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Yang Y.-J.,Nanjing University | Tian Q.-J.,Nanjing University | Tian Q.-J.,Jiangsu Center For Collab Innovation In Geographical Information Resource Development And Application
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2015

With the development of hyperspectral remote sensing technology, there are more and more researches which are related to monitoring the growth condition of rice by it. However, most of recent researches focus on the biochemical component content by monitoring hyperspectral of rice leaf. As a consequence, there are rare researches which estimate rice leaf area index by analyzing canopy hyperspectral feature at different phenological periods. After field investigation, we find that from tillering to jointing, the rice's canopy structure changed obviously and LAI increased fast. The situation of rice's growth at this stage has an incredible influence on its late growth and yield. After jointing stage, the change tendency of LAI tends to be steady and the characteristic change of canopy structure is unapparent. If we get hyperspectral of rice's canopy at the right time, we can analyze the characteristics and predict the tendency of canopy. It's also valuable on guiding the management of rice field. On the other hand, this paper also gives useful reference on crop condition monitoring using hyperspectral. For all this, using ASD and LAI-2000 to measure rice canopy spectral reflectance and LAI in tillering and jointing stage. Then the relationship between spectral reflectance and LAI is analyzed in two periods. In order to quantitatively describe the correlation, the relationship between red edge parameters and LAI is studied and rice LAI estimation model is build. Finally, using measured data to evaluate this model. The results show that using hyperspectral feature of rice to estimate LAI is feasible. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.


Wang M.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Wang M.,University of Chinese Academy of Sciences | Wang J.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Wang J.,Jiangsu Center For Collab Innovation In Geographical Information Resource Development And Application
International Conference on Geoinformatics | Year: 2016

Science data sharing has many advantages for both scientific research and education. Knowing about behaviors of science data sharing participants is valuable to support informed decision making on data sharing policy and data sharing website design. Nowadays, data sharing is mainly carried through the Internet, and web usage mining provides an ideal approach to uncover user behaviors of data sharing. This paper presents a data preprocessing framework for further user behavior mining of a geoscience data sharing portal (geodata.cn). The preprocessing steps included data cleaning, user identification, session identification, and data modeling. Web server logs served as the major data source of this study. Heuristic algorithms were employed to accomplish data cleaning and user identification. Different session identification methods were applied for comparison. Users' geolocation were identified using an online Geo-IP lookup tool, which provides geographical coordinates of an IP address. On the basis of all the preprocessing procedures, a web usage data model of science data sharing portal were proposed for further user behavior mining, such as user classification and spatial association rules mining. © 2015 IEEE.


Bai Z.-Q.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Bai Z.-Q.,University of Chinese Academy of Sciences | Wang J.-L.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Wang J.-L.,Jiangsu Center For Collab Innovation In Geographical Information Resource Development And Application
International Conference on Geoinformatics | Year: 2016

An adequate knowledge of population distribution in the long term is increasingly being used in both science and policy. In this paper, we proposed a GIS based approach using remotely sensed land use, land cover, night light emissions, and NDVI data to redistribute the aggregated population statistics at township level into a regular 100m ∗ 100m grid in 2000 and 2010 across the core area of Loess Plateau, China. Nighttime light emission data from the DMSP satellites was firstly combined with NDVI to generate a Vegetation Adjusted Nighttime Light Urban Index (VANUI) map with less saturation and more variation within inter-urban area. The land use (or land cover) data was then reclassified and rasterized to provide a 100-m resolution map. Then, VANUI was matched to the land use classes across the research area. The entire township units of the research area were divided into three different zones according to their population density. Stepwise regression method was used to derive the model of relationship between census population counts (at township level) and land use area and night light indicators for each zone. Based on these equations, we redistribute the statistics of every township unit into the 100m ∗ 100m grid. All the relationship models of each zone were seen to be good with a relative high R2 and low SEE and the generated population distribution map is spatially explicit and quantitatively detailed. In summary, the method here is illustrated to be effective to model the population distribution in long term with a high resolution and the population distribution maps in 2000 and 2010 in Loess Plateau is expected to greatly assist related researches in the region. © 2015 IEEE.


Liu L.,Nanjing University of Information Science and Technology | Zhang X.,Nanjing University of Information Science and Technology | Wang S.,Nanjing University of Information Science and Technology | Zhang W.,Nanjing University of Information Science and Technology | And 2 more authors.
Atmospheric Environment | Year: 2016

A systematic dataset of an observation network on a national scale has been organized to investigate the spatial distribution of bulk sulfur (S) deposition (Sdep) throughout China during 2000-2013, representing by far the most detailed data set to track the bulk sulfur deposition throughout China since 2000. Such a dataset is needed for ecosystem studies and for developing emission control policies. Bulk Sdep values showed great variations, ranging from 2.17 to 70.55 kg ha-1 y-1, with an average of 22.99 kg ha-1 y-1. The average rate of bulk Sdep located in East Coastal region (35.97 kg ha-1 y-1), Middle Yangtze region (57.90 kg ha-1 y-1), Middle Yellow River region (23.42 kg ha-1 y-1), North Coastal region (42.19 kg ha-1 y-1), Northeast region (34.28 kg ha-1 y-1), South Coastal region (36.97 kg S ha-1 y-1), Southwest region (33.85 kg ha-1 y-1) was 4.50, 7.24, 2.93, 5.28, 4.29, 4.63 and 4.24 times than that in Northwest region (7.99 kg ha-1 y-1). Bulk Sdep over China was mainly from fossil fuel combustion (76.96%), biomass burning (7.64%), crust (6.22%), aged sea salt (5.48%) and agriculture (3.68%). A systematic observation network on a national scale should be established to conduct a long-term monitoring atmospheric Sdep (including wet and dry deposition), based on exiting ecological stations administrated by different departments in China. © 2016 Elsevier Ltd.


Liao Y.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Liao Y.,U.S. Center for Disease Control and Prevention | Ouyang R.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Ouyang R.,U.S. Center for Disease Control and Prevention | And 6 more authors.
BMC Public Health | Year: 2015

Background: A large number of hand, foot and mouth disease (HFMD) outbreaks was reported during 2008 in China. However, little is known about the effects of meteorological conditions on different temporal and spatial scales on HFMD incidence in children. The aim of this study was to explore the relationship between meteorological data on various temporal and spatial scales and HFMD incidence among children in Shandong Province, China. Methods: The association between weekly HFMD cases and meteorological data on different temporal and spatial scales in Shandong Province from May 2008 to July 2008 and September 2008 to October 2008 was analyzed, using buffer analysis and the singular value decomposition method. Results: Wind speed within a 50-km buffer circle of counties in Shandong Province with two-week lag and RH within a 10-km buffer circle of counties with eight-week lag were significantly associated with HFMD incidence. We found a positive correlation between wind speed within the 50-km buffer circle in the prior two weeks and wind speed within the province in the prior one week. Conclusions: This study revealed strong associations between HFMD incidence in children and wind speed and RH. Thus, meteorological anomalies in the prior two or eight weeks could be used as a valid tool for detecting anomalies during the peak periods of infectious disease. © 2015 Liao et al.

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