Markov chains and cellular automata to predict environments subject to desertification

Spatial and temporal changes in shorebird habitats under different land use scenarios along the Yellow and Bohai Sea coasts in China

The effect of coastal wetland loss on shorebird habitat in recent years has been widely reported in previous studies. Various coastal wetland conservation and restoration measures have been implemented or will soon be implemented in China. The extent to which these measures will affect the area and structure of coastal wetland habitat in the future remains unclear. Here, we predicted changes in habitat area and structure for 39 common shorebird species along the coasts of the Yellow and Bohai Seas using a cellular automata-Markov (CA-Markov) land use scenario model and a maximum entropy species distribution model, along with terrain factors (slope, aspect, and digital evaluation model) and climate factors (temperature and precipitation) from the Data Centre for Resources and Environmental Sciences at the Chinese Academy of Sciences, land cover maps interpreted using the human-computer interactive method, and citizen science data of shorebird occurrences derived from eBird, Global Biodiversity Information Facility, and Bird Report. We found that shorebird habitat was most abundant along the coasts of Bohai Bay, Laizhou Bay, and Yancheng. The area of habitat decreased and became increasingly fragmented between 2000 and 2020 for more than half of the 39 species. Under the future business-as-usual scenario, the area of shorebird habitat decreased from 2020 to 2050, and the remaining habitat became increasingly fragmented. Under the ecological protection (EP) scenario, habitat loss was mitigated, and habitat connectivity was improved. The area of habitat was lower in 2050 under the EP scenario than in 2000 for most species, especially threatened species, suggesting that the area of habitat will not return to year-2000 levels under the EP scenario. These results emphasize the need to protect remaining shorebird habitats and implement ecological conservation measures to ensure the long-term preservation of coastal wetlands.

Evaluation of historical and future coastal wetland change in the Yellow and Bohai Seas using satellite images and a land use model

Predicting the future distribution of coastal wetlands and characterizing changes in the area of wetlands between historical and future periods are important for the formulation of wetland conservation and management plans. Here, we used a cellular automata-Markov model and satellite images to simulate the future distribution of coastal wetlands under the business-as-usual scenario (BAU) and ecological protection scenario (EP) along the Yellow and Bohai Seas in China; we also explored historical (from 1990 to 2020) and future (from 2020 to 2050) changes in wetlands and the factors driving these changes. We found that the area of tidal flats gradually decreased because of increases in the area of saltpans, and the aquaculture area increased because of land reclamation and the invasion of Spartina alterniflora; most of the tidal flat area was fragmented into multiple small patches. If the current rate of degradation continues (BAU), the area of tidal flats will decrease by 21.25%, and the area of saltpans and aquaculture will increase by 13.83% and 21.25%, respectively. By contrast, under EP, the area of tidal flats will increase by 13.81%, and this increase will mainly stem from the conversion of areas with S. alterniflora (174.49 km2, 33.22%) to aquaculture areas (155.17 km2, 29.54%). Clear differences between historical and future periods were observed among Liaohe Estuary, Bohai Bay, Laizhou Bay, and the Yancheng-Nantong coasts. Land reclamation is the main factor inducing changes in the area of tidal flats, saltpans, and aquaculture in Liaohe Estuary, Bohai Bay, and Laizhou Bay. Land reclamation and the S. alterniflora invasion both affect the distribution of wetlands along the Yancheng-Nantong coasts.

Using neural networks and a fuzzy inference system to evaluate the risk of wildfires and the pinpointing of firefighting stations in forests on the northern slopes of the Zagros Mountains, Iran (case study: Shimbar national wildlife preserve)

Predicting potential fire hazard zones in natural areas is one of the means of mitigating and managing fires. The current research focuses on the prioritizing of elements which contribute to the spread of fire and the special zoning of potentially dangerous areas in addition to the pinpointing of locations for the establishment of fire stations in forested areas in the Shimbar national reserve based on historical data spanning 2001 to 2018. The study utilizes elements (physiological, vegetation cover, meteorological, anthropological factors) contributing to wildfires as inputs into an artificial neural network and the development of a fuzzy inference system in order to produce fire zoning maps for the region under study. The map is divided into five sectors, i.e., minimum, low, moderate, high, and maximum risk of fire. The validation of the fire zoning map was evaluated at 0.83 and the RMSE error was 0.75. The results obtained show that 20% of the area under study is within the average risk category, 11% is within the high-risk category, and 10% is within the very high-risk category of a potential fire hazard. The most important variables were distance from a flowing source, i.e., river or stream, the land formation type, elevation, and the minimum temperature. The identification of suitable locations for firefighting stations was carried out by merging the fuzzy inference system model and Arc GIS, and the results obtained defined 16 possible locations. It was concluded that the application of hybrid models when dealing with the aforementioned variables is effective when seeking to determine locations for the establishment of firefighting stations and rural safety services; moreover, such hybrid models are highly efficacious for determining of fire hazard zones. It is proposed that hybrid models be applied on a large scale for the prevention, control, and management of fires throughout the country.

An Integrated Spatial Autoregressive Model for Analyzing and Simulating Urban Spatial Growth in a Garden City, China

In the past, the research on models related to urban land-use change and prediction was greatly complicated by the high precision of models. When planning some garden cities, we should explore a more applicable, specific, and effective macro approach than the community-level one. In this study, a model consisting of spatial autoregressive (SAR), cellular automata (CA), and Markov chains is constructed. One It can well-consider the spatial autocorrelation and integrate the advantages of CA into a geographical simulation to find the driving forces behind the expansion of a garden city. This framework has been applied to the urban planning and development of Chengdu, China. The research results show that the application of the SAR model shows the development trend in the southeast region and the needs to optimize the central region and protect the western region as an ecological reserve. The descriptive statistics and the spatial autocorrelation of the residuals are reliable. The influence of spatial variables from strong to weak is distance to water, slope, population density, GDP, distance to main roads, distance to railways, and distance to the center of the county (district). Taking 2005 as the initial year, the land-use situation in 2015 was simulated and compared with the actual land-use situation. It seems that the Kappa coefficient of the construction-land simulation is 0.7634, with high accuracy. Therefore, the land use in 2025 and 2035 is further simulated, which provides a reference for garden cities to formulate a reasonable urban space development strategy.

Assessment of current and future land use/cover changes in soil erosion in the Rio da Prata basin (Brazil)

In recent years, the Cerrado biome in Brazil (Brazilian savannah) has faced severe environmental problems due to abrupt changes in land use/cover (LUC), causing increased soil loss, sediment yield and water turbidity. Thus, this study aimed to evaluate the impacts of soil loss and sediment delivery ratio (SDR) over the last 30 years to simulate future scenarios of soil losses from 2050 to 2100 and to investigate an episode of sediment delivery that occurred in the Rio da Prata Basin (RPB) in 2018. In this study, the following were used: an estimation of soil losses for 1986, 1999, 2007 and 2016 using the Revised Universal Soil Loss Equation (RUSLE), an estimation of SDR, sediment export and sediment deposition using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, an association of RUSLE factor C to LUC data for 2050 and 2100 based on the CA-Markov hybrid model, and an estimation of future soil erosion scenarios for 2050 and 2100. The results show that over the last 30 years (1986-2016), there has been a reduction in the areas of highly intense and severe degrees. Future soil erosion scenarios (2050-2100) showed a 13.84% increase in areas of soil loss >10 Mg ha^-1 year^-1. The results highlighted the importance of assessing the impacts of LUC changes on soil erosion and the export of sediments to agricultural watersheds in the RPB, one of the best ecotourism destinations in Brazil. In addition, the increase in soil loss in the region intensified sediment yield events and increased water turbidity. Furthermore, riparian vegetation, although preserved, was not able to protect the watercourse, showing that it is essential to adopt the best management practices in the agricultural production areas of the basin, especially where ramps are extensive or the slope is greater than 2%, to reduce the runoff velocity and control the movement of sediments on the surface towards the drainage canals. The results of this study are useful for drawing up a soil and water conservation plan for the sustainable production of agriculture and maintenance of ecosystem services in the region.

A New Perspective for Urban Development Boundary Delineation Based on the MCR Model and CA-Markov Model

In order to control the development of urban space, it is important to explore scientific methods to provide a reference for regional territorial space planning. On the basis of the minimum cumulative resistance (MCR) model and the cellular automaton (CA)-Markov model, we constructed a new technical method for delineating urban development boundaries, exploring the temporal and spatial distribution characteristic of land use in Wuhan from 2010 to 2020 through nighttime and remote sensing images, and simulating the urban development boundaries of Wuhan from 2025 to 2035. The results show that: (1) the scales of Wuhan City’s built-up areas in 2010, 2015, and 2020 were 500 km2, 566.13 km2, and 885.11 km2, respectively, and the trends of expansion run to the east and southeast, and (2) on the basis of the MCR model, the urban development boundary scale of Wuhan City in 2025, 2030, and 2035 from the perspective of actual supply will be 903.52 km2, 937.48 km2, and 1021.44 km2, respectively, and based on the CA-Markov model, the urban development boundary scales of Wuhan City in 2025, 2030, and 2035 from the perspective of ideal land demand will be 912.75 km2, 946.40 km2, and 1041.91 km2, respectively. By combining the results of the two methods, we determined areas of 901.62 km2, 944.39 km2, and 1015.36 km2 as the urban development boundaries of Wuhan City in 2025, 2030, and 2035, respectively. According to the principle of supply–demand balance, the urban development boundary delineated by the integration of the MCR model and CA-Markov model, which is in line with the spatial expansion trend of growing cities, could optimize the urban development pattern; solve the contradiction between urban development, farmland protection, and ecological protection; and provide a methodological reference and decision-making basis for planning practice.

Coupling Ecological Security Pattern Establishment and Construction Land Expansion Simulation for Urban Growth Boundary Delineation: Framework and Application

Reasonable delineation of the urban growth boundary (UGB) plays a vital role in guiding orderly urban space growth and ensuring urban environmental health. Existing methodologies for UGB delineation have failed to address the significance of ecological security. Therefore, this study presents a framework that couples ecological security pattern (ESP) establishment and construction land expansion (CLE) simulation to delineate the UGB. The proposed framework is applied to the Nanchang Metropolitan Area (NCMA) in southeastern China. First, we established the regional ESP of the NCMA in 2018 based on an improved minimum cumulative resistance model. The areas of low-, medium-, and high-level ESP were 1050.75, 736.42, and 720.59 km2, respectively. Second, we implemented a multi-scenario simulation of CLE in the NCMA in 2025 based on a cellular automata–Markov model. A natural development scenario was superior to urban growth and ecological protection scenarios for social, economic, and ecological development at the regional scale. Accordingly, we delineated the UGB of the NCMA in 2025 with a scale of 687.87 km2, based on dynamic adjustment using the results of ESP establishment and CLE simulation in the natural development scenario. The rationality and scientificity of the proposed framework were verified by comparing the scale and layout of the delineated UGB with the regional planning of Nanchang City. The framework incorporating dynamic adjustment with ESP establishment and multi-scenario CLE simulation provides a useful tool for the delineation of the UGB in similar urbanized cities. Its application is conducive to achieving a win–win outcome of regional ecological security and urban development.

Hydrological Response of the Wami–Ruvu Basin to Land-Use and Land-Cover Changes and Its Impacts for the Future

The evaluation of the hydrological responses of river basins to land-use and land-cover (LULC) changes is crucial for sustaining water resources. We assessed the impact of LULC changes (1990–2018) on three hydrological components (water yield (WYLD), evapotranspiration (ET), and sediment yield (SYLD)) of the Wami–Ruvu Basin (WRB) in Tanzania, using the Soil and Water Assessment Tool (SWAT). The 1990 LULC imagery was used for SWAT simulation, and imagery from 2000, 2010, and 2018 was used for comparison with modelled hydrological parameters. The model was calibrated (1993–2008) and validated (2009–2018) in the SWAT-CUP after allowing three years (1990–1992) for the warm-up period. The results showed a decrease in WYLD (3.11 mm) and an increase in ET (29.71 mm) and SYLD (from 0.12 t/h to 1.5 t/h). The impact of LULC changes on WYLD, ET, and SYLD showed that the increase in agriculture and built-up areas and bushland, and the contraction of forest led to the hydrological instability of the WRB. These results were further assessed with climatic factors, which revealed a decrease in precipitation and an increase in temperature by 1 °C. This situation seems to look more adverse in the future, based on the LULC of the year 2036 as predicted by the CA–Markov model. Our study calls for urgent intervention by re-planning LULC and re-assessing hydrological changes timely.

Spatial modeling of land-use change in a rapidly urbanizing landscape in central Iran: integration of remote sensing, CA-Markov, and landscape metrics

In the present paper, land use/land cover (LULC) change was predicted in the Greater Isfahan area (GIA), central Iran. The GIA has been growing rapidly in recent years, and attempts to simulate its spatial expansion would be essential to make appropriate decisions in LULC management plans and achieve sustainable development. Several modeling tools were employed to outline sustainable scenarios for future dynamics of LULCs in the region. Specifically, we explored past LULC changes in the study area from 1996 to 2018 and predicted its future changes for 2030 and 2050. For this purpose, we performed object-oriented and decision tree techniques on Landsat and Sentinel-2 satellite images. The CA-Markov hybrid model was utilized to analyze past trends and predict future LULC changes. LULC changes were quantitatively measured using landscape metrics. According to the results, the majority of changes were related to increasing residential areas and decreasing irrigated lands. The results indicated that residential lands would grow from 27,886.87 ha to 67,093.62 ha over1996-2050 while irrigated lands decrease from 99,799.4 ha to 50,082.16 ha during the same period of time. The confusion matrix of the 2018 LULC map was built using a total of 525 ground truth points and yielded a Kappa coefficient and overall accuracy of 78% and 82%, respectively. Moreover, the confusion matrix constructed base on the Sentinel-2 map, as a reference, to judge the predicted 2018 LULC map with a Kappa coefficient of 88%. The results of this study provide useful insights for sustainable land management. The results of this research also proved the promising capability of remote sensing algorithms, CA-Markov model and landscape metrics future LULC planning in the study area.

An Efficacy Predictive Method for Diabetic Ulcers Based on Higher-Order Markov Chain-Set Pair Analysis

Background

Clinical comprehensive decision-making of diabetic ulcers includes curative effect evaluation and curative effect prediction. Nevertheless, there are few studies on the prediction of diabetic ulcers.

Methods

Set pair analysis (SPA) was used to assess the curative effect evaluation, and therapeutic effect was evaluated by connection degree (CD). The higher-order Markov chain-SPA curative effect prediction model was established to predict the future curative effect development. The predicted results with higher-order Markov chain-SPA and traditional first-order Markov-SPA model were compared with the actual results of the patients to verify the effectiveness of prediction.

Results

The connection degree of index levels I and II of 15 patients with diabetic ulcers after traditional Chinese medicine (TCM) treatment increased with time, while that of index levels IV and V decreased, indicating that the curative effect tends to improve. The higher-order Markov chain-SPA model was used to predict the curative effect. The results showed that the relative errors were fewer than the traditional first-order Markov-SPA model.

Conclusions

The present study suggests that a method of SPA combined with higher-order Markov-SPA is relatively effective and can be applied to the clinical prediction of diabetic ulcers, which has higher accuracy than traditional first-order curative effect prediction model.

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 CA_GWR models for reproducing rapid urban expansion at Chongqing from 2005 to 2010. We then used the CA_GWR model with the best bandwidth to predict future urban scenarios out to 2030. Our modeling shows that CA_GWR 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.