Entity

Time filter

Source Type


Bo Y.-C.,Beijing Normal University | Bo Y.-C.,Beijing Key Laboratory of Environmental Remote Sensing and Digital City | Song C.,Beijing Normal University | Song C.,Beijing Key Laboratory of Environmental Remote Sensing and Digital City | And 4 more authors.
BMC Public Health | Year: 2014

Background: There have been large-scale outbreaks of hand, foot and mouth disease (HFMD) in Mainland China over the last decade. These events varied greatly across the country. It is necessary to identify the spatial risk factors and spatial distribution patterns of HFMD for public health control and prevention. Climate risk factors associated with HFMD occurrence have been recognized. However, few studies discussed the socio-economic determinants of HFMD risk at a space scale. Methods. HFMD records in Mainland China in May 2008 were collected. Both climate and socio-economic factors were selected as potential risk exposures of HFMD. Odds ratio (OR) was used to identify the spatial risk factors. A spatial autologistic regression model was employed to get OR values of each exposures and model the spatial distribution patterns of HFMD risk. Results: Results showed that both climate and socio-economic variables were spatial risk factors for HFMD transmission in Mainland China. The statistically significant risk factors are monthly average precipitation (OR = 1.4354), monthly average temperature (OR = 1.379), monthly average wind speed (OR = 1.186), the number of industrial enterprises above designated size (OR = 17.699), the population density (OR = 1.953), and the proportion of student population (OR = 1.286). The spatial autologistic regression model has a good goodness of fit (ROC = 0.817) and prediction accuracy (Correct ratio = 78.45%) of HFMD occurrence. The autologistic regression model also reduces the contribution of the residual term in the ordinary logistic regression model significantly, from 17.25 to 1.25 for the odds ratio. Based on the prediction results of the spatial model, we obtained a map of the probability of HFMD occurrence that shows the spatial distribution pattern and local epidemic risk over Mainland China. Conclusions: The autologistic regression model was used to identify spatial risk factors and model spatial risk patterns of HFMD. HFMD occurrences were found to be spatially heterogeneous over the Mainland China, which is related to both the climate and socio-economic variables. The combination of socio-economic and climate exposures can explain the HFMD occurrences more comprehensively and objectively than those with only climate exposures. The modeled probability of HFMD occurrence at the county level reveals not only the spatial trends, but also the local details of epidemic risk, even in the regions where there were no HFMD case records. © 2014 Bo et al.; licensee BioMed Central Ltd. Source


Xu W.,Beijing Normal University | Xu W.,Beijing Key Laboratory of Environmental Remote Sensing and Digital City | Sun R.,Beijing Normal University | Sun R.,Beijing Key Laboratory of Environmental Remote Sensing and Digital City | Jin Z.,Zhejiang Climate Center
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2016

Tea is the most consumed natural plant drink in the world, and it plays an important role in human's daily life. The spatial distribution information of tea plantation is helpful for the government management and decision-making. Songyang county is located in southwest part of Zhejiang province, China, and the topography is characterized by basin in the central and surrounded by hills and mountains. The humid and cloudy climate is very suitable for tea planting, which accounts for the large proportion of tea plantation area, 68.5% of county's whole cultivated land. In this paper, ZhangXi Town, ZhaiTan Town, YeCun Town and ZhuYuan Town of SongYang County in Zhejiang Province were chosen as study area, and ZY-3 satellite images acquired on December 25, 2012 and June 9, 2013 were used to study the method of tea plantations extraction. Eight categories including roads, water, buildings, shadows, bare soil, forest, other crops and tea plantation were identified after conducting visual interpretation and field surveys. The decision tree method was adopted to extract the tea plantations. Due to the fact that tea plants in plain areas and mountains areas show different characteristics in their planting patterns, planting area and growth status,, the decision trees were built separately for these two different areas. The threshold values in the decision tree were determined by gradually changing their values in a certain range. Spectral curve analysis shows the range of the difference between band4 (0.77-0.89 μm) and band3 (0.63-0.69 μm) on December 25, 2012 is 20-30. The normalized difference vegetation index (NDVI) is almost unchanged or decreased from summer to winter for forest lands as they are covered mainly by evergreen broad-leaved forest, deciduous broad-leaved forest, bamboo forest and mixed forest. As for tea plant, due to its seasonal harvest and pruning in summer, NDVI in summer is a little lower than that in winter and the threshold value of NDVI difference between summer (June 9, 2013) and winter (December 25, 2012) was 0~0.1.As tea plants are terraced planted along the contour in mountain area, texture features characterized with nearly parallel line trend for tea plantations are presented in the image. The panchromatic data on December 25, 2012 was used to derive texture features. Anisotropic strength with a range of 0 to 1 was obtained after conducting the anisotropic strength algorithm. The classification results with different threshold values were compared with region-of-interest data and threshold values with the highest overall accuracy and Kappa coefficient were selected as final threshold. For plain areas, the difference between band4 and band3 was used to roughly exclude roads, water, buildings, bare soil, other crops and part of the forest from tea plantations with the value above 26.Then the threshold value of 0 for NDVI difference between summer and winter was adopted to exclude the remaining forest. Spectral feature and textural feature were both used to extract tea plantations in mountainous areas. The threshold value of 20 for band4 and band3 difference and 0 for NDVI difference between summer and winter were firstly adopted to exclude water, buildings, crops, roads, bare soil and part of forest. And the threshold of 0.35 for anisotropic strength was then adopted to exclude the remaining forest. The classification maps were validated with ground verification data and compared with results derived from neural network (NN) classification. The results show that decision tree method combining with spectral and textural information can significantly improve the classification accuracy. The overall accuracy and Kappa coefficient in the plain area were 95.00% and 0.85, respectively, increased by 5.46% and 0.19 when compared with NN classification. The overall accuracy and Kappa coefficient in the mountain area were 92.97% and 0.69, respectively, increased by 7.57% and 0.61 when compared with NN classification. The presented study could provide a reference for government forecasting crop production and preventing disaster for tea plantation. © 2016, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved. Source


He W.,Beijing Normal University | He W.,Beijing Key Laboratory of Environmental Remote Sensing and Digital City | Yang H.,Beijing Normal University | Yang H.,Beijing Key Laboratory of Environmental Remote Sensing and Digital City
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2013

MODIS sensors, carried onboard Terra and Aqua satellites, scan the same location daily at a fixed time. Because of the sequential multidirectional information contributed by satellite orbit drift along with multi-channel spectral responses, MODIS data greatly enriches the observations of land surface targets, which makes it possible to estimate the land surface parameters accurately and timely, such as leaf area index (LAI). Many researchers have focused on LAI estimation using MODIS data, among whom most used the multispectral data of a single satellite in one day or eight days, while few comprehensively utilized the multispectral and multidirectional information obtained by the both MODIS sensors in some sequence of days. MODIS LAI products have developed a series of generations, the fifth version (MODIS V005) has integrated data from both Terra and Aqua. It is proven that this version is improved with single satellite data, however, it only utilizes red and near-infrared band observations. It has been suggested that taking the shortwave infrared band observations into consideration can help improve the accuracy of LAI estimation. Moreover, some validation studies indicate that there are still some limitations in applying current MODIS LAI products, e.g., LAI is overestimated or underestimated to some extent in different regions. Therefore, this paper investigates the methods of winter-wheat LAI retrieval using multispectral and multidirectional observations of Terra/Aqua MODIS in consecutive days. In this study, data preprocessing, including cloud status and data quality checking, was used first to remove the observations with partial or complete cloud cover, cloud shadow, or low pixel quality in the study area. Then, LAI, average leaf angle (ALA), chlorophyll content (Cab), water content (Cw) and dry matter content (Cm) were selected as the inversion parameters through sensitivity analysis. Other parameters were fixed by drawing upon previous studies and a priori knowledge obtained from field measurements. Accordingly, a look-up table (LUT) of the PROSAIL model was generated. In order to determine the optimal bands and angles of observations, some tests were done with simulated data before the inversion. The RMSE (root mean square error) and R2 (determination coefficient ) between the estimated and the true LAI illustrates that the accuracy is improved when the data of 648, 858, 550, 1240, 1640 and 2130 nm wavelengths and multidirectional observations are chosen. Finally, LAI was estimated by searching the LUT and the mean of the 50 best cases were taken as the final solution. Comparison between the LAI results and the NDVI derived from HJ-1 CCD and MODIS data shows that they are consistent in spatial pattern. Validation using field-measured LAI illustrates the results of our method are better than MODIS LAI products in temporal variation characteristics and closer to the field-measured LAI. Nevertheless, the retrieved LAI of winter wheat is usually lower than the field-measured values, regardless of whether the PROSAIL model or MODIS 3D radiative transfer model is used. This may be partly caused by the effect of mixed pixels, which needs to be verified by further studies with high spatial resolution data. Another reason is that a saturation phenomenon often occurs at high LAI levels resulting from the low sensitivity of canopy reflectance in this domain. Source


Chen Y.,Beijing Normal University | Chen Y.,Beijing Key Laboratory of Environmental Remote Sensing and Digital City | Zhang W.,Beijing Normal University | Zhang W.,Beijing Key Laboratory of Environmental Remote Sensing and Digital City | And 3 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2013

Building boundary is important for the urban mapping and real estate industry applications. The reconstruction of building boundary is also a significant but difficult step in generating city building models. As Light detection and ranging system (Lidar) can acquire large and dense point cloud data fast and easily, it has great advantages for building reconstruction. In this paper, we combine Lidar data and images to develop a novel building boundary reconstruction method. We use only one scan of Lidar data and one image to do the reconstruction. The process consists of a sequence of three steps: project boundary Lidar points to image; extract accurate boundary from image; and reconstruct boundary in Lidar points. We define a relationship between 3D points and the pixel coordinates. Then we extract the boundary in the image and use the relationship to get boundary in the point cloud. The method presented here reduces the difficulty of data acquisition effectively. The theory is not complex so it has low computational complexity. It can also be widely used in the data acquired by other 3D scanning devices to improve the accuracy. Results of the experiment demonstrate that this method has a clear advantage and high efficiency over others, particularly in the data with large point spacing. © 2013 SPIE. Source


Zhang W.,Beijing Normal University | Zhang W.,Beijing Key Laboratory of Environmental Remote Sensing and Digital City | Zhang X.,Beijing Normal University | Zhang X.,Beijing Key Laboratory of Environmental Remote Sensing and Digital City | And 8 more authors.
Computers and Geosciences | Year: 2013

Acquiring high resolution topography products of the Moon is an important issue for further lunar exploration. In this paper, we present a data processing flow of 3D reconstruction based on Chang'E-1 (CE-1) three-line imagery (120. m) and laser altimeter (LAM) data to acquire pixel-level resolution DEM. First, initial global disparity estimation was generated, and then corresponding points were acquired pixel-by-pixel by Adaptive Support-Weight Approach. A Least Square Image matching method was used to achieve sub-pixel accuracy, ultimately resulting in pixel-level resolution DEM (120. m). Global lunar 100. m DEM (GLD100 DEM) from LROC WAC stereo imagery is employed for comparison analysis and the mean elevation difference between two DEMs is less than 120. m. Experimental results show that the processing flow is effective and reliable for acquiring pixel-level DEM based on CE-1 data. © 2013 Elsevier Ltd. Source

Discover hidden collaborations