Integrated Grid Based Ecological and Economic (INGRID) landscape model – A tool to support landscape management decisions

The aim of the INGRID landscape model is to simulate the ecological effects of management schemes for dry grasslands and to calculate costs in order to serve as decision tool for nature conservation agencies. To predict the local and regional risk of extinction of plants and animals with respect to different management scenarios/disturbance regimes, we apply modelling approaches on different scales and levels of hierarchy. We integrate abiotic and biotic state variables, processes and complex interactions in a spatially explicit way into the INGRID modelling shell. Data and parameters necessary for reliable modelling were determined empirically in a study site in southern Germany. Subsystems of the overall model are empirically parameterised and validated by means of extensive field surveys. The INGRID landscape model is still in development to be customised to administrative application. In this paper we give an overview on the landscape modelling shell and demonstrate the general structure of the INGRID landscape model. Preliminary results are exemplified with respect to habitat modelling, nature conservation evaluation, and economic modelling of two management scenarios.     

Reconstructing long time series of burned areas in arid grasslands of southern Russia by satellite remote sensing

Fire is an important natural disturbance process in many ecosystems, but humans can irrevocably change natural fire regimes. Quantifying long-term change in fire regimes is important to understand the driving forces of changes in fire dynamics, and the implications of fire regime changes for ecosystem ecology. However, assessing fire regime changes is challenging, especially in grasslands because of high intra- and inter-annual variation of the vegetation and temporally sparse satellite data in many regions of the world. The breakdown of the Soviet Union in 1991 caused substantial socioeconomic changes and a decrease in grazing pressure in Russia's arid grasslands, but how this affected grassland fires is unknown. Our research goal was to assess annual burned area in the grasslands of southern Russia before and after the breakdown. Our study area covers 19,000 km² in the Republic of Kalmykia in southern Russia in the arid grasslands of the Caspian plains. We estimated annual burned area from 1985 to 2007 by classifying AVHRR data using decision tree algorithm, and validated the results with RESURS, Landsat and MODIS data. Our results showed a substantial increase in burned area, from almost none in the 1980s to more than 20% of the total study area burned in both 2006 and 2007. Burned area started to increase around 1998 and has continued to increase, albeit with high fluctuations among years. We suggest that it took several years after livestock numbers decreased in the beginning of the 1990s for vegetation to recover, to build up enough fuel, and to reach a threshold of connectivity that could sustain large fires. Our burned area detection algorithm was effective, and captured burned areas even with incomplete annual AVHRR data. Validation results showed 68% producer's and 56% user's accuracy. Lack of frequent AVHRR data is a common problem and our burned area detection approach may also be suitable in other parts of the world with comparable ecosystems and similar AVHRR data limitations. In our case, AVHRR data were the only satellite imagery available far enough back in time to reveal marked increases in fire regimes in southern Russia before and after the breakdown of the Soviet Union.     

Modelling functional resilience of microbial ecosystems: Analysis of governing processes

Functional stability of microbial ecosystems subjected to disturbances is essential for maintaining microbial ecosystem services such as the biodegradation of organic contaminants in terrestrial environments. Functional responses to disturbances are thus an important aspect which is, however, not well understood yet. Here, we present a microbial simulation model to investigate key processes for the recovery of biodegradation. We simulated single disturbances with different spatiotemporal characteristics and monitored subsequent recovery of the biodegradation dynamics. After less intense disturbance events local regrowth governs biodegradation recovery. After highly intense disturbance events the disturbance pattern's spatial configuration is decisive and processes governing local functional recovery vary depending on habitat location with respect to the spatial disturbance pattern. Local regrowth may be unimportant when bacterial dispersal from undisturbed habitats is high. Hence, our results suggest that spatial dynamics are crucial for the robust delivery of the ecosystem service biodegradation under disturbances in terrestrial environments.     

An agent-based simulation system for concert venue crowd evacuation modeling in the presence of a fire disaster

A key activity in emergency management is planning and preparation for disaster. If the right safety measures are implemented beforehand, harmful effects can be significantly mitigated. However, evaluation and selection of effective measures is difficult due to the numerous scenarios that exist in most emergency environments coupled with the high associated cost of testing such scenarios. An agent-based system employs a computational model of autonomous interacting agents in an environment with the purpose of assessing the emergent behavior of the group. This paper presents a prototype of a computer simulation and decision support system that uses agent-based modeling to simulate crowd evacuation in the presence of a fire disaster and provides for testing of multiple disaster scenarios at virtually no cost. The prototype is unique in the current literature as it is specifically designed to simulate a concert venue setting such as a stadium or auditorium and is highly configurable allowing for user definition of concert venues with any arrangement of seats, pathways, stages, exits, and people as well as the definition of multiple fires with fire and smoke dynamics included.     

基于迭代函数系统IFS的动态树木模拟

植物作为自然景物中最常见的现象之一,模拟的方法是应用数学和图形学领域的一个重要课题。迭代函数系统IFS是分形理论的重要分支,由于植物结构的自相似性,利用IFS(Iterated Function System)可以逼真地模拟各植物形态,简述几种模拟植物的方法,主要研究迭代函数系统IFS模型,并在VC++6.0环境下基于IFS模型构造出静态蕨叶和树木,详细讨论利用带参量的IFS随机系统实现动画的过程,并利用双缓冲技术,形象逼真地模拟随风摇摆的蕨叶和生长树木的动画效果。实验结果表明,带参数的IFS可使图像发生预期的变化,如果让参数在适当的范围保持连续变化,则动画效果良好。     

Detecting wind disturbance severity and canopy heterogeneity in boreal forest by coupling high-spatial resolution satellite imagery and field data

Wind disturbance events can impact spatially heterogeneous patterns in vegetation structure and disturbance severity in forested landscapes. Characterizing these patterns in forested ecosystems with remote sensing data has been a persistent challenge as variation in severity may be heterogeneous at fine spatial scales. Yet the degree and pattern of disturbance severity are an important influence on successional dynamics. This study explored how spectral and textural characteristics of high-spatial resolution IKONOS imagery reflected patterns of disturbance severity across a windstorm damaged, 121-km² area of the Boundary Waters Canoe Area Wilderness (BWCAW) in northeastern Minnesota, USA. In this study, spectral and spatial features of high-spatial resolution (1-m panchromatic and 4-m multispectral) IKONOS satellite imagery from a single post-disturbance date are coupled with field observations of disturbance within 0.045-ha field plots to access the potential for empirically modeling disturbance severity across this heterogeneous landscape within the BWCAW. Combining textural and spectral features led to a multiple regression model that explained 68% of the variance, and predicted disturbance severity equally well for ground data not included in the model development. The results suggest the utility of combining spatial and spectral data for detecting differences in forest structure caused by ecological processes such as disturbance.     

A mobility constraint model to infer sensor behaviour in forest fire risk monitoring

Wireless sensor networks (WSNs) play an important role in forest fire risk monitoring. Various applications are in operation. However, the use of mobile sensors in forest risk monitoring remains largely unexplored. Our research contributes to fill this gap by designing a model which abstracts mobility constraints within different types of contexts for the inference of mobile sensor behaviour. This behaviour is focused on achieving a suitable spatial coverage of the WSN when monitoring forest fire risk. The proposed mobility constraint model makes use of a Bayesian network approach and consists of three components: (1) a context typology describing different contexts in which a WSN monitors a dynamic phenomenon; (2) a context graph encoding probabilistic dependencies among variables of interest; and (3) contextual rules encoding expert knowledge and application requirements needed for the inference of sensor behaviour. As an illustration, the model is used to simulate the behaviour of a mobile WSN to obtain a suitable spatial coverage in low and high fire risk scenarios. It is shown that the implemented Bayesian network within the mobility constraint model can successfully infer behaviour such as sleeping sensors, moving sensors, or deploying more sensors to enhance spatial coverage. Furthermore, the mobility constraint model contributes towards mobile sensing in which the mobile sensor behaviour is driven by constraints on the state of the phenomenon and the sensing system.     

Control relevant estimation of plant and disturbance dynamics

Estimating models for both plant and disturbance dynamics is important in control design applications that focus on disturbance rejection. Several methods for low-order approximate model estimation on the basis of closed-loop data exist in the literature, but fail to address the simultaneous estimation of low-order approximate models of both plant and disturbance dynamics. In this paper a new extended two-stage methodology is proposed that allows for low-order approximate disturbance model estimation. In the proposed extended two-stage method the first stage is used to estimate high-order models for filtering purposes. In the second stage, filtered signals are used to provide the means for low-order approximate model estimation of both plant and disturbance dynamics.     

Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China

This research investigates the potential of an integrated Markov chain analysis and cellular automata model to better understand the dynamics of Shanghai’s urban growth. The model utilizes detailed land cover categories to simulate and assess landscape changes under three different scenarios, i.e., baseline, Service Oriented Center, and Manufacturing Dominant Center scenarios. In the study, multi-temporal land use datasets, derived from remotely-sensed images from 1995, 2000, and 2005, were used for simulation and validation. Urban growth patterns and processes were then analyzed and compared with the aid of landscape metrics. This research represents the first scenario-based simulations of the future growth of Shanghai, and is one of the few studies to use landscape metrics to analyze urban scenario-based simulation results with detailed land use categories. The results indicate that the future expansion of both high-density and low-density residential/commercial zones is always located around existing built-up urban areas or along existing transportation lines. In contrast to the baseline and Service Oriented Center scenarios, industrial land under the Manufacturing Dominant Center scenario in 2015 and 2025 will form industrial parks or industrial belts along the transportation channels from Shanghai to Nanjing and Hangzhou. The study’s approach, which combines scenario-based urban simulation modeling and landscape metrics, is shown to be effective in representing, understanding, and predicting the spatial-temporal dynamics and patterns of urban evolution, including urban expansion trends.     

Integrating fire spread patterns in fire modelling at landscape scale

Fire spread modelling in landscape fire succession models needs to improve to handle uncertainty under global change processes and the resulting impact on forest systems. Linking fire spread patterns to synoptic-scale weather situations are a promising approach to simulating fire spread without fine-grained weather data. Here we present MedSpread—a model that evaluates the weights of five landscape factors in fire spread performance. We readjusted the factor weights for convective, topography-driven and wind-driven fires (n = 123) and re-assessed each fire spread group's performance against seven other control simulations. Results show that for each of the three fire spread patterns, some landscape factors exert a higher influence on fire spread simulation than others. We also found strong evidence that separating fires by fire spread pattern improves model performances. This study shows a promising link between relevant fire weather information, fire spread and fire regime simulation under global change processes.     

New features of a flat (4 + 1)-dimensional cosmological model with a perfect fluid in gauss-bonnet gravity

We study a flat multidimensional cosmological model in Gauss-Bonnet gravity in the presence of matter in the form of a perfect fluid. We find analytically new stationary regimes (these results are valid for an arbitrary number of spatial dimensions) and study their stability by means of numerical recipes in the (4 + 1)-dimensional case. In the vicinity of the stationary regime, we find numerically another nonsingular regime which appears to be periodical. Finally, we demonstrate that the presence of matter in the form of a perfect fluid lifts some constraints on the dynamics of the (4 + 1)-dimensional model which have been found earlier.     

A LANDIS-II extension for incorporating land use and other disturbances

Forest landscape models (FLMs) are widely used to examine the influence of disturbances on long-term and broad-scale forest ecosystem dynamics. However, FLMs are not well-suited to simulating some types of management or disturbance regimes, including land-use change. Consequently, there are situations in which a researcher may wish to estimate the timing and location of events externally, either from a different model, empirical observations, or some other source, and then incorporate them into an FLM. We present Land Use Plus (LU+), an extension for the LANDIS-II FLM that allows users to integrate externally-developed, spatially and temporally explicit representations of land use or other disturbance into simulations. LU+ allows users to model the proximate effects of these events on forest composition and biomass, as well as subsequent dynamics, including tree establishment and the potential for future management. LU+ will significantly increase the breadth of research questions for which LANDIS-II may be appropriately used.     

Forecasting and visualization of wildfires in a 3D geographical information system

This paper describes a wildfire forecasting application based on a 3D virtual environment and a fire simulation engine. A novel open-source framework is presented for the development of 3D graphics applications over large geographic areas, offering high performance 3D visualization and powerful interaction tools for the Geographic Information Systems (GIS) community. The application includes a remote module that allows simultaneous connections of several users for monitoring a real wildfire event. The system is able to make a realistic composition of what is really happening in the area of the wildfire with dynamic 3D objects and location of human and material resources in real time, providing a new perspective to analyze the wildfire information. The user is enabled to simulate and visualize the propagation of a fire on the terrain integrating at the same time spatial information on topography and vegetation types with weather and wind data. The application communicates with a remote web service that is in charge of the simulation task. The user may specify several parameters through a friendly interface before the application sends the information to the remote server responsible of carrying out the wildfire forecasting using the FARSITE simulation model. During the process, the server connects to different external resources to obtain up-to-date meteorological data. The client application implements a realistic 3D visualization of the fire evolution on the landscape. A Level Of Detail (LOD) strategy contributes to improve the performance of the visualization system.     

A multi-scale approach for modeling fire occurrence probability using satellite data and classification trees: A case study in a mountainous Mediterranean region

Fires constitute one major ecological disturbance which influences the natural cycle of vegetation succession and the structure and function of ecosystems. There is no single natural scale at which ecological phenomena are completely understood and thus the capacity to handle scale is beneficial to methodological frameworks for analyzing and monitoring ecosystems. Although satellite imagery has been widely applied for the assessment of fire related topics, there are few studies that consider fire at several spatial scales simultaneously. This research explores the relationships between fire occurrence and several families of environmental factors at different spatial observation scales by means of classification and regression tree models. Predictors accounting for vegetation status (estimated by spectral indices derived from Landsat imagery), fire history, topography, accessibility and vegetation types were included in the models of fire occurrence probability. We defined four scales of analysis by identifying four meaningful thresholds related to fire sizes in the study site. Sampling methodology was based on random points and the power-law distribution describing the local fire regime. The observation scale drastically affected tree size, and therefore the achieved level of detail, and the most explanatory variables in the trees. As a general trend, trees considering all the variables showed a spectral index ruling the most explicative split. According to the comparison of the four pre-determined analysis scales, we propose the existence of three eventual organization levels: landscape patch or ecosystem level, local level and the basic level, the most heterogeneous and complex scale. Rules with three levels of complexity and applicability for management were defined in the tree models: (i) the repeated critical thresholds (predictor values across which fire characteristics change rapidly), (ii) the meaningful final probability classes and (iii) the trees themselves.     

Monitoring the dynamics of wetland inundation by random sets on multi-temporal images

General models are required for better understanding the dynamics of wetland inundations and their water regimes. Extraction by imagery snapshots and use of a crisp data model do not value the inherent uncertainties in space and time. This study addresses parameterization of a mixed Gaussian random set model in a multi-temporal analysis. The model is applied to monitor annual variation of wetland inundation extents from a series of Landsat TM images in 2004 and HJ images in 2009 on the Poyang Lake national nature reserve (PLNNR) in China. We use related indices to represent spatial uncertainties of inundated areas and to delineate the transition zone between wetland vegetation and open water. The PLNNR water regime is investigated by accumulating a series of random sets during one year and determining the water covering days (WCD) at the pixel level. Random sets provide detailed spatial configurations of the WCD which has a strong negative correlation with the underwater DEM. Comparing 2004 and 2009, the study shows that almost half of the PLNNR area experienced drought. We conclude that the mixed Gaussian random set model with three components presented in this study serves as a general method to parameterize the random set model for large datasets. Moreover, it is well suited to capture detailed information on spatial temporal dynamic of wetland inundation and contributes to our understanding of wetlands water regimes from multi-temporal images.     

Mapping and monitoring riparian vegetation distribution,structure and composition with regression tree models and post-classification change metrics

Riparian systems have become increasingly susceptible to both natural and human disturbances as cumulative pressures from changing land use and climate alter the hydrological regimes. This article introduces a landscape dynamics monitoring protocol that incorporates riparian structural classes into the land-cover classification scheme and examines riparian change within the context of surrounding land-cover change. We tested whether Landsat Thematic Mapper (TM) imagery could be used to document a riparian tree die-off through the classification of multi-date Landsat images using classification and regression tree (CART) models trained with physiognomic vegetation data. We developed a post-classification change map and used patch metrics to examine the magnitude and trajectories of riparian class change relative to mapped disturbance parameters. Results show that catchments where riparian change occurred can be identified from land-cover change maps; however, the main change resulting from the die-off disturbance was compositional rather than structural, making accurate post-classification change detection difficult.     

Mapping fire scars in a southern African savannah using Landsat imagery

The spectral, spatial and temporal characteristics of the Landsat data record make it appropriate for mapping fire scars. Twenty-two annual fire scar maps from 1972–2002 were produced from historical Landsat imagery for a semi-arid savannah landscape on the South Africa–Botswana border, centred over Madikwe Game Reserve (MGR) in South Africa. A principal components transformation (PCT) helped differentiate the spectral signal of fire scars in each image. A simple, nonparametric, supervised classification (parallelepiped) of the PCT data differentiated burned and unburned areas. During most years, fire occurrences and the percentage of area burned annually were lowest in Botswana, highest in MGR, and intermediate in South Africa outside MGR. These fire scar maps are aiding MGR managers, who are endeavouring to restore a more active fire regime following decades of fire exclusion.     

Flexible modelling and altitude control for powered parafoil system based on active disturbance rejection control

For the accurate altitude control of the powered parafoil system, a novel modelling method and control methodology are designed in this paper. Firstly, based on computational fluid dynamics, the proposed model can accurately simulate the actual flight state of the powered parafoil system. Then, the double closed-loop controller based on active disturbance rejection control is designed. With this methodology, the precise altitude control can be realised. By analysing the theory of active disturbance rejection control, the proposed control method will be stable and valid. At last, after the detailed verification in the hardware-in-the-loop simulations, the flight experiment is carried out. After the simulation with the proposed model, the adjusted controller parameters can be applied directly in the actual experiment. The results show that the proposed method can provide effective guidance to the flight experiment. It also proves the validity and effectiveness of the proposed modelling and control method.     

Modelling Mediterranean landscape succession-disturbance dynamics: A landscape fire-succession model

We present a spatially explicit Landscape Fire-Succession Model (LFSM) developed to represent Mediterranean Basin landscapes and capable of integrating modules and functions that explicitly represent human activity. Plant-functional types are used to represent spatial and temporal competition for resources (water and light) in a rule-based modelling framework. Vegetation dynamics are represented using a rule-based community-level modelling approach that considers multiple succession pathways and vegetation climax states. Wildfire behaviour is represented using a cellular-automata model of fire spread that accounts for land-cover flammability, slope, wind and vegetation moisture. Results show that wildfire spread parameters have the greatest influence on two aspects of the model: land-cover change and the wildfire regime. This sensitivity highlights the importance of accurately parameterising this type of grid-based model for representing landscape-level processes. We use a pattern-oriented modelling approach in conjunction with wildfire power-law frequency-area scaling exponent β to calibrate the model. Parameters describing the role of soil moisture on vegetation dynamics are also found to significantly influence land-cover change. Recent improvements in understanding the role of soil moisture and wildfire fuel loads at the landscape-level will drive advances in Mediterranean LFSMs.