景谷县森林蓄积量遥感估测及其动态变化分析

以云南省景谷县为研究区,基于Landsat5和Landsat8遥感数据,利用2012年和2017年两期全国森林资源连续清查实地调查数据,建立多元逐步回归和随机森林模型对景谷县森林蓄积量进行遥感估测对比研究。结果显示:综合建模精度来看,随机森林法在相同样地数量条件下具有更好的估测效果;从估测结果与林地保护利用规划和林地变更数据结果相比较,随机森林法估测值接近真实值,估测精度较高。     

基于距离相关系数和KNN回归模型的森林蓄积量估测研究

【目的】探究Landsat8 OLI数据和KNN算法在森林蓄积量估测中的潜力。【方法】以湖南省湘潭县为研究区,采用Landsat8 OLI数据和同时期的二类调查数据,通过距离相关系数筛选特征,分别采用线性回归模型(MLR)、K-近邻模型(KNN)、距离加权KNN模型(DW-KNN)和优化欧式KNN模型(FW-KNN)对森林蓄积量进行估测。使用十折交叉方法进行精度检验,对检验结果进行对比分析。【结果】3种KNN模型的估测结果均高于传统的线性模型,并且在3种KNN模型中,FW-KNN算法效果最好,决定系数达到0.69,为3种模型中最高;3种KNN模型中,本研究优化欧氏距离KNN模型的估测精度最高,其均方根误差为30.3%,相比于传统KNN模型的均方根误差降低了5.1%,相比于DW-KNN模型降低了3.3%。【结论】采用DW-KNN蓄积量估测结果明显优于其他两种模型,说明通过特征与蓄积量的相关性优化样本间的距离是一种可行的KNN优化方法。     

河北省主要树种单木和林分生长率模型研建

利用第六次至第九次全国森林资源清查河北省2001,2006,2011,2016年4个年度的固定样地调查数据,采用非线性回归估计方法,建立了18个树种组的单木胸径生长率和材积生长率模型,以及12个树种组的林分材积生长率模型。结果表明,单木生长率模型的平均预估误差(MPE)基本都在3%以内,而平均百分标准误差(MPSE)、胸径生长率模型大都在10%以内,材积生长率模型大都在20%左右;林分生长率模型的平均预估误差(MPE)基本都在5%以内,平均百分标准误差(MPSE)大都在25%以内。所建模型可为河北省开展森林资源年度更新提供技术支撑。     

播期对河套地区玉米灌浆进度的影响

旨在为玉米的高产栽培提供理论支撑。采用分期播种法,利用试验数据,建立28个生长模型（方程均通过0.01的极显著检验）,将玉米灌浆期百粒干重、百粒体积随灌浆日数增加的时段分为三阶段,即渐增期、快增期和缓增期。适时播种的玉米吐丝后百粒体积、百粒干重增加进入渐增期,终止日分别为吐丝后的6天和20天,此后进入快速增长期,时间分别为22天和18天;其后到籽粒体积和干重增加进入缓慢增长期。适时播种的玉米百粒干重增加逐渐增长期为吐丝后活动积温区间0~502.4℃·d,增幅0.014 g/℃·d;快速增长期为吐丝后活动积温区间502.4~938.4℃·d,增幅0.049 g/℃·d;缓慢增长期为吐丝后活动积温区间938.4~1355.1℃·d,增幅0.018 g/℃·d。     

偏高岭土对高强机制砂混凝土性能的影响

利用扫描电子显微镜(SEM)、压汞法(MIP)、快速氯离子迁移系数法(RCM)等手段,研究了偏高岭土(MK)掺量对高强机制砂混凝土抗压强度以及抗氯离子渗透性能的影响.研究发现: 掺入MK能够有效改善高强机制砂混凝土的力学性能和抗氯离子渗透性能:随MK掺量增加,抗压强度与抗氯离子渗透性能均呈先增大后减小的趋势,最佳MK掺量为8%,此时28 d抗压强度为98.5 MPa,较基准组增加13%,氯离子迁移系数为1.89×10^-12 m²/s,较基准组减小60%.微观结构试验表明适宜的偏高岭土掺量能够有效促进水泥水化;偏高岭土颗粒的晶核效应和微集料填充效应协同作用,使得机制砂混凝土的微观结构更加均匀密实,机制砂混凝土的多害孔、有害孔减少,少害孔增加,从而提高了机制砂混凝土抗压强度和抗氯离子渗透性能.     

茶树植保机械及减量施药技术研究进展

农药防治是茶树病虫害综合防治的重要组成部分,其在病虫害突发或爆发时具有快速高效的防治优势。茶树叶片表面具有亲水性,常量施药会造成茶叶农残超标、生态环境破坏等问题,实现茶树减量施药是减少茶叶农残的有效手段。系统综述了茶树生物特性、茶树病虫害预测诊断及防治方法、茶树植保机械及施药技术,强调提高茶树低容量喷雾的农药有效利用率是实现茶树减量施药的关键。针对目前茶园地面工况复杂及农药利用率低的问题,本文从低容量仿形喷雾机、茶树病虫害喷雾决策及智能终端等六个方面提出茶树病虫害施药技术及装备的研究建议,指出低量化、精准化及智能化是未来茶树植保喷雾机械及施药技术的发展方向。     

A new lean no time test baking method with improved discriminating power

In response to customer concerns related to gluten strength in commercial baking, the Canadian Grain Commission assessed whether the Canadian Short Process (CSP) test bake method was generating useful data related to intrinsic strength of wheat varieties. Assessment of CSP loaf volume data for Canadian variety trials spanning 2003 to 2013 showed very little correlation with dough strength parameters as measured by farinograph and extensigraph. A lean no time (LNT) test baking method was developed that can better discriminate genotypes and provide objective indicators of the effect of intrinsic dough strength on baking quality. From early method development, through method validation and verification using diverse sets of samples targeting different Canadian wheat classes and grown in three different crop years, results showed the LNT method to be more discriminating and easily adopted by other laboratories. In 2015, the LNT method was adopted as the method of choice in future Canadian variety registration trials. The LNT method is fast, simple and well-suited to high throughput test baking conditions encountered in the evaluation of large numbers of breeder lines. A new objective parameter, loaf top ratio, was also introduced and found to correlate well with dough strength and dough handling properties.     

极端暴雨条件下黄土丘陵沟壑区土壤蓄水能力和入渗规律

黄土高原退耕还林还草工程实施后,下垫面环境条件的变化可能对流域水文过程、水文通量、水量平衡以及生态系统产生十分重要的影响。研究极端暴雨条件下剖面土壤蓄水能力和入渗规律,对于阐明流域产汇流过程和影响机制具有重要的科学价值。采用土壤墒情仪对陕北"7·26"特大暴雨事件下黄土丘陵沟壑区草地剖面土壤水分进行了实时动态监测,分析了极端暴雨条件下剖面土壤水分的动态变化和蓄水过程,利用Horton入渗模型模拟了剖面土壤水分湿润锋的运动过程,揭示了极端暴雨条件下剖面土壤水分的入渗规律。结果表明:(1)极端暴雨条件下,黄土丘陵沟壑区坡面草地不同深度层次土壤水分与降雨过程的响应不同,具有层次性和明显的滞后效应,其中,0～140 cm是影响该地区土壤水文过程的关键层次;(2)土壤水分再分配结束时,湿润锋最深深度达140 cm,土壤蓄水量达225.99 mm,较降雨前95.37 mm增加了1.37倍;(3)极端暴雨过程中湿润锋的运动随时间呈对数递减关系,其稳渗速率随容重增加而减小,呈指数函数递减;(4)极端降雨过程中该地区坡面草地的产流机制仍以超渗产流为主,对于揭示流域的产汇流机制和完善水文预报模型具有重要的科学意义。     

Improving wood volume predictions in dry tropical forest in the semi-arid Brazil

The volumetric variability of dry tropical forests in Brazil and the scarcity of studies on the subject show the need for the development of techniques that make it possible to obtain adequate and accurate wood volume estimates. In this study, we analyzed a database of thinning trees from a forest management plan in the Contendas de Sincorá National Forest, southwestern Bahia State, Brazil. The data set included a total of 300 trees with a trunk diameter ranging from 5 to 52 cm. Adjustments, validation and statistical selection of four volumetric models were performed. Due to the difference in height values for the same diameter and the low correlation between both variables, we do not suggest models which only use the diameter at breast height (DBH) variable as a predictor because they accommodate the largest estimation errors. In comparing the best single entry model (Hohenald-Krenn) with the Spurr model (best fit model), it is noted that the exclusion of height as a predictor causes the values of 136.44 and 0.93 for Akaike information criterion (AIC) and adjusted determination coefficient (R2 adj), which are poorer than the second best model (Schumacher-Hall). Regarding the minimum sample size, errors in estimation (root mean square error (RMSE) and bias) of the best model decrease as the sample size increases, especially when a larger number of trees with DBH≥15.0 cm are randomly sampled. Stratified sampling by diameter class produces smaller volume prediction errors than random sampling, especially when considering all trees. In summary, the Spurr and Schumacher-Hall models perform better. These models suggest that the total variance explained in the estimates is not less than 95%, producing reliable forecasts of the total volume with shell. Our estimates indicate that the bias around the average is not greater than 7%. Our results support the decision to use regression methods to build models and estimate their parameters, seeking stratification strategies in diameter classes for the sample trees. Volume estimates with valid confidence intervals can be obtained using the Spurr model for the studied dry forest. Stratified sampling of the data set for model adjustment and selection is necessary, since we find significant results with mean error square root values and bias of up to 70% of the total database.     

山地校园不同下垫面雨水径流重金属污染特征和健康风险评估

[目的] 了解山地校园不同下垫面雨水径流重金属污染特征及雨水径流中重金属元素对于人体的健康风险，为山地海绵城市建设提供径流污染控制参考和人员健康保护基础数据。[方法] 2019年7月22日在重庆交通大学针对屋顶、道路、停车场和广场4种典型的下垫面进行了雨水径流的取样监测，并统计分析场次降雨平均浓度（EMC）、电导率（EC）、钠吸附率（SAR）、危险商（HQ）和危险指数（HI）。[结果] 广场径流过程中重金属元素整体变化波动较大，道路雨水径流的重金属健康风险最大而屋顶健康风险最小；大多重金属污染物在产流后的10 min内出现了降幅，其中Fe和Mn的降幅较大，Zn的降幅中等，Cu的降幅最低；As和Cr是雨水径流中健康风险最高的重金属污染物。[结论] 山地校园重金属污染物冲刷速度较快，人流大的下垫面雨水径流中重金属元素变化波动较大，但健康风险较低；车流大的下垫面雨水径流中重金属元素波动较为稳定，但健康风险较高。