低空间分辨率遥感数据亚像元级水华面积提取方法

在对太湖、巢湖等大型湖泊进行业务化蓝藻水华遥感监测工作中,常以250 m空间分辨率的MODIS数据为主,但其像元多为水体和水华的混合像元,若用常规方法进行水华面积提取,势必会严重影响水华监测的精度和实际应用效果。针对上述问题,基于混合像元分解原理,通过混合像元分解得到水华组分在混合像元中的丰度(百分比),实现亚像元级的水华面积提取。该方法可直接根据图像的DN值进行水华面积提取,无需对数据进行辐射校正和大气校正等预处理。与常规水华提取法相比,该方法的水华面积提取精度提高了近30%。     

基于单时相MODIS数据的决策树自动构建及分类研究

以甘肃省为试验区,利用单时相MODIS数据的光谱信息,使用最大似然法和基于See 5.0数据挖掘的决策树分类方法,进行了分类对比研究.分类结果表明,加入温度一植被角度TVA和温度-植被距离TVD两个指数后,低植被覆盖区的分类效果得到了改善;基于See 5.0数据挖掘的决策树方法能够快速地建立决策树,且能提高较难识别地物类型的分类精度.     

玛曲湿地遥感影像提取及精度分析

以甘肃玛曲县为研究区,以区域湿地遥感信息提取为目标,采用TM多光谱数据和DEM数据,利用归一化植被指数和主成分分析得到的第一主成分作为分类特征,通过对数据的空间特征、波谱特征与统计结果的对比分析,构建湿地信息提取决策树模型,并与非监督分类法、最大似然法相比较,表明基于多特征决策树分类法能够用于湿地专题信息的提取,在研究区有较好的适用性。     

EMD与分形相结合的遥感影像水体信息提取方法

提出一种基于经验模态分解(empirical mode decomposition,EMD)和分形理论相结合的遥感影像水体信息提取方法,该方法尝试结合影像的光谱特征和纹理特征以提高分类提取精度。对影像进行主成分分析得到有效信息量最大的第一主分量,计算每个像元的分维数得到分维图,同时将第一主分量EMD分解得到有效信息量较大的前3个经验模态函数,再结合原有的波段信息作为研究数据,利用极大似然法分类器提取水体信息。该方法充分结合了EMD在降噪和区分相似光谱特征中的优势和分形理论在纹理信息提取中的优势。研究表明,该方法可有效提高水体信息的提取精度,Kappa最高到0.932 5。     

基于ASTER数据的决策树自动构建及分类研究

 在对ASTER原始9个波段数据进行各种变换处理的基础上，采用数量化指标平均可分性方法确定参与分类的最佳特征组合； 结合研究区8种主要地物类型训练数据集，分别采用最大似然法、BP神经网络法和基于See 5.0数据挖掘的决策树分类法进行分类，提取主要地物的空间分布专题信息。经过379个野外样点的验证，结果表明： 决策树算法分类性能最优，神经网络算法次之，最大似然法效果最差； 与ENVI 4.1、ERDAS 8.7提供的传统决策树建立及分类方法比较，基于数据挖掘工具See 5.0和Cart的决策树生成和分类方法具有客观、高效率、分类性能可靠和精度高等优点。     

基于多源数据的CART决策树冰川提取

介绍了一种快速简便的基于多源数据的CART决策树提取方法。以各拉丹冬冰川为例,利用TM影像提取冰川,通过与手动勾绘、最大似然法的对比,CART决策树方法具有一定的优势。从提取结果上看,CART决策树、手动勾绘、最大似然法提取的面积分别为856 km~2、858 km~2、866 km~2。以手动勾绘为标准,最大似然法提取和其他两种方法有着较大差距,CART决策树方法和手动勾绘法相差较少。总的来说,CART决策树在保证精度的同时,比最大似然法精确,比手动勾绘法简单快捷。     

基于决策树的多源遥感数据中湿地信息提取

了解湿地资源分布类型是恢复湿地生态环境的基础,因此本文提出基于决策树的多源遥感数据中湿地信息提取方法。运用QUEST方法构建决策树,对多源遥感数据中的湿地信息进行提取,并以Kappa系数评价决策树分类的有效性。结合海南东寨港国家级自然保护区实际数据,得到湿地地形信息、湿地分类体系信息、湿地空间与面积分类、湿地类型转换特征以及湿地类型动态度差异,完成湿地信息提取,为今后保护区湿地生态保护与恢复提供信息提取技术支持。     

融合Landsat ETM+和MODIS数据估算高时空分辨率地表短波反照率

提出一种通过融合高空间低时间分辨率、低空间高时间分辨率地表短波反照率,来估算高时空分辨率地表短波反照率的方法。首先,利用Landsat ETM+数据,通过窄波段到宽波段的转换得到一景或多景空间分辨率较高的ETM+蓝天空短波反照率;然后,在MODIS短波反照率产品基础上,以天空光比例因子为权重,得到空间分辨率较低的MODIS蓝天空短波反照率;最后,利用STARFM(Spatial and Temporal Adaptive Reflectance Fusion Model)模型融合ETM+短波反照率的空间变化信息和MODIS短波反照率的时间变化信息,得到高时空分辨率的地表短波反照率。针对STARFM模型在异质性区域估算精度降低的问题,通过以MODIS反照率影像各像元的端元(各地类)反照率取代MODIS像元反照率来提取时空变化等信息参与STARFM模型的融合过程,达到提高异质性区域估算精度的目的。结果显示,直接利用STARFM模型估算得到的高空间分辨率地表短波反照率处在合理的精度范围内(RMSE0.02),用改进后的STARFM模型估算得到的异质性区域短波反照率和真实ETM+短波反照率间的相关系数增大。     

基于MODIS时序影像的湖南省油菜种植面积遥感监测分析

基于时间序列影像数据的提取方法可实现快速监测大面积农作物的种植分布和面积估算.以湖南省为研究区,利用2017年500 m空间分辨率的MODIS NDVI时序数据,结合湖南省耕地分布数据和实地样点数据得到油菜物候标准曲线,采用最小二乘法与阈值法提取得到湖南省油菜种植分布.结果显示,遥感提取得到的湖南省油菜种植面积主要分布...     

最小二乘混合像元分解的端元丰度信息提取研究

针对无约束最小二乘混合像元分解算法提取地物端元丰度出现的局限性问题,通过野外实地采集的地物光谱数据建立研究区典型的地物波谱库,以Landsat OLI影像作为主要数据源,在经过Gram-Schmidt(GS)影像融合的基础上,利用纯净像元指数(PPI)及基于几何顶点的端元提取技术提取研究区典型地物端元,最后通过完全约束的最小二乘混合像元分解算法完成对研究区典型地物端元丰度的提取。结果较好地解决了无约束最小二乘混合像元分解算法提取的端元丰度信息出现负值的情况,并且提高了典型地物丰度信息提取的精度。完全约束最小二乘混合像元分解算法的RMSE误差均控制在0.174 913左右,在很大程度上提高了混合像元分解精度及实用性。     

Improved maize cultivated area estimation over a large scale combining MODIS–EVI time series data and crop phenological information

The accurate and timely information of crop area is vital for crop production and food security. In this study, the Enhanced Vegetation Index (EVI) data from MODerate resolution Imaging Spectroradiometer (MODIS) integrated crop phenological information was used to estimate the maize cultivated area over a large scale in Northeast China. The fine spatial resolution China’s Environment Satellite (HJ-1 satellite) images and the support vector machine (SVM) algorithm were employed to discriminate distribution of maize in the reference area. The mean MODIS–EVI time series curve of maize was extracted in the reference area by using multiple periods MODIS–EVI data. By analysing the temporal shift of crop calendars from northern to southern parts in Northeast China, the lag value was derived from phenological data of twenty-one agro-meteorological stations; here integrating with the mean MODIS–EVI time series image of maize, a standard MODIS–EVI time series image of maize was obtained in the whole study area. By calculating mean absolute distances (MAD) map between standard MODIS–EVI image and mean MODIS–EVI time series images, and setting appropriate thresholds in three provinces, the maize cultivated area was extracted in Northeast China. The results showed that the overall classification accuracy of maize cultivated area was approximately 79%. At the county level, the MODIS-derived maize cultivated area and statistical data were well correlated (R² = 0.82, RMSE = 283.98) over whole Northeast China. It demonstrated that MODIS–EVI time series data integrated with crop phenological information can be used to improve the extraction accuracy of crop cultivated area over a large scale.     

综合多特征的极化SAR图像随机森林分类算法

为抑制相干斑噪声对极化SAR图像分类结果的干扰,本文提出一种综合多特征的极化SAR图像随机森林分类方法。该方法首先利用简单线性迭代聚类(SLIC)算法生成超像素作为分类单元;然后,基于高维极化特征图像,利用训练好的随机森林模型,统计决策树的分类投票数,计算各超像素的类别概率;最后,利用超像素间的空间邻域特征,采用概率松弛算法(PLR)迭代修正超像素的类别后验概率,并依据最大后验概率(MAP)准则得到分类结果;实现综合利用超像素和空间邻域特征,降低相干斑噪声干扰的极化SAR图像分类方法。实验对比结果表明:本文方法能得有效抑制极化SAR图像中相干斑噪声的干扰,得到高精度且光滑连续的分类结果。     

基于EOS/MODIS若尔盖高原地区湿地信息提取及变化监测

以若尔盖高原地区为研究区,利用多时相中分辨率成像光谱仪MODIS(Moderate Resolution Imaging Spectroradiometer)遥感影像数据,采用基于归一化植被指数(NDVI)的时间序列谐波分析方法,对2001~2013年夏季的MODIS/NDVI和MODIS/EVI进行重构,去除云干扰,采用决策树分类方法获取若尔盖高原地区2001~2013年夏季湿地信息的分布数据并作统计。结果表明:基于EOS/MODIS遥感数据,采用决策树分类方法获取若尔盖高原地区的湿地信息数据是可行的;若尔盖高原地区的湿地面积是随年际的变化呈锐减趋势,若尔盖高原地区湿地的退化主要是受到近年来气候暖干化的影响,人类活动则加剧了湿地萎缩及退化的趋势。     

Estimation of aboveground biomass of Robinia pseudoacacia forest in the Yellow River Delta based on UAV and Backpack LiDAR point clouds

Forest plantations are an important source of terrestrial carbon sequestration. The forest of Robinia pseudoacacia in the Yellow River Delta (YRD) is the largest artificial ecological protection forest in China. However, more than half of the forest has appeared different degrees of dieback and even death since the 1990s. Timely and accurate estimation of the forest aboveground biomass (AGB) is a basis for studying the carbon cycle of forests. Light Detecting and Ranging (LiDAR) has been proved to be one of the most powerful methods for forest biomass estimation. However, because of an irregular and overlapping shape of the broadleaved forest canopy in a growing season, it is difficult to segment individual trees and estimate the tree biomass from airborne LiDAR data. In this study, a new method was proposed to solve this problem of individual tree detection in the Robinia pseudoacacia forest based on a combination of the Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) with the Backpack-LiDAR. The proposed method mainly consists of following steps: (i) at a plot level, trees in the UAV-LiDAR data were detected by seed points obtained by an individual tree segmentation (ITS) method from the Backpack-LiDAR data; (ii) height and diameter at breast height (DBH) of an individual tree would be extracted from UAV and Backpack LiDAR data, respectively; (iii) the individual tree AGB would be calculated through an allometric equation and the forest AGB at the plot level was accumulated; and (iv) the plot-level forest AGB was taken as a dependent variable, and various metrics extracted from UAV-LiDAR point cloud data as independent variables to estimate forest AGB distribution in the study area by using both multiple linear regression (MLR) and random forest (RF) models. The results demonstrate that: (1) the seed points extracted from Backpack-LiDAR could significantly improve the overall accuracy of individual tree detection (F = 0.99), and thus increase the forest AGB estimation accuracy; (2) compared with MLR model, the RF model led to a higher estimation accuracy (p < 0.05); and (3) LiDAR intensity information selected by both MLR and RF models and laser penetration rate (LP) played an important role in estimating healthy forest AGB.     

兼容多源数据的森林湿地信息提取和比较分析

针对以光谱特征差异为依据,提取森林湿地信息精度低的问题,该文采用兼容多源数据的分类回归树(CART)提取方法,并以大沾河国家森林湿地进行实证研究。基于Landsat8遥感数据、Radarsat-2极化雷达数据和地形辅助数据,采用SPM软件分别构建3种特征变量组合的CART决策树模型,并获取分类规则,最后根据规则对研究区的森林湿地信息进行提取。结果表明:3种特征变量组合中,兼容光谱、纹理、雷达与地形辅助数据的CART决策树的森林湿地信息提取精度最高,用户精度和制图精度分别达到了88.46%和82.14%。研究结果体现了雷达数据与地形辅助数据有助于提取森林湿地信息。     

基于ICESatGLAS的云南省森林地上生物量反演

结合机载、星载激光雷达对GLAS(地球科学激光测高系统)光斑范围内的森林地上生物量进行估测,并利用MODIS植被产品以及MERIS土地覆盖产品进行了云南省森林地上生物量的连续制图。机载LiDAR扫描的260个训练样本用于构建星载GLAS的森林地上生物量估测模型,模型的决定系数(R2)为0.52,均方根误差(RMSE)为31Mg/ha。研究结果显示,云南省总森林地上生物量为12.72亿t,平均森林地上生物量为94Mg/ha。估测的森林地上生物量空间分布情况与实际情况相符,森林地上生物量总量与基于森林资源清查数据的估测结果相符,表明了利用机载LiDAR与星载ICESatGLAS结合进行大区域森林地上生物量估测的可靠性。     

Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data

Forest cover plays a key role in climate change by influencing the carbon stocks, the hydrological cycle and the energy balance. Forest cover information can be determined from fine-resolution data, such as Landsat Enhanced Thematic Mapper Plus (ETM+). However, forest cover classification with fine-resolution data usually uses only one temporal data because successive data acquirement is difficult. It may achieve mis-classification result without involving vegetation growth information, because different vegetation types may have the similar spectral features in the fine-resolution data. To overcome these issues, a forest cover classification method using Landsat ETM+ data appending with time series Moderate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data was proposed. The objective was to investigate the potential of temporal features extracted from coarse-resolution time series vegetation index data on improving the forest cover classification accuracy using fine-resolution remote sensing data. This method firstly fused Landsat ETM+ NDVI and MODIS NDVI data to obtain time series fine-resolution NDVI data, and then the temporal features were extracted from the fused NDVI data. Finally, temporal features combined with Landsat ETM+ spectral data was used to improve forest cover classification accuracy using supervised classifier. The study in North China region confirmed that time series NDVI features had significant effects on improving forest cover classification accuracy of fine resolution remote sensing data. The NDVI features extracted from time series fused NDVI data could improve the overall classification accuracy approximately 5% from 88.99% to 93.88% compared to only using single Landsat ETM+ data.     

Linear unmixing of MODIS albedo composites to infer subpixel land cover type albedos

We use a linear unmixing approach to test how land use and forestry maps, in combination with the MODIS BRDF/albedo product, can be used to estimate land cover type albedos in boreal regions. Operational land use maps from three test areas in Finland and Canada were used to test the method. The resulting endmember albedo estimates had low standard errors of the mean and were realistic for the main land cover types. The estimated albedos were fairly consistent with albedo measurements conducted with a telescope mast and pure pixel albedos. Problems with the method are the possible errors in the land cover maps, lack of good quality winter MODIS albedo composites and the mismatch between the MODIS pixels and the true observation area. The results emphasize the role of tree species as determinant of forest albedo. Comprehensive spatial and temporal measurements of land cover albedo are usually not possible with in situ mast measurements, and the spatial resolution of MODIS albedo product is often too low to allow direct comparison of pixel albedos and land cover types in areas with heterogeneous vegetation. Hence, and since local forestry maps exist for most temperate and boreal regions, we believe that the proposed method will be useful in estimating average regional land cover type albedos as well as in tracking changes in them.     

Above Ground Forest Phytomass Assessment in Southern Gujarat

Spectral modeling of above ground biomass (AGB) with field data collected in 48 field sites representing moist deciduous forest in Surat district is reported. Models were generated using LISS-III and MODIS data. The plot-wise field data was aggregated to MODIS pixel (250 m) using area weightages of forest/vegetation. The study reports that above ground phytomass varied from 6.13 t/ha to 389.166 t/ha while AGB phytomass estimated using area-weights for sites of 250×250 m, ranged from 5.534 t/ha to 134.082 t/ha. The contribution of bamboo in AGB has been found very high. The analysis indicated that the highest correlation between AGB phytomass and red band (R) of MODIS satellite data of October was (R²=0.7823) and R²=0.6998 with both NDVI of October data as well as NDVI_max. High correlation (R²=0.402) with IR band of February month was also found. The phytomass range obtained by using MODIS data varies from 0.147 t/ha to 182.16 t/ha. The mean biomass is 40.50 t/ha. Total biomass is 31.44 Mt. The mean Carbon density is 19.44 tC/ha in forest areas. The study is validation of region-wise spectral modeling approach that will be adopted for mapping vegetation carbon pool of the India under National Carbon Project of ISRO-Geosphere Biosphere Programme.     

基于MODIS影像多特征的CART决策树分类

以山东省为研究区域,利用2009年9月MODIS的8 d合成波段反射率产品MOD09,选择特征变量植被指数(NDVI、EVI)、NDWI、NDMI、NDSI及辅助信息DEM,通过选取其中的影像特征组合来确定分类方案,构建各波段组合的CART决策树,对MODIS影像进行分类,得到CART决策树的最优波段组合。结果表明,特征变量DEM、NDVI、EVI对分类结果贡献较大;将CART决策树的分类结果与其相对应的最大似然分类结果进行比较可知,基于影像多特征的CART决策树分类方法能明显提高分类精度。