Resources potential assessment is one of the fields in geosciences,which is able to take great advantage of GIS technology as a substitution of traditional working methods.The gold resources potential in the eastern K...Resources potential assessment is one of the fields in geosciences,which is able to take great advantage of GIS technology as a substitution of traditional working methods.The gold resources potential in the eastern Kunlun Mountains,Qinghai Province,China was assessed by combining weights-of-evidence model with GIS spatial analysis technique.All the data sets used in this paper were derived from an established multi-source geological spatial database,which contains geological,geophysical,geochemical and remote sensing data.Three multi-class variables,i.e.,structural intersection,Indosinian k-feldspar granite and regional fault,were used in proximity analysis to examine their spatial association with known gold deposits.A prospectivity map was produced by weights-of-evidence model based on seven binary evidential maps,all of which had passed a conditional independence test.The study area was divided into three target zones of high potential,moderate potential and low potential areas,among which high potential areas and moderate potential areas accounted for 20% of the total area and contained 32 of the 43 gold deposits.The results show that the gold resources potential assessment in the eastern Kunlun Mountains has a higher precision.展开更多
These problems of nonlinearity, fuzziness and few labeled data were rarely considered in traditional remote sensing image classification. A semi-supervised kernel fuzzy C-means （SSKFCM） algorithm is proposed to over...These problems of nonlinearity, fuzziness and few labeled data were rarely considered in traditional remote sensing image classification. A semi-supervised kernel fuzzy C-means （SSKFCM） algorithm is proposed to overcome these disadvantages of remote sensing image classification in this paper. The SSKFCM algorithm is achieved by introducing a kernel method and semi-supervised learning technique into the standard fuzzy C-means （FCM） algorithm. A set of Beijing-1 micro-satellite＇s multispectral images are adopted to be classified by several algorithms, such as FCM, kernel FCM （KFCM）, semi-supervised FCM （SSFCM） and SSKFCM. The classification results are estimated by corresponding indexes. The results indicate that the SSKFCM algorithm significantly improves the classification accuracy of remote sensing images compared with the others.展开更多
Retrieving snow surface reflectance is difficult in optical remote sensing. Hence, this letter evaluates five surface reflectance models, including the Ross-Li, Roujean, Walthall, modified Rahman and Staylor models, i...Retrieving snow surface reflectance is difficult in optical remote sensing. Hence, this letter evaluates five surface reflectance models, including the Ross-Li, Roujean, Walthall, modified Rahman and Staylor models, in terms of their capacities to capture snow reflectance signatures using ground measurements in Antarctica. The biases of all the models are less than 0.0003 in both visible and near-infrared regions. Moreover, with the exception of the Staylor model, all models have root-mean-square errors of around 0.02, indicating that they can simulate the reflectance magnitude well. The R2 performances of the Ross-Li and Roujean models are higher than those of the others, indicating that these two models can capture the angle distribution of snow surface reflectance better.展开更多
We developed a method for analyzing the change in snow cover using MODIS imagery.The method was applied to images of western Sichuan Province,China taken between 2002 and 2008.The model for extracting data on snow cov...We developed a method for analyzing the change in snow cover using MODIS imagery.The method was applied to images of western Sichuan Province,China taken between 2002 and 2008.The model for extracting data on snow cover from MODIS images was created by spectral analysis.The multi-temporal snow layers were used to evaluate the temporal and spatial change in the area under snow cover between 2002 and 2008 using overlay and statistical analysis in ARCGIS.The majority(60.4%) of western Sichuan was rarely covered by snow and only 0.3% was covered by perennial snow in 2002.Snow cover was pri-marily distributed in Garzê and Aba.The area under snow cover was significantly and negatively correlated with the average monthly temperature and rainfall in 2002.The largest area under snow cover was measured in 2006 and the smallest was in 2007.Similarly,the area of snowmelt was the highest in 2006 and lowest in 2007.In general,the elevation of the snow line in-creased throughout the period 2002-2008;however,the elevation decreased in some years.Our results provide an important insight into the distribution of snow in this region,and may be useful for climate modeling and predicting the availability of water resources and the occurrence of floods and droughts.展开更多
A simple data assimilation method for improving estimation of moderate resolution imaging spectroradiometer （MODIS） leaf area index （LAI） time-series data products based on the gradient inverse weighted filter and...A simple data assimilation method for improving estimation of moderate resolution imaging spectroradiometer （MODIS） leaf area index （LAI） time-series data products based on the gradient inverse weighted filter and object analysis is proposed. The properties and quality control （QC） of MODIS LAI data products are introduced. Also, the gradient inverse weighted filter and object analysis are analyzed. An experiment based on the simple data assimilation method is performed using MODIS LAI data sets from 2000 to 2005 of Guizhou Province in China.展开更多
An improved Pan-sharpening algorithm appropriate to vegetation applications is proposed to fuse a set of IKONOS panchromatic （PAN） and multispectral image （MSI） data. The normalized difference vegetation index （N...An improved Pan-sharpening algorithm appropriate to vegetation applications is proposed to fuse a set of IKONOS panchromatic （PAN） and multispectral image （MSI） data. The normalized difference vegetation index （NDVI） is introduced to evaluate the quality of fusion products. Compared with other methods such as principal component analysis （PCA）, wavelet transform （WT）, and curvelet transform （CT）, this algorithm has a better trade-off between keeping the spatial and spectral information. The NDVI performances indicate that the fusion product of this method is more suitable for vegetation applications than the other methods.展开更多
基金Under the auspices of National High-tech R & D Program of China(No.2007AA12Z227)National Natural Science Foundation of China(No.40701146)
文摘Resources potential assessment is one of the fields in geosciences,which is able to take great advantage of GIS technology as a substitution of traditional working methods.The gold resources potential in the eastern Kunlun Mountains,Qinghai Province,China was assessed by combining weights-of-evidence model with GIS spatial analysis technique.All the data sets used in this paper were derived from an established multi-source geological spatial database,which contains geological,geophysical,geochemical and remote sensing data.Three multi-class variables,i.e.,structural intersection,Indosinian k-feldspar granite and regional fault,were used in proximity analysis to examine their spatial association with known gold deposits.A prospectivity map was produced by weights-of-evidence model based on seven binary evidential maps,all of which had passed a conditional independence test.The study area was divided into three target zones of high potential,moderate potential and low potential areas,among which high potential areas and moderate potential areas accounted for 20% of the total area and contained 32 of the 43 gold deposits.The results show that the gold resources potential assessment in the eastern Kunlun Mountains has a higher precision.
基金Supported by the National High Technology Research and Development Programme （No.2007AA12Z227） and the National Natural Science Foundation of China （No.40701146）.
文摘These problems of nonlinearity, fuzziness and few labeled data were rarely considered in traditional remote sensing image classification. A semi-supervised kernel fuzzy C-means （SSKFCM） algorithm is proposed to overcome these disadvantages of remote sensing image classification in this paper. The SSKFCM algorithm is achieved by introducing a kernel method and semi-supervised learning technique into the standard fuzzy C-means （FCM） algorithm. A set of Beijing-1 micro-satellite＇s multispectral images are adopted to be classified by several algorithms, such as FCM, kernel FCM （KFCM）, semi-supervised FCM （SSFCM） and SSKFCM. The classification results are estimated by corresponding indexes. The results indicate that the SSKFCM algorithm significantly improves the classification accuracy of remote sensing images compared with the others.
基金supported by the National "863" Program of China (No. 2009AA122101)the National Natural Science Foundation of China (Nos. 40871160and 60841006)
文摘Retrieving snow surface reflectance is difficult in optical remote sensing. Hence, this letter evaluates five surface reflectance models, including the Ross-Li, Roujean, Walthall, modified Rahman and Staylor models, in terms of their capacities to capture snow reflectance signatures using ground measurements in Antarctica. The biases of all the models are less than 0.0003 in both visible and near-infrared regions. Moreover, with the exception of the Staylor model, all models have root-mean-square errors of around 0.02, indicating that they can simulate the reflectance magnitude well. The R2 performances of the Ross-Li and Roujean models are higher than those of the others, indicating that these two models can capture the angle distribution of snow surface reflectance better.
基金supported by the National High-Tech Research & Devel-opment Program of China (Grant No.2009AA12Z140)the National Basic Research Program of China (Grant Nos. 2009CB421105 and 2007CB714401)+1 种基金the National Natural Science Foundation of China (Grant No. 40771144)SCYSF (Grant No. 08ZQ026-047)
文摘We developed a method for analyzing the change in snow cover using MODIS imagery.The method was applied to images of western Sichuan Province,China taken between 2002 and 2008.The model for extracting data on snow cover from MODIS images was created by spectral analysis.The multi-temporal snow layers were used to evaluate the temporal and spatial change in the area under snow cover between 2002 and 2008 using overlay and statistical analysis in ARCGIS.The majority(60.4%) of western Sichuan was rarely covered by snow and only 0.3% was covered by perennial snow in 2002.Snow cover was pri-marily distributed in Garzê and Aba.The area under snow cover was significantly and negatively correlated with the average monthly temperature and rainfall in 2002.The largest area under snow cover was measured in 2006 and the smallest was in 2007.Similarly,the area of snowmelt was the highest in 2006 and lowest in 2007.In general,the elevation of the snow line in-creased throughout the period 2002-2008;however,the elevation decreased in some years.Our results provide an important insight into the distribution of snow in this region,and may be useful for climate modeling and predicting the availability of water resources and the occurrence of floods and droughts.
基金This work was supported by the China Postdoctoral Science Foundation(No.20060390326)the key international S&T cooperation project of China(No.2004DFA06300).
文摘A simple data assimilation method for improving estimation of moderate resolution imaging spectroradiometer （MODIS） leaf area index （LAI） time-series data products based on the gradient inverse weighted filter and object analysis is proposed. The properties and quality control （QC） of MODIS LAI data products are introduced. Also, the gradient inverse weighted filter and object analysis are analyzed. An experiment based on the simple data assimilation method is performed using MODIS LAI data sets from 2000 to 2005 of Guizhou Province in China.
基金supported by the National Natural Science Foundation of China(No.40701146)the National "863" Program of China(No.2007AA12Z227)the National "973" Program of China(No.2007CB714406).
文摘An improved Pan-sharpening algorithm appropriate to vegetation applications is proposed to fuse a set of IKONOS panchromatic （PAN） and multispectral image （MSI） data. The normalized difference vegetation index （NDVI） is introduced to evaluate the quality of fusion products. Compared with other methods such as principal component analysis （PCA）, wavelet transform （WT）, and curvelet transform （CT）, this algorithm has a better trade-off between keeping the spatial and spectral information. The NDVI performances indicate that the fusion product of this method is more suitable for vegetation applications than the other methods.
