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 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.