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1.
Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) has been used for the blending of Landsat and MODIS data. Specifically, the 30 m Landsat-7 ETM+ (Enhanced Thematic Mapper plus) surface reflectance was predicted for a period of 10 years (2000–2009) as the product of observed ETM+ and MODIS surface reflectance (MOD09A1) on the predicted and observed ETM+ dates. A pixel based analysis for six observed ETM+ dates covering winter and summer crops showed that the prediction method was more accurate for NIR band (mean r2 = 0.71, p ≤ 0.01) compared to green band (mean r2 = 0.53; p ≤ 0.01). A recently proposed chlorophyll index (CI), which involves NIR and green spectral bands, was used to retrieve gross primary productivity (GPP) as the product of CI and photosynthetic active radiation (PAR). The regression analysis of GPP derived from closet observed and synthetic ETM+ showed a good agreement (r2 = 0.85, p ≤ 0.01 and r2 = 0.86, p ≤ 0.01) for wheat and sugarcane crops, respectively. The difference between the GPP derived from synthetic and observed ETM+ (prediction residual) was compared with the difference in GPP values from observed ETM+ on the two dates (temporal residual). The prediction residuals (mean value of 1.97 g C/m2 in 8 days) was found to be significantly lower than the temporal residuals (mean value of 4.46 g C/m2 in 8 days) that correspondence to 12% and 27%, respectively, of GPP values (mean value of 16.53 g C/m2 in 8 days) from observed ETM+ data, implying that the prediction method was better than temporal pixel substitution. Investigating the trend in synthetic ETM+ GPP values over a growing season revealed that phenological patterns were well captured for wheat and sugarcane crops. A direct comparison between the GPP values derived from MODIS and synthetic ETM+ data showed a good consistency of the temporal dynamics but a systematic error that can be read as bias (MODIS GPP over estimation). Further, the regression analysis between observed evapotranspiration and synthetic ETM+ GPP showed good agreement (r2 = 0.66, p ≤ 0.01).  相似文献   

2.
Gross primary production (GPP) is a parameter of significant importance for carbon cycle and climate change research. Remote sensing combined with other climate and meteorological data offers a convenient tool for large scale GPP estimation. This paper presents a study of GPP estimation using three methods with in situ measurements of canopy reflectance, LAI, and the photosynthetically active radiation (PAR). First, because LAI is considered as an indicator of the factor of absorbed PAR (fAPAR), it provides reasonable estimates of GPP for all types of wheat with coefficient of determination R2 of 0.7353. The second method uses four kinds of vegetation indices (VIs) to estimate GPP because these indices are suggested to be reliable candidates in the estimation of light use efficiency (LUE). Good determination coefficients were acquired in estimating GPP with R2 ranging from the lowest of 0.7604 for NDVI to the highest of 0.8505 for EVI. A new method was proposed for the estimation of GPP following the Monteith logic, which considering GPP as a product of VI × VI × PAR. Results indicated that this method can provide the best estimates of GPP as determination coefficient R2 increased largely compared to the other two methods. EVI × EVI × PAR was demonstrated to be the most suitable for the estimation of GPP with the highest R2 of 0.9207, which was about 10% larger as compared to GPP estimated from the single EVI. These results will be helpful for the development of new models of GPP estimation with all remote sensing inputs.  相似文献   

3.
Spurious evidence and spurious spatial associations between target mineral deposits and certain classes of spatial data undermine GIS-based data-driven modeling of mineral prospectivity. In a case study application of data-driven evidential belief functions, such problems were recognized and then, based on sound geological judgment, were addressed accordingly. By invoking knowledge of genetic associations between mineral deposits of the type sought and spatial geological attributes (lithology, fault/fracture density, hydrothermal alteration intensity), spurious spatial associations depicted in ‘original’ evidence maps were addressed by treatment of input spatial data via applications of certain basic GIS functionalities in order to derive ‘treated’ evidence maps. By invoking knowledge of geological processes involved in the formation of mineral deposits of the type sought and knowledge of how operations to combine evidence maps function, the integration of evidence maps was guided such that the inter-play of geological processes involved in the formation of mineral deposits of the type sought is represented in the modeling procedure and such that spurious evidence is filtered and not transmitted into the output map representing likelihood of mineral deposit occurrence in every location within a study area. The results show that: (a) using ‘treated’ evidence maps, instead of ‘original’ evidence maps, results in better mineral prospectivity maps and, thus, (b) knowledge-guided data-driven modeling of mineral prospectivity is better than a ‘purely’ data-driven modeling of mineral prospectivity.  相似文献   

4.
Mineral mapping is an important step for the development and utilization of mineral resources. The emergence of remote sensing technology, especially hyperspectral imagery, has paved a new approach to geological mapping. The k-means clustering algorithm is a classical approach to classifying hyperspectral imagery, but the influence of mixed pixels and noise mean that it usually has poor mineral mapping accuracy. In this study, the mapping accuracy of the k-means algorithm was improved in three ways: similarity measurement methods that are insensitive to dimensions are used instead of the Euclidean distance for clustering; the spectral absorption features of minerals are enhanced; and the mineral mapping results are combined as the number of cluster centers (K) is incremented from 1. The improved algorithm is used with combined spectral matching to match the clustering results with a spectral library. A case study on Cuprite, Nevada, demonstrated that the improved k-means algorithm can identify most minerals with the kappa value of over 0.8, which is 46% and 15% higher than the traditional k-means and spectral matching technology. New mineral types are more likely to be found with increasing K. When K is much greater than the number of mineral types, the accuracy is improved, and the mineral mapping results are independent of the similarity measurement method. The improved k-means algorithm can also effectively remove speckle noise from the mineral mapping results and be used to identify other objects.  相似文献   

5.
Estimating forest structural attributes using multispectral remote sensing is challenging because of the saturation of multispectral indices at high canopy cover. The objective of this study was to assess the utility of hyperspectral data in estimating and mapping forest structural parameters including mean diameter-at-breast height (DBH), mean tree height and tree density of a closed canopy beech forest (Fagus sylvatica L.). Airborne HyMap images and data on forest structural attributes were collected from the Majella National Park, Italy in July 2004. The predictive performances of vegetation indices (VI) derived from all possible two-band combinations (VI(i,j) = (Ri − Rj)/(Ri + Rj), where Ri and Rj = reflectance in any two bands) were evaluated using calibration (n = 33) and test (n = 20) data sets. The potential of partial least squares (PLS) regression, a multivariate technique involving several bands was also assessed. New VIs based on the contrast between reflectance in the red-edge shoulder (756–820 nm) and the water absorption feature centred at 1200 nm (1172–1320 nm) were found to show higher correlations with the forest structural parameters than standard VIs derived from NIR and visible reflectance (i.e. the normalised difference vegetation index, NDVI). PLS regression showed a slight improvement in estimating the beech forest structural attributes (prediction errors of 27.6%, 32.6% and 46.4% for mean DBH, height and tree density, respectively) compared to VIs using linear regression models (prediction errors of 27.8%, 35.8% and 48.3% for mean DBH, height and tree density, respectively). Mean DBH was the best predicted variable among the stand parameters (calibration R2 = 0.62 for an exponential model fit and standard error of prediction = 5.12 cm, i.e. 25% of the mean). The predicted map of mean DBH revealed high heterogeneity in the beech forest structure in the study area. The spatial variability of mean DBH occurs at less than 450 m. The DBH map could be useful to forest management in many ways, e.g. thinning of coppice to promote diameter growth, to assess the effects of management on forest structure or to detect changes in the forest structure caused by anthropogenic and natural factors.  相似文献   

6.
Soil erosion rates in alpine regions are related to high spatial variability complicating assessment of risk and damages. A crucial parameter triggering soil erosion that can be derived from satellite imagery is fractional vegetation cover (FVC). The objective of this study is to assess the applicability of normalized differenced vegetation index (NDVI), linear spectral unmixing (LSU) and mixture tuned matched filtering (MTMF) in estimating abundance of vegetation cover in alpine terrain. To account for the small scale heterogeneity of the alpine landscape we used high resolved multispectral QuickBird imagery (pixel resolution = 2.4 m) of a site in the Urseren Valley, Central Swiss Alps (67 km2). A supervised land-cover classification was applied (total accuracy 93.3%) prior to the analysis in order to stratify the image. The regression between ground truth FVC assessment and NDVI as well as MTMF-derived vegetation abundance was significant (r2 = 0.64, r2 = 0.71, respectively). Best results were achieved for LSU (r2 = 0.85). For both spectral unmixing approaches failed to estimate bare soil abundance (r2 = 0.39 for LSU, r2 = 0.28 for MTMF) due to the high spectral variability of bare soil at the study site and the low spectral resolution of the QuickBird imagery. The LSU-derived FVC map successfully identified erosion features (e.g. landslides) and areas prone to soil erosion. FVC represents an important but often neglected parameter for soil erosion risk assessment in alpine grasslands.  相似文献   

7.
Cholera has been a public health burden in Ghana since the early 1970s. Between 1999 and 2005, a total of 25,636 cases and 620 deaths were officially reported to the WHO. In one of the worst affected urban cities, fecal contamination of surface water is extremely high, and the disease is reported to be prevalent among inhabitants living in close proximity to surface water bodies. Surface runoff from dump sites is a major source of fecal and bacterial contamination of rivers and streams in the study area. This study aims to determine (a) the impacts of surface water contamination on cholera infection and (b) detect and map arbitrary shaped clusters of cholera. A Geographic Information System (GIS) based spatial analysis is used to delineate potential reservoirs of the cholera vibrios; possibly contaminated by surface runoff from open space refuse dumps. Statistical modeling using OLS model reveals a significant negative association between (a) cholera prevalence and proximity to all the potential cholera reservoirs (R2 = 0.18, p < 0.001) and (b) cholera prevalence and proximity to upstream potential cholera reservoirs (R2 = 0.25, p < 0.001). The inclusion of spatial autoregressive coefficients in the OLS model reveals the dependency of the spatial distribution of cholera prevalence on the spatial neighbors of the communities. A flexible scan statistic identifies a most likely cluster with a higher relative risk (RR = 2.04, p < 0.01) compared with the cluster detected by circular scan statistic (RR = 1.60, p < 0.01). We conclude that surface water pollution through runoff from waste dump sites play a significant role in cholera infection.  相似文献   

8.
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9.
Urban geological hazards involving ground instability can be costly, dangerous, and affect many people, yet there is little information about the extent or distribution of geohazards within Europe’s urban areas. A reason for this is the impracticality of measuring ground instability associated with the many geohazard processes that are often hidden beneath buildings and are imperceptible to conventional geological survey detection techniques. Satellite radar interferometry, or InSAR, offers a remote sensing technique to map mm-scale ground deformation over wide areas given an archive of suitable multi-temporal data. The EC FP7 Space project named PanGeo (2011–2014), used InSAR to map areas of unstable ground in 52 of Europe’s cities, representing ∼15% of the EU population. In partnership with Europe’s national geological surveys, the PanGeo project developed a standardised geohazard-mapping methodology and recorded 1286 instances of 19 types of geohazard covering 18,000 km2. Presented here is an analysis of the results of the PanGeo-project output data, which provides insights into the distribution of European urban geohazards, their frequency and probability of occurrence. Merging PanGeo data with Eurostat’s GeoStat data provides a systematic estimate of population exposures. Satellite radar interferometry is shown to be as a valuable tool for the systematic detection and mapping of urban geohazard phenomena.  相似文献   

10.
Estimation of forest structural parameters by field-based data collection methods is both expensive and time consuming. Satellite remote sensing is a low-cost alternative in modeling and mapping structural parameters in large forest areas. The current study investigates the potential of using WordView-2 multispectral satellite imagery for predicting forest structural parameters in a dryland plantation forest in Israel. The relationships between image texture features and the several structural parameters such as Number of Trees (NT), Basal Area (BA), Stem Volume (SV), Clark-Evans Index (CEI), Diameter Differentiation Index (DDI), Contagion Index (CI), Gini Coefficient (GC), and Standard Deviation of Diameters at Breast Heights (SDDBH) were examined using correlation analyses. These variables were obtained from 30 m × 30 m square-shaped plots. The Standard Deviation of Gray Levels (SDGL) as a first order texture feature and the second order texture variables based on Gray Level Co-occurrence Matrix (GLCM) were calculated for the pixels that corresponds to field plots. The results of the correlation analysis indicate that the forest structural parameters are significantly correlated with the image texture features. The highest correlation coefficients were calculated for the relationships between the SDDBH and the contrast of red band (r = 0.75, p < 0.01), the BA and the entropy of blue band (r = 0.73, p < 0.01), and the GC and the contrast of blue band (r = 0.71, p < 0.01). Each forest structural parameter was modeled as a function of texture measures derived from the satellite image using stepwise multi linear regression analyses. The determination coefficient (R2) and root mean square error (RMSE) values of the best fitting models, respectively, are 0.38 and 109.56 ha−1 for the NT; 0.54 and 1.79 m2 ha−1 for the BA; 0.42 and 27.18 m3 ha−1 for the SV; 0.23 and 0.16 for the CEI; 0.32 and 0.05 for the DDI; 0.25 and 0.06 for the CI; 0.50 and 0.05 for the GC; and 0.67 and 0.70 for the SDDBH. The leave-one-out cross-validation technique was applied for validation of the best-fitted models (R2 > 0.50). In conclusion, cross-validated statistics confirmed that the structural parameters including the BA, SDDBH, and GC can be predicted and mapped with a reasonable accuracy using the texture features extracted from the spectral bands of WorldView-2 image.  相似文献   

11.
A method of correlating spectral signatures of vegetation with soil chemistry and, in turn, of inferring the presence of subsurface mineral deposits (oil in particular) is outlined. Variations in the content of nitrogen, sulfur, and trace elements in the soil, known to correspond, for example, with underground deposits of oil, induce changes in chlorophyll content of vegetation that are detectable on imagery in different zones of the spectrum, which upon statistical analysis can be used to predict the presence of oil in underlying strata. Translated from: Izvestiya vysshikh uchebnykh zavedeniy, Geodeziya i aerofotos'yemka, 1986, No. 4, pp. 88-93.  相似文献   

12.
上黑龙江成矿带主要由早-中侏罗世河流-湖泊-洪积相含煤碎屑岩组成,目前已发现多个中小型矿床,在该区进行成矿预测意义重大。文章利用MORPAS成矿预测系统,采用证据权重法进行了成矿预测工作,在充分研究区内已知矿床的成矿规律基础上,以1：257Y地质图为背景,物化探异常分析子系统对1：20万区域化探数据进行C—A分形确定异常下限,综合现有的找矿标志,提取有利的成矿要素,进行针对处理,制成上黑龙江成矿带MORPAS金成矿预测分析图,提供成矿靶区,为森林高覆盖区提供找矿有利单元。  相似文献   

13.
The research evaluated the information content of spectral reflectance (laboratory and airborne data) for the estimation of needle chlorophyll (CAB) and nitrogen (CN) concentration in Norway spruce (Picea abies L. Karst.) needles. To identify reliable predictive models different types of spectral transformations were systematically compared regarding the accuracy of prediction. The results of the cross-validated analysis showed that CAB can be well estimated from laboratory and canopy reflectance data. The best predictive model to estimate CAB was achieved from laboratory spectra using continuum-removal transformed data (R2cv = 0.83 and a relative RMSEcv of 8.1%, n = 78) and from hyperspectral HyMap data using band-depth normalised spectra (R2cv = 0.90, relative RMSEcv = 2.8%, n = 13). Concerning the nitrogen concentration, we observed somewhat weaker relations, with however still acceptable accuracies (at canopy level: R2cv = 0.57, relative RMSEcv = 4.6%). The wavebands selected in the regression models to estimate CAB were typically located in the red edge region and near the green reflectance peak. For CN, additional wavebands related to a known protein absorption feature at 2350 nm were selected. The portion of selected wavebands attributable to known absorption features strongly depends on the type of spectral transformation applied. A method called “water removal” (WR) produced for canopy spectra the largest percentage of wavebands directly or indirectly related to known absorption features. The derived chlorophyll and nitrogen maps may support the detection and the monitoring of environmental stressors and are also important inputs to many bio-geochemical process models.  相似文献   

14.
The spatial and temporal distribution of absorption of chromophoric dissolved organic matter at 440 nm (aCDOM (440)) in the Mandovi and Zuari estuaries situated along the west coast of India, has been analysed. The study was carried out using remotely sensed data, obtained from the Ocean Colour Monitor (OCM) on board the Indian Remote Sensing satellite — P4, together with in situ data during the period January to December 2005. Satellite retrieval of CDOM absorption was carried out by applying an algorithm developed for the site. A good correlation (R=0.98) was obtained between satellite derived CDOM and in situ data. Time series analysis revealed that spatial distribution of CDOM has a direct link with the seasonal hydrodynamics of the estuaries. The effect of remnant fresh water on CDOM distribution could be analysed by delineating a plume in the offshore region of the Zuari estuary. Though fresh water flux from terrestrial input plays a major role in the distribution of CDOM throughout the Mandovi estuary, its role in the Zuari estuary is significant up to the middle zone. Other processes responsible for feeding CDOM in both the estuaries are coastal advection, in situ production and resuspension of bottom settled sediments. The highest value of aCDOM(440) was observed in the middle zone of the Mandovi estuary during the post-monsoon season. The relation between aCDOM(440) and S (spectral slope coefficient of CDOM) could differentiate CDOM introduced in to estuaries through multiple sources. The algorithm developed for the Mandovi estuary is S=0.003 [aCDOM(440)−0.7091] while for the Zuari estuary, S=0.0031 [aCDOM(440)−0.777], respectively.  相似文献   

15.
The gross primary production (GPP) at individual CO2 eddy covariance flux tower sites (GPPTower) in Dali (DL), Wenjiang (WJ) and Linzhi (LZ) around the southeastern Tibetan Plateau were determined by the net ecosystem exchange of CO2 (NEE) and ecosystem respiration (Re). The satellite remote sensing-VPM model estimates of GPP values (GPPMODIS) used the satellite-derived 8-day surface reflectance product (MOD09A1), including satellite-derived enhanced vegetation index (EVI) and land surface water index (LSWI). In this paper, we assembled a subset of flux tower data at these three sites to calibrate and test satellite-VPM model estimated GPPMODIS, and introduced the satellite data and site-level environmental factors to develop four new assimilation models. The new assimilation models’ estimates of GPP values were compared with GPPMODIS and GPPTower, and the final optimum model among the four assimilation models was determined and used to calibrate GPPMODIS. The results showed that GPPMODIS had similar temporal variations to the GPPTower, but GPPMODlS were commonly higher in absolute magnitude than GPPTower with relative error (RE) about 58.85%. While, the assimilation models’ estimates of GPP values (GPPMODEL) were much more closer to GPPTower with RE approximately 6.98%, indicating that the capacity of the simulation in the new assimilation model was greatly improved, the R2 and root mean square error (RMSE) of the new assimilation model were 0.57–4.90% higher and 0.74–2.47 g C m−2 s−1 lower than those of the GPPMODIS, respectively. The assimilation model was used to predicted GPP dynamics around the Tibetan Plateau and showed a reliable result compared with other researches. This study demonstrated the potential of the new assimilation model for estimating GPP around the Tibetan Plateau and the performances of site-level biophysical parameters in related to satellite-VPM model GPP.  相似文献   

16.
This study empirically compared noise reduction techniques for the normalized difference vegetation index (NDVI) time-series based on a new absolute measure using a time-series of 16-day composite NDVI images extracted from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) products covering the Poyang Lake area in China. We proposed an approach to accurately extract representative NDVI temporal profiles for the 12 land cover cluster types by clustering profiles, selecting optimal number of clusters, merging and labeling clusters, and selecting the representative NDVI profiles. The geometric average of the mean average distance between the reconstructed profile and the raw profiles, and the mean average distance between the reconstructed profile and the upper envelope (Dg(nr, c)) was selected as the most appropriate measure substitutive to RMSE for the evaluation of the noise reduction effects, when the ‘true’ profiles were not available. The running median, mean value, maximum operation, end point processing, and Hanning smoothing (RMMEH) filter and iterative Savitzky–Golay filter were the two most appropriate noise reduction techniques for the NDVI temporal profiles of the study area in the evaluation of noise reduction effects by the seven techniques. The robust framework using the proposed approach for the accurate extraction of representative NDVI temporal profiles and (Dg(nr, c)) in this study, is applicable in the evaluation of noise reduction effects using different techniques and in other study areas.  相似文献   

17.
This study describes the retrieval of state variables (LAI, canopy chlorophyll, water and dry matter contents) for summer barley from airborne HyMap data by means of a canopy reflectance model (PROSPECT + SAIL). Three different inversion techniques were applied to explore the impact of the employed method on estimation accuracies: numerical optimization (downhill simplex method), a look-up table (LUT) and an artificial neural network (ANN) approach. By numerical optimization (Num Opt), reliable estimates were obtained for LAI and canopy chlorophyll contents (LAI × Cab) with r2 of 0.85 and 0.94 and RDP values of 1.81 and 2.65, respectively. Accuracies dropped for canopy water (LAI × Cw) and dry matter contents (LAI × Cm). Nevertheless, the range of leaf water contents (Cw) was very narrow in the studied plant material. Prediction accuracies generally decreased in the order Num Opt > LUT > ANN. This decrease in accuracy mainly resulted from an increase in offset in the obtained values, as the retrievals from the different approaches were highly correlated. The same decreasing order in accuracy was found for the difference between the measured spectra and those reconstructed from the retrieved variable values. The parallel application of the different inversion techniques to one collective data set was helpful to identify modelling uncertainties, as shortcomings of the retrieval algorithms themselves could be separated from uncertainties in model structure and parameterisation schemes.  相似文献   

18.
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   

19.
遥感技术在海南岛环境与资源调查中的应用   总被引：3，自引：1，他引：2  
应用航空航天遥感技术对海南岛地质灾害、重点地区矿产资源、旅游资源进行了综合调查,系统分析了地质灾害形成条件与发展趋势,编制了海南岛地质灾害综合区划图;研究了重点地区金属矿、砂石矿分布规律,预测了成矿有利地段和找矿靶区;系统分析了岛内旅游资源分布现状与开发建设中存在的问题,编制了全省旅游规划卫星影像图,其成果为海南省资源开发与环境治理提供了重要基础资料。  相似文献   

20.
This paper evaluates the potential of a terrestrial laser scanner (TLS) to characterize forest canopy fuel characteristics at plot level. Several canopy properties, namely canopy height, canopy cover, canopy base height and fuel strata gap were estimated. Different approaches were tested to avoid the effect of canopy shadowing on canopy height estimation caused by deployment of the TLS below the canopy. Estimation of canopy height using a grid approach provided a coefficient of determination of R2 = 0.81 and an RMSE of 2.47 m. A similar RMSE was obtained using the 99th percentile of the height distribution of the highest points, representing the 1% of the data, although the coefficient of determination was lower (R2 = 0.70). Canopy cover (CC) was estimated as a function of the occupied cells of a grid superimposed upon the TLS point clouds. It was found that CC estimates were dependent on the cell size selected, with 3 cm being the optimum resolution for this study. The effect of the zenith view angle on CC estimates was also analyzed. A simple method was developed to estimate canopy base height from the vegetation vertical profiles derived from an occupied/non-occupied voxels approach. Canopy base height was estimated with an RMSE of 3.09 m and an R2 = 0.86. Terrestrial laser scanning also provides a unique opportunity to estimate the fuel strata gap (FSG), which has not been previously derived from remotely sensed data. The FSG was also derived from the vegetation vertical profile with an RMSE of 1.53 m and an R2 = 0.87.  相似文献   

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