has been applied for the estimation of mensuration variables, such as Yield Class, Top Height, Basal Area, Volume and mean DDB plus other variables like fractional cover and LAI, over 120K ha of woodlands across the North of England. Derived point cloud metrics were combined with current growth models in sub-areas of 30x30 m² inside each forest stand. As different parts of the country were surveyed in different years, the growth models helped us to reduce this temporal decorrelation and to bring all estimates to 2022. The comparison of results against field observations produced R² above 0.95 for all estimates. Afterwards, Sentinel-1 has been analysed to detect the perimeter of areas affected by wind damage after the Arwen storm in November 2021. Then, volume lost has been calculated as the intersection of polygons representing wind damage and pixels containing volume estimates.

2. Assessing the effects of drought and stress on productivity using satellite data, field experiments and biomass modelling - UK

Monitoring of drought stress processes in Sitka spruce plantations in Kielder Forest using time-series analysis of different vegetation indices derived from Sentinel-2 and Landsat. In parallel, polytunnel experiments with control and drought groups for this species tracked hyperspectral changes in reflectance to determine early onset drought indicators and identify differing drought tolerances between breeding population clonal types. This process helps us to isolate relevant pigment changes under mild-to-moderate drought stress and to identify drought sensitive indices. Additional work with NDVI, derived from MODIS and Sentinel-2 satellites, was used to drive a biophysical vegetation model to study carbon sequestration over commercial Sitka Spruce plantations. Future work will look to integrate more drought sensitive indices into existing models to improve model accuracy under drought conditions.

Other attempts to detect signs of plant stress in the time series of Vegetation Indices (VIs) computed from Sentinel-2, involved the use of Principal Component Analysis applied to the pixel time series of the VIs to learn features of the data common to most observations (e.g. be that phenological or instrumental) and the learned principal components are used as basic functions in a simple linear regression model to filter out these signals. Remaining features in the observations that cannot be well explained by these components may be indicative of stress.

3.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. Furthermore, the GF-6 data will be used for Pu'er forest classification, and the classification results will be compared with the Sentinel-2 classification results to obtain the optimal forest covered map.

4. Multi-Scale Biomass Mapping Using Airborne LiDAR Data - China

The LiDAR biomass index (LBI) was extended to airborne LiDAR data for multi-scale forest biomass estimation. Through suitability compensating the laser point clouds of each tree and using a small number of trees measured in the field for model calibration, robust and highly accurate results were obtained. The method was verified by the field measurement data of 20 analytical trees, 133 sample plots and 39 subcompartments with larch plantations in three forest farms. Good performances were demonstrated (R2=0.98 and RMSD=11.85 kg at tree scale, R2=0.77 and RMSD=28.74 t/ha at plot scale and R2=0.86 and RMSD= 144.15 t at stand scale). Through comparing with the existing methods of predicting DBH based on tree height and crown width to calculate biomass, the proposed method shows higher accuracy and obvious versatility among different forest farms. In comparison with the existing LiDAR metrics method, it can obtain more detailed results, but only a small amount of measurement data was needed to calibrate the model.

5. Forest gap identification based on UAV LiDAR

The remote sensing of UAV LiDAR can quickly obtain the three-dimensional spatial information of the forest. The study site is located in the Puer Sun River Reserve in Yunnan, China. The canopy height model (CHM) was derived from the point cloud data of UAV LiDAR. 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. The forest gaps in the study site area were mainly small and medium gaps, showing that there were fewer disturbance events. The spatial distribution of forest gaps and its spatial characteristics in small area subtropical natural forests can be mapped by UAV LiDAR data. 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.