灰色GM（1,1）模型的性质及其应用

灰色ＧＭ（１，１）模对生态预测有重要应用，本文讨论了它的一个改进模ＧＭ（１，１），并深化了文（１）提出的两个问题，给出了较简明的证法，还确定了文（１）所希望的一个“合适的”常数。     

Canopy closure estimation in a temperate forest using airborne LiDAR and LANDSAT ETM+ data

Aims　Forest canopy closure is one of the essential factors in forest survey, and plays an important role in forest ecosystem management. It is of great significance to study how to apply LiDAR (light detection and ranging) data efficiently in remote sensing estimation of forest canopy closure. LiDAR can be used to obtain data fast and accurately and therefore be used as training and validation data to estimate forest canopy closure in large spatial scale. It can compensate for the insufficiency (e.g. labor-intensive, time-consuming) of conventional ground survey, and provide foundations to forest inventory.Methods　In this study, we estimated canopy closure of a temperate forest in Genhe forest of Da Hinggan Ling area, Nei Mongol, China, using LiDAR and LANDSAT ETM+ data. Firstly, we calculated the canopy closure from ALS (Airborne Laser Scanning) high density point cloud data. Then, the estimated canopy closure from ALS data was used as training and validation data to modeling and inversion from eight vegetation indices computed from LANDSAT ETM+ data. Three approaches, multi-variable stepwise regression (MSR), random forest (RF) and Cubist, were developed and tested to estimate canopy closure from these vegetation indices, respectively.Important findings The validation results showed that the Cubist model yielded the highest accuracy compared to the other two models (determination coefficient (R²) = 0.722, root mean square error (RMSE) = 0.126, relative root mean square error (rRMSE) = 0.209, estimation accuracy (EA) = 79.883%). The combination of LiDAR data and LANDSAT ETM+ showed great potential to accurately estimate the canopy closure of the temperate forest. However, the model prediction capability needs to be further improved in order to be applied in larger spatial scale. More independent variables from other remotely sensed datasets, e.g. topographic data, texture information from high-resolution imagery, should be added into the model. These variables can help to reduce the influence of optical image, vegetation indices, terrain and shadow and so on. Moreover, the accuracy of the LiDAR-derived canopy closure needs to be further validated in future studies.     

ARIMA乘积季节模型在河南省风疹疫情预测中的应用

目的建立自回归求和移动平均(ARIMA)乘积季节模型,探讨其在河南省风疹发病趋势预测应用中的可行性。方法收集河南省2004—2013年报告的各月风疹病例数,建立ARIMA乘积季节模型,用2014年1—12月报告的风疹病例数,验证模型预测效果。结果河南省2004—2013年报告的风疹病例数呈现明显的季节效应,2009年前呈现逐年增多的趋势,2009年后呈现逐年减少的趋势;模型ARIMA(1,0,0)(0,1,1)12能较好的拟合既往的风疹病例数,且对2014年1—12月份风疹病例数的预测值与实际值基本吻合。结论 ARIMA乘积季节模型对河南省风疹发病趋势的预测具有可行性。     

欢迎订阅2006年《特种经济动植物》

前列腺增生症是下尿路梗阻常见的原因之一．是男性老年人常见疾病。患者以进行性排尿困难为突出表现。排尿困难进一步发展，会发生急性尿潴留。急性尿潴留的处理要点是引流尿液，缓解症状，保护肾功能。导尿术是懈除急性尿潴留最直接有效的方法，而老年前列腺肥大致急性尿潴留的患者，由于疾病原因，常给插管带来一定的困难。近年来，我们在导尿时改用14F弯头气囊尿管并加用1％利多卡因，取得了满意效果，现将经验总结如下。     

一个具暂时免疫且总人数可变的传染病动力学模型

建立了一个具常恢复率和接触率依赖于总人数的SIRS传染病动力学模型，讨论了系统平衡点的存在性和稳定性，对双线性传染率的特殊情形，给出了传染病平衡点的全局稳定性结论，推广和改进了已有的相应结果。     

科学新知

难以放松的假期你是否想要度过一个悠长假期，却始终无法忘掉工作安心度假？为了揭开其中奥秘，研究人员设计了一可模拟大脑“能量转化为思想”过程的计算机模型。     

浙江省生境质量时空演变与生态红线评估

生境质量是反映生物多样性状况与局地生态功能的重要指标,在高质量发展背景下研究区域生境质量的时空演变具有重要意义。以浙江省为研究区,基于InVEST模型、热点分析及地理探测器模型探究生境质量的时空演变与影响因素,并利用生境质量结果对浙江省生态红线开展了定量评估。结果表明:(1)2000-2015年,浙江省生境质量均值呈减速下降趋势,空间上形成了西北、西南、中东高和东北、中部低的分布格局;生境退化度呈现"中心-外围"的圈层辐射结构。(2)热点分析显示,生境质量与生境退化度在乡镇尺度上集聚特征相似、冷热点空间分布趋势相反。(3)地理探测分析发现,地形(高程、坡度)是影响浙江省生境质量的主要因素,植被因素(NPP、NDVI)的贡献度随时间推移逐渐增大;浙江省生境质量空间分异受到自然因子与社会经济因子的协同作用。(4)浙江省生态红线的生境质量整体较高且稳定,不同红线类型的生境质量存在差异;高生境质量区与生态红线的错位区域主要分布在浙西南、西北部山区,而北部、中部以及东部相对较少。基于此,对生态红线调整、区域生态功能区划提出对应的策略,以期提升浙江省生态空间管控。