公路工程土类分类统一的建议

工程土类的划分主要反映土的工程性质，目前公路工程在各专业中的分类有所不同，针对公路工程的特点，结合相关行业的分类方法，提出了目前公路工程土类分类的建议。

基于孔压静力触探的修正土类指数土体分类方法与实例应用

孔压静力触探(piezocone penetration test,简称CPTU)常被应用于土层划分、土体分类,其各种土体分类方法有一定的适用性和局限性。引用两组实际工程案例,对5种常用的基于CPTU的土体分类图进行了分析比较。分析表明,已有分类图虽然能够区分黏性土和无黏性土,但对于粉质黏土、粉土和粉砂之间的判别均存在较大误差,且多数分类图采用过渡带分类,导致分类结果的准确性偏低。基于Robertson土类指数分类图,根据实际CPTU测试数据分类结果,对土类指数I_(c)进行了修正,将其同心圆模型修正为平行线模型,由此提出一种基于CPTU的修正土类指数土体分类方法。根据对实测数据的应用表明,提出的修正土类指数土体分类方法可以更好地满足土体分类,且能够满足粉质黏土、粉土和粉砂之间的判别。

优化的CPTU土分类方法在大丰海上风电勘察中的应用

将Robertson土类指数分类图和江苏省《孔压静力触探技术规程》土分类方法分别用于大丰某海上风电场,结果表明两者虽都能较好地划分无黏性土和黏性土,准确率超过90%,但无法进一步划分粉质黏土、粉土以及砂土,基于此,该文提出一种先初步分类再二次分类的划分方法。将土类分布图先划分为4个区域,其中2区和3区为过渡性区域,再分别对落于2区和3区的测试点采用锥尖阻力和孔隙水压力进一步划分,该方法初步分类平均准确率达96.2%,二次分类准确率接近80%,分类准确率较高。对于场地揭露的淤泥质土夹有较多粉土或粉砂,锥尖阻力失真,无法有效划分,建议将锥尖阻力小于0.7MPa的黏性土划分为淤泥质土。

Application of GIS in Ecological Land Type(ELT)mapping--A case in Changbai Mountain area

This paper depicted the physiographic landscape features and natural vegetation situation of study area (the eastern Jilin Province), and expatiates the definition, basic characters and its development of Ecological Land Classification (ELC). Based on the combination of relief map, satellite photography for study area and vegetation inventory data of 480 sample sites, a 5-class and a 15-class ecological land type map was concluded according to 4 important factors including slope, aspect, vegetation and elevation. Ecological Classification System (ECS) is a method to identify, characterize, and map ecosystems. The Ecological Land Type (ELT) was examined and applied initially in eastern Jilin Province.

Spatial pattern of soil carbon and nutrient storage at the Alpine tun-dra ecosystem of Changbai Mountain, China

In August 2003, we investigated spatial pattern in soil carbon and nutrients in the Alpine tundra of Changbai Moun-tain, Jilin Province, China. The analytical results showed that the soil C concentrations at different depths were significantly (p<0.05) higher in Meadow alpine tundra vegetation than that in other vegetation types; the soil C (including inorganic carbon) concentrations at layer below 10 cm are significantly (p<0.05) higher than at layer of 1020 cm among the different vegetation types; the spatial distribution of soil N concentration at top surface of 0-10 cm depth was similar to that at 1020 cm; the soil P concentrations at different depths were significantly (p<0.05) lower at Lithic alpine tundra vegetation than that at other vegetation types; soil K concentration was significantly (p<0.05) higher in Felsenmeer alpine tundra vegetation and Lithic alpine tundra vegetation than that in Typical alpine tundra, Meadow alpine tundra, and Swamp alpine tundra vegetations.. However, the soil K had not significant change at different soil depths of each vegetation type. Soil S concentration was dramatically higher in Meadow alpine tundra vegetation than that in other vegetation types. For each vegetation type, the ratios of C: N, C: P, C: K and C: S generally decreased with soil depth. The ratio of C: N was significantly higher at 010 cm than that at 1020 cm for all vegetation types except at the top layer of the Swamp alpine tundra vegetation. Our study showed that soil C and nutrients storage were significantly spatial heterogeneity.

Knowledge-Based Classification in Automated Soil Mapping

A machine-learning approach was developed for automated building of knowledgebases for soil resources mapping by using a classification tree to generate knowledge from trainingdata. With this method, building a knowledge base for automated soil mapping was easier than usingthe conventional knowledge acquisition approach. The knowledge base built by classification tree wasused by the knowledge classifier to perform the soil type classification of Longyou County,Zhejiang Province, China using Landsat TM bi-temporal images and CIS data. To evaluate theperformance of the resultant knowledge bases, the classification results were compared to existingsoil map based on a field survey. The accuracy assessment and analysis of the resultant soil mapssuggested that the knowledge bases built by the machine-learning method was of good quality formapping distribution model of soil classes over the study area.

APPLYING PPE MODEL BASED ON RAGA TO CLASSIFYAND EVALUATE SOIL GRADE

The research of soil classification and soil grade evaluation is often based on fuzzy theory. So, the traditional method has an inevitable problem about weight matrix which given by some experts, and the final result can be influenced by artificial factors. The essentials of fuzzy synthetically judge is to handle the data of high dimension. That is to reducing the dimension number. The weight matrix in fuzzy theory is corresponding to low dimension projection value of each index. But we can′t define whether the weight matrix given by experts is the best projection value or not. So, the authors apply a new technique of falling dimension named projection pursuit to soil study, through using the improved real coding based accelerating genetic algorithm to optimize the projection direction. Thus, it can transfer multi dimension data into one dimension data, through searching for the optimum projection direction to realize the soil classification and its grade evaluation. The method can avoid the artificial disturbance, and acquire preferably effect. Thus, the paper provides a new method to the research of soil classification and grade evaluation.

New and newly recorded species of the subgenus Flagellozetes(Cosmogalumna)(Acari:Oribatida:Galumnidae) from China

Material and methods Soil sample were collected by soil-corer and afterwards extracted using Tullgren funnels. Measurements and descriptions are based on specimens mounted in temporary cavity slides and studied using a light microscope equipped with a drawing tube. All body measurements are presented in micrometers. The body length was measured in lateral view, from the tip of the rostrum to the posterior edge of the ventral plate, to avoid discrepancies caused by different degrees of notogastral distension. Notogastral width refers to the maximum width in dorsal aspect. Lengths of body setae were measured in lateral aspect. Formulas for leg setation are given in parentheses according to the sequence trochanter-femur-genu-tibia-tarsus （famulus included）.

Integrating TM and Ancillary Geographical Data with Classification Trees for Land Cover Classification of Marsh Area

The main objective of this research is to determine the capacity of land cover classification combining spec- tral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjiang Plain, Heilongjiang Province, China. Semi-variograms and Z-test value were calculated to assess the separability of grey-level co-occurrence texture measures to maximize the difference between land cover types. The degree of spatial autocorrelation showed that window sizes of 3×3 pixels and 11×11 pixels were most appropriate for Landsat TM im- age texture calculations. The texture analysis showed that co-occurrence entropy, dissimilarity, and variance texture measures, derived from the Landsat TM spectrum bands and vegetation indices provided the most significant statistical differentiation between land cover types. Subsequently, a Classification and Regression Tree (CART) algorithm was applied to three different combinations of predictors: 1) TM imagery alone (TM-only); 2) TM imagery plus image texture (TM+TXT model); and 3) all predictors including TM imagery, image texture and additional ancillary GIS in- formation (TM+TXT+GIS model). Compared with traditional Maximum Likelihood Classification (MLC) supervised classification, three classification trees predictive models reduced the overall error rate significantly. Image texture measures and ancillary geographical variables depressed the speckle noise effectively and reduced classification error rate of marsh obviously. For classification trees model making use of all available predictors, omission error rate was 12.90% and commission error rate was 10.99% for marsh. The developed method is portable, relatively easy to im- plement and should be applicable in other settings and over larger extents.

CLASSIFICATION OF CRYOSOLS?SIGNIFICANCE,ACHIEVEMENTS AND CHALLENGES

International concerns about the effects of global change on permafrost-affected soils and responses of permafrost terrestrial landscapes to such change have been increasing in the last two decades. To achieve a variety of goals including the determining of soil carbon stocks and dynamics in the Northern Hemisphere,the understanding of soil degradation and the best ways to protect the fragile ecosystems in permafrost environment,further study development on Cryosol classification is being in great demand. In this paper the existing Cryosol classifications contained in three representative soil taxonomies are introduced,and the problems in the practical application of the defining criteria used for category differentiation in these taxonomic systems are discussed. Meanwhile,the resumption and reconstruction of Chinese Cryosol classification within a taxonomic frame is proposed. In dealing with Cryosol classification the advantages that Chinese pedologists have and the challenges that they have to face are analyzed. Finally,several suggestions on the study development of the further taxonomic frame of Cryosol classification are put forward.

Remote sensing classification of western Sierra Leone using landsat TM and ETM+

The study examines the changes of land cover/use resources for the period under investigation.An unsupervised vegetation classification is being performed that provides five distinctive classes and thus assesses these changes in five broad land cover classes-high/moist forests,forest regrowth,mixed savanna,bare land/ grass and water.The remote sensing images used in this work are both images of TM and ETM+in different time periods(1986 to 2001)to determine land cover/use changes.A fairly accuracy report is recorded after performing the unsupervised classification,which shows vegetation has been depleted for over the years.Changes created are mostly human and to a lesser extent environment.Human activities are mainly encroachment thus altering the landscape through activities such as population growth,agriculture,settlements,etc.and environment due to some perceive climatic changes.This vegetation classification highlights the importance to acquire and publish information about the country's partial vegetation cover and vegetation change including vegetation maps and other basic vegetation influencing factors,leading to an understanding of its evolution for a period.

Essential Oil Composition of Four Endemic Tanacetum L. （Asteraceae） Taxa from Turkey and a Chemotaxonomic Approach

In this study the essential oil components aerial parts of Tanacetum heterotomum （Bornm.） Grierson, T. zahlbruckneri （Nab.） Griersson, T. densum （Lab.） Schultz Bip. subsp, amani Heywood and T. cadmeum （Boiss.） Heywood subsp, orientale were examined by HS-SPME/GC-MS technique. Thirty six, thirty nine, forty and forty five constituents were determined representing 88.9%, 90.1%, 90.8% and 91.5% of the oil, respectively. The main compounds of studied Tanacetum L. taxa; borneol, α-pinene, 1,8-cineole, β-pinene, camphor, germacrene D, spathulenol are determined. Studied Tanacetum taxa showed congruency with the discription in Flora of Turkey as morphological properties; on the contrary essential oil composition were detected very quiet diverse infrageneric level. Chemotypes of Tanacetum L. taxa were reported as borneol, germacrene D, spathulenol, α-pinene, 1,8-cineole, β-pinene and camphor. The results obtained from this study were discussed in terms of chemotaxonomy and natural products.

Comparative analysis on soil engineering classifications of China and America

Based on China National Standard of Soil Engineering Classification (GB/T 50145-2007) and the Unified Soil Classification System of American Society for Testing Materials (ASTM D-2478), two kinds of soil laboratory engineering classification methods were discussed and analyzed from the aspects of the definition in particle fraction, classification of soil type and evaluation standard for soil gradation. There is a same limit of fine grains fraction in the two standards, and there are three main types of soil in GB/T 50145-2007 and two in ASTM D-2487. Different evaluation standards of gradation are put forward for gravels and sands in ASTM D-2487. Same criteria of A line, B line and controlling value of plastic index are in the plasticity chart of both standards.

Assessment of Soil Organic Carbon Stock in the Upper Yangtze River Basin

Soil organic carbon is of great importance to terrestrial ecosystems. Studies on the amount and spatial distribution of soil organic carbon stock in various types of soil can help to better understand the role of soil in the global carbon cycle and provide a scientific basis for the assessment of the magnitude of carbon stored in a given area. Here we present estimates of soil organic carbon stock in soils in the upper reaches of the Yangtze River based on soil types as defined by Chinese Soil Taxonomy and recently compiled into a digital soil database. The results showed that the total soil organic carbon stock of the upper Yangtze River to a depth of 100 cm was 1.452x1013 kg. The highest soil organic carbon stock was found in felty soils （2.419x10TM kg）, followed by dark brown soils （1.269x10=kg）, and dark feltysoils （L139x10=kg）. Chernozems and irrigation silting soils showed the lowest soil organic carbon stock, mainly due to the small total area of such soils. The soil organic carbon density of these major soil types ranged from 5.6 to 26.1 kg m2- The average soil organic carbon density of the upper reaches of the Yangtze River was 16.4 kg m-2, which was higher than that of the national average. Soil organic carbon density indicated a distinct decreasing trend from west to east, which corresponds to the pattern of increasing temperature from cold to subtropical.

Object-Based Method Outperforms Per-Pixel Method for Land Cover Classification in a Protected Area of the Brazilian Atlantic Rainforest Region

Conventional image classification based on pixels hinders the possibilities to obtain information contained in images, while modern object-based classification methods increase the acquisition of information about the object and the context in which it is inserted in the image. The objective of this study was to investigate the performance of different classification methods for land cover mapping in the vicinity of the Alto Ribeira Tourist State Park, a Brazilian Atlantic rainforest area. Two classification methods were tested, including i） a hybrid per-pixel classification using the image processing software ERDAS Imagine version 9.1 and ii） an object-based classification using the software eCognition version 5. In the first method, six different classes were established, while in the second method, another two classes were established in addition to the six classes in the first method. Accuracy assessment of the classification results presented showed that the object-based classification with a Kappa index value of 0.8687 outperformed the per-pixel classification with a Kappa index value of 0.2224. Application of the user＇s knowledge during the object-based classification process achieved the desired quality; therefore, the use of inter-relationships between objects, superelasses, subclasses, and neighboring classes were critical to improving the efficiency of land cover classification.

Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm

There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.