陕北丘陵沟壑区撂荒地自然恢复植被的组成结构与数量分类

采用样带调查与TWINSPAN分类等方法,对陕北丘陵沟壑区延安、安塞和吴旗174个撂荒地样方的物种组成、出现频率与盖度、及群落类型进行了统计与分类。植被组成结构的统计结果表明:该区自然恢复的植被几乎一半是由禾本科、菊科、豆科和蔷薇科的物种组成,北温带、旧世界温带、世界与泛热带分布成分占到总物种数近75%,且以中旱生、中生和旱生的草本类植物为主,具有典型的温带地面芽植物气候特征。植被的数量分类表明:调查样方基本包括了该区自然恢复的主要植被类型,延安、安塞和吴旗的植被在1年生草本群落到多年生蒿禾类草本群落阶段,依次均以猪毛蒿(Artemisia scoparia)、赖草(Leymussecalinus)、长芒草(Stipa bungeana)、达乌里胡枝子(Lespedeza davurica)、铁杆蒿(Artemisia gmelinii)、茭蒿(Artemisia giraldii)、白羊草(Bothriochloa ischaemun)等为主要优势物种构成的不同组合的植物群落,且这些物种具有较高的盖度和频度;但在植被演替后期,不同植被带及阴阳坡的演替方向却发生了明显的变化。以延安为代表的森林带,阴坡可形成黄...

辽西低山丘陵区生态系统退化程度的定量确定

Exactly measuring the degradation degree of ecosystems is the basis and precondition of restoring ecosystems. At present,degradation degree is usually analyzed qualitatively.In this paper,quantitative classification method was used to study the degradation degree of ecosystem in hilly region of western Liaoning,which was measured by the degradation degree of habitat.The result shows that the habitat in the northern slope of mountains has been degraded to certain stage between shrubbery and pioneer tree forest,which provides theoretical basis for vegetation restoration practice. Moreover,the result is beneficial for studying the degradation degree of ecosystems in other regions or of other types.

Impact of cloudiness on net ecosystem exchange of carbon dioxide in different types of forest ecosystems in China

Asia 3 Foresight Program [30721140307]; National Key Research and Development Program [2010CB833500]; National Natural Science Foundation of China [30590381, 30900198];

Land-cover classification of China: integrated analysis of AVHRR imagery and geophysical data

Over last two decades, numerous studies have used remotely sensed data from the Advanced Very High Resolution Radiometer (AVHRR) sensors to map land use and land cover at large spatial scales, but achieved only limited success. In this paper, we employed an approach that combines both AVHRR images and geophysical datasets (e.g. climate, elevation). Three geophysical datasets are used in this study: annual mean temperature, annual precipitation, and elevation. We first divide China into nine bio-climatic regions, using the long-term mean climate data. For each of nine regions, the three geophysical data layers are stacked together with AVHRR data and AVHRR-derived vegetation index (Normalized Difference Vegetation Index) data, and the resultant multi-source datasets were then analysed to generate land-cover maps for individual regions, using supervised classification algorithms. The nine land-cover maps for individual regions were assembled together for China. The existing land-cover dataset derived from Landsat Thematic Mapper (TM) images was used to assess the accuracy of the classification that is based on AVHRR and geophysical data. Accuracy of individual regions varies from 73% to 89%, with an overall accuracy of 81% for China. The results showed that the methodology used in this study is, in general, feasible for large-scale land-cover mapping in China.

The regional types of China's floating population: Identification methods and spatial patterns

With the rapid increase of the number and influence of floating population in China, it is urgently needed to understand the regional types of China's floating population and their spatial characteristics. After reviewing the current methods for identifying regional types of floating population, this paper puts forward a new composite-index identification method and its modification version which is consisted of two indexes of the net migration rate and gross migration rate. Then, the traditional single-index and the new composite-index identification methods are empirically tested to explore their spatial patterns and characteristics by using China's 2000 census data at county level. The results show: (1) The composite-index identification method is much better than traditional single-index method because it can measure the migration direction and scale of floating simultaneously, and in particular it can identify the unique regional types of floating population with large scale of immigration and emigration. (2) The modified composite-index identification method, by using the share of a region's certain type of floating population to the total in China as weights, can effectively correct the over- or under-estimated errors due to the rather large or small total population of a region. (3) The spatial patterns of different regional types of China's floating population are closely related to the regional differentiation of their natural environment, population density and socio-economic development level. The three active regional types of floating population are mainly located in the eastern part of China with lower elevation, more than 800 mm precipitation, rather higher population densities and economic development levels.

Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression

Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated.

Discriminative Orthogonal Neighborhood-Preserving Projections for Classification

Orthogonal neighborhood-preserving projection (ONPP) is a recently developed orthogonal linear algorithm for overcoming the out-of-sample problem existing in the well-known manifold learning algorithm, i.e., locally linear embedding. It has been shown that ONPP is a strong analyzer of high-dimensional data. However, when applied to classification problems in a supervised setting, ONPP only focuses on the intraclass geometrical information while ignores the interaction of samples from different classes. To enhance the performance of ONPP in classification, a new algorithm termed discriminative ONPP (DONPP) is proposed in this paper. DONPP 1) takes into account both intraclass and interclass geometries; 2) considers the neighborhood information of interclass relationships; and 3) follows the orthogonality property of ONPP. Furthermore, DONPP is extended to the semisupervised case, i.e., semisupervised DONPP (SDONPP). This uses unlabeled samples to improve the classification accuracy of the original DONPP. Empirical studies demonstrate the effectiveness of both DONPP and SDONPP.

Biologically Inspired Features for Scene Classification in Video Surveillance

Inspired by human visual cognition mechanism, this paper first presents a scene classification method based on an improved standard model feature. Compared with state-of-the-art efforts in scene classification, the newly proposed method is more robust, more selective, and of lower complexity. These advantages are demonstrated by two sets of experiments on both our own database and standard public ones. Furthermore, occlusion and disorder problems in scene classification in video surveillance are also first studied in this paper.

Phase identification in a series of liquid crystalline TPP polyethers and copolyethers having highly ordered mesophase structures. 7. Phase structures in a series of copolyethers containing odd and even numbers of methylene units of different compositions

A series of liquid crystalline copolyethers have been synthesized from 1-(4-hydroxy-4'-biphenyl)-2-(4-hydroxyphenyl)propane with 1,7-dibromoheptane and 1,12-dibromododecene [coTPPs(7/12)], which represents copolyethers containing both odd and even numbers of methylene units. The molar ratio of odd to even methylene units in this series ranges from 1/9 to 9/1. The coTPPs(7/12) exhibit multiple phase transitions during cooling and heating in differential scanning calorimetry experiments. For all these thermal transitions, a small undercooling and superheating dependence is observed upon cooling and heating at different rates. Three types of phase behaviors can be classified in coTPPs(7/12) on the basis of the structural analyses by wide-angle X-ray diffraction on powder and fiber samples and by electron diffraction experiments in transmission electron microscopy. At room temperature, highly ordered smectic and smectic crystal (SC) phases are identified in coTPPs(7/12: 1/9 and 2/8), which is similar to the homopolymer TPP(m = 12). The coTPPs(7/12: 3/7, 4/6, and 5/5) possess a hexagonal columnar (Phi(H)) phase in which the molecular and columnar axes are parallel to the fiber direction and perpendicular to the hexagonal lateral packing. The coTPPs(7/12: 6/4, 7/3, and 8/2) possess a tilted hexagonal columnar (Phi(TH)) phase with a single tilt angle which increases with the increasing composition of the seven-numbered methylene units. However, in coTPP(7/12: 9/1), a Phi(TH) phase with multiple tilt angles is found. Upon heating, phase structures in most coTPPs(7/12) involving the columnar phases enter directly into the nematic (N) phase, while the coTPP(7/12: 1/9) exhibits a highly ordered smectic F (S-F) phase before it reaches the N phase. One exception is found in coTPP(7/12: 2/8), wherein the transformation from the S-F to Phi(H) occurs prior to the N phase. Combining the copolymer phase behaviors observed with the corresponding homopolymers TPP(n = 7) and TPP(m = 12), a phase diagram describing transition temperatures with respect to the composition can be constructed.

Classification of valence changes of trivalent rare earth ions in alkaline earth borates using artificial neural networks

The investigations of classification on the valence changes from RE3+ to RE2+ (RE = Eu, Sm, Yb, Tm) in host compounds of alkaline earth berate were performed using artificial neural networks (ANNs). For comparison, the common methods of pattern recognition, such as SIMCA, KNN, Fisher discriminant analysis and stepwise discriminant analysis were adopted. A learning set consisting of 24 host compounds and a test set consisting of 12 host compounds were characterized by eight crystal structure parameters. These parameters were reduced from 8 to 4 by leaps and bounds algorithm. The recognition rates from 87.5 to 95.8% and prediction capabilities from 75.0 to 91.7% were obtained. The results provided by ANN method were better than that achieved by the other four methods. (C) 1999 Elsevier Science B.V. All rights reserved.

MULTIVARIATE CLASSIFICATION BASED ON METAL CONTENTS IN HUMAN SENILE CATARACT LENSES

Multivariate classification methods were used to evaluate data on the concentrations of eight metals in human senile lenses measured by atomic absorption spectrometry. Principal components analysis and hierarchical clustering separated senile cataract lenses, nuclei from cataract lenses, and normal lenses into three classes on the basis of the eight elements. Stepwise discriminant analysis was applied to give discriminant functions with five selected variables. Results provided by the linear learning machine method were also satisfactory; the k-nearest neighbour method was less useful.