数字化地籍图数据处理方法的研究     

吕亮卿  毕援朝 《山西农业大学学报(自然科学版)》1998,18(3):222-227

本文系统、全面地讨论了数字化地籍图数据处理的各种方法、特性及其应用范围。并结合实例对利用已知面积校正数字化坐标的平差计算方法作了详细的讨论。通过一系列的数据处理 ,可以向地理信息系统提供精度可靠的地理基础数据。  相似文献   

云南撒坝猪H-FABP基因多态性及与部分生产性能的关系     

李国治  连林生  鲁绍雄  严达伟  苟潇  赵桂英 《家畜生态》2008,(6)

采用PCR-RFLP技术检测H-FABP基因5′上游区1125~1817区段HinfⅠ酶切位点在撒坝猪群体中的多态性分布,并采用最小二乘分析模型初步统计分析检测位点多态性与部分生产性能的相关性。结果表明,H-FABP基因等位基因H及基因型Hh在群体中占优势,频率分别为0.6022和0.4194;H-FABP基因在群体中处于Hardy-Weinberg平衡状态;H-FABP基因检测位点基因型间在生长和繁殖性状上不存在不利影响。  相似文献   

2022年夏季汉江中下游水华生消驱动因子及其贡献率量化研究          下载免费PDF全文

查悉妮  辛小康  付婷  李建  白凤朋  林枭  舒鹏 《水生态学杂志》2024,45(1):143-151

2022年夏季，长江流域遭遇了罕见的“汛期反枯”极端水文事件，在此期间，汉江中下游首次发现了蓝藻水华。本研究选取叶绿素a浓度作为衡量水华的关键指标，基于偏最小二乘回归（Partial least squares regression，PLSR）量化了不同环境因子对2022年夏季汉江中下游（仙桃、宗关断面）水华生消的贡献率。结果表明：（1）仙桃和宗关断面叶绿素a浓度与溶解氧、pH值和水温均呈现出显著的正相关关系。溶解氧、pH值和水温对2022年夏季汉江中下游水华生消的贡献程度最高，三者对仙桃和宗关断面水华生消的贡献率分别为15.18%、13.68%、14.50%和18.06%、15.93%、15.65%。（2）基于偏最小二乘路径模型（Partial Least Squares Path Modeling，PLS-PM），本研究进一步解析了各环境因子对叶绿素a浓度变化的影响路径。结果表明气象因子是2022年夏季汉江中下游蓝藻水华暴发的诱导因子，高温无雨的极端天气导致水温和pH上升，加速了藻类的代谢反应速率。同时，河道流量减小延长了有机物和营养盐的传输和滞留时间，为藻类生长提供了稳定的营养条件。并且水华“萌发”时段可能与汉江中下游“涝旱急转”时段重叠。因此，本文建议下一步研究应结合准确的中长期气象预报信息，在“涝旱急转”时段实时优化汉江中下游水华防控调度的下泄流量与下泄时机，这将有可能大大提高水华防控的效率和效果。  相似文献   

Prediction of watermelon quality based on vibration spectrum     

《Postharvest Biology and Technology》2013

Judging watermelon quality based on its apparent properties such as size or skin color is difficult. A non-destructive method is employed here, based on vibrational response spectrum, to determine the quality indices of watermelon (Charleston gray). The responses of samples to vibration excitation were recorded by laser Doppler vibrometry (LDV). The phase shift between input and output signals were extracted over a wide frequency range. The total soluble solids (TSS), titratable acidity (TA) and TSS/TA ratio also measured as watermelon quality characters. Stepwise multiple linear regression (SMLR) as well as partial least square regression (PLS) was applied to extracted vibration spectrums to construct prediction models of watermelon quality. The results showed that performance of SMLR models were better than PLS. The determination coefficients (R²) of SMLR validation models were 0.9976, 0.9985 and 0.9542 for TSS, TA and TSS/TA respectively. It is likely that reduction of cell wall materials to soluble solids during ripening process changes viscoelastic properties of watermelon reflected by vibrational response. This study demonstrated the feasibility of mentioned method for predicting the quality of watermelons in an industrial grading system.  相似文献   

Predicting equations of main factors affecting regional climate in the "Three-North" Protective Forest Area     

张志秀  刘鹏  杨德威 《林业研究》1999,(1)

lntroductionInordertosolvetheseriousecologicaIproblemwefacedandimprovethephenomenonoflanddesertifi-cationinthenorthareaofChina.ChinahasbeguntoconstructtheThree-NorthProtectiveForestSystemsince1978.Withthehardworkoftwentyyears,itistold"Thegreatestecologicalprojectoftheworld"and"ThegreengreatwaIl".AIthoughthecountryinvestedIotsofcapitaItobuiIdtheThree-Northshelter-forestsystemthroughmanywayssuchasThree-Northspecialinvestment,financeappropriatefunds,finan-cialloansandtakeoutfundsforbringup…  相似文献   

黄山松人工林球果生物量的测定     

郑少玲 《福建林业科技》2001,28(2):65-67

采取平均木法实测不同立地条件的 10年生黄山松球果生物量 ,阐明黄山松球果生物量与立地条件以及测树因子之间的相互关系。 10年生黄山松山脊球果生物量最高平均每株 0 5 8kg ,而山坡最低为 0 4kg。应用树高和胸径分别推导出预测黄山松球果生物量的回归方程。  相似文献   

基于LS-SVM的光伏最大功率跟踪控制方法     

蔡纪鹤  孙玉坤  李蓓  徐艳 《农业机械学报》2014,45(S1):213-218

为解决自然环境剧烈变化条件下,传统光伏最大功率跟踪控制中存在的控制精度低和误跟踪现象,建立了基于最小二乘支持向量机的最大工作点电压预测模型,通过该模型预测光伏发电系统的最大工作点电压,并用预测电压来修正恒电压控制法的参考电压,从而实现光伏发电系统的最大功率跟踪控制。仿真结果表明预测模型具有较高的精度,相对误差在0.04以内,控制方法能够快速、稳定地实现光伏发电系统的最大功率跟踪,有效避免误跟踪现象。  相似文献   

Rainfall erosivity and erosivity density through rainfall synthetic series for São Paulo State,Brazil: Assessment,regionalization and modeling     

《国际水土保持研究(英文)》2022,10(3):355-370

Rainfall is the main cause of erosion of Brazilian soils, which makes assessing the rainfall erosivity factor (RE) and the erosivity density (ED) fundamental for soil and water conservation. Therefore, the objectives of this study were: i) to estimate the RE and ED for São Paulo State, Brazil, using synthetic series of pluviographic data; ii) to define homogeneous regions regarding rainfall erosivity; and iii) to generate regression models for rainfall erosivity estimates in each of the homogeneous regions. Synthetic series of pluviographic data were initially obtained on a sub-daily scale from the daily rainfall records of 696 rainfall gauges. The RE values were then estimated from the synthetic rainfall data, and ED was calculated from the relationship between erosivity and rainfall amounts. Monthly and annual maps for RE and ED were obtained. Hierarchical clustering analysis was used to define homogeneous regions in terms of rainfall erosivity, and regionalized regression models for estimating RE were generated. The results demonstrate high spatial variability of RE in São Paulo, where the highest annual values were observed in the coastal region. December to March concentrate approximately 60% of the intra-annual erosivity. The highest values of annual ED were observed in regions with intense agricultural activity. The definition of five homogeneous regions concerning the rainfall erosive potential evidenced distinct seasonal patterns of the spatial distribution of erosivity. Finally, the high predictive accuracy of the regionalized models obtained characterizes them as essential tools for reliable estimates of rainfall erosivity, and contribute to better soil conservation planning.  相似文献   

A comparison of linear and exponential regression for estimating diffusive CH₄ fluxes by closed-chambers in peatlands     

Inke Forbrich  Lars Kutzbach  Annabell Hormann 《Soil biology & biochemistry》2010,42(3):507-515

The closed-chamber method is the most common approach to determine CH₄ fluxes in peatlands. The concentration change in the chamber is monitored over time, and the flux is usually calculated by the slope of a linear regression function. Theoretically, the gas exchange cannot be constant over time but has to decrease, when the concentration gradient between chamber headspace and soil air decreases. In this study, we test whether we can detect this non-linearity in the concentration change during the chamber closure with six air samples. We expect generally a low concentration gradient on dry sites (hummocks) and thus the occurrence of exponential concentration changes in the chamber due to a quick equilibrium of gas concentrations between peat and chamber headspace. On wet (flarks) and sedge-covered sites (lawns), we expect a high gradient and near-linear concentration changes in the chamber. To evaluate these model assumptions, we calculate both linear and exponential regressions for a test data set (n = 597) from a Finnish mire. We use the Akaike Information Criterion with small sample second order bias correction to select the best-fitted model. 13.6%, 19.2% and 9.8% of measurements on hummocks, lawns and flarks, respectively, were best fitted with an exponential regression model. A flux estimation derived from the slope of the exponential function at the beginning of the chamber closure can be significantly higher than using the slope of the linear regression function. Non-linear concentration-over-time curves occurred mostly during periods of changing water table. This could be due to either natural processes or chamber artefacts, e.g. initial pressure fluctuations during chamber deployment. To be able to exclude either natural processes or artefacts as cause of non-linearity, further information, e.g. CH₄ concentration profile measurements in the peat, would be needed. If this is not available, the range of uncertainty can be substantial. We suggest to use the range between the slopes of the exponential regression at the beginning and at the end of the closure time as an estimate of the overall uncertainty.  相似文献   

Hyper-spectral estimation of wheat biomass after alleviating of soil effects on spectra by non-negative matrix factorization     

《European Journal of Agronomy》2017

Hyper-spectral technology has been proven to be an effective method for the fast and non-destructive monitoring of crop biomass. However, the biomass estimation accuracy of this method is limited due to the effects of background factors, such as soils and water. In this study, a spectral separation method, non-negative matrix factorization (NMF), was proposed to alleviate the effects of soil on spectra. With the application of the NMF method, pure vegetation spectra were extracted from the field-observed spectra of wheat canopy, which were collected in four growing seasons from the tillering to the booting stages of wheat. Then, prediction models of wheat biomass (WB) were established and validated using the extracted spectra with the partial least squares regression (PLSR) method. The results showed that the NMF method could effectively separate the vegetation spectra from the mixed canopy spectra. Based on the extracted vegetation spectra, the WB prediction accuracy could be greatly improved with an increase of 31.7% for the R²_p and an increase of 46.6% for the ratio of performance to deviation (RPD) as compared to the original spectra, indicating that the NMF method could significantly improve the performance of the WB prediction model. This method has potential application in the estimation of biomass using remote sensing technology.  相似文献