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排序方式: 共有401条查询结果,搜索用时 46 毫秒
1.
利用高光谱数据进行植被生化成分反演方法研究 总被引:3,自引:0,他引:3
高光谱数据包含着丰富光谱信息,能够定量地分析物质成分[1]。由高光谱数据,可以运用多元逐步线性回归方法反演植被生化组分含量,从而达到监测植被生长状况的目的[2]。本文具体介绍了多元逐步线性回归方法,及其在氮、木质素和纤维素含量估算中的应用。 相似文献
2.
Natural radioactivity in sediment of Wei River,China 总被引:1,自引:0,他引:1
The concentrations of natural radionuclides in sediment of Wei River of China were measured using γ-ray spectrometry with
the aim of estimating the radiation hazard as establishing a database for radioactivity levels of river sediment of China.
The activity concentrations of 226Ra, 232Th and 40K in sediment samples ranged from 10.4 to 39.9 Bq kg−1, 15.3 to 54.8 Bq kg−1 and 514.8 to 1,175.5 Bq kg−1, respectively. The concentrations of these radionuclides were compared with the typical world values and the average activities
of Chinese soil and Shaanxi soil. Radium equivalent activity (Raeq), external hazard index (H
ex) and representative level index (I
γr) were calculated for the samples to assess the radiation hazards arising due to the use of these sediment samples in the
construction of dwellings. All the sediment samples have Raeq lower than the limit of 370 Bq kg−1, H
ex less than unity and I
γr close to 1 Bq kg−1. The overall mean outdoor terrestrial gamma dose rate is 64.8 nGy h−1 and the corresponding outdoor annual effective dose is 0.079 mSv. None of the studied location is considered a radiological
risk and sediment can be safely used in construction. 相似文献
3.
J.G.P.W. Clevers L. KooistraM.E. Schaepman 《International Journal of Applied Earth Observation and Geoinformation》2008
Canopy water content (CWC) is important for mapping and monitoring the condition of the terrestrial ecosystem. Spectral information related to the water absorption features at 970 nm and 1200 nm offers possibilities for deriving information on CWC. In this study, we compare the use of derivative spectra, spectral indices and continuum removal techniques for these regions. Hyperspectral reflectance data representing a range of canopies were simulated using the combined PROSPECT + SAILH model. Best results in estimating CWC were obtained by using spectral derivatives at the slopes of the 970 nm and 1200 nm water absorption features. Real data from two different test sites were analysed. Spectral information at both test sites was obtained with an ASD FieldSpec spectrometer, whereas at the second site HyMap airborne imaging spectrometer data were also acquired. Best results were obtained for the derivative spectra. In order to avoid the potential influence of atmospheric water vapour absorption bands the derivative of the reflectance on the right slope of the canopy water absorption feature at 970 nm can best be used for estimating CWC. 相似文献
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6.
矿产资源开发环境影响评价的指标体系及方法 总被引:7,自引:0,他引:7
通过对矿产资源开发环境影响因素的系统分析,建立了环境影响评价的指标体系,它包括自然环境和社会经济环境两大指标体系;简评了矿产资源开发的环境影响评价方法;提出了建立矿产资源开发环境影响评价信息系统的设想,并简要介绍了该信息系统的组成、主要功能及值得重视研究的几个问题。 相似文献
7.
Soil erodibility, which is difficult to estimate and upscaling, was determined in this study using multiple spectral models of soil properties (soil organic matter (SOM), water-stable aggregates (WSA) > 0.25 mm, the geometric mean radius (Dg)). Herein, the soil erodibility indicators were calculated, and soil properties were quantitatively analyzed based on laboratory simulation experiments involving two selected contrasting soils. In addition, continuous wavelet transformation was applied to the reflectance spectra (350–2500 nm) of 65 soil samples from the study area. To build the relationship, the soil properties that control erodibility were identified prior to the spectral analysis. In this study, the SOM, Dg and WSA >0.25 mm were selected to represent the most significant soil properties controlling erodibility and describe the erodibility indicator based on a logarithmic regression model as a function of SOM or WSA > 0.25 mm. Five, six and three wavelet features were observed to calibrate the estimated soil properties model, and the best performance was obtained with a combination feature regression model for SOM (R2 = 0.86, p < 0.01), Dg (R2 = 0.79, p < 0.01) and WSA >0.25 mm (R2 = 0.61, p < 0.01), respectively. One part of the wavelet features captured amplitude variations in the broad shape of the reflectance spectra, and another part captured variations in the shape and depth of the soil dry substances. The wavelet features for the validated dataset used to predict the SOM, WSA >0.25 mm and Dg were not significantly different compared with the calibrated dataset. The synthesized spectral models of soil properties, and the formation of a new equation for soil erodibility transformed from the spectral models of soil properties are presented in this study. These results show that a spectral analytical approach can be applied to complex datasets and provide new insights into emerging dynamic variation with erodibility estimation. 相似文献
8.
探索利用高光谱数据的岩性填图新方法是遥感地质应用领域的重要需求之一。本文运用随机森林方法和EO-1Hyperion高光谱数据,对新疆塔里木西北部柯坪地区的局部区域进行岩性分类,并对相关问题进行分析。分别利用光谱特征以及加入光谱一阶导数特征进行岩性分类,并对不同特征对岩性分类的重要性进行分析,同时与现有的基于光谱角制图方法(SAM)进行比较。结果表明,与SAM方法相比,随机森林方法得到了更高精度的岩性分类结果,是一种有效可行的岩性分类方法。根据特征重要性的排序,蓝绿光波段、短波红外波段以及相应的一阶导数特征对研究区Hyperion数据的沉积岩岩性分类贡献更大。 相似文献
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10.
Michael J. Friedel Massimo Buscema Luiz Eduardo Vicente Fabio Iwashita Andréa Koga-Vicente 《International Journal of Digital Earth》2018,11(7):670-690
An unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely sensed hyperspectral imagery. The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibirací, Minas Gerais, Brazil. The proposed workflow includes subset feature selection, learning, and estimation algorithms. Network training with landscape feature class realizations provide a hypersurface from which to estimate mixtures of soil (e.g. 0.5 exceedance for pixels: 75% clay-rich Nitisols, 15% iron-rich Latosols, and 1% quartz-rich Arenosols) and vegetation (e.g. 0.5 exceedance for pixels: 4% Aspen-like trees, 7% Blackberry-like trees, 0% live grass, and 2% dead grass). The process correctly maps forests and iron-rich Latosols as being coincident with existing drainages, and correctly classifies the clay-rich Nitisols and grasses on the intervening hills. These classifications are independently corroborated visually (Google Earth) and quantitatively (random soil samples and crossplots of field spectra). Some mapping challenges are the underestimation of forest fractions and overestimation of soil fractions where steep valley shadows exist, and the under representation of classified grass in some dry areas of the Hyperion image. These preliminary results provide impetus for future hyperspectral studies involving airborne and satellite sensors with higher signal-to-noise and smaller footprints. 相似文献