共查询到19条相似文献，搜索用时 906 毫秒
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黄土高原地区草地资源两次遥感调查比较研究 总被引：2，自引：0，他引：2
本文对黄土高原地区草地资源的2000年遥感快查和1986年遥感调查进行了比较研究.2000年遥感快查应用1999-2000年TM陆地卫星影像、地理信息系统技术和GPS全球定位技术全数字作业进行,1986年的遥感调查利用TM陆地卫星影像和常规地面调查进行.两次调查的草地类型分类系统、比例尺和调查精度均相同,而方法、手段、结果不同.对草地资源变化趋势进行了初步分析.在此基础上,阐述了遥感(RS)、地理信息系统(GIS)和全球定位系统(GPS)技术在草地资源调查中应用的方法,为建立、完善草地地理信息系统与草地资源的动态监测研究提供一定的技术基础. 相似文献
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中国草地资源遥感快查技术方法的研究 总被引：9，自引：0，他引：9
本文阐述了全国草地资源第二次遥感快速调查的总体技术路线以及遥感技术、计算机技术、地理信息系统技术在草地资源调查中的应用与方法.调查采用与20世纪80年代全国首次草地资源调查相同的草地属性界定标准和草地分类系统;基本信息源为1999-2000年TM和中国陆地卫星影像;参考信息源为1;25万中国科学院土地利用现状数据库数字影像图.遥感调查采用全数字作业方式,应用MGE/Microstation遥感制图专用软件和人机交互判断方法,完成草地资源信息的提取和草地资源图的编制,作业比例尺为1:25万.同时选择松嫩、甘南和伊犁等7片重点牧区草原,进行野外路线实地调绘,建立判读标志,测定草地生产力.经过判读、制图和验证,生成草地资源的图形数据和属性数据,再行数据编辑、集成、生成数据库后,进行图幅面积平差、面积量算、分类统计,在二年时间内完成全国草地资源的快速清查. 相似文献
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基于遥感资料的植被类型划分能快速获得大尺度的植被覆盖变化数据。采用遥感和GIS技术,利用天山北坡典型草地2008年Landsat 5 TM遥感影像和1999年Landsat 7 ETM+全色波段数据,在对遥感影像预处理的基础上,进行了数据融合处理。根据融合影像的纹理特征进行监督分类,将研究区域的植被初步分为8种主要覆盖类型,在监督分类的基础上借助专家知识系统构建决策树,进一步将草地分为5类,包括平原荒漠、平原沙漠、低山荒漠、温性草甸和高寒草甸,最后对分类结果进行精度评价,总精度在95%以上,总Kappa系数为0.939 6,间隔9年的影像融合和决策树分类方法在研究区植被分类中具有较高的可行性。 相似文献
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新疆福海县草地资源变化研究 总被引：1，自引：1，他引：0
采用1999年TM和2008年ETM+遥感影像,结合野外调查,利用GIS软件人机交互式解译提取福海县草地资源信息,以此进行其在时空上的变化.研究结果表明:福海县草地退化严重,低、中、高覆盖度草地均有不同程度的退化,退化面积分别占其总面积的22.06%,45.01%和62.25%,高覆盖度草地退化最严重;分析了导致研究区... 相似文献
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TM影像应用于草地资源类型调查与制图的研究 总被引：2，自引：0，他引：2
ＴＭ影像为大范围、高效率、低成本调查草地资源提供了一种新技术手段。本文在介绍ＴＭ影像的特点及其解译原则的基础上，对ＴＭ影像应用于草地资源类型调查与制图可靠性进行了初步分析，结合“三北”防护林甘青宁类型区遥感综合调查研究课题，提出ＴＭ影像应用于草地资源调查与制图方法。强调欲提高ＴＭ影像目视解译的精度，须把室内综合预判与野外选线考察紧密结合起来。 相似文献
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Dawei Xu Baorui Chen Beibei Shen Xu Wang Yuchun Yan Lijun Xu Xiaoping Xin 《Strength and Conditioning Journal》2019,72(2):318-326
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. 相似文献
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草地遥感估产的原理与方法 总被引：5，自引：2，他引：3
在介绍草地遥感估产的原理和方法的基础上,分析了单纯的遥感模型、遥感模型与地学模型相结合等不同估产方法及草地面积动态监测的特点和存在问题,并对草地遥感估产方法发展趋势做了总结。随着遥感技术的发展,草地遥感估产技术和方法也将继续发展,基于遥感数据和方法的草地估产模型更加趋于成熟,其应用也将向草原生态系统、草地退化监测、草地植被生长监测等领域扩展。 相似文献
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In order to promote the application of hyperspectral remote sensing in the quantification of grassland areas’ physiological and biochemical parameters, based on the spectral characteristics of ground measurements, the dry AGB and multisensor satellite remote sensing data, including such methods as correlation analysis, scaling up, and regression analysis, were used to establish a multiscale remote sensing inversion model for the alpine grassland biomass. The feasibility and effectiveness of the model were verified by the remote sensing estimation of a time-space sequence biomass of a plateau grassland in northern Tibet. The results showed that, in the ground spectral characteristic parameters of the grassland’s biomass, the original wave bands of 550, 680, 860, and 900 nm, as well as their combination form, had a good correlation with biomass. Also, the remote sensing biomass estimation model established on the basis of the two spectral characteristics (VI2 and Normalized Difference Vegetation Index [NDVI]) had a high inversion accuracy and was easy to realize, with a fitting R2 of 0.869 and an F test value of 92.6. The biomass remote sensing estimate after scale transformation had a standard deviation of 53.9 kg/ha from the fitting model established by MODIS NDVI, and the estimation accuracy was 89%. Therefore, it displayed the ability to realize the estimation of large-scale and long-time sequence remote sensing biomass. The verification of the model’s accuracy, comparison of the existing research results of predecessors, and analysis of the regional development background demonstrated the effectiveness and feasibility of this method. 相似文献