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Identification of paddy fields is essential for monitoring the rice cultivated area and predicting rice productivity. Timely and accurate extraction of rice distribution can bring vital information for national food security, agricultural policy formulation, and regional environmental sustainability. Conventional classification methods usually suffered from low accuracy, multi-class training samples, or demanding imagery requirements. This paper proposes to use one-class support vector classification (OCSVC) to extract rice cultivated area with Landsat Optical Land Imager (OLI) imagery. Instead of sampling and training all land cover types as performed by multi-class classification methods, OCSVC only used the training samples of target class (rice) for rice mapping. The performance of OCSVC was evaluated in terms of the classification accuracy of rice mapping and rice acreage estimation based on high-resolution imagery, field survey data and rice acreage data from government reports for Jiangsu Province, China. At the county-level, OCSVC was also compared with the commonly used multi-class support vector classification (MCSVC), decision tree classification (DTC), and vegetation index-based thresholding (VIT). Our results demonstrated that OCSVC produced a comparable overall accuracy to DTC and outperformed MCSVC and VIT. The computational efficiency of OCSVC increased approximately ten times as compared to MCSVC. The OCSVC produced the best correlation between its classified area and reported area among the four classification methods evaluated. When applied to the provincial level, the classification overall accuracy for OCSVC was 88.54%. The detected rice planting area for Jiangsu Province was 22,602 km 2 , which was consistent with the statistics from the National Bureau of Statistics (22,948 km 2 ). This OCSVC-based mapping strategy provides a practical and efficient way to detect the rice planting extent with Landsat imagery at a large scale.

The goal of this study was to investigate the impacts of various sedimentary-diagenetic conditions on the macroscopic petrophysical parameters and microscopic pore structures of tight sandstones from the Lower Jurassic Badaowan Formation in the Southern Junggar Basin, China. Based on the traditional methods for establishing pore size distribution, including integrating the results of high-pressure mercury injection, nuclear magnetic resonance, and scanning electron microscopy, the constrained least squares algorithm was employed to automatically determine the porosity contributions of pore types with different origins. The results show that there are six genetic pore types: residual intergranular pores (RIPs), feldspar dissolution pores (FDPs), rock fragment dissolution pores (RFDPs), clay mineral intergranular pores (CIPs), intercrystalline pores of kaolinite (IPKs), and matrix pores (MPs). Four lithofacies were identified: the quartz cemented-dissolution facies (QCDF), carbonate cemented facies (CCF), authigenic clay mineral facies (ACMF), and matrix-caused tightly compacted facies (MCTF). Modified by limited dissolution, the QCDF with a high proportion of macropores (RIPs, FDPs, and RFDPs) exhibited a slightly higher porosity and considerably higher permeability than those of others. A large number of micropores (MPs, CIPs, and IPKs) in MCTF and ACMF led to slightly lower porosities but considerably lower permeabilities. Due to the tightly cemented carbonates in the CCF, its porosity reduced sharply, but the permeability of the CCF remained much higher those of the MCTF and ACMF. The results highlight that a high proportion of macropores with large radii and regular shapes provide more effective percolation paths than storage spaces. Nevertheless, micropores with small radii and complex pore structures have a limited contribution to flow capability. The fractal dimension analysis shows that a high proportion of MPs is the major reason for the heterogeneity in tight sandstones. The formation of larger macropores with smooth surfaces are more conductive for oil and gas accumulation.

Indicator-geostatistics based super-resolution mapping (IGSRM) is a popular super-resolution mapping (SRM) method. Unlike most existing SRM methods that produce only one SRM result each, IGSRM generates multiple equally plausible super-resolution realizations (i.e., SRM results). However, multiple super-resolution realizations are not desirable in many applications, where only one SRM result is usually required. These super-resolution realizations may have different strengths and weaknesses. This paper proposes a novel two-step combination method of generating a single SRM result from multiple super-resolution realizations obtained by IGSRM. In the first step of the method, a constrained majority rule is proposed to combine multiple super-resolution realizations generated by IGSRM into a single SRM result under the class proportion constraint. In the second step, partial pixel swapping is proposed to further improve the SRM result obtained in the previous step. The proposed combination method was evaluated for two study areas. The proposed method was quantitatively compared with IGSRM and Multiple SRM (M-SRM), an existing multiple SRM result combination method, in terms of thematic accuracy and geometric accuracy. Experimental results show that the proposed method produces SRM results that are better than those of IGSRM and M-SRM. For example, in the first example, the overall accuracy of the proposed method is 7.43-10.96% higher than that of the IGSRM method for different scale factors, and 1.09-3.44% higher than that of the M-SRM, while, in the second example, the improvement in overall accuracy is 2.42-4.92%, and 0.08-0.90%, respectively. The proposed method provides a general framework for combining multiple results from different SRM methods.