Performance Evaluation of the SLEUTH Model in the Shenyang Metropolitan Area of Northeastern China

Abstract Performance evaluation is crucial for the development and improvement of an urban cellular automata model, such as SLEUTH. In this paper, we employed multiple methods for map comparison and model validation to evaluate the simulation performance of the SLEUTH urban growth model in the Shenyang metropolitan area of China. These multiple methods included the relative operating characteristic (ROC) curve statistic, multiple-resolutions error budget, and landscape metrics. They were used to quantitatively examine model performance in terms of the amount and spatial location of urban development, urban spatial pattern and prediction ability. The assessment results showed that SLEUTH performed well in the way of the quantitative simulation of urban growth for this case study. Similar to other urban growth models, however, the simulation accuracy for spatial location of new development at the pixel scale and urban spatial pattern still needs to be improved greatly. These inaccuracies might be attributed to the structure and nature of SLEUTH, local urban development characteristics, and the temporal and spatial scale of its application. Finally, many valuable suggestions had been put forward to improve simulation performance of SLEUTH model for spatial location of urban development in the Shenyang metropolitan area

Data in support of environmental controls on the characteristics of mean number of forest fires and mean forest area burned (1987–2007) in China

Fire frequency and size are two important parameters describing fire characteristics. Exploring the spatial variation of fire characteristics and understanding the environmental controls are indispensable to fire prediction and sustainable forest landscape management. To illustrate the spatial variation of forest fire characteristics over China and to quantitatively determine the relative contribution of each of the environmental controls to this variation, forest fire characteristic data (mean number of forest fires and mean burned forest area) and environmental data (climate, land use, vegetation type and topography) at provincial level were derived. These data sets can potentially serve as a foundation for future studies relating to fire risk assessment, carbon emission by forest fires, and the impact of climate change on fire characteristics. This data article contains data related to the research article entitled “Environmental controls on the characteristics of mean number of forest fires and mean forest area burned (1987–2007) in China” by chang et al. [1]

Assessing the Effects of Management Alternatives on Habitat Suitability in a Forested Landscape of Northeastern China

Abstract Forest management often has cumulative, longlasting effects on wildlife habitat suitability and the effects may be impractical to evaluate using landscape-scale field experiments. To understand such effects, we linked a spatially explicit landscape disturbance and succession model (LANDIS) with habitat suitability index (HSI) models to assess the effects of management alternatives on habitat suitability in a forested landscape of northeastern China. LANDIS was applied to simulate future forest landscape changes under four management alternatives (no cutting, clearcutting, selective cutting I and II) over a 200-year horizon. The simulation outputs were linked with HSI models for three wildlife species, the red squirrel (Sciurus vulgaris), the red deer (Cervus elaphus) and the hazel grouse (Bonasa bonasia). These species are chosen because they represent numerous species that have distinct habitat requirements in our study area. We assessed their habitat suitability based on the mean HSI values, which is a measure of the average habitat quality. Our simulation results showed that no one management scenario was the best for all species and various forest management scenarios would lead to conflicting wildlife habitat outcomes. How to choose a scenario is dependent on the trade-off of economical, ecological and social goals. Our modeling effort could provide decision makers with relative comparisons among management scenarios from the perspective of biodiversity conservation. The general simulation results were expected based on our knowledge of forest management and habitat relationships of the species, which confirmed that the coupled modeling approach correctly simulated the assumed relationships between the wildlife, forest composition, age structure, and spatial configuration of habitat. However, several emergent results revealed the unexpected outcomes that a management scenario may lead to

Variations in Growing-Season NDVI and Its Response to Permafrost Degradation in Northeast China

Permafrost is extremely sensitive to climate change. The degradation of permafrost has strong and profound effects on vegetation. The permafrost zone of northeastern China is the second largest region of permafrost in China and lies on the south edge of the Eurasian cryolithozone. This study analyzed the spatiotemporal variations of the growing-season Normalization Difference Vegetation Index (NDVI) in the permafrost zone of northeastern China and analyzed the correlation between NDVI and ground surface temperatures (GST) during the years 1981–2014. Mean growing-season NDVI (MGS-NDVI) experienced a marked increase of 0.003 year−1 across the entire permafrost zone. The spatial dynamics of vegetation cover had a high degree of heterogeneity on a per pixel scale. The MGS-NDVI value increased significantly (5% significance level) in 80.57%, and this increase was mostly distributed in permafrost zone except for the western steppe region. Only 7.72% experienced a significant decrease in NDVI, mainly in the cultivated and steppe portions. In addition, MGS-NDVI increased significantly with increasing growing-season mean ground surface temperature (GS-MGST). Our results suggest that a warming of GS-MGST (permafrost degradation) in the permafrost region of northeastern China played a positive role in increasing plant growth and activities. Although increasing ground surface temperature resulted in increased vegetation cover and growth in the short time of permafrost degradation, from the long term point of view, permafrost degradation or disappearance may weaken or even hinder vegetation activities

Latin hypercube sampling and geostatistical modeling of spatial uncertainty in a spatially explicit forest landscape model simulation

Abstract Geostatistical stochastic simulation is always combined with Monte Carlo method to quantify the uncertainty in spatial model simulations. However, due to the relatively long running time of spatially explicit forest models as a result of their complexity, it is always infeasible to generate hundreds or thousands of Monte Carlo simulations. Thus, it is of great importance to generate a relatively small set of conditional realizations capturing most of the spatial variability. In this study, we introduced an effective sampling method (Latin hypercube sampling) into a stochastic simulation algorithm (LU decomposition simulation). Latin hypercube sampling is first compared with a common sampling procedure (simple random sampling) in LU decomposition simulation. Then it is applied to the investigation of uncertainty in the simulation results of a spatially explicit forest model, LANDIS. Results showed that Latin hypercube sampling can capture more variability in the sample space than simple random sampling especially when the number of simulations is small. Application results showed that LANDIS simulation results at the landscape level (species percent area and their spatial pattern measured by an aggregation index) is not sensitive to the uncertainty in species age cohort information at the cell level produced by geostatistical stochastic simulation algorithms. This suggests that LANDIS can be used to predict the forest landscape change at broad spatial and temporal scales even if exhaustive species age cohort information at each cell is not available

Performance Evaluation of the SLEUTH Model in the Shenyang Metropolitan Area of Northeastern China

Abstract Performance evaluation is crucial for the development and improvement of an urban cellular automata model, such as SLEUTH. In this paper, we employed multiple methods for map comparison and model validation to evaluate the simulation performance of the SLEUTH urban growth model in the Shenyang metropolitan area of China. These multiple methods included the relative operating characteristic (ROC) curve statistic, multiple-resolutions error budget, and landscape metrics. They were used to quantitatively examine model performance in terms of the amount and spatial location of urban development, urban spatial pattern and prediction ability. The assessment results showed that SLEUTH performed well in the way of the quantitative simulation of urban growth for this case study. Similar to other urban growth models, however, the simulation accuracy for spatial location of new development at the pixel scale and urban spatial pattern still needs to be improved greatly. These inaccuracies might be attributed to the structure and nature of SLEUTH, local urban development characteristics, and the temporal and spatial scale of its application. Finally, many valuable suggestions had been put forward to improve simulation performance of SLEUTH model for spatial location of urban development in the Shenyang metropolitan area

Simulate urban growth based on RS, GIS, and SLEUTH model in Shenyang-Fushun metropolitan area northeastern China

Shenyang and Fushun are two most nearest mega cities in China. Integration of the two cities as one sub-administrative economic region is a state and province policy to promote economy development of Liaoning province. How the urban patterns of the mega cities will grow is interested to city planners, decision-makers, land managers, ecologists, geographers, and resource managers because it's special policy and spatiotemporal dynamic complexity. This study explore the combined application of remote sensing, geographical information system and SLEUTH urban growth model to analyze and model urban growth pattern in Shenyang-Fushun metropolitan area northeastern China. The sequential RS images can give quantitative descriptors of the geometry of urban form to be computed and compared over time. The investigation is based on a 16-year time series data set compiled from interpreted historical TM satellite imagery. The SLEUTH model was calibrated using the mutil-temporal data set for the 6391.12km2 area where has experienced rapid urbanization in recent years. The model allowed a spatial forecast of urban growth, and future growth was projected out to 2050 assuming four different policy scenarios: (1) current trends (CT), (2) accelerated urban development (AUD), (3) protected urban development (PUD), and (4) limitative urban growth (LUD). The predicted urban growth shows similar compact pattern under each scenario except the current trends scenario that shows diffused urban growth pattern and most diffused growth appears at south of region. Edge growth and road gravity growth are the main growth types in the future. The accelerated urban development scenario shows the most urban growth area. The limitative urban growth scenario shows the least urban growth area. The protected urban development scenario shows moderate urban growth area and good protection to other land resources. The urban land of two mega cities will connect firstly to a whole on the south bank of Hun River about in 2040 in the accelerated urban development and the current trends scenarios, and will not connect on the other two scenarios until 2050. The combined method using remote sensing, geographical information system and SLEUTH urban growth model is powerful for representation, modeling and prediction of the spatiotemporal urban growth, and useful for understanding the alternative future planning scenarios, but location accuracy and scenarios design must be further considered for local application. ©2009 IEEE

Predicting Impacts of Climate Change on the Aboveground Carbon Sequestration Rate of a Temperate Forest in Northeastern China

<div><p>The aboveground carbon sequestration rate (ACSR) reflects the influence of climate change on forest dynamics. To reveal the long-term effects of climate change on forest succession and carbon sequestration, a forest landscape succession and disturbance model (LANDIS Pro7.0) was used to simulate the ACSR of a temperate forest at the community and species levels in northeastern China based on both current and predicted climatic data. On the community level, the ACSR of mixed Korean pine hardwood forests and mixed larch hardwood forests, fluctuated during the entire simulation, while a large decline of ACSR emerged in interim of simulation in spruce-fir forest and aspen-white birch forests, respectively. On the species level, the ACSR of all conifers declined greatly around 2070s except for Korean pine. The ACSR of dominant hardwoods in the Lesser Khingan Mountains area, such as Manchurian ash, Amur cork, black elm, and ribbed birch fluctuated with broad ranges, respectively. Pioneer species experienced a sharp decline around 2080s, and they would finally disappear in the simulation. The differences of the ACSR among various climates were mainly identified in mixed Korean pine hardwood forests, in all conifers, and in a few hardwoods in the last quarter of simulation. These results indicate that climate warming can influence the ACSR in the Lesser Khingan Mountains area, and the largest impact commonly emerged in the A2 scenario. The ACSR of coniferous species experienced higher impact by climate change than that of deciduous species.</p></div

Species vital attributes in the Lesser Khingan mountains area, Northeastern China.

<p>LONG: longevity (years); MTR: age of maturity (years); ST: shade tolerance (1–5); FT: fire tolerance (1–5); ESD: effective seeding distance (m); MSD: maximum seeding distance (m); VP: vegetative production probability (0–1); MVP: minimum age of vegetative reproduction (years); MD: maximum diameter at breast height (cm); CCC: carbon content coefficient (0–1).</p