Conference Agenda

Remote sensing technologies hold great promise for archaeology and heritage conservation. In particular, satellite imagery provides an efficient means of surveying large areas for the detection and monitoring of archaeological sites and features. In this Dragon-6 project, a collaboration between the University of Bern (Switzerland), the National Research Council (Italy), Wuhan University (China), the Aerospace Information Research Institute of the Chinese Academy of Sciences (China), and Guizhou University (China), we aim to use multi-sensor remote sensing to address critical issues in archaeology and heritage conservation.

Our project focuses on test sites in China, like the Great Wall, as well as several other sites, e.g., in Italy and Kazakhstan. Mainly, we consider the following:

1. Cultural Heritage Protection, including looting detection and deformation monitoring
2. Detection of targets of archaeological interest using AI-based approaches and sub-surface sensing
3. Contextual landscape analysis

For looting detection, we build upon our work from Dragon-5 to identify the wide range of looting activities, from organized industrial-scale looting to small-scale illicit excavations by individuals. Although large-scale looting can be identified in remote sensing images, detecting small looting holes (1-2 meters in size) is more challenging and requires automated methods.

Deformation monitoring using Persistent Scatterer Interferometry (PSInSAR) and SqueeSAR techniques can provide valuable information for heritage conservation. PSInSAR is widely used but has limitations in cultural heritage sites with few persistent scatterers. SqueeSAR overcomes this by analyzing statistically homogeneously distributed scatterers (DS). We will refine the DS phase estimation and selection to better handle deformation gradients and identify the finer-scale deformation patterns relevant to heritage sites.

Seasonal deformation patterns over a multi-year time series can also serve as indicators of subsurface archaeological features, acting as crop marks. Extremely small differences in deformation associated with buried structures require advanced time series analysis. Using phase-preserving DS approaches, we aim to process large areas over long time spans to identify time-series outliers indicative of subsurface features. Machine learning will be applied to detect both generic outliers and patterns associated with specific types of buried archaeological remains (e.g., walls, roads, and tombs) based on training data. To detect archaeological sites and features exposed at the surface, we will develop AI-based methods for automated site and feature detection using optical and SAR data. Landscape archaeological approaches will also be tested to understand the sites within their broader geomorphological and environmental contexts.

Multi-sensor fusion will be explored to synergistically combine these different threads and leverage the complementary strengths of optical, SAR, multispectral, and other remote-sensing data for more comprehensive archaeological mapping and heritage monitoring.

Field work will be conducted to validate remote-sensing-based detections and to iteratively refine our methods based on ground truth data. This collaboration, building upon the unique strengths and expertise of each team, will develop innovative Earth observation methods to help protect cultural heritage and enable new archaeological discoveries.

Amidst the global population explosion, food security has emerged as a pressing issue in the 21st century. In this context, global warming and extended drought periods are of major concern – with agricultural droughts increasingly challenging sustainable food supply. Timely and accurate monitoring of crop water stress stands as a promising way forward for mitigating the detrimental impacts on crop yield caused by droughts. In our Dragon 6 project, we aim to synergistically utilise thermal infrared (TIR) and solar-induced fluorescence (SIF) observations from ESA and Chinese satellites to monitor crop water stress over North China Plain, Iberian Peninsula, and Luxembourg. The key scientific objectives of this project are: 1) advancing evapotranspiration partitioning into soil evaporation and plant transpiration using TIR observations based on an analytical model, 2) developing a method for estimating plant transpiration using SIF observations, 3) constructing downscaling algorithms for land surface temperature and SIF data to fine spatial resolutions, and 4) enhancing crop water stress monitoring capability based on plant transpiration and SIF estimates.

The project will advance our understanding and modelling skills in plant water and carbon cycles by synthesizing the expertise in TIR remote sensing (European colleagues) and SIF remote sensing (Chinese colleagues), thereby strengthening the scientific exchanges between the Sino-European teams. This collaboration is expected to take full advantage of the European and Chinese satellite data to achieve the project’s scientific goals and develop a crop water stress monitoring system. The dissemination of the project results will be performed through submissions to scientific journals and ESA and NRSCC joint reports, along with presentations at Dragon Annual Symposia, thereby promoting the visibility of the expected achievements. Additionally, the young scientists involved in the project will benefit from a unique portfolio of highly innovative training, fostering their professional knowledge growth and transferable skill development that is crucial for successful career advancement, including research management, knowledge utilisation, and scientific writing.

Wetlands, known as the "kidneys of the Earth," are formed by the interaction between water and land, and characterized by seasonal and annual waterlogged. Meanwhile, they are unique and resilient ecosystem with biodiversity. Wetlands and water are the most important resources for human and other living things. The harmony of human-water relations is the core issue of global sustainable development. The contradiction between human and water is more prominent in high-quality development stage of China. However, water is not scarce but uneven and pollution under the condition of global climate change. We face several challenges for resilient wetlands and harmony of human-water relations, such as, the critical points of resilient wetland at drought and flooding conditions, simulation and estimation the non-point source pollution load of studying watershed with the influence of global climate changes and human activities, harmony of human-water relations in watershed and so on.

Advanced satellite remote sensing technology provide timely and accurate scientific data, technical support, effective valuation and management system for dynamic monitoring and comprehensive management of resilient wetlands and human-water relationships in watersheds, including spatiotemporal changes of water resources, environmental changes in watershed, and the impact of human activities, dynamic monitoring of water and drought disasters, watershed ecological restoration, and non-point source pollution estimation in watershed and so on.

However, raw remote sensing data is not enough to support the evaluation of resilient wetlands and human-water relationship in watersheds. Effective wetland and watershed ecological indicators and their the dynamic monitoring are prominent for the coordination of resilient wetlands and human-water relationships. An effective and reliable wetland and watershed dynamic monitoring and management platform should be developed urgently to monitor resilient wetland and human-water relations based on advanced remote sensing. Poyang Lake watershed is selected as one of the study area in this proposal. This proposal includes three works, such as, 1) evaluation of resilient wetlands at drought and flooding conditions, 2) watershed runoff and non-point pollution modeling under impacts of human activities and climate change and 3) virtual and real collaborative geographic experiments for the human-water relationship.

Salt lake monitoring and information retrieval is important in ecological protection, water resource management, economic development, and tourism culture. Understanding the ecosystem and biodiversity of the salt lake helps to preserve its natural environment. Understanding the hydrological characteristics and water quality of salt lakes is helpful for the scientific management and utilization of water resources. Salt lakes also provide salt resources, which contribute to economic growth, and have unique natural landscape and cultural values, attracting tourists and promoting tourism.

Salt lake monitoring from space has great advantages in efficiency and continuity, which is mainly reflected in the following aspects: Firstly, salt lakes are often located in remote areas, which are difficult to reach and monitor directly. Remote sensing satellites can cover a wide surface area and provide high-resolution data, making it easier and more comprehensive to monitor and study salt lakes. Secondly, remote sensing satellites can obtain data over a long period of time, which can help us understand the salt crust changes, water volume and quality status, surrounding vegetation coverage and other information of salt lakes, and provide a scientific basis for natural resources management and ecological protection. In addition, remote sensing satellites can also provide multi-frequency and multi-polarimetric data, which can be used for the exploration of salt resources in related economic and scientific research.

Microwave remote sensing is capable of earth observation in full-time and full climate condition, especially polarimetric SAR can reflect the geometrical and bio-physical information of salt lakes, so as to reveal the interaction process of salt crystal precipitation and typical environmental elements, and finally realize the fine and quantitative acquisition of salt lake areas. On the other hand, optical remote sensing has significant advantages in salt lake monitoring as water change in terms of geochemistry components, associated with modification of alga and bacteria’s populations involving change in water colors. By analyzing the multispectral and/or hyperspectral data, the physical and chemical information of the salt lake water body and the surrounding environment can be obtained.

The objectives of this project is to observe salt lakes, an ecosystem that evolves from wet to dry, over the year/year meteorological and exploitation conditions, using microwave and optical remote sensing EO data, including: 1) To establish the feature mapping of polarimetric SAR data associated with terrain types, and develop water segmentation and fine classification methods based on the physical scattering mechanism of salt lake; 2) To analyze the optical reflectivity and absorption characteristics of different wavelength bands, and to obtain the physical and chemical information of the salt lake water body and the surrounding environment; 3)To fuse the mapping and classification results of joint microwave and optical satellite remote sensing data. A synergistic use of multi-source EO sensors will be developed in order to accurately monitor the temporal evolution of salt lakes.

Typical salt lakes located in China, France and Spain will be investigated for the methodology demonstration. For microwave satellite data, Sentinel 1A&1B, GF3, LT1, as well as ALOS2 and Radarsat2 SLC data in the test sites will be collected. While for optical satellite data, Sentinel 2, GF1, Landsat8&9 multispectral data, as well as optical Jilin-1 and the hyperspectral GF 5 (01A) and if possible EnMap/Prisma data in the test sites will be applied.

The deliverables of the project consist in joint reporting during Dragon 6 meetings, joint publications in international conferences and peer-reviewed journals, student exchanges, jointly organized tutorials, and dissemination of processing methods.