用改进的综合顺序分类法对黄土高原草地分类的探讨

用改进的综合顺序分类法将黄土高原草地划分为13类，各草地类分布情况基本符合黄土高原草地植被的分布规律，说明用综合顺序分类法对黄土高原草地进行分类是可行的。     

人工草地分类的新系统—综合顺序分类法

提出了人工草地系统分类的原则及类、亚类、型三级分类体系。类按热量带的特征划分，亚类按土壤特征划分，型按饲用植物种的特征划分。同时，这一人工草地的分类系统可以在各个级别与天然摹的综合顺序分类法平行衔接，因此这一方法可称为人工草地的综合顺序分类法。     

乐都县天然草地分类探讨

根据综合顺序分类法，将乐都县天然草地划分为四个类，九个亚类，二十六个草地型。草地类随海拔升高，依次分布着微温微干－微温微润－冷温潮湿－寒温潮湿类草地，呈明显的垂直分布规律。     

乐都县天然草地分类探讨

对乐都县天然草地分类进行了探讨，根据综合顺序分类法，乐都县天然草地可分为４个类，９个亚类，２６个草地型，同时指出，草地类随海拔升高，依次分布着微温微干类－微温微润类－冷温潮湿类－寒温潮湿类。并呈明显的垂直分布规律。     

改进的综合顺序分类法在青海草地分类中的应用

本研究统计分析了青海省50个气象台站1961－2006年的气象资料,利用改进后的综合顺序分类法划分了青海草原类型第1级-“类”。根据划分结果将全省草地划分为11类,基本符合青海草地类型分布特征,说明了改进的综合顺序分类法在青海进行植被类型划分是可行的。选择刚察、玛多、格尔木3个有代表性的地区,统计分析了它们的湿润度K值和>0℃年积温的变化趋势,同时分析了草场的演替趋势。     

河南草地的分类

依据改进的草原综合顺序分类法的分类原则,河南的草地可分为9个类。类的划分有助于更深刻地揭示天然草地的自然特性及经济特性,为科学地指导草业发展服务。     

河南草地的分类

依据改进的草原综合顺序分类法的分类原则，河南的草地可分为９个类，类的划发有助于更深刻地揭示天然草地的自然特性及经济特性，为科学地指导草业发展服务。     

基于草地综合顺序分类法的CASA模型改进

将任继周等提出的草原综合顺序分类法的草原分类气候指标＞0℃年积温(Σθ)和湿润度指标(K)引入CASA模型,对水分胁迫因子的计算进行改进,避免了复杂的土壤参数,仅用＞0℃年积温和K值就可计算水分胁迫因子,极大地简化了模型.同时调整了CASA模型最大光能利用率的固定取值,使之成为适合中国草地净第一性生产力(NPP)估算的模型.改进的CASA模型实现了草地NPP模拟与草地综合顺序分类系统的耦合,能更好地反映草地类型与草地NPP的关系,为研究草地NPP的区域分布和全球分布提供了理论依据.     

适用于3S技术的草地综合顺序分类法水热指标改进

草地类型理论和类型划分是草地科学研究和经营管理的基础。草地综合顺序分类法是有较高科学性的分类体系,但在实际应用中,尤其是引入3S技术时出现了水热条件的以点代面,以相邻生境代实际生境的问题,影响了草地综合顺序分类法的准确性和实际应用。通过对其问题的梳理,提出利用定量遥感反演地表参数以解决上述问题,即用面状的土壤水热指标数据替换点状的站点大气水热指标,旨在为我国草地的精确分类提供新的思路和创新方法。     

基于草原综合顺序分类法的中国山地草地亚类分类研究北大核心CSCD

草地作为陆地生态系统的重要组成部分,精准界定草地类型对草地资源研究具有重要意义。本研究基于数字高程模型(DEM),采用草原综合顺序分类法(CSCS),对中国山地草地亚类进行定量划分,并分析了草地亚类在山地区域的空间分布特征。结果表明:1)山地亚类共划分为20种,以小起伏山地亚类为主,面积分布最广泛;丘陵、中起伏山地亚类面积分布相当;大起伏和极大起伏山地亚类面积分布较少;2)原则上每种草地类型可划分为20种草地亚类,由于山地水热条件的空间差异,在特定环境下某些草地亚类并不存在;3)寒冷潮湿多雨冻原、高山草甸类(ⅠF36)的山地亚类面积分布最大;寒温潮湿寒温性针叶林类(ⅡF37)的山地亚类面积占第二位;暖热潮湿落叶-常绿阔叶林类(ⅤF40)的山地亚类面积居第三;亚热极干亚热带荒漠类(ⅥA6)的山地亚类面积分布最小。该研究结果可为草原开发利用、培育保护和经营管理,以及畜牧业的发展提供一定科学依据。     

中国草地生态系统分类初步研究

按照草地生态系统的特征、功能过程特点进行草地生态系统分类。具体可考虑5个原则:生物群落特征;无机环境特征;重点功能过程;动态特征以及尽可能与生物群区结合。利用五级制,陆地生态系统型—-草地生态系统纲—-生态系统目—-生态系统属—-生态系统丛。暂将全国分成7个草地生态系统目,19个属,130个丛。     

河南草地的分类及家畜生态环境的研究

按照综合顺序分类系统的原则及类的划分体系，研究草地的生态环境，并把草地划为九类。类的划分对于进一步认识、开发和利用天然草地提供了重要依据。     

The Classification of Grassland Types Based on Object-Based Image Analysis with Multisource Data

The spatial distribution of different grassland types is important for effectively analyzing spatial patterns, obtaining key vegetation parameters using remote sensing (e.g., biomass, leaf area index, net primary production), and using and protecting grasslands. Existing classifications of grasslands by remote sensing are mostly divided according to the fractional vegetation cover or biomass, but classifications according to grassland types are scarce. In this study, we focused on the classification of different grassland types using remote sensing based on object-based image analysis (OBIA) with multitemporal images in combination with a 30-m digital elevation model (DEM) and the normalized difference vegetation index (NDVI). The grasslands were located in Hulunber, Inner Mongolia, and an autonomous region of China. The support vector machine (SVM) and random forest (RF) machine learning classifiers were selected for the classification. The results revealed the following: 1) It is feasible to generally extract different grassland types on the basis of OBIA with multisource data; the overall classification accuracy and Kappa value exceeded 90% and 0.9, respectively, using the SVM and RF machine learning classifiers, and the classification accuracy of the different grassland types ranged from 61.64% to 98.71%; 2) Multitemporal images and auxiliary data (DEM and NDVI) improved the separability of different grassland types. The information in the growing season was conducive for distinguishing temperate meadow steppe from temperate steppe and was favorable for extracting lowland meadow and swamp in the nongrowing season. The DEM and NDVI also effectively reduced the number of image segmentation objects and improved the segmentation effects; 3) Spectral and textural features were more important than geometric features in this study. A few main variables played a major role in the classification, while a large number of variables had either no significant effect or a negative effect on the classification results when the optimal feature subset was determined. This study provides a scientific basis and reference for the classification of various grassland types by remote sensing, including the data selection, image segmentation, feature selection, classifier selection, and parameter settings.     

世界草地综合顺序分类图的制作

通过对全球1961～1990年气象数据的分析与统计,将7 775个站点的气温与降水换算成草地综合顺序分类指标-＞0℃的年积温(Σθ)和湿润度(K)值,在Arc GIS平台上,配以世界行政区划图,依据综合顺序分类理论,制作世界草地综合顺序分类图.研究结果表明:可将世界陆域草地分为38类,基本符合全球气候带以及植被的分布,...     

天祝县草地综合顺序分类及生态系统服务价值评价

按照草地综合顺序分类方法,利用天祝县及周边地区117个气象台站的资料和DEM分区修正,将天祝县草地分为寒温微干山地草原、寒温微润山地草甸草原、寒温湿润山地草甸、微温微干温带典型草原、微温微润草甸草原和微温湿润森林草原6类.同时考虑土地利用方式和草地退化程度,结合天祝县实际的草地资源分布图和草地的不同等级,利用地理信息系统软件(ArctGIS)工具,对天祝县草地生态系统服务价值进行估算,结果表明:天祝县草地的生态系统服务总价值为5.685亿元/年,其中微温微润草甸草原贡献率最高,达31.52%,寒温微干山地草原草地生态系统服务价值最低,贡献率为8.45%.     

生态文明建设背景下中国草原多维分类方法探讨

草原分类是草原科学管理的主要依据和基础，为合理开发草原资源和有效保护草原生态提供科学依据。我国现有的草原分类体系仅从发生学单一维度确定各类草原的联系和区别，在生产和实践中很难从草原功能、权属、经营方向等维度实现精准管理。为适应生态文明建设新时代草原工作从生产为主转向生态为主、从林草矛盾转向林草融合的需求，实现山水林田湖草沙冰生命共同体的系统治理，草原分类工作需从多元化角度出发，形成科学性、系统性和综合性的多维分类体系。本文充分吸收了国内外草原分类的先进理论方法和技术体系，借鉴我国林地多元化分类的框架体系，提出了我国草原多维分类体系的原则、方法和指标，构建了基于发生学、功能用途、产权属性、经营程度等四个维度的草原分类系统，不仅考虑了草原发生发展的气候、地形和土壤等自然属性，而且考虑了草原经营程度、功能用途和产权特征等社会经济属性，实现了各个分类系统的融合性和互补性，为新时期草原研究、保护、修复、利用、建设等分门别类的专项管理提供基础支撑，同时为新时期草原资源和生态的综合管理、山水林田湖草生命共同体的系统治理提供科学依据。     

Functional Classification of Feed Items in Pampa Grassland,Based on Their Near-Infrared Spectrum

Native pastures that present high botanical richness produce a variety of feed items for grazing herbivores. Diet selection takes place at the bite scale and, at a daily scale, the sum of bites composing the intake determines diet characteristics. The objective of this paper is to bring an original contribution to the understanding of diet selection in diverse foraging environments such as native Pampa grasslands. The issues are 1) to improve grazing management in Pampa ecosystems and 2) to evaluate the ability of native pastures to feed a herd in order to reduce supplementation and to improve the economic sustainability of extensive systems. We hypothesize that the information contained in the infrared spectra of plant samples corresponding to potential bites can be useful for establishing functional bite categories, which, in turn, can be used to understand and manage pastoral ecosystems. We analyzed the near-infrared spectrum (NIRS) of 1 515 bite samples gathered in 2012 using the continuous bite monitoring method applied to heifers continuously grazing on Pampa grassland at five grazing intensities (randomized block design with two replicates, applied since 1987). On the basis of the main wavelengths, a statistical classification of bite samples was produced and five classes were described: 1) small bites of wetland grasses and short grass; 2) medium bites of forbs and short grass; 3) small bites of short Poaceae and mixed species; 4) large bites on tussocks of Poaceae; and 5) extralarge bites on stiff tussock of Poaceae and Apiaceae. In order to evaluate the functional value of the classification, we tested its ability to take seasonal changes in pasture composition into account and to describe the variations in animal intake and performance in contrasted grazing conditions. We concluded that the NIRS is a good support for establishing functional classes of feed items that can be used to understand the foraging behavior and diet composition of grazing herbivores. Our approach was tested in Pampa grasslands grazed by cattle but should be applicable to a wide range of ecosystems, provided that chemical composition is more relevant for selection than feed item accessibility.     

川西北退化高寒草地分类分级治理技术要点

在退化高寒草地现状诊断分级和退化高寒草地治理轻简实用技术研究基础上,综合考虑川西北高寒草地当前生产力水平、不同类型草地的生态定位和发展潜力、国家政策导向、草地使用传统习惯、技术成本和推广难易等因素,集成形成针对性和可操作性强、成本低、效果好、易推广的高寒草地分类分级治理技术。     

三江源区"黑土滩"退化草地的分类分级及治理模式

通过对三江源区120个野外调查样区的数据分析,依据地形条件以及工程的需要,将"黑土滩"退化草地划分为三种类型,即Ⅰ滩地:坡度在0.7°之间,Ⅱ缓坡地:坡度在≥7～25°之间,Ⅲ陡坡地:坡度≥25°.通过聚类分析和主成分分析,优选出秃斑地比例和可食牧草比例作为"黑土滩"退化草地分级的主要指标,将其分为轻度、中度和重度,并提出了相应的治理模式.     

青海草地类型在中国草地分类系统中的归并初探

青海现行的草地类型分类系统,主要依据<青海草地资源>,建立于20世纪80年代.1988年,青海省在全省草地资源调查中,依据1984年厦门会议制定的草地分类系统,编写了<青海草地资源>,将青海全省天然草地划分为9个草地类、9个草地组、173个草地型.由于<青海草地资源>成文早于<中国草地资源>,分类方法和分类单位与<中国草地资源>不同,导致青海草地分类系统与全国草地分类系统差异很大.为了统一规范青海草地分类系统,与全国草地分类系统相一致,现将青海省原用的草地类型归并其中,形成包括10个草地类、8个草地组、167个草地型的青海草地分类系统,通过今后的草地资源调查实践,进一步得到补充和完善,最终形成一个科学的青海草地分类体系.