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3-D CHARACTERIZATION AND TEMPORAL ANALYSIS OF FORESTS AND VEGETATED AREAS USING TIME-SERIES OF POLARIMETRIC SAR DATA AND TOMOGRAPHIC PROCESSING
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Based on the experience accumulated during the DRAGON-1 to -4 projects, we intend, under the DRAGON-5 project to strengthen the established fruitful collaborations between European and Chinese partners and experts in PolSAR, PolInSAR and [...] |
Dr. Laurent Ferro-Famil, University of Rennes 1, FRANCE |
Prof.. Erxue Chen, Institute of forest resources information technique, CHINA |
Ecosystems |
Based on the experience accumulated during the DRAGON-1 to -4 projects, we intend, under the DRAGON-5 project to strengthen the established fruitful collaborations between European and Chinese partners and experts in PolSAR, PolInSAR and PolTomo-TomoSAR, for the 3-D characterization and temporal analysis of forests and vegetated areas using time-series of polarimetric SAR data and tomographic processing. This project aims to promote the use of existing spaceborne SAR sensors with polarimetric and interferometric diversities, for the temporal monitoring of forested and vegetated areas and to pave the way for future spaceborne missions and concepts. The proposed project contains 4 main scientific topics with the following objectives: 1) Development of physical parameter retrieval methods for the quantitative 3-D characterization of vegetated areas using low frequency sensors, whose penetration properties are well suited to the imaging of the intrinsic properties of natural volumes. This topic aims at developing vegetation parameters extraction methods based on the complementary aspects of PolSAR and PolTomSAR, for volumetric land-cover characterization. Among the many descriptors of vegetated areas, key ecosystem parameter for biomass stock successions, and growth dynamics, such as forest structure and Above Ground Biomass will be addressed, as well as classical indicators like tree height. Special attention will be dedicated to the estimation of the underlying ground dielectric and roughness properties, over both wild and cultivated areas. 2) Development of innovative vector signal processing techniques for high-resolution 3-D imaging. The recent history of SAR tomography shows that the possibilities for characterizing 3-D environments using Multi-Baseline Pol-inSAR data are highly linked to both the quality of the signal processing techniques used to perform 3-D focusing and to the acquisition configuration. During this project, several options, related to original CS- and Wavelet-CS based imaging solutions will be tested, and original configurations, like multi-temporal Tandems, Bistatic Tomographic pairs, will be analyzed and explored at various application scales. 3) Temporal monitoring of forested and vegetated areas using time-series of acquisitions. Time series of Sentinel 1 or ALOS sensors will be used to detect and monitor Forest and grassland disasters, forest mapping, AGB estimation, as well as nearly real-time deforestation mapping over 3 continents. 4) PolSARpro Software v6.0 is a polarimetric SAR data processing and educational tool developed under contract to the European Space Agency. It is proposed in this project to include all the new algorithms and scientific procedures that will be developed during the DRAGON-5 project. It will thus increase the great collection of well-established algorithms and tools designed to handle PolSAR and Pol-InSAR data from airborne and spaceborne sensors. The PolSARpro software could thus become also an important communication tool, advertising the international Geoscience and Remote Sensing community for promoting the most important scientific developments conducted during the DRAGON-5 project. The funding of the project in general and of the Young Scientists in particular is not problematic at all since both European and Chinese partners are used work on the topic proposed in this project and can, through national or regional funding, guarantee that the project will not suffer from a lack of human or material resources
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CEFO: CHINA-ESA FOREST OBSERVATION
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The key aim of CEFO proposal is to develop methods and data products which will support the sustainable economic development of the key forestry sector in China, thereby alleviating poverty among this community. The CEFO project will apply and [...] |
Dr. Juan Suarez, Northern Research Station, UK |
Prof.. Yong Pang, Chinese Academy of Forestry, CHINA |
Ecosystems |
The key aim of CEFO proposal is to develop methods and data products which will support the sustainable economic development of the key forestry sector in China, thereby alleviating poverty among this community. The CEFO project will apply and evaluate innovative remote sensing methods to improve sustainable forestry management for Chinese forests, in a close collaboration between Chinese forest researchers, stakeholders, and the UK research team. The research focuses on priority areas of application of remote sensing to change detection, yield and forest carbon sequestration, and improved detection of stress related to growth and forest health. It will develop the joint use and evaluation of Chinese and European satellites (Sentinel-2, Gaofen 1/2/6/7), the planned Chinese Terrestrial Ecosystem Carbon Monitoring Satellite and ESA BIOMASS mission. To achieve this, it will apply a set of spectroscopy, radiative transfer modelling and time series analysis methods to Chinese forests, recently developed within collaborative research projects with NASA, EU and MOST funding. In particular, the project will develop methods based on fundamental tree physiology that can be extended for the future monitoring of hazards affecting Chinese forests using remote sensing. It will also integrate remote sensing data for model and algorithm development to advance data visualisation and simulation techniques; to detect change using time series observations to inform policy, monitor vegetation condition, and provide growth model inputs to assess yield and to estimate carbon sequestration. The project will also contribute expertise and state-of-the art equipment towards capacity building for remote sensing field and lab analysis. Some funding sources include the joint project funded by the National Science Foundation of China (41871278) and China Gaofen Forest Application Project, Northern Research Station of UK and the Chinese Academy of Forestry. They will support the normal progressing of the Dragon 5 cooperation and the participating of the annual symposium. Some scholarships from ESA will be very helpful to have 1 or 2 graduate students focus on this project. We will try to apply some funding from the GFOI (Global Forest Observation Initiative) related activities.
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GRASSLAND DEGRADATION DETECTION AND ASSESSMENT BY REMOTE SENSING
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Monitoring grassland degradation on a large scale has proven very difficult. Early estimates of the extent of degraded grasslands in dry areas put their number as about 3,050 million hectares (ha), or 94% of all degraded drylands, but relied on [...] |
Prof.. Alan Grainger, School of Geography, University of Leeds, UK |
Prof.. Zhihai Gao, Chinese Academy of Forestry, CHINA |
Ecosystems |
Monitoring grassland degradation on a large scale has proven very difficult. Early estimates of the extent of degraded grasslands in dry areas put their number as about 3,050 million hectares (ha), or 94% of all degraded drylands, but relied on subjective assessments. Attempts to base estimates on measurements, e.g. by means of the Normalised Difference Vegetation Index (NDVI) derived from low resolution satellite data, came in for much criticism. Continuing difficulties of this kind prevented the Third Edition of the World Atlas of Desertification from displaying maps of the extent of dryland degradation based on satellite data. So there is an urgent need to devise new methods for measuring grassland degradation using satellite sensors. This project aims to fill this gap by devising such methods. It will experiment with various combinations of optical and radar data with resolutions varying from as much as 30 m (medium resolution) to ≤ 1 m (very high resolution). The research will focus on grasslands in China, which cover 400 million ha, or 42% of national land area, and this enables it to take advantage of data collected by both Chinese and ESA satellites. Its findings will make it feasible to monitor the degradation of grasslands, and drylands generally, in a reliable way, and to tackle the even more challenging task of measuring the combination of degradation and restoration that is required for monitoring progress in achieving the goal of land degradation neutrality, which are included in one of the sustainable development goals. Without reliable measurements of human-induced degradation, catalysed by droughts, it will be impossible to estimate the likely impact of long-term global climate change. Topics and methods： (1) Mapping and dynamic monitoring of grassland types: Referring to the traditional grassland categorizing system, the remote sensing classification system of the study area would be firstly constructed on the basis of analyzing the charicteristics of grassland types in the study area and payloads of China and ESA’s EO satellites. Then methods on mapping and dynamic monitoring of grassland types will be studied by multi-source remote sensing, especially with the utilization of Very High Resoultuion (VHR) datasets. (2) Quantitative estimation of grassland ecological parameters：The estimation and dynamic monitoring technologies of grassland ecological parameters, including grassland vegetation coverage, NPP and grassland biomass, will be developed by means of the combination of multi-scale and multi-source remote sensing and field investigation, and a grassland ecological static and dynamic monitoring technological system is expected to build (3) Degraded Grassland detection and assessment: The regularity of grassland ecological change and its spatio-temporal coupling relationship with climatic factors, soil characteristics and intensity of grazing will be analyzied synthetically. A remote sensing method for detection of degraded grassland which can greatly eliminate the impact of climate fluctuation will be developed, and the degree of grassland degradation will be assessed scientifically by grading the vegetation productivity and soil characteristics within degraded grassland. Availability of funding to run the project： For the European side, the School of Geography has the necessary institutional capacity to run the project. In addition, during the 4 years research, two funding projects, sponsored by National Science and Technology Major Project (No. 21-Y30B02-9001-19/22) and Fundamental Research Funds for the Central Non-Prof.it Research Institution of CAF (CAFYBB2019ZB004), are being undertaking by the Chinese team members. They will support the normal progressing of the Dragon 5 cooperation and the participating of the annual symposium.
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MAPPING FOREST PARAMETERS AND FOREST DAMAGE FOR SUSTAINABLE FOREST MANAGEMENT FROM DATA FUSION OF SATELLITE DATA
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This project concerns the topic Ecosystems and spans the subtopics Collaborative estimation of forest quality parameters and Forest and grassland disaster monitoring with the objective to develop new methods for the respective areas. The forest [...] |
Dr. Johan Fransson, Swedish University of Agricultural Sciences, Department of Forest Resource Management, SWEDEN |
Prof.. Xiaoli Zhang, Beijing Forestry University, CHINA |
Ecosystems |
This project concerns the topic Ecosystems and spans the subtopics Collaborative estimation of forest quality parameters and Forest and grassland disaster monitoring with the objective to develop new methods for the respective areas. The forest quality parameters include biomass, tree species and new quality parameters. For biomass, new tools will be developed to map forest state and change from satellite-borne radar with support of airborne laser scanning data. The deliverables are algorithms for correction of synthetic aperture radar (SAR) data for inconsistent weather conditions, decreasing stationary map uncertainties of forest estimates due e.g. fluctuating model uncertainty, wavelength dependence, and improper reference data, and using forest change estimations and auxiliary data to derive forest site-index (SI) maps. For tree species, the methods will be based on a combination of remotely sensed data types: medium-resolution satellite imagery, satellite SAR data, high-resolution images and LiDAR data to derive information both from spectral information structural information. The deliverables are algorithms for tree species identification from satellite images, SAR data, combining satellite images and ALS data, as well as a tree species map for a study area in Sweden. The new quality parameters are related to tree retention elements in the landscape, aiming to characterize and quantify forest structures that are key elements for ecological biodiversity. This objective will be addressed using several high-resolution data sources, such as multispectral satellite imagery and LiDAR data. The deliverables here are algorithms for mapping tree retention elements in the landscape from high-resolution multispectral satellite imagery and LiDAR data. The method for collaborative estimation of forest quality parameters includes the following steps: (1) extraction of features from Remote Sensing (RS) data, (2) linking RS data to reference data on sample plots, (3) training of estimators and classifiers using the reference data, and (4) prediction or supervised classification. For storm damage, the objective is to develop methodology and algorithms and to perform a scientific evaluation of SAR data from two or more satellite sensors for detecting and mapping changes in boreal forests. The approach for mapping storm-felled forest is to use change detection based on backscatter SAR images before and after the changes. Simulated wind-thrown fellings offer a unique in situ dataset with a setting very similar to a natural storm damaged forest. The deliverables here are algorithms for detecting and mapping changes in boreal forests, in particular storm damage, from SAR data from two or more satellite sensors. Two kinds of forest insect damage will be studied: (1) The European spruce bark beetle (Ips typographus [L.]), (2) pine wood nematode (Bursaphelenchus xylophilus). The objectives here are two-fold: (1) Developing the methods for forest insect damage detection at an early stage. (2) Analyzing distribution and spreading patterns of the forest insect damage. The deliverables here are methods for early detection, forest infestation spreading patterns and forecasting models, and prediction maps for large area application. The joint teams currently own 12 ongoing projects (2020 – 2022) with 4 339 700 EUR funding and 4 planned/submitted applications (2021 – 2023) with 1 078 740 EUR that are related to this proposal for Dragon 5 collaboration. For a coherent and comprehensive scientific research, field inventory and other remote sensing data have been collected in 2018 and 2019, and planned more data acquisition in 2020 – 2024. Together with satellite images provided by the Dragon 5 project, the large amount and various datasets will support our studies for forest information extraction.
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