Dynamic simulation and projection of ESV changes in arid regions caused by urban growth under climate change scenarios.

Spatial simulation and projection of ecosystem services value (ESV) changes caused by urban growth are important for sustainable development in arid regions. We developed a new model of cellular automata based grasshopper optimization algorithm (named GOA-CA) for simulating urban growth patterns and assessing the impacts of urban growth on ESV changes under climate change scenarios. The results show that GOA-CA yielded overall accuracy exceeding 98%, and FOM for 2010 and 2020 were 43.

Long-term analysis of the urban heat island effect using multisource Landsat images considering inter-class differences in land surface temperature products.

It is imperative to quantitatively analyze the long-term temporal and spatial characteristics of the urban heat island (UHI) effect on cities for applications, such as urban expansion and environmental protection. Owing to the high spatial resolution and availability of long time-series data, remote sensing images from Landsat satellites are widely used for land surface temperature (LST) retrieval. However, limited by the satellite revisit cycle and image quality, the use of multisource Landsat images in a long-term study of the UHI effect is inevitable.

Spatiotemporal spread pattern of the COVID-19 cases in China.

The COVID-19 pandemic is currently spreading widely around the world, causing huge threats to public safety and global society. This study analyzes the spatiotemporal pattern of the COVID-19 pandemic in China, reveals China's epicenters of the pandemic through spatial clustering, and delineates the substantial effect of distance to Wuhan on the pandemic spread. The results show that the daily new COVID-19 cases mostly occurred in and around Wuhan before March 6, and then moved to the Grand Bay Area (Shenzhen, Hong Kong and Macau).

Spatially-explicit modeling and intensity analysis of China's land use change 2000-2050.

Land use change affected by wide ranges of human activities is a key driver of global climate change. In the last three decades, China has experienced unprecedented land use change accompanied by increasing environmental problems. There is a pressing need to project and analyze long-term land use scenarios that are critical for land use planning and policymaking.

Urban expansion simulation and scenario prediction using cellular automata: comparison between individual and multiple influencing factors.

Quantifying the contribution of driving factors is crucial to urban expansion modeling based on cellular automata (CA). The objective of this study is to compare individual-factor-based (IFB) models and multi-factor-based (MFB) models as well as examine the impacts of each factor on future urban scenarios. We quantified the contribution of driving factors using a generalized additive model (GAM), and calibrated six IFB-DE-CA models and fifteen MFB-DE-CA models using a differential evolution (DE) algorithm.

Evaluating land ecological security and examining its relationships with driving factors using GIS and generalized additive model.

Land ecological security (LES) refers to the environmental health and sustainability of the land resources and ecosystems, which are substantially affected by biophysical and socio-economic factors. We assess the spatiotemporal patterns of LES in Ningbo city on the southeast coast of China from 1975 to 2015 and explore the effects of driving factors. Expert evaluation is used to estimate the LES score for each 2×2km grid and map the patterns by Kriging.

Using exploratory regression to identify optimal driving factors for cellular automaton modeling of land use change.

Defining transition rules is an important issue in cellular automaton (CA)-based land use modeling because these models incorporate highly correlated driving factors. Multicollinearity among correlated driving factors may produce negative effects that must be eliminated from the modeling. Using exploratory regression under pre-defined criteria, we identified all possible combinations of factors from the candidate factors affecting land use change.

Impacts of aquaculture wastewater irrigation on soil microbial functional diversity and community structure in arid regions.

Aquaculture wastewater is one of the most important alternative water resources in arid regions where scarcity of fresh water is common. Irrigation with this kind of water may affect soil microbial functional diversity and community structure as changes of soil environment would be significant. Here, we conducted a field sampling to investigate these effects using Biolog and metagenomic methods.

Shifts in soil microbial metabolic activities and community structures along a salinity gradient of irrigation water in a typical arid region of China.

Saline water irrigation can change soil environment, which thereby influence soil microbial process. Based on a field experiment, the shifts in soil microbial metabolic activities and community structures under five irrigation salinities were studied using Biolog and metagenomic methods in this study. The results demonstrated that microbial metabolic activities were greatly restrained in saline water irrigated soils, as average well color development (AWCD) reduced under all saline water irrigation treatments.

Scenario prediction of emerging coastal city using CA modeling under different environmental conditions: a case study of Lingang New City, China.

The world's coastal regions are experiencing rapid urbanization coupled with increased risk of ecological damage and storm surge related to global climate and sea level rising. This urban development issue is particularly important in China, where many emerging coastal cities are being developed. Lingang New City, southeast of Shanghai, is an excellent example of a coastal city that is increasingly vulnerable to environmental change.

[Land use simulation and landscape assessment by using genetic algorithm based on cellular automata under different sampling schemes].

Simulating land use change scenarios with cellular automata (CA) can help to the policy makers in understanding the mechanisms of land change, and support the spatial decision-making for the sustainable use of land resources. Genetic algorithm (GA), an intelligent approach originally conceived from the biological process of evolution, has the capability of minimizing the difference between simulated and observed land use patterns with optimum chromosomes (i.e.