共查询到19条相似文献，搜索用时 687 毫秒
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30m全球地表覆盖遥感制图生产体系与实践 总被引：1，自引：0，他引：1
在以"多源影像最优化处理、参考资料服务化整合、覆盖类型精细化提取、产品质量多元检核"为主线的总体研究基础上,依托生产技术规范体系、全过程质量控制手段和支持环境,通过30m地表覆盖产品和技术设计、多源影像资料收集整合处理、分区按类型地表覆盖数据提取组织实施及数据产品集成与优化,构建了工程化的30m全球地表覆盖遥感制图生产体系,实现了预期的产品指标,完成了2000和2010两个基准年的30m地表覆盖数据产品研制。通过精度评价,该套数据产品分类精度达到80%以上。该生产体系的构建为开展较高分辨率全球地表覆盖数据产品研制、细化、更新奠定了基础,为开展大规模遥感影像信息提取、表达和应用起到了示范作用。 相似文献
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基于高分遥感数据的地表覆盖专题监测研究 总被引：1，自引：0，他引：1
董仲奎 《测绘与空间地理信息》2016,(12):58-59
为推动高分遥感数据在地理国情地表覆盖专题监测工作中的应用,研究了基于高分遥感数据开展地理国情地表覆盖专题监测的技术路线和数据处理与分析方法,探讨了专题监测系统的模块和功能设置,以期为地理国情地表覆盖专题监测工作提供参考。 相似文献
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正地表覆盖是地表各种生物与物理覆盖综合体,其空间分布及随时间的变化是气候模拟、生态评估、地理国情监测等不可或缺的重要基础地理信息。随着对地观测技术发展,利用遥感影像提取地表覆盖信息已成为当前的一种主要技术手段。但因地物光谱、纹理、时相等特征复杂多样,加上时相适宜、质量优良的影像数据难以全面覆盖,往往导致地表覆盖更新前后多期产品数据不一致。工程生产实践中,地表覆盖数据质检主要以人工检核为主、部分自动化为辅,耗时、费力,迫切需要一种面向地表 相似文献
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通过遥感影像与基准年数据对比获得变化信息是目前地表覆盖数据增量更新的主要变化数据来源,但现有方法不能直接更新地表覆盖矢量数据。本文设计了一种包含变化对象的空间位置和类型信息的地表覆盖增量数据模型,发展了一种引入面/面二维交细分类型的地表覆盖矢量数据增量更新方法。该方法首先采用基于目标整体交、差结果的欧拉数的E-WID层次拓扑关系模型区,分析了地表覆盖矢量数据更新中的14种二维交细分拓扑关系类型;然后根据这些二维交细分类型,设计了9条自动更新处理规则。最后开发了一套基于根据二维交细分类型处理规则的地表覆盖数据增量更新原型系统,并用实际数据验证了其正确性。 相似文献
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地表参数定量遥感反演是遥感科学研究的重要环节。21世纪以来,地球静止气象卫星数据在地表参数遥感反演中受到越来越多的重视。本文对利用地球静止气象卫星进行地表参数遥感反演研究的进展进行了综述。文章首先简单介绍了当前正在运行的欧盟Meteosat、美国GOES-R、日本葵花和中国风云静止卫星系统,随后详细总结了不同卫星系统估算各种地表参数的方法。在此基础上,文章对进一步利用静止卫星估算地表参数的研究展开讨论,指出未来的研究应重点关注几个方面:(1)探索和运用新技术提高静止卫星数据获取和处理的效率和精度;(2)融合全球多颗静止气象卫星,同时与极轨卫星融合,生产覆盖全球的长时序地表参数产品;(3)探索地表参数的高效获取方法,对静止气象卫星地表参数产品开展真实性检验,满足地表过程研究和资源环境动态监测对高质量地表参数产品的需求。 相似文献
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地理国情监测在全国范围内逐步展开,地表覆盖作为地理国情监测的基础数据之一,是地理国情监测的一个重要方面;遥感影像具有实时、获取快速等特点,用于地理国情监测中具有较大的优势。本文从遥感数据解译、地表覆盖数据处理、与经济数据相结合的分析方法等,研究了地表覆盖变化与经济发展的相互关系,充分利用地表覆盖数据为经济发展的政策制定提供依据。 相似文献
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正遥感影像变化检测是大范围地表覆盖数据更新的重要技术手段。近年来,国内外研究学者从不同的角度提出了大量的变化检测算法或模型,但尚未有一种通用的方法能够适用于不同的影像数据条件、地表覆盖数据类型和地理区域特点。事实上,变化检测领域的"算法-数据"之间存 相似文献
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面向遥感影像智能分类的海量样本数据采集方法 总被引：1，自引：0，他引：1
以地理国情监测高分辨率遥感影像及高精度地表覆盖分类产品为数据源，提出了一种面向遥感影像智能分类、基于位置匹配技术的全国尺度海量样本数据采集方法。根据数据源特征，研究了县域采集数量权重设置、坐标投影转换、栅格灰度重采样、无效样本数据过滤、地表覆盖分类码映射、样本数据命名标识、特定地表覆盖类型样本数据采集等关键技术，构建了位置匹配的遥感影像数据与分类标签数据组成的样本数据对，开发了样本数据自动采集软件。利用该方法，以县级行政区划为单元，实现了全国尺度海量样本数据采集。选取其中5个县域的成果，评估了方法的实用性及运算性能。研究表明：该方法提升了生产全国尺度海量样本数据的计算响应速度；采集的样本数据能够满足遥感影像智能分类对样本源高质量、大规模的需求，提升了遥感影像分类与预测的准确度。 相似文献
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Change detection with remotely sensed imagery plays an important role in land cover mapping, process analysis and dynamic information services. Euclidean distance, correlation and other mathematic metrics between spectral curves have been used to calculate change magnitude in most change detection methods. However, many pseudo changes would also be detected because of inter-class spectral variance, which remains a significant challenge for operational remote sensing applications. In general, different land cover types have their own spectral curves characterized by typical spectral values and shapes. These spectral values are widely used for designing change detection algorithms. However, the shape of spectral curves has not yet been fully considered. This paper proposes to use spectral gradient difference (SGD) to quantitatively describe the spectral shapes and the differences in shape between two spectra. Change magnitude calculated in the new spectral gradient space is used to detect the change/no-change areas. Then, a chain model is employed to represent the SGD pattern both qualitatively and quantitatively. Finally, the land cover change types are determined by pattern matching with the knowledgebase of reference SGD patterns. The effectiveness of this SGD-based change detection approach was verified by a simulation experiment and a case study of Landsat data. The results indicated that the SGD-based approach was superior to the traditional methods. 相似文献
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地表覆盖与更新是地理国情监测、环境变化评估、生态系统保护等不可或缺的基础地理信息。遥感制图技术已成为地表覆盖信息提取的重要手段，但因地物光谱、纹理及时相等特征复杂性，地表覆盖更新数据往往存在错分、漏分，从而导致地表覆盖时空目标不一致。现有地表覆盖更新数据不一致性探测主要以人工检查为主、部分自动化为辅的方式，生产实践中需要大量的作业人员与时间，缺乏行之有效的不一致性自动化探测工具。本文研究分析了栅格地表覆盖更新数据不一致性检查面临的挑战，提出了基于复合逻辑量词的栅格空间拓扑关系计算方法、基于置信区间的更新期地表覆盖错分目标初判规则构建，以及利用空间约束多重匹配的更新期错分目标后验判断，形成了“关系-规则-判断”的地表覆盖时空目标不一致性探测体系。试验以山东临朐、垦利GlobeLand30数据为研究对象，经与统计一致性检核方法对比分析、参照真实地表影像数据，实现了地表覆盖时空目标不一致性探测与有效性检验，验证了探测方法可行性。 相似文献
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This paper discusses the development and implementation of a method that can be used with multi-decadal Landsat data for computing general coastal US land use and land cover (LULC) maps consisting of seven classes. With Mobile Bay, Alabama as the study region, the method that was applied to derive LULC products for nine dates across a 34-year time span. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and Coastal Change and Analysis Program value-added products. Each classification’s overall accuracy was assessed by comparing stratified random locations to available high spatial resolution satellite and aerial imagery, field survey data and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall κ statistics ranging from 0.78 to 0.89. Accurate classifications were computed for all nine dates, yielding effective results regardless of season and Landsat sensor. This classification method provided useful map inputs for computing LULC change products. 相似文献
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Galen J. Maclaurin 《地理信息系统科学与遥感》2016,53(6):759-777
Regional and national level land cover datasets, such as the National Land Cover Database (NLCD) in the United States, have become an important resource in physical and social science research. Updates to the NLCD have been conducted every 5 years since 2001; however, the procedure for producing a new release is labor-intensive and time-consuming, taking 3 or 4 years to complete. Furthermore, in most countries very few, if any, such releases exist, and thus there is high demand for efficient production of land cover data at different points in time. In this paper, an active machine learning framework for temporal updating (or backcasting) of land cover data is proposed and tested for three study sites covered by the NLCD. The approach employs a maximum entropy classifier to extract information from one Landsat image using the NLCD, and then replicate the classification on a Landsat image for the same geographic extent from a different point in time to create land cover data of similar quality. Results show that this framework can effectively replicate the land cover database in the temporal domain with similar levels of overall and within class agreement when compared against high resolution reference land cover datasets. These results demonstrate that the land cover information encapsulated in the NLCD can effectively be extracted using solely Landsat imagery for replication purposes. The algorithm is fully automated and scalable for applications at landscape and regional scales for multiple points in time. 相似文献
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O.R. van Lier J.E. Luther D.G. Leckie W.W. Bowers 《International Journal of Applied Earth Observation and Geoinformation》2011
Monitoring ecological indicators is important for assessing impacts of human activities on ecosystems. A means of identifying and applying appropriate indicators is a prerequisite for: environmental assessment; better assessment and understanding of ecosystem health; elucidation of biogeochemical trends; and more accurate predictions of future responses to global change, particularly those due to anthropogenic disturbance. The challenge is to derive meaningful indicators of change that capture the complexities of ecosystems yet can be monitored consistently over large areas and across time. In this study, methods for monitoring indicators of land cover (LC) and forest change were developed using multi-sensor Landsat imagery. Mapping and updating procedures were applied to the Humber River Basin (HRB) in Newfoundland and Labrador, one of four test sites in Canada selected for testing the development of national-scale methods. Procedures involved unsupervised clustering and labeling of baseline imagery, followed by image-to-image spectral clustering to derive binary change masks within which new LC types were classified for non-baseline imagery. Updated maps were compatible with the baseline map and reflected change in LC for three time periods: 1976–1990, 1990–2001, and 2001–2007. From the LC products, several change indicators were quantified including: forest depletion, forest regeneration, forest change, net forest change, and annual rates of change. The procedures were validated using field plots to assess the accuracy of the 2007 LC product (74.2% for 10 LC classes) and change classes observed from 2001 to 2007 (87.8% for four change classes: depletion, regeneration, non-treed class no change, and treed class no change). Methods were considered to be highly efficient and operationally feasible over large areas spanning multiple Landsat scenes. Specific results for the test site provided trend information supporting land and resource management in the HRB region. 相似文献
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Abstract The objective of this study was to explore the utility of multi‐temporal, multi‐spectral image data acquired by the IKONOS satellite system for monitoring detailed land cover changes within shrubland habitat reserves. Sub‐pixel accuracy in date‐to‐date registration was achieved, in spite of the irregular relief of the study area and the high spatial resolution of the imagery. Change vector classification enabled features ranging in size from tens of square meters to several hectares to be detected and six general land cover change classes to be identified. Interpretation of the change vector classification product in conjunction with visual inspection of the multi‐temporal imagery enabled identification of specific change types such as: vegetation disturbance and associated increase in soil exposure, shrub removal, urban edge vegetation clearing and fire maintenance, increase in vegetation cover, spread of invasive plant species, fire scars and subsequent recovery, erosional scouring, trail and road development, and expansion of bicycle disturbances. 相似文献
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Crowdsourcing geospatial data 总被引：6，自引：0，他引：6
In this paper we review recent developments of crowdsourcing geospatial data. While traditional mapping is nearly exclusively coordinated and often also carried out by large organisations, crowdsourcing geospatial data refers to generating a map using informal social networks and web 2.0 technology. Key differences are the fact that users lacking formal training in map making create the geospatial data themselves rather than relying on professional services; that potentially very large user groups collaborate voluntarily and often without financial compensation with the result that at a very low monetary cost open datasets become available and that mapping and change detection occur in real time. This situation is similar to that found in the Open Source software environment.We shortly explain the basic technology needed for crowdsourcing geospatial data, discuss the underlying concepts including quality issues and give some examples for this novel way of generating geospatial data. We also point at applications where alternatives do not exist such as life traffic information systems. Finally we explore the future of crowdsourcing geospatial data and give some concluding remarks. 相似文献