Prediction of pine mistletoe infection using remote sensing imaging: A comparison of the artificial neural network model and logistic regression model

In this study, the prediction of pine mistletoe distribution in Scots pine ecosystems was explored using remote sensing variables to compare the multilayer perceptron (MLP) artificial neural network (ANN) and logistic regression (LR) model performances. For this purpose, 109 sample plots were distinguished in pure Scots pine forests (natural) in the Eastern Black Sea Region of Turkey. Distinguishing mistletoe-infected stands (69) and uninfected stands (40) was performed with field observations. The variables acquired from Landsat 8 (Level 1) images were used as independent variables for independent-sample t-test, MLP ANN and LR models. Remote sensing variables indicated that mistletoe-infected stands were in drier areas with a lower vegetation-leaf area index. Based on the performance results of both models, the sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and accuracy of the MLP ANN model were superior to those of the LR model. The prediction percentages (SEN, SPE, PPV and NPV) of mistletoe-infected stands were better than the prediction percentages of uninfected stands. The prediction accuracies of LR and MLP ANN models were 74.3% and 89.6%, respectively. However, all remote sensing variables were included in the prediction equation of the MLP ANN model, while the thermal infrared 1 (TIRS1) variable was included in the LR model. In the MLP ANN model, the TIRS1 variable also had the highest normalized importance (100%). The area under the curve (AUC) value for identifying the mistletoe-infected stands of Scots pine forests used by the MLP ANN model (0.892 ± 0.034) was higher than in the LR model (0.838 ± 0.039), explaining the more accurate predictions obtained from the MLP ANN model. The MLP ANN model showed much better performance than the LR model. The results of this study are expected to make important contributions to the identification of potential mistletoe-infected areas.     

长白山林区主要可燃物类型地表可燃物载量分析

对长白山林区主要可燃物类型地表可燃物载量进行了对比分析和聚类分析。通过可燃物类型载量的对比分析,得出易燃的可燃物类型是云冷杉林(鱼鳞云杉103a)、落叶松林(115a)和云冷杉林(鱼鳞云杉80a),最不易燃的是岳桦林(142a)、白桦林(87a)和岳桦林(115a);释放能量最大的可燃物类型是落叶松林(115a),释放能量最小的可燃物类型是白桦林(87a)。通过各可燃物类型的1h时滞可燃物载量和地表总可燃物载量的聚类分析,可把13个可燃物类型化为6类。     

绥化市秦家镇农田防护林网络架构及生态农业效益分析

对松嫩平原上一典型土地开发整理项目区深入调查研究基础上,在人均土地面积较少的农村,构建出由农田防护林、水利排灌、道路、农田4项网络构成的高效农田防护林复合经营与多样型林带乔、灌立体混交经营模式,实现了林带由单一结构向多层次的立体结构转移,架构了生态立体农业体系,旨为重新调整土地利用结构,实现山、水、田、林、路的全面治理提供依据。     

嫩江沙地杨树用材林生产力的研究

通过对嫩江沙地杜蒙和讷河2个区域12个杨树样地标准木调查资料的分析,确定了几种杨树的地上净生产力范围;通过对生物量和生产力构成因素的综合分析初步确定了杨树合理的经营密度,表明林分密度应根据不同的造林目的和立地条件实行动态管理.     

国有林场森林资源信息管理系统研究及应用

以登封国有林场为试验对象,开发了国有林场森林资源信息管理系统,并得到应用。基本上实现了登封国有林场森林资源管理的信息化,为森林资源的科学管理和决策,以及可持续发展提供了技术支持。     

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。     

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.     

生态景观林林分改造工程建设理论与千岛湖区实践研究

马尾松和杉木林是受人为干扰下形成的次生的退化森林生态系统,群落结构不稳定,在千岛湖区选择针叶林阔叶化林分改造工程符合当地森林植被的自然演替规律,同时也被生产实践证明是有效的。本文回顾自1963年以来千岛湖区生态景观林林分改造工程的历程,对千岛湖区生态景观林林分改造工程建设指导思想、技术措施和建设成效进行了思考,同时提出千岛湖区生态景观林林分改造工程建设应加强对建设成效的监测,林分结构调整时应适当加大上层马尾松林的间伐强度,以及林分改造应与生物防火林带建设和森林病虫害的预防相结合等建议。     

城市森林系统评价指标体系研究

城市森林建设是人与自然和谐的重要内容之一,是实现森林城市的重要途径。针对近年来全国不少城市提出“城市森林”、“森林城”的建设目标,作者以森林生态学、景观生态学、城市生态学等理论为指导,在系统分析国内外现有城市森林的基础上,探讨了城市森林的评价指标体系,并介绍了指标的内涵及选取评价指标的原则,旨在为城市森林规划建设提供科学合理的量化依据。     

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.