1982-2006年华北植被指数时空变化特征

利用1982-2006年GIMMS/NDVI数据,研究华北近25 a来植被指数在时间和空间上的变化。结果表明:①植被指数的年内变化呈单峰型,最大值出现在夏季,其中北京市NDVI最大,森林NDVI比农田和草地NDVI大;②华北年平均NDVI呈增加趋势,河北省植被指数增加最快,北京市次之,不同土地覆盖类型中,农田NDVI增加最快,草地次之,森林最小;③空间趋势分析结果显示,华北植被指数改善的面积占整个地区面积的15.96%,退化面积占11.86%,其中,河北省改善面积最大,内蒙古退化较明显,不同土地覆盖类型中,农田植被改善最明显;④基于华北近年暖干化发展趋势下,人类活动对该区域NDVI变化起到了重要作用。     

窝铺山区森林植被水土保持效益及资源植物开发利用途径的研究

森林植被在涵养水源保持水土,减少土壤流失的作用很大,植物措施投资少,见效快、可长期发挥效益.对窝铺流域三年的治理及观测研究表明:森林植被保持水土效益明显;土壤侵蚀量小于其它植被类型;改善了生态环境;利于建立新的生态平衡,保证农业高产、优质稳定地发展.作者同时对研究区内的资源植物进行了调查分类,对资源植物的保护和开发利用提出了几个值得注意的问题.     

晋西黄土丘陵边缘土石山区天然次生林群落β多样性研究

对晋西黄土丘陵边缘土石山区不同海拔高度木本植物物种丰富度以及各群落间β多样性特征进行了分析.结果表明,木本植物总物种丰富度随海拔高度的升高呈下降趋势,乔木种在海拔较高地段丰富度较大,灌木种则是在海拔较低地段丰富度较大.群落相异性指数Cody指数和Whittaker 指数多以相邻海拔群落间的指数值最小,群落间海拔差异越大指数值越大.而群落相似性指数Jaccard和Sorenson指数则表现出相反的变化规律,即随着群落间海拔差距的增大,指数值呈逐渐减小的趋势,相邻群落间的群落相似性指数值最大.     

麻栎 马尾松天然次生混交林生物量结构及根系分布特征调查研究

通过对马尾松、麻栎天然次生混交林林分生物量的测定,对林分生物量积累、根系分布特征的差异进行了研究。结果表明,两类树种混交造林方式能在一定程度上提高树干的生产量,天然次生麻栎较马尾松具有主根、水平根系均发达,且须根分布均匀的特点。     

基于因子分析的中国林业发展潜力

立足于我国林业资源可持续发展,为制定正确的林业资源发展战略,研究林业发展潜力并揭示潜力提升空间是十分必要的。选择27个省作为研究对象,运用《中国林业统计年鉴2008》数据,采用因子分析法,将反映林业发展潜力的10项主要评价指标转化为评价的5个主要因子,并对27个省进行排名发现,各地区林业资源占有和经营、林业资源质量和管理,以及自然、培育和人文转型潜力发展不平衡,南方各省的林业具有很大的发展潜力。并提出了提高南方集体林区森林活立木蓄积量,继续和深化国有森林资源管理改革和创新,大力发展森林旅游业等有力措施。     

三峡库区林草间作及肉羊高效养殖模式探讨

从牧草引种、疏林地和果园种草、肉羊养殖等方面,对三峡库区的林草间作及肉羊高效养殖技术模式进行了探讨.通过研究,筛选出了适合库区种植的优质高产牧草种类及水土保持的牧草种类,提出了林草间作的技术要点,同时应用肉羊高效养殖技术饲养肉山羊,把退耕还林还草和草地畜牧业有机地结合在一起,并强调了二者的相互依赖性,实现可持续发展.     

连作及轮作土壤微生物菌群对黄瓜幼苗生长及土壤酶活性的影响

通过植物-土壤反馈试验,研究连作及轮作土壤微生物菌群对黄瓜幼苗生长和土壤酶活性的影响。结果表明,轮作土壤微生物处理黄瓜植株鲜质量、叶绿素含量、土壤中性磷酸酶、蔗糖酶活性显著高于连作土壤微生物处理;连作及轮作土壤微生物处理增加了黄瓜幼苗干质量、鲜质量、叶绿素含量及总叶面积,显著提高了土壤中性磷酸酶、蔗糖酶、脲酶活性。综上,连作及轮作土壤微生物菌群对黄瓜幼苗生长均产生正反馈作用,并且轮作土壤微生物的正反馈作用大于连作土壤微生物。     

经济林种子生理研究进展

经济林种子是育苗、造林的物质基础,种子品质的好坏直接关系到苗木生产及造林的成效。本文就经济林种子的休眠与萌发、种子的老化及劣变、种子活力等种子生理工作研究现状进行了较为系统的总结,并提出了今后经济林种子生理的研究方向和发展趋势。     

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.