基于MODIS遥感数据的宏观土地覆盖特征分类方法与精度分析研究

针对宏观土地覆盖遥感分类的现状,充分利用MODIS相对于AVHRR数据具有的多光谱和分辨率优势,提出了利用MODIS数据进行分类特征选择与提取并结合多时相特征进行宏观土地覆盖分类的分类方法,并在中国山东省进行了分类试验,得出以下结论:①不同比例下的训练样本与验证样本影响着总体分类精度;②从MODIS数据中得到的植被指数EVI、白天地表温度Tday、水体指数NDWI、纹理特征局部平稳Homogeneity等可以作为分类特征配合参与到多波段地表反射率Ref1-7遥感影像中,能明显提高分类精度,而土壤亮度指数NDSI则没有贡献;③提取的分类特征对总体分类精度贡献大小为:EVI贡献最大,提高近6个百分点,其次是Homogeneity、NDWI,均提高近4个百分点,而最少的Tday也贡献了近3个百分点;④各分类特征对不同地物类别具有不同的分离度,在提高某些类别的分离性时,有可能降低了其它类别的分离性。试验结果表明:在没有其它非遥感信息的前提下,仅利用MODIS遥感自身信息对宏观土地覆盖分类就可达到较高精度。     

基于MODIS数据的宏观土地利用/土地覆盖监测研究

建立土地动态监测系统,快速提供各种土地利用变化信息是实现资源保护的基本手段。而及时准确地获取覆盖广的地表影像资料是大范围动态监测的重要环节。介绍了MODIS影像数据的特点,并与其它遥感数据源进行了比较,就其在土地利用中的应用进行了探讨。分析了松嫩平原2001年9月MODIS影像的特征,准确地解译出该区在宏观上的土地利用方式,为监测该区土地沙化、盐碱化和农作物生长提供了科学依据。     

面向土地覆盖分类的MODIS影像融合研究

MODIS影像的多波段及其1、2波段的250 m中等分辨率为大区域中空间分辨率的土地覆盖制图提供了可能。为了有效利用MODIS影像的空间和光谱信息,使用SFIM、HPF和PCA变换等遥感影像融合方法,分别采用MODIS影像的波段1(b1)和波段2(b2)对3~7(b3~b7)波段进行融合,并就融合影像的光谱保真度和分类精度对6种不同融合结果进行评价。结果表明不同的融合结果得到的分类精度均有不同程度的提高;3种融合方法中使用b2的融合效果均优于b1;SFIM变换在光谱失真较小的情况下能够较大程度地提高分类精度。因此使用b2的SFIM变换可以用于提高MODIS土地覆盖图的空间分辨率和精度。     

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。     

基于MODIS—NDVI数据的秦岭中段土地覆被分类

基于地物光谱特征的监督分类一直是用遥感影像解译土地覆被类型的常规方法。基于可见光和近红外波段的光谱反射率构建的NDVI指数的水平高低及时序变化特征对土地覆被类型有高度敏感性和较好的指示性。本文基于时序MODIS-NDVI数据,通过合理选择训练样区对MODIS影象进行监督分类,最终实现对秦岭中部地区各种土地覆被的分类,通过与实地GPS调查数据比较,结果显示分类总体精度达到76．77％,kappa系数为67．22％,分类等级为较好。     

基于多源遥感数据融合的地表覆盖分类方法

针对当前地表覆盖分类结果精度低的问题,提出基于多源遥感数据融合的地表覆盖分类方法。首先,将红-绿-蓝（Red Green Blue,RBG）、数字表面模型（Digital Surface Model,DSM）和归一化植被指数（Normalized Differential Vegetation Index,NDVI）作为编码器的3个分支,生成地表覆盖遥感影像语义分割网络;其次,利用多源遥感数据融合技术构建地表覆盖分类模型;最后,通过对模型的训练和优化,输出空白域自适应与影像地表覆盖分类结果。实验结果表明,该方法可以实现对多种地表覆盖类型的高精度划分,具有一定的应用价值。     

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。     

RadarSat-2全极化数据土地覆盖分类精度评价

Radar Sat-2全极化数据能否应用于土地覆盖分类需要大量的研究和论证。本文以NLCD为参考数据,利用SVM分类器对1景旧金山地区Radar Sat-2全极化数据进行土地覆盖分类实验,并从分类类别面积一致性、空间相似性两个方面对分类结果进行分类精度评价。实验获得Radar Sat-2数据分类结果总体分类精度76.91%和kappa系数0.65,表明Radar Sat-2全极化数据用于土地覆盖分类分类精度较高,可以达到很好的分类质量。     

RadarSat-2 全极化数据土地覆盖分类精度评价

RadarSat-2 全极化数据能否应用于土地覆盖分类需要大量的研究和论证。本文以NLCD 为参考数据,利用SVM 分类器对1 景旧金山地区RadarSat-2 全极化数据进行土地覆盖分类实验,并从分类类别面积一致性、空间相似性两个方面对分类结果进行分类精度评价。实验获得RadarSat-2 数据分类结果总体分类精度76.91%和kappa 系数0.65,表明RadarSat-2 全极化数据用于土地覆盖分类分类精度较高,可以达到很好的分类质量。     

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.     

面向宏观地表分类的特征选择算法比较研究

为了成功将土地覆盖进行分类,选择合适的特征是至关重要的。针对利用MODIS数据进行宏观土地覆盖的分类问题,对三种典型的特征选择方法进行了比较研究。研究结果表明：分支定界法（BB）最适合于该土地覆盖分类问题,与此同时,ReliefF和mRMR方法在目标应用中的精度非常接近。研究结果同样表明进行特征选择是非常必要的,它不仅能够大大地降低计算复杂度,而且分类精度能够保持不变,甚至更高。     

Spatial, spectral and temporal patterns of tropical forest cover change as observed with multiple scales of optical satellite data

This article describes the development of a methodology for scaling observations of changes in tropical forest cover to large areas at high temporal frequency from coarse resolution satellite imagery. The approach for estimating proportional forest cover change as a continuous variable is based on a regression model that relates multispectral, multitemporal MODIS data, transformed to optimize the spectral detection of vegetation changes, to reference change data sets derived from a Landsat data record for a study site in Central America. A number of issues involved in model development are addressed here by exploring the spatial, spectral and temporal patterns of forest cover change as manifested in a time-series of multi-scale satellite imagery.The analyses highlighted the distinct spectral change patterns from year-to-year in response to the possible land cover trajectories of forest clearing, regeneration and changes in climatic and land cover conditions. Spectral response in the MODIS Calibrated Radiances Swath data set followed more closely with the expected patterns of forest cover change than did the spectral response in the Gridded Surface Reflectance product. With forest cover change patterns relatively invariant to the spatial grain size of the analysis, the model results indicate that the best spectral metrics for detecting tropical forest clearing and regeneration are those that incorporate shortwave infrared information from the MODIS calibrated radiances data set at 500-m resolution, with errors ranging from 7.4 to 10.9% across the time periods of analysis.     

Land cover assessment with MODIS imagery in southern African Miombo ecosystems

Global land use and land cover products in highly dynamic tropical ecosystems lack the detail needed for natural resource management and monitoring at the national and provincial level. The MODIS sensor provides improved opportunities to combine multispectral and multitemporal data for land use and land cover mapping. In this paper we compare the MODIS Global Land Cover Classification Product with recent land use and land cover maps at the national level over a characteristic location of Miombo woodlands in the province of Zambezia, Mozambique. The performances of three land cover-mapping approaches were assessed: single-date supervised classification, principal component analysis of band-pair difference images, and multitemporal NDVI analysis. Extensive recent field data were used for the definition of the test sites and accuracy assessment. Encouraging results were achieved with the three approaches. The classification results were refined with the help of a digital elevation model. The most consistent results were achieved using principal component analysis of band-pair difference images. This method provided the most accurate classifications for agriculture, wetlands, grasslands, thicket and open forest. The overall classification accuracy reached 90%. The multitemporal NDVI provided a more accurate classification for the dense forest cover class. The selection of the right image dates proved to be critical for all the cases evaluated. The flexibility of these alternatives makes them promising options for rapid and inexpensive land cover mapping in regions of high environmental variability such as tropical developing countries.     

高光谱影像波段选择算法研究*

基于高光谱影像数据的特点,分析了高光谱数据的降维方法。着重探讨了波段选择的若干算法:熵及联合熵、最佳指数因子、自动子空间划分、自适应波段选择、波段指数和最优波段指数等算法。分析了各种算法的有效性、局限性和计算复杂度,并针对波段指数的不足,设计了最优波段指数(OBI)波段选择新算法。最后通过具体的试验,验证了各种算法的性能。     

Estimating proportional change in forest cover as a continuous variable from multi-year MODIS data

This article describes a series of fundamental analyses designed to test and compare the utility of various MODIS data and products for detecting land cover change over a large area of the tropics. The approach for estimating proportional forest cover change as a continuous variable was based on a reduced major axis regression model. The model relates multispectral and multi-temporal MODIS data, transformed to optimize the spectral detection of vegetation changes, to reference change data sets derived from a Landsat data record for several study sites across the Central American region. Three MODIS data sets with diverse attributes were evaluated on model consistency, prediction accuracy and practical utility in estimating change in forest cover over multiple time intervals and spatial extents.A spectral index based on short-wave infrared information (normalized difference moisture index), calculated from half-kilometer Calibrated Radiances data sets, generally showed the best relationships with the reference data and the lowest model prediction errors at individual study areas and time intervals. However, spectral indices based on atmospherically corrected surface reflectance data, as with the Vegetation Indices and Nadir Bidirectional Reflectance Distribution Function - Adjusted Reflectance (NBAR) data sets, produced consistent model parameters and accurate forest cover change estimates when modeling over multiple time intervals. Models based on anniversary date acquisitions of the one-kilometer resolution NBAR product proved to be the most consistent and practical to implement. Linear regression models based on spectral indices that correlate with change in the brightness, greenness and wetness spectral domains of these data estimated proportional change in forest cover with less than 10% prediction error over the full spatial and temporal extent of this study.     

HJ-1-CCD与MODIS数据的NDVI比较分析

利用辐射传输模型对HJ-1-CCD数据进行大气校正并反演出植被指数,和MODIS标准产品数据进行对比,并对典型区域的结果相关性和差异性进行了统计分析.结果显示:①当下垫面为相对均一的地表时,如耕地、有植被覆盖的山地、水体等,两传感器反演出的植被指数相关性(r)和相对差异分别为耕地(0.74,10％)、山地(0.69,12％)、水体(0.78,5％);②当下垫面为较为复杂的地表时,如建筑用地(含居民住宅区)等,两传感器的相关性(r)仅为0.58、相对差异达到18％;③在估算小范围区域且地表覆盖较为复杂的下垫面植被指数时,HJ-1-CCD因其相对较高的空间分辨率,能有效减弱混合像元的影响,进而提供更加丰富的植被指数信息.     

垃圾图像判别中的特征提取与选择研究*

对垃圾图像判别问题中的特征提取和特征选择研究现状进行了总结。从特征的可区分性、鲁棒性和提取效率三个方面比较了垃圾图像判别中的主要特征,分析了特征的优缺点。结合分类学习算法、仿真实验结果,对已有的主要特征选择和分析方法进行比对,为进一步研究特征提取、特征选择方法,提高垃圾图像分类器的性能和效率提供有价值的参考。     

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。