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.
A. Prof. Heng Dong
Wuhan University of Technology
Research Area：Environmental Remote Sensing
Title: Estimation of Global Terrestrial Gross Primary Productivity based on Solar-Induced Chlorophyll Fluorescence
Abstract: Solar-induced chlorophyll fluorescence (SIF) is directly related to plant photosynthesis. It is a kind of light signal emitted by the photosystem after plants absorb solar energy. With the development of remote sensing technology and SIF extraction algorithm，it is a new way to monitor the growth status of vegetation and estimate the gross primary productivity（GPP）of the terrestrial ecosystem. However, the relationship between SIF and GPP at the canopy scale is affected by many factors such as canopy structure, plant species, external environmental stress. The most widely used GPP-SIF empirical linear estimation model currently lacks universality.
In this study, after analyzing the fluorescence emission mechanism at different spatial scales, and the GPP-SIF empirical linear estimation model, some factors affecting the photosynthetic capacity of the vegetation and the canopy SIF emission were introduced to construct a new GPP estimation method. Combining GOME-2 SIF products and FLUXNET2015 data set to measure GPP and MODIS related products, we analyzed model accuracy with different vegetation types .The research results show that the estimation accuracy of the model on all vegetation types has been greatly improved compared with the empirical linear estimation model, and the model can better reflect the seasonal change characteristics of the different vegetation types GPP.The model’s estimate of 128.86 Pg C/year (fair value 123±8 Pg C/year) GPP globally in 2010 shows the effectiveness of the model's application on a global scale.
摘要：日光诱导叶绿素荧光（solar-induced chlorophyll fluorescence，SIF）与植物光合作用直接关联，是植物吸收太阳光能后由光系统发射的一种光信号，随着遥感技术和SIF提取算法的发展，开辟了监测植被生长状态和估算陆地生态系统总初级生产力（Gross Primary Productivity，GPP）的新途径。不过由于在冠层尺度上SIF与GPP的关系还受到了冠层结构、植物种类、外界环境胁迫等多种因素影响，导致冠层GPP-SIF相互关系变化机理依然不明确，使得目前使用最广泛的GPP-SIF经验线性估算模型仍缺乏普适性。
本研究通过分析不同空间尺度上的荧光发射机理，在GPP-SIF经验线性估算模型的基础上，引入一些影响植被光合能力和影响冠层SIF发射的一些因素，构建了基于近红外荧光的GPP估算理论模型。结合GOME-2 SIF产品、FLUXNET2015数据集中实测GPP和MODIS相关产品，在不同植被类型上构建该理论模型并进行估算精度验证分析。研究结果显示：模型在所有植被类型上的估算精度都较经验线性估算模型有了很大的提高，同时模型能较好地体现出各站点所代表的不同植被类型GPP的季节性变化特征。模型对于2010年全球128.86 Pg C/year （公允值123±8 Pg C/year）GPP的估算量更是表明了模型在全球尺度应用的有效性。
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.