Selection of tree species by principal component analysis for abandoned farmland in southeastern Horqin Sandy Land,China

With changes in global climate and land use,the area of desertified farmland in southeastern Horqin Sandy Land(HSL)has increased in recent years,and farmlands are being abandoned.These abandoned farmlands(AFs)nega-tively impact the local ecology.Therefore,the aim of the present study was to select suitable trees and shrubs for those AFs to prevent and control the desertification tendency.In this study,three AFs were fenced for 2 years,then 37 arbor and shrub species or varieties of 21 families were planted in the fenced AFs and grown for 10 years.The ecological adaptability of the species was evaluated and ranked using a principal component analysis.The results showed that the biodiversity of the AFs significantly improved after 2 years of fencing;the Shannon-Wiener index and species rich-ness of perennial grasses and forbs were 1.45 and 3.6 times higher,respectively,than for the unfenced AF.Among all species planted in fenced AFs,nine tree species had posi-tive comprehensive F(CF)values;Pinus sylvestris(Russian Shira steppe provenance),Populus alba‘Berolinensis’and Gleditsia triacanthos had CF greater than 1,and the first(PC1),second(PC2)and third(PC3)principal component values(F_(1),F_(2),F_(3))were all positive.Among the shrubs,only Lespedeza bicolor and Rosa xanthina f.normalis had CF greater than 0.All these results suggest that fencing improves biodiversity and that planting trees and shrubs that have higher CF values on the basis of fencing is an effective way to green and beautify AFs in HSL.

Combining spatial and economic criteria in tree-level harvest planning

Background:Modern remote sensing methods enable the prediction of tree-level forest resource data.However,the benefits of using tree-level data in forest or harvest planning is not clear given a relative paucity of research.In particular,there is a need for tree-level methods that simultaneously account for the spatial distribution of trees and other objectives.In this study,we developed a spatial tree selection method that considers tree-level(relative value increment),neighborhood related(proximity of cut trees)and global objectives(total harvest).Methods:We partitioned the whole surface area of the stand to trees,with the assumption that a large tree occupies a larger area than a small tree.This was implemented using a power diagram.We also utilized spatially explicit tree-level growth models that accounted for competition by neighboring trees.Optimization was conducted with a variant of cellular automata.The proposed method was tested in stone pine(Pinus pinea L.)stands in Spain where we implemented basic individual tree detection with airborne laser scanning data.Results:We showed how to mimic four different spatial distributions of cut trees using alternative weightings of objective variables.The Non-spatial selection did not aim at a particular spatial layout,the Single-tree selection dispersed the trees to be cut,and the Tree group and Clearcut selections clustered harvested trees at different magnitudes.Conclusions:The proposed method can be used to control the spatial layout of trees while extracting trees that are the most economically mature.

Which trees should be removed in thinning treatments?

Background： In economically optimal management, trees that are removed in a thinning treatment should be selected on the basis of their value, relative value increment and the effect of removal on the growth of remaining trees. Large valuable trees with decreased value increment should be removed, especially when they overtop smaller trees. Methods： This study optimized the tree selection rule in the thinning treatments of continuous cover managemen when the aim is to maximize the profitability of forest management. The weights of three criteria （stem value, relative value increment and effect of removal on the competition of remaining trees） were optimized together with thinning intervals. Results and conclusions： The results confirmed the hypothesis that optimal thinning involves removing predominantly large trees. Increasing stumpage value, decreasing relative value increment, and increasing competitive influence increased the likelihood that removal is optimal decision. However, if the spatial distribution of trees is irregular, it is optimal to leave large trees in sparse places and remove somewhat smaller trees from dense places. However, the benefit of optimal thinning, as compared to diameter limit cutting is not usually large in pure one-species stands. On the contrary, removing the smallest trees from the stand may lead to significant （30-40 %） reductions in the net present value of harvest incomes.

Optimal Selection of Raw Material Forest Species for Edible Fungi in Guizhou Province

A comprehensive evaluation system,which focused on optimal selection of raw material forest species for edible fungi,was established by combination of Analytic Hierarchy Process（ AHP） and Experts Grading Method（ EGM）. The evaluation system had 4 indices of grade I and 12 indices of grade II. Among the 12 indices of grade II,the weighted values of production quality of edible fungi P2（ 0. 287 4）,usable time P7（ 0. 187 3）,annual average increment P8（ 0. 187 3）,edible fungi production suitability P1（ 0. 095 8） were larger than the values of others. Based on the comprehensive evaluation system,this study analyzed and screened 47 broadleaf species of 40 genera of 25 families. There were 16 broadleaf species having the comprehensive evaluation scores of equal to or greater than2. 400 0,which were available as major tree species for edible fungi development of Guizhou Province,especially species such as Liriodendron chinense,Quercus acutissima,Alnus cremastogyne,Betula luminfera,Elaeocarpus duclouxii,Elaeocarpus sylvestris,Choerospondias axillaris. The 10 broadleaf tree species with comprehensive evaluation score of 2. 100 0 ≤ Y 2. 400 0 were recommended as candidates for edible fungi production,while the 21 broadleaf species with the comprehensive evaluation score of less than 2. 100 0 were not recommended.

华北地区高速公路绿化树种选择

文章对华北地区高速公路绿化的功能和作用、绿化设计原则及树种选择原则等进行研讨。并以山东泰淮线高速公路泰安段绿化设计为例,从高速公路特点、分区绿化、植物配置和绿化景观动态变化等原则出发综合考虑绿化树种的选择。

Non-Blocking Join Algorithm Based on Hash-Merge for Improving Query Response Times

In data streams or web scenarios at highly variable and unpredictable rates, a good join algorithm should be able to ＂hide＂ the delays by continuing to output join results. The non-blocking algorithms allow some tuples to be flushed onto disk, with the goal of producing results continuously when data transmission is suspended. But state-of-the-art algorithms have trouble with the constraint of allocated memory. To make better use of memory, a novel non-blocking join algorithm based on hash-merge for improving query response times is proposed. The reduced data structure of in-memory tuples helps to improve memory utility. A replacement selection tree is applied to adjust memory by expanding or shrinking the size of the tree and separates one external join transaction into multi-subtasks. In addition, a cost model to estimate task output rate is proposed to select the in-disk portion that promises to produce the fastest results in the external join stage. Experiments show that the technique, with far less memory, delivers results faster than the three non-blocking join algorithms （ XJoin, HMJ and RPJ ） , with up to almost two-fold improvement in reliable network and one order of magnitude improvement in unreliable network in terms of the number of the reported tuples.

A new correlation-based approach for ensemble selection in random forests

Purpose-Ensemble methods have been widely used in the field of pattern recognition due to the difficulty offinding a single classifier that performs well on a wide variety of problems.Despite the effectiveness of thesetechniques,studies have shown that ensemble methods generate a large number of hypotheses and thatcontain redundant classifiers in most cases.Several works proposed in the state of the art attempt to reduce allhypotheses without affecting performance.Design/methodology/approach-In this work,the authors are proposing a pruning method that takes intoconsideration the correlation between classifiers/classes and each classifier with the rest of the set.The authorshave used the random forest algorithm as trees-based ensemble classifiers and the pruning was made by atechnique inspired by the CFS(correlation feature selection)algorithm.Findings-The proposed method CES(correlation-based Ensemble Selection)was evaluated onten datasets from the UCI machine learning repository,and the performances were compared to sixensemble pruning techniques.The results showed that our proposed pruning method selects a smallensemble in a smaller amount of time while improving classification rates compared to the state-of-the-artmethods.Originality/value-CES is a new ordering-based method that uses the CFS algorithm.CES selects,in a shorttime,a small sub-ensemble that outperforms results obtained from the whole forest and the other state-of-thearttechniques used in this study.

Instance-Specific Algorithm Selection via Multi-Output Learning

Instance-specific algorithm selection technologies have been successfully used in many research fields,such as constraint satisfaction and planning. Researchers have been increasingly trying to model the potential relations between different candidate algorithms for the algorithm selection. In this study, we propose an instancespecific algorithm selection method based on multi-output learning, which can manage these relations more directly.Three kinds of multi-output learning methods are used to predict the performances of the candidate algorithms：（1）multi-output regressor stacking;（2） multi-output extremely randomized trees; and（3） hybrid single-output and multioutput trees. The experimental results obtained using 11 SAT datasets and 5 Max SAT datasets indicate that our proposed methods can obtain a better performance over the state-of-the-art algorithm selection methods.

Bird community comparisons of four plantations and conservation concerns in South China

Plantations of non-native,fast-growing trees are increasing in the tropics and subtropics,perhaps with negative consequences for the native avifauna.We studied bird diversity in 4 types of plantations in South China to deter-mine which plantation types are especially detrimental,and compared our findings with studies in nearby natu-ral forests to assess the magnitude of the negative impact.A total of 57 species was recorded.The mean capture rate of understory birds was 1.7 individuals 100-net-h-1.Bird richness and capture rate were lower in plantations than in nearby natural forests.Babblers(Timaliidae),primarily forest-dependent species in South China,were particularly under-represented in plantations.Species richness,composition and bird density,particularly of un-derstory birds,differed between plantation types.Plantations of Schima,which is native to South China,had the highest species richness according to point count data.Plantations of Acacia(non-native)supported the highest understory species richness and produced the highest capture rate of understory birds,probably because of their complex structure and high arthropod abundance.If bird diversity is to be considered,we strongly recommend that future re-afforestation projects in South China should,as far as possible,use mixed native tree species,and especially Schima,ahead of the other species.

A Comprehensive Method to Reject Detection Outliers by Combining Template Descriptor with Sparse 3D Point Clouds

We are using a template descriptor on the image in order to try and find the object. However, we have a sparse 3D point clouds of the world that is not used at all when looking for the object in the images.Considering there are many false alarms during the detection, we are interested in exploring how to combine the detections on the image with the 3D point clouds in order to reject some detection outliers. In this experiment we use semi-direct-monocular visual odometry(SVO) to provide 3D points coordinates and camera poses to project 3D points to 2D image coordinates. By un-projecting points in the tracking on the selection tree(TST) detection box back to 3D space, we can use 3D Gaussian ellipsoid fitting to determine object scales. By ruling out different scales of detected objects, we can reject most of the detection outliers of the object.