2021 2nd International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2021)
Countdown
Speaker

Ramli-Nazir-116.png

Prof. Ramli Nazir

Universiti Teknologi Malaysia 

Research Area:Geotechnical Engineering



陈超-116.png

A. Prof. Chao Chen

Zhejiang Ocean University

Research Area:Marine Environment Remote Sensing


Title: Spatio-temporal pattern evolution of coastlines for archipelagic regions

Abstract: The coastline is the lifeline and golden line of Marine economic development, which has important ecological functions and resource value. Accurate perception of coastline dynamic information can effectively support the sustainable development of human society. The statistical data of the former State Oceanic Administration shows that China's natural coastline has been decreasing year by year, and some of it has been seriously damaged. The coastal wetland ecosystem has been destroyed, and the restoration and renovation of the coastline are in urgent need. Zhoushan Archipelago is the first prefecture-level city established as an archipelago in China. There are 1,390 islands with an area of more than 500 square meters, accounting for 1/5 of the total number of islands in China. It is extremely rich in marine resources and is known as "the Buddhist Kingdom of the Sea and Sky, the Port City of Fishing". Because Zhoushan Islands is located in the dynamic sensitive zone of the interaction between land and sea, the ecological environment is fragile and the stability is poor, especially in recent years, the urbanization and industrialization process is accelerated, and the coastline resources are seriously damaged. Therefore, it is urgent to study the "feature-process-law" of coastline evolution of Zhoushan Islands. In this context, this paper takes Zhoushan Islands as the research object, accurately obtains the spatial location information of the coastline, analyzes the evolution characteristics of its spatial and temporal pattern, and identifies its evolution hot spots. It is of great significance to the dynamic monitoring, management and protection of shoreline resources in the archipelago region.



董恒原图.jpg

A. Prof. Heng Dong

Wuhan University of Technology

Research Area:Environmental Remote Sensing



邢学敏-116.png

A. Prof. Xuemin Xing

Changsha University of Science & Technology

Research Area:Mapping and Remote Sensing


Title: Measuring subsidence over soft clay highway based on a novel time-series InSAR deformation model: with emphasis on rheological properties and seasonal factors

Abstract: The stability management of soft clay subgrade is one of the main challenges in the field of highway engineering. It is necessary to ensure the accuracy of the long-term surface deformation monitoring for the highways built on soft clay subgrade after the embankment settlement construction. Building deformation models is a crucial step for time-series deformation monitoring. Most deformation models used in Interferometric Synthetic Aperture Radar (InSAR) modelling are empirical mathematical models, ignoring the physical mechanisms of the observed objects. In this work, a Novel InSAR time series deformation model (NM), with emphasis on the rheological properties of the soft clay and the environmental factors (temperature, humidity, and precipitation), was proposed. The NM was constructed based on the combination of the Seasonal Model and the Burgers model, which is introduced from the field. of Rheology. Two highways, namely Lungui Highway (LH) and G1508 Highway (GH)), both locate in Guangdong Province, China, are selected as the test area. The primary rheological parameters (viscosity and elastic modulus) were introduced in the NM and estimated with the time-series surface deformation generation. The NM is also utilized to assist the analyzation of the rheological properties of the soft soil in the test area. The high-pass (HP) deformation and in-situ levelling measurements are used to evaluate the reliability and accuracy of the NM. The results show that the Root Mean Square of the HP deformation obtained by the NM is lower than the three traditional models, with an improvement of 44.9% in LH and 50% in GH, respectively. The Root Mean Square Error for NM is estimated as ±3 mm compared with the levelling measurements, which is also better than the traditional models. The results prove the reliability and feasibility of the NM for the deformation monitoring of soft clay highways. The estimated rheological parameters can broaden the application of InSAR technology and provide a reference index for the stability control of highway construction engineering.