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Gao X.,Beijing Normal University | Gao X.,Beijing Key Laboratory of Environmental Remote Sensing and Digital City | Chen X.,Beijing Normal University | Chen X.,Beijing Key Laboratory of Environmental Remote Sensing and Digital City | And 4 more authors.
Huanjing Kexue Xuebao/Acta Scientiae Circumstantiae | Year: 2015

Changes of chemical composition of rainfall can indirectly reflect the characteristics of atmospheric pollution. This study explores the spatial and temporal variations of chemical composition in rainfall from four sites along an urban-to-rural transect in Beijing during rainy season in 2013-2014. Meanwhile, possible sources for major ions are identified by PMF model. Results show that the pH values of rainfall at four sites are 6.52, 6.57, 6.60 and 6.52, with slight increases from urban to rural site. Ion concentrations are high in spring and fall but low in summer. VWM of main ions can be separated into three groups. Higher concentration group includes Ca2+, NH4 + and SO4 2- with VWM between 100~400 μeq·L-1, 100~350 μeq·L-1, and 350~500 μeq·L-1, respectively. Middle concentration group includes Mg2+, Na+ and NO3 - with VWM in the range of 0~150 μeq·L-1, 0~250 μeq·L-1, and 0~150 μeq·L-1, respectively. K+, Cl- are in lower concentration group, with VWM in the range of 0~45 μeq·L-1 and 0~90 μeq·L-1, respectively. VWM of NH4 + and Mg2+ decreases from urban to rural area. SO4 2- is the major acid ion while Ca2+, NH4 + and Mg2+ are major neutralizing ions with average neutralization indices of 0.5, 0.4 and 0.1, respectively. Deposition fluxes of SO4 2- and Ca2+ are 55.66 kg·hm-2·a-1 and 24.10 kg·hm-2·a-1 at BNU and 26.03 kg·hm-2·a-1 and 10.84 kg·hm-2·a-1 at YQ, respectively. Deposition flux of nitrogen in ammonium and nitrate at four sites change from 7.27 to 14.05 kg ·hm-2·a-1 and from 2.50 to 5.07 kg ·hm-2·a-1, respectively. Flux of Mg2+ and Cl- change between 1.32~4.48 kg ·hm-2·a-1 and 3.67~6.10 kg ·hm-2·a-1 at four sites, respectively. Wet deposition is the major form of ion settlement, and co-varies seasonally with rainfall. PMF simulations show that dust and construction associated pollutants are high at the four sites. Vehicle emissions are serious in the downtown area. Secondary pollution is serious at urban site. The suburban site is greatly influenced by NH3 emission from farms and landfills. © 2015, Science Press. All right reserved.


Yang X.,Beijing Normal University | Yang X.,Beijing Key Laboratory of Environmental Remote Sensing and Digital City | Liu H.,Beijing Normal University | Liu H.,Beijing Key Laboratory of Environmental Remote Sensing and Digital City | And 6 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2016

Data-lacking regions have little information about the economic value of the cultivated land in the historical years. This research chose Lhasa city in Tibet as an example to discuss a method based on GIS (geographic information system), which was to propose the historical economic value evaluation of cultivated land in data-lacking regions and to fill the blanks of the historical economic value data of the cultivated land. This research took 3 factors based on the agricultural location theory from natural and human aspects, i.e., the slope, the stable light at nighttime, and the distance from the cultivated land to the roads at different levels. The data resources of this research were defense meteorological satellite program/ operational linescan system (DMSP/OLS), digital elevation model and the road data, all of which were free and easy to acquire. In addition, all of the data were in the year of 2010 in order to ensure the data quality and have the corresponding Google Earth data which were treated as the ground true reference. First of all, we graded each factor into 5 classes and assigned them the score of 1, 3, 5, 7 and 9, which indicated their contributions for improving the economic value for the corresponding cultivated land. Second, we decided the weights for each factor in each grade by employing the method of analytic hierarchy process. Third, the spatial weighted overlay based on GIS was introduced to calculate the final score for each cultivated land which was the reference to determine their historical economic value. The method was firstly testified in the Chengguan district, Lhasa city and then applied to the whole city. We classified the cultivated land in Chengguan district into 3 classes, and the first class cultivated land referred to the farmland which had the highest historical economic value and the third class referred to the lowest one. After that we introduced the stratified random sampling method into this research to perform the accuracy assessment. We selected 100 pieces of cultivated land according to their area and compared the evaluation result with the ground true data by visual interpretation. The overall accuracy of the historical economic value evaluation of Chengguan district was 84% which meant that the proposed method was very effective. Instead of applying the method to the whole city directly, we took the economic differences among the 8 counties into account. We classified the economic level of the 8 counties into 3 class based on their stable light intensity with the help of the nighttime light satellite imagery before the method was extended to the whole Lhasa city. The accuracy assessment was also performed by the randomly selected 1000 pieces of the cultivated land in the whole city. The overall accuracy of the evaluation was 82.6% with an overall Kappa coefficient of 0.722, the user's accuracy of the first-class cultivated land was 79.63%, and the producer's accuracy was 71.27%; the user's accuracy of the second-class cultivated land was 84.76%, and the producer's accuracy was 80.91%; the user's accuracy of the third-class cultivated land was 81.58%, and the producer's accuracy was 89.97%. The result indicated that the extended method based on the nighttime imagery was scientific and effective to apply the evaluation method proposed in this study to a larger study area, and this method was robust and easy to realize. In summary, the area of the cultivated land in middle level accounted for 58.43% of the total area. Chengguan district, as the center of Lhasa city, had the most highest cultivated land value, and the Linzhou county had the lowest economic value of cultivated land. Further work should focus on the realization of this method in other years, which helps discuss the change trajectory of the cultivated land economic value in time series. © 2016, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.


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.


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.


Zhang Y.H.,State Key Laboratory of Remote Sensing Science | Zhang Y.H.,Beijing key Laboratory of Environmental Remote Sensing and Digital City | Zhang Y.H.,Beijing Normal University | Liu H.P.,State Key Laboratory of Remote Sensing Science | And 2 more authors.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2016

China have occurred unprecedented urban growth over the last two decades. It is reported that the level of China's urbanization increased from 18% in 1978 to 41% in 2003, and this figure may exceed 65% by 2050. The change detection of long time serious remote sensing images is the effective way to acquire the data of urban land-cover change to understand the pattern of urbanization. In this paper, we proposed the similarity index (SI) and apply it in long time series urban land-cover change detection. First of all, we built possible change trajectories in four times based on the normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI) that extracted from time series Landsat images. Secondly, we applied SI in similarity comparison between the observed change trajectory and the possible trajectories. Lastly, verifying the accuracy of the results. The overall accuracy in four periods is 85.7% and the overall accuracy of each two years is about 90% and kappa statistic is about 0.85. The results show that this method is effective for time series land-cover change detection.


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.


Yang B.,Beijing Normal University | Yang B.,Beijing Key Laboratory of Environmental Remote Sensing and Digital City | Liu Z.,Beijing Normal University | Liu Z.,Beijing Key Laboratory of Environmental Remote Sensing and Digital City | And 6 more authors.
Proceedings - 2011 19th International Conference on Geoinformatics, Geoinformatics 2011 | Year: 2011

Multi-temporal remote sensing data can provide much more information that could be used to improve the accuracy of classification of vegetation types. However, it is always required to manually select a set of training samples by using conventional supervised classification methods, which is a time-consuming and costly task. In this paper, a new classification method based on spectral knowledge database (SKD) has been proposed. The spectral knowledge database is composed by a serious of spectral datasets, prior knowledge, and remote sensing models. For a specific remote sensing image, the data of object class can be simulated based on the knowledge database, which can be used to generate the classification rules. With this approach, an experiment of sugarcane identification was conducted. And the results show that the accuracy of the results by using proposed method was comparable to that by using supervised classification. © 2011 IEEE.


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.


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.

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