The spatial optimization of best management practices(BMPs) plays a critical role in precise watershed pollution control. However, the effectiveness of BMPs exhibits a complex nonlinear dependence on both configuratio...The spatial optimization of best management practices(BMPs) plays a critical role in precise watershed pollution control. However, the effectiveness of BMPs exhibits a complex nonlinear dependence on both configuration unit scale and rainfall intensity, often leading to widespread spatiotemporal mismatches during implementation. To fill this gap, this study proposes a new framework:(a) delineating configuration units based on the implementation scale differences between structural and nonstructural BMPs;(b) incorporating BMP reduction thresholds to enable dynamic adjustment of design scales according to inflow loads;and(c) developing a staged allocation strategy tailored to varying rainfall scenarios. The framework is exemplified by an agricultural catchment in the southeastern Liaohe watershed, China. The results showed that the framework could improve the assessment accuracy and cost-effectiveness of pollution control. Specifically, neglecting BMP reduction thresholds resulted in a 51.35% underestimation of treatment costs. Incorporating these thresholds and dynamically adjusting BMP design scales reduced treatment costs by 62.70%. Furthermore, the framework facilitated more precise localization of structural BMPs(1 km^(2)) and improved optimization efficiency by 95.91%. The proposed staged allocation strategy ensured water quality compliance under varying rainfall intensities. Structural BMPs primarily addressed pollution from light to moderate rainfall in the initial stage, while nonstructural BMPs targeted heavy rainfall pollution in the subsequent stage. The proposed framework may enhance the spatiotemporal adaptability of BMP configuration to respond to the threats posed by climate change and human activities. It can also be extended to other agriculture-dominated watersheds.展开更多
Image matching based on scale invariant feature transform（SIFT） is one of the most popular image matching algorithms, which exhibits high robustness and accuracy. Grayscale images rather than color images are genera...Image matching based on scale invariant feature transform（SIFT） is one of the most popular image matching algorithms, which exhibits high robustness and accuracy. Grayscale images rather than color images are generally used to get SIFT descriptors in order to reduce the complexity. The regions which have a similar grayscale level but different hues tend to produce wrong matching results in this case. Therefore, the loss of color information may result in decreasing of matching ratio. An image matching algorithm based on SIFT is proposed, which adds a color offset and an exposure offset when converting color images to grayscale images in order to enhance the matching ratio. Experimental results show that the proposed algorithm can effectively differentiate the regions with different colors but the similar grayscale level, and increase the matching ratio of image matching based on SIFT. Furthermore, it does not introduce much complexity than the traditional SIFT.展开更多
基金supported by the Fund for Innovative Research Group of the National Natural Science Foundation of China (Grant No.52221003)。
文摘The spatial optimization of best management practices(BMPs) plays a critical role in precise watershed pollution control. However, the effectiveness of BMPs exhibits a complex nonlinear dependence on both configuration unit scale and rainfall intensity, often leading to widespread spatiotemporal mismatches during implementation. To fill this gap, this study proposes a new framework:(a) delineating configuration units based on the implementation scale differences between structural and nonstructural BMPs;(b) incorporating BMP reduction thresholds to enable dynamic adjustment of design scales according to inflow loads;and(c) developing a staged allocation strategy tailored to varying rainfall scenarios. The framework is exemplified by an agricultural catchment in the southeastern Liaohe watershed, China. The results showed that the framework could improve the assessment accuracy and cost-effectiveness of pollution control. Specifically, neglecting BMP reduction thresholds resulted in a 51.35% underestimation of treatment costs. Incorporating these thresholds and dynamically adjusting BMP design scales reduced treatment costs by 62.70%. Furthermore, the framework facilitated more precise localization of structural BMPs(1 km^(2)) and improved optimization efficiency by 95.91%. The proposed staged allocation strategy ensured water quality compliance under varying rainfall intensities. Structural BMPs primarily addressed pollution from light to moderate rainfall in the initial stage, while nonstructural BMPs targeted heavy rainfall pollution in the subsequent stage. The proposed framework may enhance the spatiotemporal adaptability of BMP configuration to respond to the threats posed by climate change and human activities. It can also be extended to other agriculture-dominated watersheds.
基金supported by the National Natural Science Foundation of China(61271315)the State Scholarship Fund of China
文摘Image matching based on scale invariant feature transform（SIFT） is one of the most popular image matching algorithms, which exhibits high robustness and accuracy. Grayscale images rather than color images are generally used to get SIFT descriptors in order to reduce the complexity. The regions which have a similar grayscale level but different hues tend to produce wrong matching results in this case. Therefore, the loss of color information may result in decreasing of matching ratio. An image matching algorithm based on SIFT is proposed, which adds a color offset and an exposure offset when converting color images to grayscale images in order to enhance the matching ratio. Experimental results show that the proposed algorithm can effectively differentiate the regions with different colors but the similar grayscale level, and increase the matching ratio of image matching based on SIFT. Furthermore, it does not introduce much complexity than the traditional SIFT.
