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Wang J.-M.,University of Science and Technology of China | Wang J.-M.,Key Laboratory of Land Regulation | Yang P.-L.,China Agricultural University | Bai Z.-K.,University of Science and Technology of China | Bai Z.-K.,Key Laboratory of Land Regulation
Meitan Xuebao/Journal of the China Coal Society | Year: 2011

In order to choose the reasonable application rate and application mode of sodic soils reclaimed with desulphogypsum, control soil moisture, soil salinity and soil sodicity in the process of soil reclamation. A pot experiment was conducted to study union response of sunflower biological indicators (height, leaf area and dry mass), physiological indicators (photosynthesis rate, transpiration rate and stomata conductance) and yield to soil moisture, soil salinity and soil sodicity. Results indicate that crop growth is simultaneously affected by soil moisture, soil salinity and soil sodicity in the process of soil reclamation. Biological indicators and yield of sunflower decreases with the increase of soil moisture, soil salinity and soil sodicity. Sunflower photosynthesis rate, transpiration rate and stomata conductance increases with the increase of soil moisture, and decreases with the increase of soil salinity and soil sodicity. Soil salinity is the dominant factor of affecting sunflower yield and biological indicators, and effects of soil moisture on the sunflower physiological indicators is more obvious than the other two factors.


Yuan T.,University of Science and Technology of China | Yuan T.,Key Laboratory of Land Regulation | Zheng X.,University of Science and Technology of China | Zheng X.,Key Laboratory of Land Regulation | And 5 more authors.
PLoS ONE | Year: 2014

Objective and effective image quality assessment (IQA) is directly related to the application of optical remote sensing images (ORSI). In this study, a new IQA method of standardizing the target object recognition rate (ORR) is presented to reflect quality. First, several quality degradation treatments with high-resolution ORSIs are implemented to model the ORSIs obtained in different imaging conditions; then, a machine learning algorithm is adopted for recognition experiments on a chosen target object to obtain ORRs; finally, a comparison with commonly used IQA indicators was performed to reveal their applicability and limitations. The results showed that the ORR of the original ORSI was calculated to be up to 81.95%, whereas the ORR ratios of the quality-degraded images to the original images were 65.52%, 64.58%, 71.21%, and 73.11%. The results show that these data can more accurately reflect the advantages and disadvantages of different images in object identification and information extraction when compared with conventional digital image assessment indexes. By recognizing the difference in image quality from the application effect perspective, using a machine learning algorithm to extract regional gray scale features of typical objects in the image for analysis, and quantitatively assessing quality of ORSI according to the difference, this method provides a new approach for objective ORSI assessment. © 2014 Yuan et al.

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