ABSTRACT
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. Then, the landscape vulnerability assessment model was constructed based on the optimal scale, and its spatiotemporal evolution characteristics and spatial autocorrelation were analyzed. The result showed that: (1) The optimal granularity of landscape pattern analysis in this study area was 80 m, and the optimal amplitude was 350 × 350 m. (2) During 2002-2020, the overall vulnerability of landscape pattern in the southern part of the study area showed an increasing trend, while that in the middle and northern parts showed a decreasing trend. (3) The mean values of the vulnerability index of the overall landscape pattern in 2002, 2009, 2015 and 2020 were 0.1479, 0.1483, 0.1562 and 0.1625, respectively, showing an increasing trend year by year. In terms of land use, during 2002-2020, the average vulnerability indices of forestland and built up land increased by 23.18% and 21.43%, respectively, followed by water body and bare land, increased by 12.18% and 9.52%, respectively, while the changes of cropland and grassland were relatively small, increasing by 5.36% and 5.65%, respectively. (4) During 2002-2020, the landscape pattern vulnerability showed a significant spatial positive correlation in terms of spatial distribution. The Low-Low areas were generally transferred from the southeastern and midwestern to the middle and northern, and the High-High areas were mainly transferred from the middle to the southern. Overall, the degree of the spatial agglomeration of the landscape pattern vulnerability showed an increasing trend.
ABSTRACT
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). Finally, the impact of land use change on ESV was further analyzed based on the improved evaluation model. The result showed that (1) during 2002-2020, the area of forestland, grassland, and built-up land showed an increasing trend. The area of cropland and bare land showed a decreasing trend, and the water body area showed a slightly decreasing trend. (2) The total ESVS overall increased by 2.1759 × 107 yuan; the increased ESVS from air quality regulation, maintain biodiversity, and climate regulation were the main reasons for the increased of total ESVS, with contribution rates of 53.18%, 12.46%, and 11.29% respectively. (3) The sensitivity of ecosystem services to land use change showed a decreasing trend, and the order of elasticity index of different land use types was cropland > water body > forestland > grassland > bare land. The conversion of cropland and bare land to forestland was the main type of ESVs increase, with contribution rates of 18.35% and 10.13%, respectively. The cropland reclamation and built-up land expansion were the most significant land use changes that lead to the decline of ESVS, with contribution rates of 20.14% and 19.03% respectively. (4) The ESV showed a significant positive auto-correlation in terms of spatial distribution. The area of high-high region was mainly distributed in water body, forestland, and its surrounding areas. The area of low-low region was mainly distributed in built-up land and wasteland areas where human disturbance is relatively serious. The high-low and low-high regions were affected by landscape transition process and randomly distributed around the low-low and high-high regions, respectively. This study cannot only put forward a new method for the dynamic continuous evaluation of ESV, but also provide a reference for the rational allocation of land resources in the startup area to realize the balanced development of regional environment and economy.
Subject(s)
Ecosystem , Rivers , Humans , Conservation of Natural Resources , Forests , China , WaterABSTRACT
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). The Markov transition matrix for the performance prediction of airport runways was constructed. In order to evaluate the ability of the Markov transition matrix method to predict the trend of deterioration for PCI of the airport runway under different conditions of aircraft traffic volume and thickness of the runway pavement surface, a data set was constructed with the actual inspection data of the airport runway, and the corresponding samples were selected for analysis. The results showed that a Markov transition matrix for airport runway performance prediction, constructed based on survival analysis theory, can combine discontinuous inspection data or monitoring data with Weibull function survival curves. The method proposed in this paper can quantitatively predict the remaining service life of airport runways and provide support for cost-effective decisions about airport pavement maintenance and rehabilitation.
