共查询到19条相似文献，搜索用时 484 毫秒
1.
针对目前精度评价尺度单一的问题,提出基于直方变差图的多尺度精度评价方法,分别在像元尺度和亚像元尺度进行土地覆盖数据集精度评价。在像元尺度利用驻点作为采样工具直接评价数据集精度;亚像元尺度上,则利用非严格定义的驻点和驻点直方变差图对不同面积和空间结构的优势类进行精度评价。并以浙江北部典型区域为实验区,Landsat TM/ETM+为参考数据,对UMD、IGBP DISCover、MOD12Q1-2001、GLC2000、GlobCover2009等5种大尺度土地覆盖数据集进行多尺度精度评价实验。结果表明,多尺度精度评价方法能够全面地评价土地覆盖数据集的精度,提供更加丰富的多尺度精度信息。像元尺度精度评价可在一定程度上消除由于参考数据与数据集间的空间匹配造成的误差,评价结果更加客观;亚像元尺度精度评价能有效反映亚像元尺度优势地物面积及空间结构与精度的关系。 相似文献
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高时空分辨率的全球多类别土地覆盖数据对于地球系统的生物化学循环、气候变化等研究至关重要。目前公开的数据产品中,较高空间分辨率的全球多类别土地覆盖产品仅提供单一或短时期的数据,而全球逐年土地覆盖产品往往只有单一土地覆盖类型,难以从较长时间跨度上反映精细地物的年际变化。本文借助Google Earth Engine平台,利用现有多套全球土地覆盖产品、Landsat卫星系列影像、以及大量人工目视解译样本,结合多数据融合、时序变化检测和机器学习等的方法,研制了一套2000年—2015年全球30 m分辨率的逐年土地覆盖变化数据集AGLC-2000-2015（Annual Global Land Cover 2000-2015）。基于AGLC-2000-2015数据集,本文选择性分析了3个典型区域（中国珠江三角洲地区、青藏高原色林错湖区和亚马逊热带雨林区）的土地覆盖年际变化。结果显示,AGLC-2000-2015数据集达到了较高的精度水平：基准年份产品（AGLC-2015）的总体精度（OA）为76.10%,Kappa系数为0.72,显著优于现有30 m分辨率的全球土地覆盖产品Globeland 30（OA = 63.49%,Kappa = 0.58）、FROM-GLC（OA = 61.41%,Kappa = 0.55）和GLC-FCS30（OA = 63.46%,Kappa = 0.57）;年际间分类模型的总体精度和Kappa系数分别为84.10%和0.81,在各大洲的平均总体精度均超过80.00%,表明该模型在全球多类别土地覆盖分类中表现良好。3个典型区域的土地覆盖变化分析显示,中国珠江三角洲地区城市扩张趋势明显（195.96 km2/a）,其增量主要来源于耕地（84.88%）;青藏高原色林错湖泊对于气候变暖响应明显,湖区面积呈扩大趋势（17.95 km2/a）,湖面北岸扩张最为明显;亚马逊热带雨林南部区域毁林造田趋势明显,15 a间森林面积减少46356.53 km2,其中大部分转化为农田（39621.29 km2）。上述结果表明：AGLC-2000-2015数据集能够有效反映全球陆地区域在30 m空间分辨率下的地表覆盖分布及年际间的动态演化,为地表陆面过程研究和相关应用提供可靠的数据支撑。 相似文献
3.
西南喀斯特山区地形起伏较大,地物分布较为破碎,致使传统的光谱特征一次分类方法的精度较低。本文基于高分辨率无人机正射影像和地形指标,充分利用无人机遥感影像空间特征、光谱特征、纹理特征及地形特征,采取面向对象CART决策树算法与分层策略提取了研究区土地覆盖类型。研究表明,结合空间地形因子和分层策略的方法减少了破碎区地物间的相干扰,故具有较高的分类精度,总体分类精度达91.2%,Kappa系数为0.87,较传统一次分类精度提高了9.8%,Kappa系数提高了0.13。该方法对西南喀斯特地区土地覆盖解译精度较好,可为土地利用监测提供参考。 相似文献
4.
《国土资源遥感》2020,(1)
为了探究低空无人机遥感技术对喀斯特地貌条件下不同形态农耕区地物类型的识别精度,以桂林市3个200 m×200 m样方的农耕区为研究区,在无人机航拍影像和地面调查数据的支持下,分别将基于像元和面向对象的影像分析技术与支持向量机(support vector machine,SVM)算法相结合,构建不同地貌条件下农耕区地物遥感识别模型,并进行精度对比分析。结果表明,面向对象的SVM分类结果保留了原始地物的大致轮廓,且地块较完整,更为适用于喀斯特地貌条件下的农耕区地物识别,较基于像元的SVM分类方法总体精度高6. 54%,Kappa系数高0. 135;基于像元的SVM分类方法适用于地物分布规则的农耕区地物识别,相比面向对象的SVM分类方法总体精度高2. 92%,Kappa系数高0. 026。 相似文献
5.
综合环境卫星与MODIS数据的面向对象土地覆盖分类方法 总被引：1，自引：0，他引：1
使用面向对象方法对单时相的环境卫星数据进行土地覆盖分类时,几何特征和光谱特征相似的地物无法区分,而MODIS时序数据的空间分辨率较低,不适用于中小尺度的土地覆盖分类。应用面向对象方法,充分利用环境卫星数据的空间、光谱特征和MODIS数据的物候特征建立规则,进行分类,可以有效地解决上述困难。首先对环境卫星数据进行多尺度分割,生成待分类对象;再根据对象的特征,依据由简到难的原则进行分层分类。以双台子河口为例进行土地覆盖分类,总体精度93%,Kappa系数0.92。结果表明,综合环境卫星与MODIS数据的面向对象土地覆盖分类方法应用潜力巨大。 相似文献
6.
土地覆被作为地表自然和人工建造物的综合体，是开展土地科学相关研究的重要基础，在遥感大数据背景下，准确、快速、自动化进行土地覆被提取技术一直是遥感研究中的重点。本文基于eCognition软件，采用面向对象的多尺度分割法，综合考虑地物在遥感影像上的光谱、形状和纹理特征，建立多种地物提取规则。通过模糊函数、支持向量机（SVM）和阈值法对研究区的土地覆被进行分类提取，并与研究区的FROM-GLC10数据和土地利用变更数据进行了对比分析。结果表明：①研究区土地覆被分类的总体精度为97%，Kappa系数为0.96，分类精度较高；②基于10 m分辨率影像，综合使用形状、纹理、光谱信息对于道路的提取具有较好的效果，道路提取Kappa系数为0.84；③分类结果在面积和空间分布上都优于FROM-GLC10数据，与研究区实际土地变更数据保持较好的一致性。基于面向对象与规则的分类方法提取地物能够有效利用多种遥感影像特征，分类精度高，对于处理高分辨率遥感数据具有很好的优势。 相似文献
7.
高精度的土地覆盖数据是生态系统监测评估与区域可持续发展的重要研究基础，然而目前较少有研究对较高分辨率土地覆盖数据(10 m)在城市尺度的区域上进行研究。随着国际湿地城市的建立，也需要高质量高精度的土地覆盖数据为相关研究提供信息。本文以中国首批国际湿地城市为研究区，对3套全球10 m土地覆盖数据DW (Dynamic World)、ESA (ESA WorldCover)、ESRI (Esri Land Cover))选择2020年和2021年进行空间一致性分析和精度评价，最后提出基于空间一致性分析与精度评价的融合方法，基于空间一致性分析结果和精度评价结果进行土地覆盖数据集融合以重建生产一套新数据。结果表明：(1)任何两个数据集之间，水体、林地、耕地、建设用地这些类型一致度比较高，而湿地、草地和裸地混淆度比较高。(2) ESRI与DW的空间一致性程度最高，全部一致区域占比最高(60%以上)，全部不一致区域占比最低(6%以下)且多分布在沿海沿江，湿地广布的区域，这些区域异质性强，土地覆盖类型复杂。(3) ESA的总体精度最高，DW和ESRI的总体精度较为接近；ESA的湿地类型的精度和分类细... 相似文献
8.
用SPOT-VGT数据制作中国2000年度土地覆盖数据 总被引：13，自引：1，他引：13
土地覆盖是自然环境与人类活动相互作用的中心,准确而现势性强的土地覆盖数据是科学研究、资源管理和环境监测等应用的基础资料。该研究作为欧盟联合研究中心2000年全球土地覆盖计划(GLC2000)的一部分,利用2000年的1km空间分辨率的SPOT-4VGTS10数据与DEM、积温和降水等通过AHP方法合成的自然因子数据,采用FAO的土地覆盖分类系统(LCCS),通过非监督分类方法制作中国2000年的土地覆盖图。研究结果表明,在HANTS方法去云处理的基础上,结合气候分区,利用一年内每10天最大值合成的NDVI时间序列自然因子数据集可对除干旱区外大部分地区进行很好的分类,对干旱区则采用8月下旬的VGT原始数据取代NDVI数据参加分类,可达到较好的分类结果。 相似文献
9.
张景景 《测绘与空间地理信息》2018,(4):146-150
高分辨率遥感卫星影像是获取地物精细类别的重要数据源,快速准确地获取土地利用和土地覆盖分类信息可为土地利用规划、土地管理等提供重要的数据支撑和决策依据。本文开展了高分辨率影像面向对象分类研究,首先,利用多尺度分割方法对高分辨率影像进行分割,基于分割对象,选取不同地物类别样本并计算光谱特征、纹理特征、几何特征。然后,针对特征冗余问题,利用最大相关最小冗余算法选择优先级较高特征,在此基础上结合遗传算法对特征集进行适当扩充(m GA)。在面向对象分类过程中,通过利用遗传算法对支持向量机模型进行快速参数寻优,并在此基础上对分割对象进行分类。最终地物总体精度达到85.93%,Kappa系数为0.828 2。并将分类结果与最近邻分类和随机森林分类结果进行了比较,地物分类精度提高了4.05%和6.81%。实验结果表明:基于m GA特征优化及SVM参数选择进行改进的面向对象的分类方法是有效的。 相似文献
10.
11.
《International Journal of Digital Earth》2013,6(9):709-724
Six widely used coarse-resolution global land cover data-sets – Global Land Cover Characterization (GLCC), Global Land Cover 2000 (GLC2000), GlobCover land cover product (GlobCover), MODIS land cover product (MODIS LC), the University of Maryland land cover product (UMD LC), and the MODIS Vegetation Continuous Fields tree cover layer (MODIS VCF) disagree substantially in their estimates of forest cover. Employing a regression tree model trained on higher-resolution, Landsat-based data, these multisource multiresolution maps were integrated for an improved characterization of forest cover over North America. Evaluated using a withheld test sample, the integrated percent forest cover (IPFC) data-set has a root mean square error of 11.75% – substantially better than the 17.37% of GLCC, 17.61% of GLC2000, 17.96% of GlobCover, 15.23% of MODIS LC, 19.25% of MODIS VCF, and 15.15% of UMD LC, respectively. Although demonstrated for forest, this approach based on integration of multiple products has potential for improved characterization of other land cover types as well. 相似文献
12.
Inputs to various applications and models, current global land cover (GLC) maps are based on different data sources and methods. Therefore, comparing GLC maps is challenging. Statistical comparison of GLC maps is further complicated by the lack of a reference dataset that is suitable for validating multiple maps. This study utilizes the existing Globcover-2005 reference dataset to compare thematic accuracies of three GLC maps for the year 2005 (Globcover, LC-CCI and MODIS). We translated and reinterpreted the LCCS (land cover classification system) classifier information of the reference dataset into the different map legends. The three maps were evaluated for a variety of applications, i.e., general circulation models, dynamic global vegetation models, agriculture assessments, carbon estimation and biodiversity assessments, using weighted accuracy assessment. Based on the impact of land cover confusions on the overall weighted accuracy of the GLC maps, we identified map improvement priorities. Overall accuracies were 70.8 ± 1.4%, 71.4 ± 1.3%, and 61.3 ± 1.5% for LC-CCI, MODIS, and Globcover, respectively. Weighted accuracy assessments produced increased overall accuracies (80–93%) since not all class confusion errors are important for specific applications. As a common denominator for all applications, the classes mixed trees, shrubs, grasses, and cropland were identified as improvement priorities. The results demonstrate the necessity of accounting for dissimilarities in the importance of map classification errors for different user application. To determine the fitness of use of GLC maps, accuracy of GLC maps should be assessed per application; there is no single-figure accuracy estimate expressing map fitness for all purposes. 相似文献
13.
Comparative assessment of CORINE2000 and GLC2000: Spatial analysis of land cover data for Europe 总被引：3，自引：0，他引：3
K. Neumann M. Herold A. Hartley C. Schmullius 《International Journal of Applied Earth Observation and Geoinformation》2007,9(4):425-437
Given the current lack of interoperability between global and regional land cover products, efforts are underway to link the new European global land cover map (GLOBCOVER) with the existing global land cover 2000 map (GLC2000) and European CORINE mapping initiative. Since both datasets apply different mapping standards, key for a successful implementation is a thorough understanding of the heterogeneities among both datasets. Thus, this paper provides an assessment of compatibilities and differences between the CORINE2000 and GLC2000 datasets. The comparative assessment considers inconsistencies between the thematic legends (using the UN land cover classification system-LCCS), class specific accuracies, and the spatial resolution and heterogeneity of the datasets. The results are summarized with implications for the development of the new GLOBCOVER datasets. 相似文献
14.
地表覆盖分类数据对区域森林叶面积指数反演的影响 总被引：2，自引：0，他引：2
以江西省吉安市为研究区,将5种全球地表覆盖分类数据(包括美国地质调查局(USGS)、马里兰大学(UMD)和波士顿大学(BU)生成的3套数据和欧洲生成的2套数据)以及由TM影像生成的区域地表覆盖分类数据,分别与MODIS1km反射率资料结合,利用基于4尺度几何光学模型的LAI反演方法生成研究区的LAI。在1km和4km两种尺度上将反演的LAI与TM资料生成的LAI进行比较,评价地表覆盖分类数据对LAI反演结果的影响。结果表明,TM和欧洲太空局的GLOBCOVER地表覆盖分类数据用于反演LAI的结果较好,在1km尺度上,反演的LAI与统计模型估算的TMLAI相关的R2分别为0.44和0.40,在4km尺度上的R2分别为0.57和0.54;其次为波士顿大学的MODIS地表覆盖分类数据,据其反演的LAI与TMLAI相关的R2在1km和4km尺度上分别为0.38和0.51;而马里兰大学的UMD和欧洲的GLC2000地表覆盖分类数据会导致反演的LAI存在较大误差,据其反演的LAI与TMLAI之间的一致性较差,在1km和4km两种尺度上平均偏低20%左右;LAI的反演结果对聚集度系数具有强的敏感性。该研究表明,为了提高区域/全球LAI反演精度,需要有高质量的地表覆盖分类数据。 相似文献
15.
沿海地区地表覆盖信息是全国地理国情普查的重要内容,遥感影像分类技术为沿海地区地表覆盖信息提供了一种重要方法。本文基于GF-1高分辨率遥感影像,建立了沿海地区地表覆盖分类系统,采用中国测绘科学研究院自主研发的面向对象GLC决策树分类方法和软件进行了地表覆盖分类。通过对某试验区进行分类试验,并结合该区地表覆盖标准分类图进行精度评价,验证了基于高分辨率影像,面向对象GLC决策树分类方法在沿海地区地表覆盖信息提取上的有效性及优越性,其总体分类精度和Kappa系数分别为87.201 8%、0.840 6,均高于SVM分类法。最后提出基于高分辨率遥感影像的沿海地区地表覆盖信息提取流程。 相似文献
16.
Monitoring agricultural land cover is highly relevant for global early warning systems such as ASAP (Anomaly hot Spots of Agricultural Production), because it represents the basis for detecting production deficits in food security assessment. Given the significant inconsistencies among existing land cover datasets, there is a need to obtain a more accurate representation of the spatial distribution and extent of agricultural area in Africa. In this research, we explore a fusion approach that combines the strength of individual datasets and minimises their limitations. Specifically, a semi-automatic method is developed, relying on multi-criteria analysis (MCA) complemented with manual fine-tuning using the best-rated datasets, to generate two hybrid and static agricultural masks – one for cropland and another for grassland. Following a comprehensive selection of land cover maps, each dataset is evaluated at country level according to five criteria: timeliness, spatial resolution, comparison with FAO statistics, accuracy assessment and expert evaluation. A sensitivity analysis is performed, based on an evaluation of the impact of weight settings on the resulting land cover. The proposed methodology is capable of improving agricultural characterisation in Africa. As a result, two static masks at 250 m spatial resolution for the nominal year 2016 are provided. 相似文献
17.
Armel Thibaut Kaptué Tchuenté Jean-Louis Roujean Steven M. De Jong 《International Journal of Applied Earth Observation and Geoinformation》2011
Land cover dynamics at the African continental scale is of great importance for global change studies. Actually, four satellite-derived land cover maps of Africa now available, e.g. ECOCLIMAP, GLC2000, MODIS and GLOBCOVER, are based on images acquired in the 2000s. This study aims at stressing the compliances and the discrepancies between these four land cover classifications systems. Each of them used different mapping initiatives and relies on different mapping standards, which supports the present investigation. In order to do a relative comparison of the four maps, a preamble was to reconcile their thematic legends into more aggregated categories after a projection into the same spatial resolution. Results show that the agreement between the four land cover products is between 56 and 69%. While all these land cover datasets show a reasonable agreement in terms of surface types and spatial distribution patterns, mapping of heterogeneous landscapes in the four products is not very successful. Land cover products based on remote sensing imagery can indeed significantly be improved by using smarter algorithms, better timing of image acquisition, improved class definitions. Either will help to improve the accuracy of future land cover maps at the African continental scale. Data producers may use the areas of spatial agreement for training area selection while users might need to verify the information in the areas of disagreement using additional data sources. 相似文献
18.
There is much interest in using volunteered geographic information (VGI) in formal scientific analyses. This analysis uses VGI describing land cover that was captured using a web-based interface, linked to Google Earth. A number of control points, for which the land cover had been determined by experts allowed measures of the reliability of each volunteer in relation to each land cover class to be calculated. Geographically weighted kernels were used to estimate surfaces of volunteered land cover information accuracy and then to develop spatially distributed correspondences between the volunteer land cover class and land cover from 3 contemporary global datasets (GLC-2000, GlobCover and MODIS v.5). Specifically, a geographically weighted approach calculated local confusion matrices (correspondences) at each location in a central African study area and generated spatial distributions of user's, producer's, portmanteau, and partial portmanteau accuracies. These were used to evaluate the global datasets and to infer which of them was 'best’ at describing Tree cover at each location in the study area. The resulting maps show where specific global datasets are recommended for analyses requiring Tree cover information. The methods presented in this research suggest that some of the concerns about the quality of VGI can be addressed through careful data collection, the use of control points to evaluate volunteer performance and spatially explicit analyses. A research agenda for the use and analysis of VGI about land cover is outlined. 相似文献
19.
土地利用/覆被（LUC）可为土地资源领域相关研究提供基础数据。本文构建了面向对象的LUC分类方法，并以沿海特殊土地类型区连云港市为例，应用Landsat 8影像开展了实证研究。结果表明：①总体分类精度达到85.06%，总体Kappa系数为0.83，超过了0.7的最低允许判别精度；②该方法可以有效地减少研究区因南北部区域耕地植被覆盖度不同导致的错分现象，并可以用于盐田与滩涂信息的提取工作；③该方法既可为研究区土地利用相关研究提供符合精度要求的数据，也可为其他沿海地区进行土地利用/覆被信息提取工作提供参考和借鉴。 相似文献