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Modeling forest structure using multi-source satellite data is beneficial to understanding the relationship between vertical and horizontal structure and image features to provide more comprehensive and abundant information for the study of forest structural complexity. This study investigates and models forest structure as a multivariate structure based on sample data and active-passive remote sensing data (Landsat8, Sentinel-2A, and ALOS-2 PALSAR) from the Saihanba Forest in Hebei Province, Northern China, to measure forest structural complexity, relying on a relationship-driven model between field and satellite data. In this study, we considered the effects of the role of satellite variables in different vertical structure types and horizontal structure ranges, used two methods to stepwise select significant variables (stepwise forward selection and Pearson correlation coefficient), and employed a multivariate modeling technique (redundancy analysis) to derive a forest composite structure index (FSI), combining both horizontal and vertical structure attributes. The results show that optical texture can better represent forest structure characteristics, polarization interferometric radar information can represent the vertical structure information of forests, and combining the two can represent 77% of the variance of multiple forest structural attributes. The new FSI can explain 93% of the relationship between stand structure and satellite variables, and the linear fit R2 to the measured data reaches 0.91, which largely shows the situation of the measured data. The generated forest structure map more accurately reflects the complexity of the forest structure in the Saihanba Forest, achieving a supplementary explanation of the measured data. [ABSTRACT FROM AUTHOR]

Climate change has resulted in an increase in extreme rainstorm events, posing the challenges of urban waterlogging and runoff pollution. Low Impact Development (LID) is widely used to address the issues above, but its effectiveness is unknown in mountainous areas. Due to a flash flood and high flood peak, storage pumping stations are also needed to drain. Thus, a framework composed of storage pumping stations and Low Impact Developments (LIDs) was proposed based on the topography and the regional upstream and downstream relationships. The water quantity in this framework is applied to YI County in Hebei Province, China. The results showed that individual LIDs effectively reduced runoff volume, with the implementation area being more crucial than the location. Combining storage pumping stations with LIDs significantly reduces peak outflow and delays it by 5 to 51 min. The combined downstream implementation of storage pumping stations and LIDs yielded the most effective results. These findings offer important insights and management strategies for controlling waterlogging in mountainous cities of developing countries. [ABSTRACT FROM AUTHOR]