In arid prairie, soil moisture has a substantial impact on the prairie resource management. The synthetic aperture radar (SAR) based soil moisture retrieval is often hampered by vegetation effects on the backscattering coefficient. A new method has been proposed to retrieve soil moisture using TerraSAR-X data. The developed method included bare soil backscattering σppsoilo simulation, vegetation effect correction, and the relationship between σppsoilo and soil moisture establishment. The bare soil surface was described using the forward advanced integral equation model. The vegetation influence was eliminated through an empirical ratio method. Soil moisture was retrieved from the estimated σppsoilo. The collected datasets in Wutumeiren prairie were used to verify the developed method. Four different vegetation variables were incorporated to separate the influence of vegetation, including leaf area index (LAI), normalized difference vegetation index, enhanced vegetation index, and vegetation water content, respectively. The lowest soil moisture inversion error was found when LAI was applied to decouple the effect of vegetation, which led the root mean square error to reach up to 5.02 vol.%. From the perspective of experiment results, LAI was recommended to characterize the scattering mechanism of vegetation. These results indicated that TerraSAR-X data have an operational potential for soil moisture retrieval in arid prairie.
Monitoring the environmental status of mountainous or hilly areas is very important for their great influence on the global ecosystem and humanity. The enhanced vegetation index (EVI) has been widely used in environmental monitoring. It can reduce background and atmospheric noise via its feedback-based format. However, the application of EVI in mountainous areas will be limited, because EVI is greatly affected by topographic effects as its soil adjustment index is not in a band ratio format. To moderate the topographic effects on EVI, we modified the EVI by changing the soil adjustment index from a constant to a variable related to the incidence angle. In the evaluation of the modified EVI, three other well-known topographic correction methods, Sun-canopy-sensor (SCS), SCS with C-correction (SCS+C), and modified Minnaert (MM), were used for comparison. The results indicated that the modified EVI and SCS+C perform better than MM and SCS by visual comparison. Quantitatively, modified EVI, which has an effect similar to SCS+C in the low incidence angle regions, largely decreased the standard deviation of the same land features and the correlation between EVI and the cosine of the incidence angle. When the incidence angle exceeds 90 deg, SCS+C and other two topographic correction methods caused overcorrections. However, modified EVI solved this problem well due to its smaller increasing curvature than other three topographic correction methods. Moreover, compared to SCS+C, modified EVI better preserved the characters of land surface features.
The use of microwave remote sensing for estimating vegetation biomass is limited in arid grassland regions because of the heterogeneous distribution of vegetation, sparse vegetation cover, and the strong influence from soil. To minimize the problem, a synergistic method of active and passive remote sensing data for retrieval of above-ground biomass (AGB) was developed in this paper. Vegetation coverage, which can be easily estimated from optical data, was combined in the scattering model. The total backscattering was divided into the amount attributed to areas covered with vegetation and that attributed to areas of bare soil. Backscattering coefficients were simulated using the established scattering model. A look-up table was established using the relationship between the vegetation water content and the backscattering coefficient for water content retrieval. Then, AGB was estimated using the relationship between the vegetation water content and the AGB. The method was applied to estimate the AGB of the Wutumeiren prairie. Finally, the accuracy and sources of error in this innovative AGB retrieval method were evaluated. The results showed that the predicted AGB correlated with the measured AGB (R 2 =0.8414 , RMSE=0.1953 kg/m 2 ). Thus, the method has operational potential for the estimation of the AGB of herbaceous vegetation in arid regions.
In order to overcome the deficiencies of traditional uncertainty assessment methods of remote sensing images
classification by error-matrix and kappa coefficient, classification uncertainties at pixel scale of Beijing-1 small satellite
multi-spectrum remote sensing images were measured and represented. Firstly, an unsupervised classification algorithm-neighborhood
EM considering spatial autocorrelation and classification fuzziness-was introduced. Then, four uncertainty
assessment indexes of neighborhood EM classification-fuzzy membership residual, relative maximum fuzzy membership
deviation, fuzzy membership entropy and relative fuzzy membership entropy - were constructed. Finally, the
experiments concerned were performed using Beijing-1 small satellite multi-spectrum remote sensing image data in
Dongkunlun, Qinghai province, China.
Hyperspectral remote sensing image classification is a challenging task in remote sensing applications because this
image always has some information redundancy and is easy to be affected by noise or lack of the separability. A semi-supervised
classification method based on principal component analysis (PCA) method and kernel fuzzy C-means
(KFCM) algorithm for hyperspectral remote sensing image is proposed in this paper. First the PCA method finds an
effective representation of spectral signature in a reduced dimensional feature space. Then a semi-supervised kernel-based
FCM algorithm, called SSKFCM algorithm by introducing semi-supervised learning technique and the kernel trick
simultaneously into conventional fuzzy C-means algorithm, is introduced to classify the feature vectors. Finally
numerical experiments are conducted on a hyperspectral remote sensing image that provides digital images of 80 spectral
bands with wavelength rang from 455 nm to 1642 nm. Classification performance is estimated by classification accuracy
and kappa coefficient. The simulation results show that the proposed approach can be effectively applied to
hyperspectral remote sensing image classification.
KEYWORDS: Data mining, Monte Carlo methods, Expectation maximization algorithms, Mining, Computer simulations, Databases, Data modeling, Fuzzy logic, Information science, Information technology
On the basis of analyzing the uncertainties of spatial data mining (SDM), and in view of the limits of traditional spatial data mining, the framework for the uncertain spatial data mining has been founded. For which, four key problems have been probed and analyzed, including uncertainty simulation of spatial data with Monte Carlo method, measurement of spatial autocorrelation based on uncertain spatial positional data, discretization of continuous data based on neighberhood EM algorithm and quality assessment of results. Meanwhile, the experiments concerned have been performed using the geo-spatial datum gotten from 37 typified cites in China.
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