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 43.2% and 38.1%, respectively, indicating the effectiveness of the model. The prairie lost the highest economic ESVs (192 million USD) and the coniferous yielded the largest economic ESV increase (292 million USD) during 2000-2020. Using climate change scenarios as urban future land use demands, we projected three scenarios of the urban growth of Urumqi for 2050 and their impacts on ESV. Our model can be easily applied to simulating urban development, analyzing its impact on ESV and projecting future scenarios in global arid regions.

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. Nonetheless, owing to the differences among multisource sensors, such as Landsat-7 and Landsat-8, there may be apparent deviations in the LST results retrieved from different sensor data, which are obtained from the same area and under similar circumstances. Consequently, it is necessary to build a relationship between the LST results generated from multisource Landsat sensors for future research on the UHI effect. In this study, Shenzhen city was studied to explore the fitting relationship between the corresponding LST products from Landsat-7 and Landsat-8 images obtained from adjacent dates with similar climatic conditions. Furthermore, factors affecting the fitting models, such as land cover types, seasonal and inter-annual differences, were analyzed. The constructed fitting model had a strong relationship with land cover types but a relatively weak relationship with seasonal and inter-annual differences; this indicates that a pseudo Landsat-8-based LST product can be generated from a Landsat-7-based LST product using a model fitted by a Landsat-7/8 pair obtained from adjacent years (or different seasons). Finally, by considering the consistency between LST products from multisource Landsat images, the spatiotemporal variations in the UHI effect in Shenzhen can be accurately explored using long time-series data.

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). The total COVID-19 cases in China were mainly distributed in the east of the Huhuanyong Line, where the epicenters accounted for more than 60% of the country's total in/on 24 January and 7 February, half in/on 31 January, and more than 70% from 14 February. The total cases finally stabilized at approximately 84,000, and the inflection point for Wuhan was on 14 February, one week later than those of Hubei (outside Wuhan) and China (outside Hubei). The generalized additive model-based analysis shows that population density and distance to provincial cities were significantly associated with the total number of the cases, while distances to prefecture cities and intercity traffic stations, and population inflow from Wuhan after 24 January, had no strong relationships with the total number of cases. The results and findings should provide valuable insights for understanding the changes in the COVID-19 transmission as well as implications for controlling the global COVID-19 pandemic spread.

Modeling urban encroachment on ecological land using cellular automata and cross-entropy optimization rules.

Rapid urban expansion often leads to substantial encroachment on ecological lands and destruction of natural environments. We developed a new cellular automata model (named CACEO) that uses cross-entropy optimization (CEO) to reproduce and project urban expansion into coastal areas and to assess urban encroachment on ecological lands. The CEO algorithm automatically searches for the near-optimal CA parameters and is capable of objectively parameterizing CA models to predict multi-objective scenarios. We calibrated CACEO by simulating urban expansion at Wenzhou from 1995 to 2005, validated the model from 2005 to 2015 using real data, and then predicted urban expansion for 2025 and 2035. End-state overall accuracies were 93.8% for 2005 and 94.4% for 2015, while figure-of-merit metrics were 27.9% for 2005 and 19.1% for 2015. We predicted four different scenarios to year 2025 and 2035: (1) a business-as-usual (BAU)-scenario using benchmark settings; (2) a District-scenario based on a district-oriented urban development strategy; (3) a Road-scenario based on a road network-oriented urban development strategy; and (4) a Coast-scenario based on a coast-oriented urban development strategy. Each scenario predicts a substantially different pattern of urban encroachment on ecological land and significant loss of farmland, forest, wetland and grassland. These scenarios should be useful in adjusting urban development strategies at Wenzhou and elsewhere.

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. Using GlobeLand30 data, we examined China's land use change from 2000 to 2010, and developed a novel LandCA model for scenario projections from 2020 to 2050. The observed and projected land use change (2000-2050) was analyzed in terms of the interval, category, and transition levels. Our findings show that land Exchange intensity is more than 3 times greater than land Quantity intensity from 2000 to 2050, and the overall rate of land use change will decelerate from 2010 to 2050. During 2000-2010, the loss of built-up land to other categories was 12.7% while the gain was 32.5%, with a growth rate 3.4 times larger than that during 2010-2050. The total amount of cultivated land continuously decreases but will not violate the Chinese "Cultivated Land Red-Line Restriction" by 2050. We speculate that the government's goal of 26% forest cover by 2050 may not be achieved, as a result of strict land use policies preventing the transformation from cultivated land to forests. This study contributes to new evaluations of long-term land use change in China for the government to adjust policies and regulations for sustainable development.

Modeling ESV losses caused by urban expansion using cellular automata and geographically weighted regression.

Driven by increasing urban demand, spatially-varying urban expansion has led to significant ecosystem degradation in China and elsewhere. Spatial nonstationarity affects the relationship between urban expansion and ecosystem service value (ESV) loss, but its significance has been under-emphasized. To study the spatially-heterogeneous ESV loss, we integrated cellular automata (CA) with geographically weighted regression (GWR) in a model that considers the relationships between urban expansion and its driving factors. We used ten GWR bandwidths to construct the CAGWR models for reproducing rapid urban expansion at Chongqing from 2005 to 2010. We then used the CAGWR model with the best bandwidth to predict future urban scenarios out to 2030. Our modeling shows that CAGWR is strongly sensitive to bandwidth, and that the overall accuracy and Figure-of-Merit are maximized with a ~2 km2 bandwidth (about 150 samples). We examined ESV losses in eleven ecosystem classes and found that climate regulation and water flow regulation are the dominant drivers of ESV loss. From 2010 to 2030, Chongqing's urban area will increase by about 87%, resulting in substantial encroachment on agricultural land, dryland and shrubs, causing significant ESV losses of about 38%. Our results constitute an early warning of ecosystem degradation caused by massive urban development. This study improves our understanding of spatially-varying urban expansion and related ESV losses in rapidly developing areas and should help improve urban planning regulation and regional policy for sustainable development to maintain environmentally-friendly cities.

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. The six IFB-DE-CA models and five MFB-DE-CA models were selected to simulate the 2005-2015 urban expansion of Hangzhou, China, and all IFB-DE-CA models were applied to project future urban scenarios out to the year 2030. Our results show that terrain (DEM) and population density (POP) are the two most influential factors affecting urban expansion of Hangzhou, indicating the dominance of biophysical and demographic drivers. All DE-CA models produced defensible simulations for 2015, with overall accuracy exceeding 89%. The IFB-DE-CA models based on DEM and POP outperformed some MFB-DE-CA models, suggesting that multiple factors are not necessarily more effective than a single factor in simulating present urban patterns. The future scenarios produced by the IFB-DE-CA models are substantially shaped by the corresponding factors. These scenarios can inform urban modelers and policy-makers as to how Hangzhou city will evolve if the corresponding factors are individually focused. This study improves our understanding of the effects of driving factors on urban expansion and future scenarios when incorporating the factors separately.

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. Five levels of LES are used: very secure, secure, neutral, insecure and very insecure. A generalized additive model (GAM) captures the relationships between LES and driving factors, and identifies the dominant factors. Our results show that the Ningbo LES has been deteriorating since 1975, and it is now very insecure in Ningbo city center and the central area of the satellite city Cixi. The dominant factors affecting LES are distances to city center (Dcity), district center (Ddistrict) and road networks (Droads), and the moving window built-up area (Dndbi). Among these, Dndbi is most important as inferred by the highest explained deviance of the GAM. This study improves our understanding of the spatiotemporal patterns of LES in Ningbo and how LES changes. As a result, it provides insight to help local governments optimize land-use configuration, potentially improving the environment and ecosystems and creating a more environmentally friendly city.

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. The results demonstrated that irrigation with aquaculture wastewater could dramatically reduce soil microbial functional diversity. The values of diversity indices and sole carbon source utilization were all significantly decreased. Increased soil salinity, especially Cl concentration, appeared primarily associated with the decreases. Differently, higher bacterial community diversity was obtained in aquaculture wastewater irrigated soils. More abundant phyla Actinobacteria, Chloroflexi, Acidobacteria, Gemmatimonadetes and fewer members of Proteobacteria, Bacteroidetes and Planctomycetes were found in this kind of soils. Changes in the concentration of soil Cl mainly accounted for the shifts of bacterial community composition. This research can improve our understanding of how aquaculture wastewater irrigation changes soil microbial process and as a result, be useful to manage soil and wastewater resources in arid regions.

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. Three combinations that incorporate five driving factors meeting pre-defined criteria were assessed. With the selected combinations of factors, three logistic regression-based CA models were built to simulate dynamic land use change in Shanghai, China, from 2000 to 2015. For comparative purposes, a CA model with all candidate factors was also applied to simulate the land use change. Simulations using three CA models with multicollinearity eliminated performed better (with accuracy improvements about 3.6%) than the model incorporating all candidate factors. Our results showed that not all candidate factors are necessary for accurate CA modeling and the simulations were not sensitive to changes in statistically non-significant driving factors. We conclude that exploratory regression is an effective method to search for the optimal combinations of driving factors, leading to better land use change models that are devoid of multicollinearity. We suggest identification of dominant factors and elimination of multicollinearity before building land change models, making it possible to simulate more realistic outcomes.

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. Although no significant difference in carbon substrate utilization of all six categories was observed among Mild, Medium, High and Severe treatments, the consumption of sole carbon source was significantly varied. Especially, asparagine, galacturonic, putrescine and 4-benzoic acid played a decisive role in dominating the differences. Soil bacterial richness and diversity increased with irrigation salinity while the number of bacterial phyla decreased. Three significantly increased (Proteobacteria, Actinobacteria and Chloroflexi), two decreased (Planctomycetes, Bacteroidetes) and two irresponsive (Gemmatimonadetes and Acidobacteria) phyla were observed as the dominant groups in saline water irrigated soils. The results presented here could improve the understanding of the soil biological process under saline circumstance.

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. Sustainable urban development requires planning that classifies and allocates coastal lands using objective procedures that incorporate changing environmental conditions. In this paper, we applied cellular automata (CA) modeling based on self-adaptive genetic algorithm (SAGA) to predict future scenarios and explore sustainable urban development options for Lingang. The CA model was calibrated using the 2005 initial status, 2015 final status, and a set of spatial variables. We implemented specific ecological and environmental conditions as spatial constraints for the model and predicted four 2030 scenarios: (a) an urban planning-oriented Plan Scenario; (b) an ecosystem protection-oriented Eco Scenario; (c) a storm surge-affected Storm Scenario; and (d) a scenario incorporating both ecosystem protection and the effects of storm surge, called the Ecostorm Scenario. The Plan Scenario has been taken as the baseline, with the Lingang urban area increasing from 45.8 km(2) in 2015 to 66.8 km(2) in 2030, accounting for 23.9 % of the entire study area. The simulated urban land size of the Plan Scenario in 2030 was taken as the target to accommodate the projected population increase in this city, which was then applied in the remaining three development scenarios. We used CA modeling to reallocate the urban cells to other unconstrained areas in response to changing spatial constraints. Our predictions should be helpful not only in assessing and adjusting the urban planning schemes for Lingang but also for evaluating urban planning in coastal cities elsewhere.

[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., feasible CA parameters) obtained through a set of selection, crossover, and mutation operations. In this paper, GA-based CA model was developed, and applied to simulate the land use change in Jiaxing City of Zhejiang Province in 1992-2008. This model was calibrated with 6% (66 samples km(-2)) and 3% (33 samples km(-2)) samplings, and the simulation results were evaluated based on confusion matrix, Kappa coefficient, and landscape metrics analysis. Over 80% of the land use features generated by the GA-based CA model matched the observed classification of land features geographically, and much higher simulation accuracy could be obtained with a larger sample. The simulation accuracy and the landscape metrics for 2001 were better than those for 2008, suggesting a tendency that the model's accuracy decreased over the simulating process.