作者机构：
[Yang Dong-xu; Zhong Yong-de] Cent South Univ Forestry & Technol, Tourism Coll, Changsha 410004, Hunan, Peoples R China.;[Yang Dong-xu] Tourism Coll Zhejiang, Hangzhou 311231, Zhejiang, Peoples R China.;[Wei Jing] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China.;[Wei Jing] Tsinghua Univ, Dept Earth Syst Sci, Beijing 100084, Peoples R China.
通讯机构：
[Zhong Yong-de] C;[Wei Jing] B;[Wei Jing] T;Cent South Univ Forestry & Technol, Tourism Coll, Changsha 410004, Hunan, Peoples R China.;Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China.
关键词：
气溶胶光学厚度;高亮地表;城市地区
摘要：
大气气溶胶是影响城市环境空气质量的重要因素, 同时对人类健康具有重要影响。 传统的气溶胶遥感反演方法多适用于海洋及植被等地表反射率较低的区域, 对于城市等高亮地表区域, 地表反射率较高且难以确定, 气溶胶反演面临巨大挑战。 针对该问题, 提出一种新的地表反射率的确定方法, 将下垫面划分为暗地表和亮地表两种类型, 分别使用可见光与短波红外的线性关系和利用长时间序列MODIS表现反射率数据使用最小值合成技术构建先验数据集的方法, 确定其地表反射率, 然后基于辐射传输方程理论利用查找表方法, 进行气溶胶光学厚度反演。 选择下垫面复杂、 空气污染问题严重的北京市作为研究区, 应用MODIS数据进行气溶胶反演实验, 最后使用北京站、 香河站、 北京CAMS站和北京RADI站4个AERONET气溶胶地基观测数据和MODIS气溶胶产品对反演结果进行对比验证。 结果表明该算法气溶胶反演结果与地基观测数据具有较高的一致性(R2=0.902), 能以较高精度实现城市等高反射率地区的气溶胶反演, 反演精度与空间连续性上较MOD04有显著提高。 Atmospheric aerosol is one of the most important factors that affect air quality of urban environment, meanwhile, it has important effects on human health. Traditional aerosol optical depth (AOD) retrieval algorithms are always suitable for dark areas with low surface reflectance including ocean and densely vegetated areas, however, for bright urban areas, surface reflectance is high and difficult to be determined, leading to great challenges. Aiming at this problem, a new improved approach of surface reflectance estimation is proposed and the underlying surfaces are divided into dark and bright areas. Surface reflectance is determined using the simulated relationships between the surface reflectance between visible and short-wave infrared channels and a priori surface reflectance dataset constructed with long time series of MODIS apparent reflectance images using the minimum value synthesis technology. Then aerosol retrieval is performed based on the radiative transfer theory with pre-calculated Look-up Tables. Beijing, which features complex surfaces and serious air pollution, is selected as the study area and the proposed algorithm is applied to the MODIS data for aerosol retrieval experiments. Four AErosol RObotic NETwork (AERONET) AOD ground-measured stations, Beijing, Xianghe, Beijing_CAMS and Beijing_RADI, and operational MODIS aerosol product (MOD04) are selected for validation and comparison purposes. Results showed that AOD retrievals are highly consistent with AERONET AOD ground measurements (R2=0.902) and showed overall higher accuracy with more detailed spatial distribution compared to MOD04 AOD products over bright urban areas.
摘要：
Mitigating tourism's emissions, in particular related to transportation is critical, to achieve the broader national emission reduction targets in developing countries. To gain a better understanding of the carbon emissions related to one of Chinese top destinations, and assess changes over time, this article analyses the travel patterns of both domestic and international visitors to Zhangjiajie, Hunan province. Two points in time are examined, namely 2009 and 2015. The results show that despite some contraction towards closer and shorter trips, the overall growth (a near doubling in arrivals) led to a substantial increase in carbon emissions. The increasing role of the private car and high-speed rail has been noted in particular. The average transportation carbon footprint of visitors to Zhangjiajie changed from 94.55 kg CO2 in 2009 to 82.97 kg in 2015 per trip, and 18.87 kg in 2009 to 16.46 kg in 2015 per visitor day. (C) 2017 Published by Elsevier Ltd.
会议名称：
2016 IEEE International Conference on Big Data Analysis (ICBDA)
会议时间：
March 2016
会议地点：
Hangzhou, China
会议论文集名称：
2016 IEEE International Conference on Big Data Analysis (ICBDA)
关键词：
tourists flows;phone big data;analysis system
摘要：
The analysis of tourist flows behavior can help to achieve the reasonable allocation of social resources for tourist area and respond effectively to the tourist destination traffic pressure, maintain social public security, etc. The traditional manual analysis methods, such as questionnaire survey, have the characters of the high cost and low efficiency. With the development and popularity of smart phones in the daily life, on the one hand, it brings convenience for people. On the other hand, the user mobile trajectory data provides the effective analysis of tourist flows behavior. This paper presents a framework for the analysis of the distributed tourist flows, and uses the multi-node processing tasks, thus to improve the performance and scalability of the algorithm. It takes advantage of the mobile track data provided by the mobile phone operators to analyze the tourist flows, and establish several models, including the vacation destination's tourist flows behavior analysis model and habitual residence's tourists analysis model. Compared with the traditional methods, this scheme has lower cost, higher efficiency and wider coverage.
