基于遥感和陆表信息集成的广东省土地覆盖分类方法研究

土地覆盖信息是估算地-气间的生物物理过程和能量交换的关键参数,也是区域和全球尺度气候和生态系统过程模型所需要的重要参量。如何高效地利用遥感数据提取土地覆盖信息是当前研究迫切需要解决的问题。面向对象的分类方法不但充分利用了遥感数据的光谱信息,同时也利用了影像的纹理结构信息和更多的地物分布信息关系,在遥感分类中具有较大的潜力。研究基于2010年多时相的环境卫星数据、TM数据以及DEM数据,并结合地表采集的4000多个样点数据,采用面向对象的分类方法对广东省土地覆盖进行分类。经采样验证,广东省土地覆盖平均精度为85%,分类结果精度远高于常规的分类算法,说明结合陆表信息的面向对象分类方法比常规的分类算法更具有优势,可以实现高精度的土地覆盖分类。     

基于亚分数混淆矩阵的中国典型区大尺度土地覆盖数据集评价

在中国东北、华北、华中、华南、西北、青藏、内蒙古7个自然地区分别选择典型区A、B、C、D、E、F、G,以Landsat TM/ETM+影像分类结果为参考数据,采用亚分数混淆矩阵对5种大尺度土地覆盖数据集的精度进行定量评价,为数据集的使用提供科学依据。亚分数混淆矩阵可避免参考数据与待评价数据尺度转换时引入的误差,能反映不同优势类比重情况下数据集的总体精度和分类方法误差。结果表明:GLC2000在全部典型区的总体精度最高,为65.64%;UMD总体精度最低,为43.06%。GLC2000在主要土地覆盖类型为林地和耕地以及草地区域具有较高的分类精度;UMD在各区域的分类精度均最低或较低。5种土地覆盖数据集对于城镇、其他的分类精度在各典型区均较低;对于草地和水体的分类精度则是在西北干旱区和青藏高原区的典型区较高。     

基于植被分区的中国植被类型分类方法

高精度的土地覆盖分类产品对定量遥感研究及遥感应用等具有非常重要的意义。目前免费的且全球覆盖的土地分类产品已有很多,但这些产品多为国外研究机构和人员所研发,由于对中国区域地形复杂、植被结构特征差异与农作物种植结构差异等没有进行充分的研究,使得这些产品用于中国区域的分类时其精度尤其是植被类型的分类精度较低。因此,生产一种针对中国区域的植被类型分类产品是非常必要的。针对中国区域地形、土壤等信息,并在借鉴现有的植被区划的基础上,发展了一种基于植被分区的中国植被类型分类方法,该分类方法以长时间序列为基础,能以较高的时间分辨率捕捉地表随时间变化的信息,从而利用地物在时间维上的差异提高分类精度,并利用该方法完成了2012年中国土地覆盖分类。此外还通过分层随机采样的方法对分类结果进行了精度评估,发现本分类产品的总体精度和Kappa系数有较大提高,其中本文产品总体精度为90.78%,Kappa系数为0.86;并通过与MODIS土地覆盖数据产品进行比较,发现该产品精度比MODIS土地覆盖数据产品在植被类型上提高了61.38%。     

物候特征辅助下的随机森林宏观尺度土地覆盖分类方法研究

通过遥感技术获取大范围土地覆盖信息对于监测、理解和预测自然资源具有重要的科学意义.MODIS数据是当今宏观尺度土地覆盖研究的主要数据源.本文以河北省为研究区,应用MOD13Q1数据产品,构建MODIS NDVI时间序列,从中反演物候特征作为参与分类的主要辅助信息,并采用随机森林分类方法进行宏观尺度土地覆被分类实验,并与单决策树(CART)进行对比分析.实验结果表明,物候特征辅助下的随机森林宏观尺度土地覆被分类方法的总体精度为87.2％,Kappa系数为0.83,比CART单一决策树精度提高了17.9％;应用物候特征参与分类,使得总体精度提高2.6％;其中,旱地和建筑用地精度分别提高了6.7％和11.9％.     

基于MODIS温度和植被指数产品的山东省土地覆盖变化研究

地表温度(LST)与归一化植被指数(NDVI)构成的NDVI-Ts特征空间具有丰富的地学和生态学内涵。MODIS数据因其优越的时间分辨率、波谱分辨率,已被广泛地运用于各个领域。在本研究中,运用遥感技术和GIS技术相结合的手段,利用NASA提供的MODIS温度产品和NDVI产品,以山东省土地利用图、山东省TM遥感影像图和基于3S技术的山东省森林资源调查项目的外业调查数据为参考和评价标准,以NDVI-Ts时间序列为指标,在进行土地覆盖分类的基础上,分析比较了山东省土地覆盖从2000年到2006年的变化情况。研究结果表明,利用MODIS产品将NDVI-Ts时间序列作为分类特征,在较大尺度范围的土地覆盖分类中具有较高的分类精度,有利于对土地覆盖变化进行动态监测。     

时序数据集构建质量对土地覆盖分类精度的影响研究

研究通过对MODIS双星数据组合、线性插值和HANTS平滑方法来提升时序数据集质量,采用随机森林的方法分类,对分类结果精度评定以分析时序数据集构建质量对分类精度的影响。结果表明：双星数据有利于提高时序数据集的时间分辨率,精确刻划覆盖变化,为后续处理提供基础;线性插值可改善像元点的质量,降低云、雨因素影响;HANTS平滑能移除异常值,平滑数据,突出曲线特征,降低分类复杂度。改进质量后的时序数据集,分类总体精度从84.32%提高至90.75%,Kappa系数从0.798 6提高至0.881 6。总之,使用时序数据进行土地覆盖分类时,应以消除异常值,真实反映地表覆盖物候特征为目的提高时序数据集的质量,从而提高分类精度。     

美国高分辨率土地覆盖信息提取技术研究进展

土地覆盖数据是进行全球变化研究的基础。美国地质调查局组织的土地覆盖特征研究项目成功的开展了两次工作, 建设了土地覆盖数据库, 得到了多方面的认可, 代表了美国相关领域的前沿技术。项目中涉及一系列技术, 包括土地覆盖分类分区、遥感影像选取、数据预处理过程、图像转换变化分析、土地覆盖分类技术、数据产品验证、土地覆盖数据库建设等。另外还有关键技术研究, 包括: 土地覆盖多边形分析、纹理特征分析、树冠密度信息提取、城市不透性表面估算等。就美国地质调查局项目中土地覆盖遥感影像数据处理方法、技术流程、数据库建设, 以及有关土地覆盖度等相关关键技术进行了介绍, 期望能够反映其进展情况, 对国内的相关工作起到启示作用。     

基于最优尺度的遥感影像土地覆盖分类仿真

土地覆盖遥感影像是国家的战略性、基础性资源,真实、准确和实时的土地覆盖类型信息对科学保护和合理利用土地资源至关重要。随着大数据时代遥感影像数量快速增长,已有算法的准确性和稳定性无法满足土地覆盖情况分类需求。为进一步提升土地覆盖分类准确率,提出一种基于最优尺度分割与特征融合的方法。首先针对预处理后的遥感影像,利用局部方差计算出分割的最优尺度,并以尺度为基准优化过分割、欠分割影像;然后以分割后的影像为基准,采用局部二值模式算子(LBP)及神经网络提取土地影像的纹理特征和光谱初级特征;最后将影像的两种特征有机融合,并利用支持向量机分类器(SVM),构建了土地遥感影像分类模型(OSF-SVM模型)。分割仿真结果表明,与已有方法相比,文中的尺度分割技术在RR、RI及ARI指标上具有所提高,平局提升了10.83%;分类仿真结果表明,较传统SVM模型相比,OSF-SVM模型在R、P以及F₁指标上分别平均提高了4.1%、3.9%和4%。因此,通过最优尺度分割和特征融合构建的OSF-SVM遥感影像土地覆盖分类模型,提高了影像分割及分类的精确度与稳定性。     

MODIS土地覆盖数据产品精度分析——以黄河源区为例

MODIS土地覆盖数据产品覆盖广、时间分辨率高,是区域土地覆盖变化监测的重要数据源。本文以中国土地资源分类系统为依据,重新归类黄河源区MODIS土地覆盖数据。利用2000年和2006年黄河源区Land-sat解译数据为参考数据,对相应的MODIS土地覆盖数据,从数量精度和形状一致性两个方面进行精度分析和适用性评价。结果表明:在形状上,加入权重的总体形状一致性皆在69%以上,其中主要地类草地的一致性达到88%以上;在数量上,加入权重的总体面积相对误差在26%以内,误差主要产生在未利用土地等地类。MODIS土地覆盖数据产品在大尺度的土地覆盖监测中仍然有重要的应用价值。     

一种利用Landsat年度时序数据的土地覆盖分类方法

针对大面积土地覆盖遥感分类中数据获取难度大、复杂度高、分类结果不够精确且易受季候变化影响等问题,提出了一种利用Landsat时间序列数据,生成年度时序特征,并结合特定算法(UniBagging)进行土地覆盖分类的方法(LandUTime)。该方法定义了一种基于时间序列数据的特征生成方式,根据时序数据特点,设计了一种基于特征子空间的集成分类算法。实现过程分为2个阶段,首先基于特定模型,在像元级别上对Landsat时间序列图像进行回归分析,生成模式特征,然后将所有特征整合成"特征块",根据特征子空间将基分类器集成到相互分离的集合中,最后通过加权投票的方法进行分类结果输出。实验结果与定量分析表明,与传统的特征提取及分类方法相比,该方法提高了分类精度,而且对高维数据具有鲁棒性;可以有效克服大面积土地覆盖分类中云遮掩、数据条带和物候变化等问题的影响,具有较高的准确性和实用性。     

Validation and comparison of 1 km global land cover products in China

Four 1 km global land cover products are currently available to the scientific community: the University of Maryland (UMD) global land cover product, the International Geosphere–Biosphere Programme Data and Information System Cover (IGBP‐DISCover), the MODerate resolution Imaging Spectrometer (MODIS) global land cover product and Global Land Cover 2000 (GLC2000). Because of differences in data sources, temporal scales, classification systems and methodologies, it is important to compare and validate these global maps before using them for a variety of studies at regional to global scales. This study aimed to perform the validation and comparison of the four global land cover datasets, and to examine the suitability and accuracy of different coarse spatial resolution datasets in mapping and monitoring cropland across China. To meet this objective, we compared the four global land cover products with the National Land Cover Dataset 2000 (NLCD‐2000) at three scales to evaluate the accuracy of estimates of aggregated cropland areas in China. This was followed by a spatial comparison to assess the accuracies of the four products in estimating the spatial distribution of cropland across China. A comparative analysis showed that there are varying levels of apparent discrepancies in estimating the cropland of China between these four global land cover datasets, and that both area totals and spatial (dis)agreement between them vary from region to region. Among these, the MODIS dataset has the best fit in depicting China's croplands. The coarse spatial resolution and the per pixel classification approach, as well as landscape heterogeneity, are the main reasons for the large discrepancies between the global land cover datasets tested and the reference data.     

A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets

Accurate and up-to-date global land cover data sets are necessary for various global change research studies including climate change, biodiversity conservation, ecosystem assessment, and environmental modeling. In recent years, substantial advancement has been achieved in generating such data products. Yet, we are far from producing geospatially consistent high-quality data at an operational level. We compared the recently available Global Land Cover 2000 (GLC-2000) and MODerate resolution Imaging Spectrometer (MODIS) global land cover data to evaluate the similarities and differences in methodologies and results, and to identify areas of spatial agreement and disagreement. These two global land cover data sets were prepared using different data sources, classification systems, and methodologies, but using the same spatial resolution (i.e., 1 km) satellite data. Our analysis shows a general agreement at the class aggregate level except for savannas/shrublands, and wetlands. The disagreement, however, increases when comparing detailed land cover classes. Similarly, percent agreement between the two data sets was found to be highly variable among biomes. The identified areas of spatial agreement and disagreement will be useful for both data producers and users. Data producers may use the areas of spatial agreement for training area selection and pay special attention to areas of disagreement for further improvement in future land cover characterization and mapping. Users can conveniently use the findings in the areas of agreement, whereas users might need to verify the informaiton in the areas of disagreement with the help of secondary information. Learning from past experience and building on the existing infrastructure (e.g., regional networks), further research is necessary to (1) reduce ambiguity in land cover definitions, (2) increase availability of improved spatial, spectral, radiometric, and geometric resolution satellite data, and (3) develop advanced classification algorithms.     

Exploiting synergies of global land cover products for carbon cycle modeling

Within the past decade, several global land cover data sets derived from satellite observations have become available to the scientific community. They offer valuable information on the current state of the Earth's land surface. However, considerable disagreements among them and classification legends not primarily suited for specific applications such as carbon cycle model parameterizations pose significant challenges and uncertainties in the use of such data sets.This paper addresses the user community of global land cover products. We first review and compare several global land cover products, i.e. the Global Land Cover Characterization Database (GLCC), Global Land Cover 2000 (GLC2000), and the MODIS land cover product, and highlight individual strengths and weaknesses of mapping approaches. Our overall objective is to present a straightforward method that merges existing products into a desired classification legend. This process follows the idea of convergence of evidence and generates a ‘best-estimate’ data set using fuzzy agreement. We apply our method to develop a new joint 1-km global land cover product (SYNMAP) with improved characteristics for land cover parameterization of the carbon cycle models that reduces land cover uncertainties in carbon budget calculations.The overall advantage of the SYNMAP legend is that all classes are properly defined in terms of plant functional type mixtures, which can be remotely sensed and include the definitions of leaf type and longevity for each class with a tree component. SYNMAP is currently used for parameterization in a European model intercomparison initiative of three global vegetation models: BIOME-BGC, LPJ, and ORCHIDEE.Corroboration of SYNMAP against GLCC, GLC2000 and MODIS land cover products reveals improved agreement of SYNMAP with all other land cover products and therefore indicates the successful exploration of synergies between the different products. However, given that we cannot provide extensive validation using reference data we are unable to prove that SYNMAP is actually more accurate. SYNMAP is available on request from Martin Jung.     

Object-based land cover change detection for cross-sensor images

Accurate and timely land cover change detection at regional and global scales is necessary for both natural resource management and global environmental change studies. Satellite remote sensing has been widely used in land cover change detection over the past three decades. The variety of satellites which have been launched for Earth Observation (EO) and the large volume of remotely sensed data archives acquired by different sensors provide a unique opportunity for land cover change detection. This article introduces an object-based land cover change detection approach for cross-sensor images. First, two images acquired by different sensors were stacked together and principal component analysis (PCA) was applied to the stacked data. Second, based on the Eigen values of the PCA transformation, six principal bands were selected for further image segmentation. Finally, a land cover change detection classification scheme was designed based on the land cover change patterns in the study area. An image–object classification was implemented to generate a land cover change map. The experiment was carried out using images acquired by Landsat 5 TM and IRS-P6 LISS3 over Daqing, China. The overall accuracy and kappa coefficient of the change map were 83.42% and 0.82, respectively. The results indicate that this is a promising approach to produce land cover change maps using cross-sensor images.     

Spatial pattern analysis of land cover change trajectories in Tarim Basin,northwest China

This study attempts to develop a methodology to quantify spatial patterns of land cover change using landscape metrics. First, multitemporal land cover types are derived based on a unified land cover classification scheme and from the classification of multitemporal remotely sensed imagery. Categorical land cover change trajectories are then established and reclassified according to the nature and driving forces of the change. Finally, spatial pattern metrics of the land cover change trajectory classes are computed and their relationships to human activities and environmental factors are analysed. A case study in the middle reach of Tarim River in the arid zone of China from 1973 to 2000 shows that during the 30‐year study period, the natural force is dominant in environmental change, although the human impact through altering water resources and surface materials has increased dramatically in recent years. The human‐induced change trajectories generally show lower normalized landscape shape index (NLSI), interspersion and juxtaposition index (IJI) and area‐weighted mean patch fractal dimension (FARC_AM), indicating greater aggregation, less association with others and simpler and larger patches in shape, respectively. The results suggest that spatial pattern metrics of land cover change trajectories can provide a good quantitative measurement for better understanding of the spatio‐temporal pattern of land cover change due to different causes.     

Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data

Information on land cover distribution at regional and global scales has become fundamental for studying global changes affecting ecological and climatic systems. The remote sensing community has responded to this increased interest by improving data quality and methodologies for extracting land cover information. However, in addition to the advantages provided by satellite products, certain limitations exist that need to be objectively quantified and clearly communicated to users so that they can make informed decisions on whether and how land cover products should be used. Accuracy assessment is the procedure used to quantify product quality. Some aspects of accuracy assessment for evaluating four global land cover maps over Canada are discussed in this paper. Attempts are made to quantify limiting factors resulting from the coarse spatial resolution of data used for generating land cover information at regional and global levels. Sub-pixel fractional error matrices are introduced as a more appropriate way for assessing the accuracy of mixed pixels. For classification with coarse spatial resolution data, limitations of the classification method produce a maximum achievable accuracy defined as the average percent fraction of dominant land cover of all pixels in the mapped area. Relationships among spatial resolution, landscape heterogeneity and thematic resolution were studied and reported. Other factors that can affect accuracy, such as misregistration and legend conversion, are also discussed.     

MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets

Information related to land cover is immensely important to global change science. In the past decade, data sources and methodologies for creating global land cover maps from remote sensing have evolved rapidly. Here we describe the datasets and algorithms used to create the Collection 5 MODIS Global Land Cover Type product, which is substantially changed relative to Collection 4. In addition to using updated input data, the algorithm and ancillary datasets used to produce the product have been refined. Most importantly, the Collection 5 product is generated at 500-m spatial resolution, providing a four-fold increase in spatial resolution relative to the previous version. In addition, many components of the classification algorithm have been changed. The training site database has been revised, land surface temperature is now included as an input feature, and ancillary datasets used in post-processing of ensemble decision tree results have been updated. Further, methods used to correct classifier results for bias imposed by training data properties have been refined, techniques used to fuse ancillary data based on spatially varying prior probabilities have been revised, and a variety of methods have been developed to address limitations of the algorithm for the urban, wetland, and deciduous needleleaf classes. Finally, techniques used to stabilize classification results across years have been developed and implemented to reduce year-to-year variation in land cover labels not associated with land cover change. Results from a cross-validation analysis indicate that the overall accuracy of the product is about 75% correctly classified, but that the range in class-specific accuracies is large. Comparison of Collection 5 maps with Collection 4 results show substantial differences arising from increased spatial resolution and changes in the input data and classification algorithm.     

Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data

Researchers from the U.S. Geological Survey, University of Nebraska-Lincoln and the European Commission's Joint Research Centre, Ispra, Italy produced a 1 km resolution global land cover characteristics database for use in a wide range of continental-to global-scale environmental studies. This database provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity of the global land surface, and presents a detailed interpretation of the extent of human development. The project was carried out as an International Geosphere-Biosphere Programme, Data and Information Systems (IGBP-DIS) initiative. The IGBP DISCover global land cover product is an integral component of the global land cover database. DISCover includes 17 general land cover classes defined to meet the needs of IGBP core science projects. A formal accuracy assessment of the DISCover data layer will be completed in 1998. The 1 km global land cover database was developed through a continent-by-continent unsupervised classification of 1 km monthly Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) composites covering 1992-1993. Extensive post-classification stratification was necessary to resolve spectral/temporal confusion between disparate land cover types. The complete global database consists of 961 seasonal land cover regions that capture patterns of land cover, seasonality and relative primary productivity. The seasonal land cover regions were aggregated to produce seven separate land cover data sets used for global environmental modelling and assessment. The data sets include IGBP DISCover, U.S. Geological Survey Anderson System, Simple Biosphere Model, Simple Biosphere Model 2, Biosphere-Atmosphere Transfer Scheme, Olson Ecosystems and Running Global Remote Sensing Land Cover. The database also includes all digital sources that were used in the classification. The complete database can be sourced from the website: http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html.     

Trajectory analysis of land cover change in arid environment of China

Remotely sensed data have been utilized for environmental change study over the past 30 years. Large collections of remote sensing imagery have made it possible for spatio‐temporal analyses of the environment and the impact of human activities. This research attempts to develop both conceptual framework and methodological implementation for land cover change detection based on medium and high spatial resolution imagery and temporal trajectory analysis. Multi‐temporal and multi‐scale remotely sensed data have been integrated from various sources with a monitoring time frame of 30 years, including historical and state‐of‐the‐art high‐resolution satellite imagery. Based on this, spatio‐temporal patterns of environmental change, which is largely represented by changes in land cover (e.g., vegetation and water), were analysed for the given timeframe. Multi‐scale and multi‐temporal remotely sensed data, including Landsat MSS, TM, ETM and SPOT HRV, were used to detect changes in land cover in the past 30 years in Tarim River, Xinjiang, China. The study shows that by using the auto‐classification approach an overall accuracy of 85–90% with a Kappa coefficient of 0.66–0.78 was achieved for the classification of individual images. The temporal trajectory of land‐use change was established and its spatial pattern was analysed to gain a better understanding of the human impact on the fragile ecosystem of China's arid environment.     

Study of Monitoring Snow Cover Using GOESGeostationary Satellite and AMSR-E

Snow cover is an important variable in global climate change research and meteorological applications.In this paper,we use visible and infrared bands of GOES-12(Geostationary Operational Environmental Satellite) geostationary satellite and AMSR-E(Advanced Microwave Scanning Radiometer for the Earth Observing System) to monitor snow cover in east of USA and discuss the application method.Snow index,multi-temporal data combination and decision-tree classification are used in monitoring the snow cover.Temporal stability test and climatic surface temperature test are used to further discriminate snow and cloud.Snow cover result also combined with AMSR-E daily snow products to remove the cloud effect.This method performs well in monitoring snow cover which removes more cloud and get more information of the surface.Using NOAA/NESDIS IMS daily snow cover products and station observations to compare and validate the snow cover images,the accuracy is about 86% and 90%,respectively.The operational geostationary satellites of China are FY-2D/2E,which have the same band information of GOES.This method can be used in monitoring snow cover of China in further study.