Analysis of landscape pattern vulnerability in Dasi river basin at the optimal scale.

Since the reform and opening up in 1978, the Dasi River Basin within Jinan's startup area from replacing old growth drivers with new ones (startup area) has experienced rapid urbanization and industrialization, and the landscape pattern has changed significantly, resulting in a series of eco-environmental problems. In order to more accurately identify the vulnerable areas of landscape pattern, understand their cause mechanism and changing laws, and provide a theoretical basis for the implementation of sustainable landscape pattern planning and management in the region. Four Landsat images of 2002, 2009, 2015 and 2020 were taken as data sources, and the optimal granularity of landscape pattern analysis was determined from the perspective of landscape level and class level by using the coefficient of variation method, granularity effect curve and information loss model, and the optimal amplitude was determined by using the grid method and semi-variance function.

An Innovation of the Markov Probability Model for Predicting the Remaining Service Life of Civil Airport Rigid Pavements.

In view of the time series update of airport runway health status detection data, the Markov chain of stochastic process theory was adopted. Considering the influence of aircraft traffic load, age, and pavement structure surface-layer thickness on the performance deterioration process of airport runways, the method of survival analysis was used. The parameter model of survival analysis was used to establish the duration function model of the four condition states of the airport runway PCI (pavement condition index).

Assessing impact of land use change on ecosystem service value in Dasi River Basin of China based on an improved evaluation model.

The aim of this study was to provide a new method for dynamic and continuous assessment of ecosystem service value (ESV) and reveal the impact of land use change on ESV in Dasi River Basin within Jinan's startup area from replacing old growth drivers with new ones. Based on four remote sensing images from 2002 to 2020, four ecological indicators were extracted, and the ecological environmental quality index (EEQI) was obtained through the approach of principal component analysis (PCA). Then, the traditional ESV evaluation method was modified by using the EEQI, grain yield, the biomass factor of cropland ecosystem, and the consumer price index (CPI).