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1.
Shenghui Fang Yuan Le Qi Liang Xiaojun Liu 《Journal of the Indian Society of Remote Sensing》2014,42(4):733-743
The aim of this study is to estimate leaf area index (LAI) in different type of plants using vegetation indices (VIs) and neural network algorithms retrieved from MODIS data. Four VI were calculated, and neural networks were built up based on MODIS surface reflectance products. Among the tested VIs, normalized difference vegetation index (NDVI) and chlorophyll index (CI) appeared to be the best candidate indices in estimating LAI across sites with different vegetation types. The models having the highest accuracy were CI for grassland and deciduous broad leaf forest with determination coefficients (R-square above 0.70, and NDVI for crop R-square?=?0.78). Neural network showed better results than VI methods except in grassland sites. The added VI information showed no significant improvement of model accuracy for the neural networks in most sites. 相似文献
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The drainage network of a sixth-order tropical river basin, viz. Ithikkara river basin, was extracted from different sources such as Survey of India topographic maps (1: 50,000; TOPO) and digital elevation data of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (30 m) and Shuttle Radar Topography Mapping Mission (SRTM) (90 m). Basin morphometric attributes were estimated to evaluate the accuracy of the digital elevation model (DEM)-derived drainage networks for hydrologic applications as well as terrain characterization. The stream networks derived from ASTER and SRTM DEMs show significant agreement (with slight overestimation of lower order streams) with that of TOPO. The study suggests that SRTM (despite the coarser spatial resolution) provides better results, in drainage delineation and basin morphometry, compared to ASTER. Further, the variability of basin morphometry among the data sources might be attributed to spatial variation of elevation, raster grid size and vertical accuracy of the DEMs as well as incapability of the surface hydrologic analysis functions in the GIS platform. 相似文献
3.
Janne Heiskanen Miina Rautiainen Lauri Korhonen Matti Mõttus Pauline Stenberg 《International Journal of Applied Earth Observation and Geoinformation》2011
Spectral invariants provide a novel approach for characterizing canopy structure in forest reflectance models and for mapping biophysical variables using satellite images. We applied a photon recollision probability (p) based forest reflectance model (PARAS) to retrieve leaf area index (LAI) from fine resolution SPOT HRVIR and Landsat ETM+ satellite data. First, PARAS was parameterized using an extensive database of LAI-2000 measurements from five conifer-dominated boreal forest sites in Finland, and mixtures of field-measured forest understory spectra. The selected vegetation indices (e.g. reduced simple ratio, RSR), neural networks and kNN method were used to retrieve effective LAI (Le) based on reflectance model simulations. For comparison, we established empirical vegetation index-LAI regression models for our study sites. The empirical RSR–Le regression performed best when applied to an independent test site in southern Finland [RMSE 0.57 (24.2%)]. However, the difference to the best reflectance model based retrievals produced by neural networks was only marginal [RMSE 0.59 (25.1%)]. According to this study, the PARAS model provides a simple and flexible modelling tool for calibrating algorithms for LAI retrieval in conifer-dominated boreal forests. The advantage of PARAS is that it directly uses field measurements to parameterize canopy structure (LAI-2000, hemispherical photographs) and optical properties of foliage and understory. 相似文献
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5.
遥感数据产品真实性检验不确定性分析研究进展 总被引：1，自引：1，他引：1
不确定性分析是遥感产品真实性检验最重要的部分,本文以叶面积指数LAI为例,从测量、模型以及蕴含在测量和模型中的尺度效应3个方面分析产品真实性检验过程的不确定性来源,并针对问题提出减小其不确定性的办法。对于相对均一的地表,地面测量的空间代表性比较好,可不考虑地表空间代表性引起的尺度效应和蕴含在模型中的尺度效应引起的不确定性。针对异质性地表,分为两种情况:若模型是线性的,蕴含在模型中的尺度效应可以忽略,只需要考虑测量的不确定性、模型本身的不确定性、以及地面测量的空间代表性引起的尺度效应;若模型是非线性的,则测量、模型和蕴含在测量和模型中的尺度效应引起的不确定性都需要考虑。 相似文献
6.
对多源遥感数据协同生产的2010年—2015年中国区域1 km空间分辨率5天合成的MuSyQ（Multi-source data Synergized Quantitative remote sensing production system）叶面积指数LAI产品进行验证。参考现有的LAI产品（MODIS c5，GLASS LAI）和中国生态系统研究网络部分农田和森林站点可用的LAI地面测量数据，从时空连续性、时空一致性、精度和准确性等方面对中国区域的MuSyQ LAI产品进行定性和定量分析与评价。结果表明：（1） MuSyQ LAI产品在保证精度优于MODIS产品的情况下，时间分辨率和时空连续性均有提高。MuSyQ LAI与其他LAI产品（MODIS c5，GLASS LAI）在整体上有很好的一致性（RMSE=1.0，RMSE=0.81），但对常绿阔叶林高值处的描述不稳定；（2） 与LAI地面测量数据相比，MuSyQ LAI产品与地面参考图对比结果较好（最高相关性（R2=0.54）和较低总体误差（RMSE=0.96）），其在阔叶作物生长季高值处有些许低估且在某些阔叶林站点有些高估。整体上，MuSyQ LAI产品呈现出较高的精度，可靠的空间分布和连续稳定的时间分布，且对森林LAI的描述具有更可靠的动态范围。 相似文献
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以往,根据遥感资料估算作物产量,多数用的是经验方法。本文提出了一种具有生物学基础的水稻估产模式。该模式包括两项基础性的工作。(1)利用Baerema在澳大利亚新南威尔士州的Riveri-na地区,在水稻整个生长季中获得的三个水稻品种、两种播种方式(空播和拖拉机播)共6组实测的叶面积指数值LAI,通过LAI与积温间关系的分析,得到一条规一化的LAI曲线,称叶面积指数轨线(LAI trajectory)(2)根据太阳辐射在植冠层内的传输理论,利用实测的水稻叶角分布和常规日射资料,用模拟计算的方法,得到水稻冠层对光合有效辐射PAR日截获率IPAR_d与LAI间的关系。并用实测资料对此作了检验,结果表明模拟计算结果是可取的。由此,我们只要知道了水稻扬花前不久某一天的LAI,利用上述两项基本关系及当地的辐射、温度资料,便可推算植冠层从扬花到生理成熟期间对光合有效辐射的截获总量TIPAR,进而再假定水稻的灌浆直接取决于对PAR的总截获量TIPAR,根据水稻籽粒产量与光能截获间的转换效率,便能估算水稻产量。 相似文献
8.
Akira Hirano Roy Welch Harold Lang 《ISPRS Journal of Photogrammetry and Remote Sensing》2003,57(5-6):356
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on-board the National Aeronautics and Space Administration's (NASA's) Terra spacecraft provides along-track digital stereo image data at 15-m resolution. As part of ASTER digital elevation model (DEM) accuracy evaluation efforts by the US/Japan ASTER Science Team, stereo image data for four study sites around the world have been employed to validate prelaunch estimates of heighting accuracy. Automated stereocorrelation procedures were implemented using the Desktop Mapping System (DMS) software on a personal computer to derive DEMs with 30- to 150-m postings. Results indicate that a root-mean-square error (RMSE) in elevation between ±7 and ±15 m can be achieved with ASTER stereo image data of good quality. An evaluation of an ASTER DEM data product produced at the US Geological Survey (USGS) EROS Data Center (EDC) yielded an RMSE of ±8.6 m. Overall, the ability to extract elevations from ASTER stereopairs using stereocorrelation techniques meets expectations. 相似文献
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The characterization of fuel types is very important for computing spatial fire hazard and risk and simulating fire growth and intensity across a landscape. However, due to the complex nature of fuel characteristic a fuel map is considered one of the most difficult thematic layers to build up. The advent of sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. In order to ascertain how well ASTER data can provide an exhaustive classification of fuel properties a sample area characterized by mixed vegetation covers was analysed. The selected sample areas has an extension at around 60 km2 and is located inside the Sila plateau in the Calabria Region (South of Italy). Fieldwork fuel type recognitions, performed before, after and during the acquisition of remote sensing ASTER data, were used as ground-truth dataset to assess the results obtained for the considered test area. The method comprised the following three steps: (I) adaptation of Prometheus fuel types for obtaining a standardization system useful for remotely sensed classification of fuel types and properties in the considered Mediterranean ecosystems; (II) model construction for the spectral characterization and mapping of fuel types based on a maximum likelihood (ML) classification algorithm; (III) accuracy assessment for the performance evaluation based on the comparison of ASTER-based results with ground-truth. Results from our analysis showed that the use ASTER data provided a valuable characterization and mapping of fuel types being that the achieved classification accuracy was higher than 90%. 相似文献
10.
基于森林模型参数先验知识估算高分辨率叶面积指数 总被引：1，自引：0，他引：1
目前，估算高分辨率叶面积指数LAI（Leaf Area Index）的常用方法是采用大量地面测量数据和遥感数据建立统计模型，再用统计模型估算LAI。然而，与农田地面测量实验相比，森林地面测量实验获取的观测数据更加有限，这使得基于统计模型的森林高分辨率LAI的估算精度低，难以满足应用需求。为此，本文提出一种基于森林模型参数先验知识、使用森林研究区少量的LAI地面测量数据和归一化植被指数NDVI数据估算森林高分辨率LAI的方法。首先，获取全球20个森林实验区的LAI地面测量数据和NDVI数据，建立LAI-NDVI统计模型并提取森林模型参数的先验知识。然后，以一个新的森林站点Concepción作为研究区，将该研究区的数据分为建模数据和验证数据两个部分。使用研究区有限的建模数据对森林模型参数先验知识进行本地化校正得到优化模型，优化模型用于估算森林高分辨率LAI，使用验证数据评价LAI的估算精度。同时，选取了Camerons站点、Gnangara站点、Hirsikangas站点评价本文方法的LAI估算精度。使用地面测量LAI验证基于森林模型参数先验知识估算高分辨率LAI的结果精度，经验证4个森林站点的均方根误差分别为0.6680，0.4449，0.2863，0.5755。研究结果表明：在仅有少量观测数据时，采用本方法能有效地提高森林高分辨率LAI的估算精度。因此，本方法可为森林高分辨率LAI的遥感估算提供参考。 相似文献
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基于中国农业科学院在呼伦贝尔草原实测的120组草地冠层光谱反射率及相应的叶面积指数(LAI)数据,在进行主成分分析(PCA)实现降维处理的基础上,利用径向基函数(radial basis function,RBF)神经网络方法对草地LAI进行了高光谱反演研究.PCA结果表明,前9个主成分的累积贡献率达到了99.782％,能包含原光谱数据的绝大部分信息.将120组LAI及相应的9个主成分样本数据随机分为校正集数据(90组)和预测集数据(30组),分别用于神经网络模型的建立和LAI的预测.所构建的神经网络模型的模拟结果表明,RBF神经网络模型对校正集样本的模拟准确率达到100％(RMSE =0.009 6,R2 =0.999);预测集样本的实测LAI和模拟LAI之间的均方误差和决定系数分别为0.218 6和0.839,取得了较好的模拟效果,有效提高了传统的多元线性回归方程(RMSE =0.416 5,R2=0.570)的计算精度. 相似文献
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Pratima Pandey Surendar Manickam Avik Bhattacharya AL. Ramanathan Gulab Singh G. Venkataraman 《国际地球制图》2017,32(4):442-454
Glaciers have a high impact in the socio-economic sectors including water supply, energy production, flood and avalanches. A high precision digital elevation model (DEM) is required to monitor glaciers and to study various glacier processes. The present study deals with the qualitative and quantitative evaluation of the DEM generated from the bistatic TanDEM-X data by comparing it with GPS, Ice, Cloud, and land Elevation Satellite (ICESat) data and standard global DEMs such as Shuttle Radar Topography Mission (SRTM) and Advanced Space-borne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM). The study area consists of highly undulating glaciated terrain in western Himalaya, India. The results reveal that TanDEM-X is slightly better than SRTM both qualitatively and quantitatively, whereas ASTER GDEM showing maximum discrepancy among the three DEMs. The Root Mean Square Error (RMSE) of the TanDEM-X DEM with respect to GPS is 3.5 m at lower relief and 11.9 m at glaciated terrain, against 6.7 and 12.5 m for SRTM and 9.3 and 19.8 m for ASTER GDEM, respectively, for the same sites. On an average, for the whole study area, the RMSE of TanDEM-X is 7.9 m, SRTM is 9.3 m and ASTER GDM is 14.2 m. The RMSE of TanDEM-X, SRTM and ASTER GDEM with respect to ICESat are 16.3, 19.9 and 101.1 m, respectively. It is evident from the analysis that though SRTM is closer to TanDEM-X in terms of accuracy in the mountainous terrain, however, TanDEM-X will be more useful for studying glacier dynamics and topography. 相似文献
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Digital elevation model (DEM) data of Shuttle Radar Topography Mission (SRTM) are distributed at a horizontal resolution of 90 m (30 m only for US) for the world, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM data provide 30 m horizontal resolution, while CARTOSAT-1 (IRS-P5) gives 2.6 m horizontal resolution for global coverage. SRTM and ASTER data are available freely but 2.6 m CARTOSAT-1 data are costly. Hence, through this study, we found out a horizontal accuracy for selected ground control points (GCPs) from SRTM and ASTER with respect to CARTOSAT-1 DEM to implement this result (observed from horizontal accuracy) for those areas where the 2.6-m horizontal resolution data are not available. In addition to this, the present study helps in providing a benchmark against which the future DEM products (with horizontal resolution less than CARTOSAT-1) with respect to CARTOSAT-1 DEM can be evaluated. The original SRTM image contained voids that were represented digitally as ?140; such voids were initially filled using the measured values of elevation for obtaining accurate DEM. Horizontal accuracy analysis between SRTM- and ASTER-derived DEMs with respect to CARTOSAT-1 (IRS-P5) DEM allowed a qualitative assessment of the horizontal component of the error, and the appropriable statistical measures were used to estimate their horizontal accuracies. The horizontal accuracy for ASTER and SRTM DEM with respect to CARTOSAT-1 were evaluated using the root mean square error (RMSE) and relative root mean square error (R-RMSE). The results from this study revealed that the average RMSE of 20 selected GCPs was 2.17 for SRTM and 2.817 for ASTER, which are also validated using R-RMSE test which proves that SRTM data have good horizontal accuracy than ASTER with respect to CARTOSAT-1 because the average R-RMSE of 20 GCPs was 3.7 × 10?4 and 5.3 × 10?4 for SRTM and ASTER, respectively. 相似文献
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针对ASTER GDEM高程精度还未得到充分验证,以江西省莲花县为试验区,使用ICESat-2数据系统分析了ASTER GDEM在坡度、地形起伏度和土地利用类型中的误差分布。结果表明,ASTER GDEM受坡度、地形起伏度影响严重,随坡度、地形起伏度增加,GDEM误差呈上升趋势;对于不同土地利用类型,GDEM误差存在较大差异,在水域误差最大,在建设用地误差最小。最后,使用后向传播神经网络(BPNN)对莲花县ASTER GDEM修正,结果发现BPNN模型可以有效改善其高程精度。 相似文献
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ASTER GDEM与SRTM3高程差异影响因素分析 总被引：3，自引：0，他引：3
作为最新发布的全球地形数据,ASTER GDEM比目前常用的SRTM3数据有着更高的分辨率和更广的覆盖范围,对于相关地学分析具有重要意义。本文以华中地区为研究区域,对ASTER GDEM与SRTM3数据进行了比较,重点分析了坡度、坡向、地形起伏度、土地利用类型、植被覆盖度、生成ASTER GDEM栅格点高程数据所用的ASTER DEM影像数等因素对2种DEM数据高程差异的影响。结果表明,在研究区域内,ASTER GDEM高程比SRTM3高程平均低5.42 m,两种DEM数据高程差异的RMS值为16.90 m;ASTER GDEM与SRTM3之间的高程差异随着坡度、地形起伏度、植被覆盖度的增大而增大,而ASTER DEM影像数越大,高程差异越小;坡向、土地利用类型对高程差异也有影响。 相似文献
16.
MODIS土地覆盖分类的尺度不确定性研究 总被引：2，自引：0，他引：2
以空间异质性较强的枯水期鄱阳湖为研究区,以搭载于同一卫星平台、具有同一观测时间和较高空间分辨率的ASTER数据为参照,分析研究了MODIS数据在土地覆盖分类中由空间尺度带来的不确定性。首先基于MODIS三角权重函数,建立了从ASTER到MODIS的尺度转换方法;然后对不同空间分辨率的数据进行土地覆盖分类,并基于误差矩阵和线性模型分析了MODIS土地覆盖分类结果的误差来源。结果表明,空间分辨率和光谱分辨率与成像方式这两类因素对MODIS与ASTER分类结果差异的贡献比例约为(6.6—11.2):2;MODIS像元尺度对研究区水体的分类不确定性影响较低,而对森林的不确定性影响可达63%。由此可见,在基于MODIS数据的土地覆盖分类研究中,空间尺度所产生的不确定性是比较显著的。这些研究结果对于土地覆盖分类及变化检测、尺度效应和景观生态学不确定性研究,有积极的参考意义。 相似文献
17.
Feizollah Masoumi Taymour Eslamkish Mehdi Honarmand Ali Akbar Abkar 《Journal of the Indian Society of Remote Sensing》2017,45(6):1039-1055
This study attempts to evaluate the capability of the advanced spaceborne thermal emission and reflection radiometer (ASTER) and the advantages of ground knowledge for generating maps portraying hydrothermally altered areas in relation to porphyry copper deposits. The northern part of the Rabor area in the Urumieh–Dokhtar magmatic belt, containing some copper mineralization occurrences, was investigated as a case study. Several image processing techniques, namely minimum noise fraction, pixel purity index, and n-dimensional visualization, contributed to the extraction of pure pixels as endmembers. The spectra of some rock samples collected around the well-known altered zones of the study area were resampled to ASTER bands and used to identify the image-extracted endmembers. Spectral analysis of the endmembers and ground sample spectra led to the identification of three types of hydrothermal alterations: (1) phyllic, (2) propylitic, and (3) argillic. The identified endmembers were used as the specified targets for mapping hydrothermal alteration zones over the study area by using a mixture tuned match filtering algorithm. Results demonstrate that high abundances within pixels correspond closely to the altered areas. Field observations, thin section, and X-ray diffraction of collected samples confirmed the accuracy of the alteration maps prepared by the application of the proposed methods. The final classified hydrothermal alteration maps showed the overall accuracy and kappa coefficient values of 85.41 and 0.72%, respectively. These results ascertain that ASTER data that use suitable image processing techniques appear consistent in mapping out the distribution of hydrothermally altered areas. In addition, ground knowledge data can act as a valuable information source to increase the image classification accuracy and reliability. 相似文献
18.
Abhishek Pandey Khem B. Thapa R. Prasad K. P. Singh 《Journal of the Indian Society of Remote Sensing》2012,40(4):709-715
Estimation of crop variables is necessary for crop type monitoring as well as crop yield forecast. At the present era artificial neural network methodology are widely used to the remote sensing domain for numerous applications like crop yield forecasting and crop type classification. In the present work, two neural network models namely general regression neural network (GRNN) and radial basis function neural network (RBFNN) are used to estimate crop variables: leaf area index (LAI), biomass (BM), plant height (PH) and soil moisture (SM) by using ground based X-band scatterometer data. The both networks are trained and tested with X-band scatterometer data. The performance of the GRNN and RBFNN networks are found that the radial basis approach is more suitable for crop variable estimation in comparison to the GRNN approach. This work presents the applicability of neural network as an estimator and method employed could be useful to estimate the crop variables of other crops. 相似文献
19.
基于遥感数据与作物生长模型同化的冬小麦长势监测与估产方法研究 总被引：23，自引：2，他引：23
本文以LAI作为结合点，讨论了利用复合型混合演化（SCE—UA）算法实现CERES—Wheat模型与遥感数据同化的可行性。CERES—Wheat模型同化后主要生育期和产量的模拟值分别与真实条件下模型相应模拟值以及实测值进行比较。结果表明，同化后CERES—Wheat模型的模拟精度对LAI外部同化数据的误差并不十分敏感。并且在LAI同化数据较少时，也可获得较好的同化结果。这一特点体现了SCE—UA算法应用于同化过程的优越性，为同化策略在区域冬小麦长势监测及估产中的应用提供了基础。 相似文献
20.
以行道树无患子为研究目标,采用地面激光扫描(TLS)技术提取单木分回波点云数据。获取全波形数据、单目标数据、首次回波数据、其余次回波数据,建立基于多回波点云的算法,利用消光系数法提取不同投影分辨率0.01 m、0.02 m和0.03 m的的单株树叶面积指数(LAI)。利用2维影像数据数字半球影像(DHP)和LAI2200提取对应单株树的叶面积指数,进行比较分析,以检验其精度。结果表明:点云投影的分辨率与激光回波都对LAI有极显著影响,其中分辨率为0.02 m和0.03 m的估算结果与LAI2200所得估算结果相近,且差异不显著;单目标回波数据用于LAI的解算,可以同LAI2200的2维影像数据结果进行相互验证。使用单目标回波数据,0.02 m投影分辨率可以最大程度的保证单株LAI的精度,其与LAI2200测定的数据进行截距为0的线性回归,斜率达到0.827。本研究所做多回波地面激光数据计算叶面积指数的算法拓展了地面激光扫描的应用领域,为立木生长量信息准确提取和树木精确建模提供了重要的技术参考。 相似文献