Monitoring of winter wheat distribution and phenological phases based on MODIS time-series： A case study in the Yellow River Delta, China

Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta（YRD） region using moderate resolution imaging spectroradiometer（MODIS） time-series data. The normalized difference vegetation index（NDVI） was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection.

Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data

By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.

The Relevant Analysis on the Stop Time in Winter and Times of Plateau and the Index in Early Days for Garrisonned Plateau Constructors

Objective: Study the influence of the stop time in winter, times of plateau on the index in early days for plateau constructors meet;Method:Is it participate in plateau construction of 2002-2004 to choose,enter into the plateau again of 2003-2005 practise clothes finish to mate 326 materials “physical examination in front of the worker",which is passed in Nanshankou Hospital in early days, divided into 3 groups according to the difference of year for the physical examination, examine by leaning towards relevant analytical methods;Result: （1） In the situation of day controlling about the stop time in winter, times of garrison in plateau and blood and oxygen saturation lever （SaO2）, the systolic pressure （sBP） is presented and shouldered relevantly winter to control. Present positive correlation with the value of hemoglobin （Hb）;（2） It is stopped that in case of controlling and is garrisoned in the number of times of plateau in winter day and blood and oxygen saturation lever （SaO2） to present positive correlation. Present and shoulder with the hemoglobin （Hb） relevantly.Conclusion: In order to ensure the health of plateau constructors to the maximum extent, should try one’s best to reduce the number of times of returning to the plateau in possible cases. At the same time each one constructs for year and returns to the time that the hinterland concentrates rest should be on above 90 days.

基于Holt-Winters的电离层总电子含量预报

利用IGS中心提供的不同经纬度平静期、活跃期14d的电离层TEC格网点数据,以前8d的TEC值作为样本序列,分别采用Holt-Winters加法模型和乘法模型建立TEC预报模型,并预报后6d的TEC值。结果表明,无论在电离层平静期还是活跃期,2种模型所得预报结果大致相同,并与实际观测数据吻合较好,但加法模型的预测结果能更好地反映电离层TEC的变化特性。

综合Winters模型和ARMA模型预测GDP

以广东省GDP的时间序列数据为依据,分别应用Winters模型和ARMA模型以及加权综合这两个模型的方法对其进行季度性GDP值的短期预测,通过比较由不同方法得出的预测结果的绝对百分误差的大小,选择出绝对百分误差最小的方法·

大伙房水库入库径流的Holter-Winter预报模型——兼与付文艺老师商榷

为研究大伙房水库入库径流序列的波动规律,运用时间序列分析理论和方法对径流序列进行分析和建模。分析的结果显示,大伙房水库径流序列是不具有ARMA模型相关性统计特征的平稳时间序列。而运用指数平滑法建立的Holter-Winter模型能较好地拟合径流序列波动规律,且预测效果好。因此,Holter-Winter模型可作为大伙房水库入库径流量预报模型使用。

Holt-Winters模型与X-11模型在预测中的比较研究

以实际数据为例,对一时间序列分别建立Holt-Winters模型与X-11模型并进行预测。从而对这两种统计模型进行比较研究,得出对于既有趋势又有季节性的时间序列进行预测X-11模型优于Holt-Winters模型。

基于Holt-Winters的时间序列预测在大数据监控系统中的分析与应用

针对大数据监控系统对时间序列预测准确性和实时性的需求,以及大数据监控系统中时间序列呈现趋势性和季节性变化的特点,选择Holt-Winters算法建立时间序列预测模型。首先介绍时间序列的概念和特点,然后分析Holt-Winters算法的原理以及预测条件。选取合适的平滑系数是影响Holt-Winters算法预测准确性的关键,结合L-BFGS算法在不同时间区间求最优解,实现动态平滑系数的选取。最后以用户2天的页面访问量作为实验数据,通过相对误差指标的比较分析,验证该算法能满足大数据监控系统对时间序列预测的需求,具有较好的实际应用效果。

Size matters: wintering ducks stay longer and use fewer habitats on largest Chinese lakes

Background:Evidence suggests that wintering waterbirds have become conspicuously more concentrated at two largest lakes of the Yangtze River Floodplain,East Dong Ting Lake(Hunan Province,29°20′N,113°E)and Poyang Lake(Jiangxi Province,29°N,116°20′E),relative to other lakes,despite the establishment of reserves elsewhere.While this relationship is likely due to greater extent of undisturbed habitats in larger lakes,we understand little of the drivers affecting individual behaviours behind this tendency.Methods:We tracked wintering movements of three duck species(Eurasian Wigeon Mareca penelope,Falcated Duck M.falcata and Northern Pintail Anas acuta)using GPS transmitters,examining differences between the two largest lakes and other smaller lakes in ducks’habitat use,duration of stay at each lake and the daily distances moved by the tagged birds while at these sites.Results:The Eurasian Wigeon and Falcated Duck stayed five times longer and almost exclusively used natural habitat types at the two large lakes(91‒95%of positions)compared to length of stay time at smaller lakes,where they spent 28‒33 days on average(excluding the capture site)and exploited many more different habitats(including c.50%outside lakes).Conclusions:Our study is the first to show that shorter length of stay and more varied habitat use by ducks at small lakes may contribute to explaining the apparent regional concentration of numbers present of these and other species at the largest lakes in recent years.This compares with their declining abundance at smaller lakes,where habitat loss and degradation has been more manifest than on the larger lakes.

Holt-Winters与ARIMA模型在电离层总电子含量预报中的比较

利用IGS中心发布的15天TEC数据作为样本,采用Holt-Winters加法、Holt-Winters乘法和ARIMA模型分别进行5天的TEC值预报。分析了太阳活动、纬度、预报时长对3种模型预报精度的影响。实验结果表明,3种模型的预报精度会随着纬度的降低、预报时长的增加而呈下降趋势;电离层活动平静期的预报精度要优于活动活跃期。在平静期,3种模型在高纬度地区的预报效果大致相同;在中、低纬度地区加法模型的预报效果要优于另外两种模型。在活跃期,3种模型在高纬度地区的预报效果大致相同;中纬度地区乘法模型和ARIMA模型预报效果要优于加法模型;在低纬度地区,加法模型和乘法模型的预报效果要优于ARIMA模型。加法模型精确预报时长可以达到5天左右,而乘法模型和ARIMA模型只能达到3天左右。

Using microscopic video data measures for driver behavior analysis during adverse winter weather:opportunities and challenges

This paper presents a driver behavior analysis using microscopic video data measures including vehicle speed, lane-changing ratio, and time to collision. An analytical framework was developed to evaluate the effect of adverse winter weather conditions on highway driving behavior based on automated （computer） and manual methods. The research was conducted through two case studies. The first case study was conducted to evaluate the feasibility of applying an au- tomated approach to extracting driver behavior data based on 15 video recordings obtained in the winter 2013 at three dif- ferent locations on the Don Valley Parkway in Toronto, Canada. A comparison was made between the automated approach and manual approach, and issues in collecting data using the automated approach under winter conditions were identified. The second case study was based on high quality data collected in the winter 2014, at a location on Highway 25 in Montreal, Canada. The results demonstrate the effectiveness of the automated analytical framework in analyzing driver behavior, as well as evaluating the impact of adverse winter weather conditions on driver behavior. This approach could be applied to evaluate winter maintenance strategies and crash risk on highways during adverse winter weather conditions.

Winters模型在保费收入预测中的应用——以大连市寿险原保险保费收入预测为例

以大连市寿险原保险保费收入的时间序列数据为依据,分别应用Winters加法模型和Winters乘法模型的方法对其进行月度保费收入值的预测,通过比较由不同方法得出的预测结果的绝对百分误差的大小,选择出绝对百分误差最小的方法。

Holt-Winters模型在卷烟预测中的应用

提出基于Holt-Winters卷烟预测模型,对该预测模型设计了相应的算法,并以实际数据为例验证了Holt-Winters模型预测方法的有效性和实用性。

Fusing multi-source data to map spatio-temporal dynamics of winter rape on the Jianghan Plain and Dongting Lake Plain, China

Mapping crop distribution with remote sensing data is of great importance for agricultural production, food security and agricultural sustainability. Winter rape is an important oil crop, which plays an important role in the cooking oil market of China. The Jianghan Plain and Dongting Lake Plain (JPDLP) are major agricultural production areas in China. Essential changes in winter rape distribution have taken place in this area during the 21st century. However, the pattern of these changes remains unknown. In this study, the spatial and temporal dynamics of winter rape from 2000 to 2017 on the JPDLP were analyzed. An artificial neural network (ANN)-based classification method was proposed to map fractional winter rape distribution by fusing moderate resolution imaging spectrometer (MODIS) data and high-resolution imagery. The results are as follows:(1) The total winter rape acreages on the JPDLP dropped significantly, especially on the Jianghan Plain with a decline of about 45% during 2000 and 2017.(2) The winter rape abundance keeps changing with about 20–30% croplands changing their abundance drastically in every two consecutive observation years.(3) The winter rape has obvious regional differentiation for the trend of its change at the county level, and the decreasing trend was observed more strongly in the traditionally dominant agricultural counties.

基于小波变换和季节性Holt-Winters模型的短期负荷预测方法

精准的负荷预测对售电公司在电力市场中的运行起着十分重要的作用,而企业用户的负荷受多种因素的影响具有不平稳的特性,对此,提出了基于离散小波分解和粒子群优化的季节性Holt⁃Winters模型的短期负荷预测方法。针对原始负荷序列周期性不平稳的特性,利用离散小波变换对原始负荷序列进行分解,并采用季节性Holt⁃Winters模型进行预测,同时借助小波去噪和粒子群算法进一步提高预测模型的准确性。小波去噪在过滤原始数据中潜在的噪声的同时,对数据进行平滑处理,而粒子群算法能让Holt⁃Winters模型在训练过程找到最优参数。采用该模型来预测具有不同变化趋势的日负荷曲线,结果表明所提出的模型具有较高的预测精度,可适用于不同用电类型的用户负荷短期预测。

ARMA模型和Holt-Winters指数平滑模型在企业用电量预测中的应用与分析

本文通过对时间序列的研究分析,提出基于ARMA模型和基于Holt-Winters指数平滑模型进行企业用电量预测的方法。对采集的中山市某企业用电量数据样本分别用上述两种模型建模和后续30天的用电量数据预测,分析样本数量对预测准确度的影响、用累计短期高频数据预测中长期数据的效果,比较两种模型的预测准确度。结果显示:基于ARMA模型的预测方法对于企业未来30天逐日用电量预测能够达到平均95%以上的拟合度,基于Holt-Winters指数平滑模型累计逐日预测数据预测下1个月用电量数据具有高达99%以上的拟合度,二者组合应用具有较高的可行性和推广价值。

基于Holt-Winters模型的天然气负荷预测

探讨Holt-Winters乘法模型在企业天然气供应负荷预测中的应用价值。以2015—2018年各季度天然气负荷为基础,运用SPSS分别建立Holt-Winters加法模型和乘法模型,采用2019年各季度的实际数据验证模型,采用均方根误差(RMAE)作为模型评价指标。结果:RMAE的比较结果表明加法模型优于乘法模型,具有较高的预测精度,可较好的拟合企业天然气负荷的变化趋势。

Oil-Price Forecasting Based on Various Univariate Time-Series Models

Time-series-based forecasting is essential to determine how past events affect future events. This paper compares the performance accuracy of different time-series models for oil prices. Three types of univariate models are discussed: the exponential smoothing (ES), Holt-Winters (HW) and autoregressive intergrade moving average (ARIMA) models. To determine the best model, six different strategies were applied as selection criteria to quantify these models’ prediction accuracies. This comparison should help policy makers and industry marketing strategists select the best forecasting method in oil market. The three models were compared by applying them to the time series of regular oil prices for West Texas Intermediate (WTI) crude. The comparison indicated that the HW model performed better than the ES model for a prediction with a confidence interval of 95%. However, the ARIMA (2, 1, 2) model yielded the best results, leading us to conclude that this sophisticated and robust model outperformed other simple yet flexible models in oil market.

适时一次灌溉对8个冬小麦品种产量和生理特性的影响

为了探讨适时一次灌溉的灌溉效果和增产机制,以黄淮海麦区主推的8个小麦品种为材料,在大田和池栽条件下,设置适时一次灌溉和无灌溉(自然降雨)2种处理,测定抽穗期、灌浆前期、灌浆中期、灌浆后期旗叶生理指标和成熟期籽粒产量,研究适时一次灌溉对冬小麦产量和旗叶生理特性的影响。结果表明,适时一次灌溉改善了4个生育时期的旗叶生理特性。大田、池栽条件下适时一次灌溉处理旗叶超氧化物歧化酶(SOD)活性分别较无灌溉处理增加23.63%~39.07%、27.82%~54.53%,过氧化物酶(POD)活性增加16.39%~53.76%、16.93%~38.62%,过氧化氢酶(CAT)活性增加33.74%~51.59%、26.13%~45.06%,可溶性糖含量增加27.73%~46.34%、24.75%~37.31%,脯氨酸含量增加26.29%~37.84%、21.41%~41.33%,可溶性蛋白含量增加16.56%~42.06%、14.85%~31.69%,净光合速率增加11.51%~47.25%、24.36%~43.33%,丙二醛(MDA)含量下降12.32%~18.64%、14.85%~22.79%,小麦籽粒产量平均增加17.95%和29.27%。适时一次灌溉条件下,不同品种间比较,以周麦32和周麦36较无灌溉增产幅度较大,分别为31.50%和28.23%,可作为丰产抗旱性品种在有限灌溉旱作区进行推广。本研究结果为冬小麦适时一次灌溉研究及品种优化布局提供了参考。

低温等离子体处理对冬枣贮藏品质及风味物质的影响

该文以冬枣为试材,对其进行不同时间(0、5、10 min)的低温等离子体处理,通过测量处理后冬枣的微生物数量、品质相关指标和采用顶空气相色谱离子迁移谱(headspace gas chromatography ion migration spectrometry,HS-GC-IMS)测量其风味物质的变化,探究低温等离子体处理对冬枣贮藏品质和风味物质变化的影响。结果表明,与CK组相比,低温等离子体处理对冬枣表面菌落总数、霉菌和酵母菌总数具有显著的抑制作用,其中70 d时,10 min处理组和5 min处理分别比CK组菌落总数低0.6 lg CFU/g和0.39 lg CFU/g;分别比CK组霉菌和酵母菌总数低0.42 lg CFU/g和0.28 lg CFU/g。低温等离子体处理可以减缓冬枣呼吸强度的升高,缓解可溶性糖含量、可滴定酸含量和维生素C含量的下降;抑制冬枣硬度的降低,其中,5 min处理组效果最为显著,冬枣硬度在第70天时5 min处理组比CK组升高47%;5 min处理组冬枣中原果胶含量比CK组高43%,可溶性果胶含量比CK组低55%,纤维素含量和半纤维素含量也分别比CK组高14%和74%。HS-GC-IMS结果分析表明,贮藏后期5 min处理组中2,3-丁二酮、2-苯基乙醛、柠檬烯、己酸等物质含量高于CK组和10 min处理组,但处理组中2,3-丁二酮和2,5-二甲基吡嗪含量低于CK组。综合分析表明,低温等离子体处理对抑制采后冬枣品质劣变及风味物质变化具有积极作用。