The 2007 crisis and Greek wildfires: a multivariate analysis of suppression times

Shifts in government priorities in response to the 2007 global recession have affected wildfire management and natural disaster funding arrangements, leading to a reduced effectiveness of fire suppression actions and increasing fire vulnerability. Our study investigates the role of local socioeconomic contexts on fire suppression effectiveness under economic expansion and recession in a Mediterranean region (Attica, Greece) strongly affected by 2007 crisis and displaying a persistently high density of peri-urban wildfires. Basic characteristics of wildfires (spatial distribution, intensity, and land use preferences) were investigated in the study area over two consecutive 8-year time intervals characterized by economic expansion (2000–2007) and recession (2008–2015). An integrated approach based on multivariate statistics and artificial neural networks was implemented to evaluate latent relationships between fire suppression time, wildfire characteristics, and socioeconomic dynamics. Controlling for wildfires’ characteristics over the two time intervals, fire time length increased under crisis—mainly for small and medium-sized fires—possibly as an indirect response to reduced effectiveness of forest land management. Local contexts and political decisions influenced by economic downturns are relevant factors shaping wildfires’ severity in the Mediterranean region. With recession, local contexts vulnerable to wildfires require more effective fire prevention measures, sustainable forest management, and regional planning.     

Wildfire seasonality and land use: when do wildfires prefer to burn?

Because of the increasing anthropogenic fire activity, understanding the role of land-use in shaping wildfire regimes has become a major concern. In the last decade, an increasing number of studies have been carried out on the relationship between land-use and wildfire patterns, in order to identify land-use types where fire behaves selectively, showing a marked preference (or avoidance) in terms of fire incidence. By contrast, the temporal aspects of the relationship between landuse types and wildfire occurrence have received far less attention. The aim of this paper is, thus, to analyze the temporal patterns of fire occurrence in Sardinia (Italy) during the period 2000–2006 to identify land-use types where wildfires occur earlier or later than expected from a random null model. The study highlighted a close relationship between the timing of fire occurrence and land-cover that is primarily governed by two complementary processes: climatic factors that act indirectly on the timing of wildfires determining the spatial distribution of land-use types, and human population and human pressure that directly influence fire ignition. From a practical viewpoint, understanding the temporal trends of wildfires within the different land-use classes can be an effective decision-support tool for fire agencies in managing fire risk and for producing provisional models of fire behavior under changing climatic scenarios and evolving landscapes.     

An integrated methodology for estimation of forest fire-loss using geospatial information

These days, wildfires are prevalent in almost all areas of the world. Researchers have been actively analyzing wildfire damage using a variety of satellite images and geospatial datasets. This paper presents a method for detailed estimation of wildfire losses using various geospatial datasets and an actual case of wildfire at Kang-Won-Do, Republic of Korea in 2005. A set of infrared (IR) aerial images acquired after the wildfire were used to visually delineate the damaged regions, and information on forest type, diameter class, age class, and canopy density within the damaged regions was retrieved from GIS layers of the Korean national forest inventory. Approximate tree heights were computed from airborne LIDAR and verified by ground LIDAR datasets. The corresponding stand volumes were computed using tree volume equations (TVE). The proposed algorithm can efficiently estimate fire loss using the geospatial information; in the present case, the total fire loss was estimated as $5.9 million, which is a more accurate estimate than $4.5 million based on conventional approach. The proposed method can be claimed as a powerful alternative for estimating damage caused by wildfires, because the aerial image interpretation can delineate and analyze damaged regions in a comprehensive and consistent manner; moreover, LIDAR datasets and national forest inventory data can significantly reduce field work.     

Prediction of near-surface soil moisture at large scale by digital terrain modeling and neural networks

The capability of Artificial Neural Network models to forecast near-surface soil moisture at fine spatial scale resolution has been tested for a 99.5 ha watershed located in SW Spain using several easy to achieve digital models of topographic and land cover variables as inputs and a series of soil moisture measurements as training data set. The study methods were designed in order to determining the potentials of the neural network model as a tool to gain insight into soil moisture distribution factors and also in order to optimize the data sampling scheme finding the optimum size of the training data set. Results suggest the efficiency of the methods in forecasting soil moisture, as a tool to assess the optimum number of field samples, and the importance of the variables selected in explaining the final map obtained.     

人工神经网络用于铅的化学形态模拟计算

用前馈线性网络法求解水体系中Ｐｂ（２＋）与ＯＨ－之间的反应常数，不同训练算法对求解结果的精度、收敛速度及权值均有影响．结果表明，批处理算法的精度最好，权值不出现负值，但运算时间最长；在线算法的精度虽不如批处理算法，而比数据变换－在线算法好，权值有时会出现负值．运算时间较长；数据变换－在线算法的优点是运算时间短，但相对误差较大，权值出现负值的机会多。采用反馈网络模拟计算铅的各种化学形态的浓度．用物料核算的方法对反馈网络模型进行检验表明，此种模型用于平衡计算是可行的，详细分析了理论模拟和实验曲线的差异的原因，温度的影响最小，在４＜ｐＨ＜９时，ＣＯ有重要的影响．在国代检验时，ｎ值取整所引入的误差的影响亦不可忽视。从本文的结果可以看到，采用前馈网络和反馈网络相结合的方法考察水体中的化学形态是可行的．从而为解决这一类问题提供了一种可能的途径．     

Structural characteristics of post-wildfire and clearcut landscapes

A continuing discussion in the field of ecology and forest management concerns the implications of clearcutting as a functional replacement for wildfire in disturbance-driven ecosystems. At the landscape level, spatial pattern has been shown to influence many ecologically important processes. Satellite imagery allows the evaluation of structural patterns created by alternative forest management activities at broad scales. In Northwestern Ontario, both clearcutting and wildfire have occurred over large contiguous areas. Spatial characteristics including composition, patch size, patch shape, and interspersion were calculated from classified Landsat Thematic Mapper (TM) data at two thematic scales and used to compare post-wildfire and clearcut landscapes. Patches in the clearcut landscape were found to be larger in size, and had a more irregular shape than those in the wildfire landscape. Differences in landscape structure were much more pronounced at broad scales than at fine thematic scales.     

Assessment of effects of climate change and grazing activity on grassland yield in the Three Rivers Headwaters Region of Qinghai–Tibet Plateau, China

Inter-annual dynamics of grassland yield of the Three Rivers Headwaters Region of Qinghai–Tibet Plateau of China in 1988–2005 was analyzed using the GLO-PEM model, and the herbage supply function was evaluated. The results indicate that while grassland yield in the region showed marked inter-annual fluctuation there was a trend of increased yield over the 18 years of the study. This increase was especially marked for Alpine Desert and Alpine Steppe and in the west of the region. The inter-annual coefficient of variation of productivity increased from the east to the west of the region and from Marsh, Alpine Meadow, Alpine Steppe, Temperate Steppe to Alpine Desert grasslands. Climate change, particularly increased temperatures in the region during the study period, is suggested to be the main cause of increased grassland yield. However, reduced grazing pressure and changes to the seasonal pattern of grazing could also have influenced the grassland yield trend. These findings indicate the importance of understanding the function of the grassland ecosystems in the region and the effect of climate change on them especially in regard to their use to supply forage for animal production. Reduction of grazing pressure, especially during winter, is indicated to be critical for the restoration and sustainable use of grassland ecosystems in the region.     

Advancing effects analysis for integrated, large-scale wildfire risk assessment

In this article, we describe the design and development of a quantitative, geospatial risk assessment tool intended to facilitate monitoring trends in wildfire risk over time and to provide information useful in prioritizing fuels treatments and mitigation measures. The research effort is designed to develop, from a strategic view, a first approximation of how both fire likelihood and intensity influence risk to social, economic, and ecological values at regional and national scales. Three main components are required to generate wildfire risk outputs: (1) burn probability maps generated from wildfire simulations, (2) spatially identified highly valued resources (HVRs), and (3) response functions that describe the effects of fire (beneficial or detrimental) on the HVR. Analyzing fire effects has to date presented a major challenge to integrated risk assessments, due to a limited understanding of the type and magnitude of changes wrought by wildfire to ecological and other nonmarket values. This work advances wildfire effects analysis, recognizing knowledge uncertainty and appropriately managing it through the use of an expert systems approach. Specifically, this work entailed consultation with 10 fire and fuels program management officials from federal agencies with fire management responsibilities in order to define quantitative resource response relationships as a function of fire intensity. Here, we demonstrate a proof-of-concept application of the wildland fire risk assessment tool, using the state of Oregon as a case study.     

Evaluation of stream chemistry trends in US Geological Survey reference watersheds, 1970–2010

The Hydrologic Benchmark Network (HBN) is a long-term monitoring program established by the US Geological Survey in the 1960s to track changes in the streamflow and stream chemistry in undeveloped watersheds across the USA. Trends in stream chemistry were tested at 15 HBN stations over two periods (1970–2010 and 1990–2010) using the parametric Load Estimator (LOADEST) model and the nonparametric seasonal Kendall test. Trends in annual streamflow and precipitation chemistry also were tested to help identify likely drivers of changes in stream chemistry. At stations in the northeastern USA, there were significant declines in stream sulfate, which were consistent with declines in sulfate deposition resulting from the reductions in SO₂ emissions mandated under the Clean Air Act Amendments. Sulfate declines in stream water were smaller than declines in deposition suggesting sulfate may be accumulating in watershed soils and thereby delaying the stream response to improvements in deposition. Trends in stream chemistry at stations in other part of the country generally were attributed to climate variability or land disturbance. Despite declines in sulfate deposition, increasing stream sulfate was observed at several stations and appeared to be linked to periods of drought or declining streamflow. Falling water tables might have enhanced oxidation of organic matter in wetlands or pyrite in mineralized bedrock thereby increasing sulfate export in surface water. Increasing sulfate and nitrate at a station in the western USA were attributed to release of soluble salts and nutrients from soils following a large wildfire in the watershed.     

Application of artificial neural network approach and remotely sensed imagery for regional eco-environmental quality evaluation

Eco-environment quality evaluation is an important research theme in environment management. In the present study, Fuzhou city in China was selected as a study area and a limited number of 222 sampling field sites were first investigated in situ with the help of a GPS device. Every sampling site was assessed by ecological experts and given an Eco-environment Background Value (EBV) based on a scoring and ranking system. The higher the EBV, the better the ecological environmental quality. Then, three types of eco-environmental attributes that are physically-based and easily-quantifiable at a grid level were extracted: (1) remote sensing derived attributes (vegetation index, wetness index, soil brightness index, surface land temperature index), (2) meteorological attributes (annual temperature and annual precipitation), and (3) terrain attribute (elevation). A Back Propagation (BP) Artificial Neural Network (ANN) model was proposed for the EBV validation and prediction. A three-layer BP ANN model was designed to automatically learn the internal relationship using a training set of known EBV and eco-environmental attributes, followed by the application of the model for predicting EBV values across the whole study area. It was found that the performance of the BP ANN model was satisfactory and capable of an overall prediction accuracy of 82.4%, with a Kappa coefficient of 0.801 in the validation. The evaluation results showed that the eco-environmental quality of Fuzhou city is considered as satisfactory. Through analyzing the spatial correlation between the eco-environmental quality and land uses, it was found that the best eco-environmental areas were related to forest lands, whereas the urban area had the relatively worst eco-environmental quality. Human activities are still considered as a major impact on the eco-environmental quality in this area.     

Modeling of SO<Subscript>2</Subscript> distribution in Istanbul using artificial neural networks

Urban air pollution is a growing problem in developing countries. Some compounds especially sulphur dioxide (SO₂) is considered as typical indicators of the urban air quality. Air pollution modeling and prediction have great importance in preventing the occurrence of air pollution episodes and provide sufficient time to take the necessary precautions. Recently, various stochastic image-processing algorithms such as Artificial Neural Network (ANN) are applied to environmental engineering. ANN structure employs input, hidden and output layers. Due to the complexity of the problem, as the number of input–output parameters differs, ANN model settings such as the number of neurons of these layers changes. The ability of ANN models to learn, particularly capability of handling large amounts (or sets) of data simultaneously as well as their fast response time, are invariably the characteristics desired for predictive and forecasting purposes. In this paper, ANN models have been used to predict air pollutant parameter in meteorological considerations. We have especially focused on modeling of SO₂ distribution and predicting its future concentration in Istanbul, Turkey. We have obtained data sets including meteorological variables and SO₂ concentrations from Istanbul-Florya meteorological station and Istanbul-Yenibosna air pollution station. We have preferred three-layer perceptron type of ANN which consists of 10, 22 and 1 neurons for input, hidden and output layers, respectively. All considered parameters are measured as daily mean. The input parameters are: SO₂ concentration, pressure, temperature, humidity, wind direction, wind speed, strength of sunshine, sunshine, cloudy, rainfall and output parameter is the future prediction of SO₂. To evaluate the performance of ANN model, our results are compared to classical nonlinear regression methods. The over all system finds an optimum correlation between input–output variables. Here, the correlation parameter, r is 0.999 and 0.528 for training and test data. Thus in our model, the trend of SO₂ is well estimated and seasonal effects are well represented. As a result, we conclude that ANN is one of the compromising methods in estimation of environmental complex air pollution problems.     

The Biodiversity Crisis and Adaptation to Climate Change: A Case Study from Australia's Forests

If current trends continue, human activities will drastically alter most of the planet's remaining natural ecosystems and their composite biota within a few decades. Compounding the impacts on biodiversity from deleterious management practices is climate variability and change. The Intergovernmental Panel on Climate Change (IPCC) recently concluded that there is ample evidence to suggest climate change is likely to result in significant impacts on biological diversity. These impacts are likely to be exacerbated by the secondary effects of climate change such as changes in the occurrence of wildfire, insect outbreaks and similar disturbances. Current changes in climate are very different from those of the past due to their rate and magnitude, the direct effects of increased atmospheric CO₂ concentrations and because highly modified landscapes and an array of threatening processes limit the ability of terrestrial ecosystems and species to respond to changed conditions. One of the primary human adaptation option for conserving biodiversity is considered to be changes in management. The complex and overarching nature of climate change issues emphasises the need for greatly enhanced cooperation between scientists, policy makers, industry and the community to better understand key interactions and identify options for adaptation. A key challenge is to identify opportunities that facilitate sustainable development by making use of existing technologies and developing policies that enhance the resilience of climate-sensitive sectors. Measures to enhance the resilience of biodiversity must be considered in all of these activities if many ecosystem services essential to humanity are to be sustained. New institutional arrangements appear necessary at the regional and national level to ensure that policy initiatives and research directed at assessing and mitigating the vulnerability of biodiversity to climate change are complementary and undertaken strategically and cost-effectively. Policy implementation at the national level to meet responsibilities arising from the UNFCCC (e.g., the Kyoto Protocol) and the UN Convention on Biological Diversity require greater coordination and integration between economic sectors, since many primary drivers of biodiversity loss and vulnerability are influenced at this level. A case study from the Australian continent is used to illustrate several key issues and discuss a basis for reform, including recommendations for facilitating adaptation to climate variability and change.     

The use of artificial neural networks to forecast biological atmospheric allergens or pathogens only as Alternaria spores

The monitoring of atmospheric Alternaria spores is of major importance due to their adverse effects on crops and their role as human allergens. Most species act as plant pathogens, prompting considerable economic losses worldwide on important crops such as potato, tomato or wheat. Fungal spores can also have serious detrimental effects on human health, triggering respiratory diseases and allergenic processes. The aim of this study was not only to examine the relationship between the atmospheric Alternaria spore content and the prevailing meteorological parameters, but also to predict the atmospheric Alternaria spore content in the Northwest Spain using a novel data analysis technique, ANNs (Artificial Neural Networks). A Hirst-type LANZONI VPPS 2000 volumetric 7-day recording sampler was used to collect the airborne spores from 1997 to 2008. Neural networks provided us with a good tool for forecasting Alternaria airborne spore concentration, and thus could help the automation of the prediction system in the aerobiological information diffusion to the population suffering from allergic problems or the prevention of considerable economic worldwide losses on important crops. Our proposed model would be applied to different geographical areas; nevertheless, the adjustment of the model, by using the available and adequate variables, would be realised in each case.     

Use of Forest Ecosystem Classification systems in fire management

Forest Ecosystem Classification (FEC) systems have been used in the past mainly for forest management decision-making. FEC systems can also serve an important role for decision-making in other disciplines, such as fire management for both wildfire suppression and prescribed burning operations. FEC systems can provide an important means of identifying potential fuels that may be present on a forest site. This fuel information, in combination with current fire weather conditions, as determined by the Canadian Forest Fire Weather Index (FWI) system, can assist fire managers in determining potential fire behaviour if ignition should occur. FEC systems provide a means of identifying the possible presence of a live understory vegetation component, a fuel layer that has been largely ignored in the past due to a lack of information. Dense understory vegetation can produce a very moist microlimate that can effectively hinder fire spread. The use of FEC systems can help in setting priorities on which wildfires need to be attacked aggressively. For prescribed burning, FEC systems can assist in achieving burn objectives better and more safely.     

干旱区内陆艾比湖流域平原区景观生态安全评价研究

在构建基于景观尺度的生态安全评价模型基础上,分析了1990-2005年干旱区内陆艾比湖流域平原区的景观生态安全变化特征。结果表明:研究区景观生态安全状况可以分为3个层次,并以河流、湖泊及沼泽为主的湿地景观所在区域的生态安全程度相对较高。1990-2005年,研究区生态安全状况呈现"V"字型变化趋势,其中生态安全评价指数相对较低区域的面积所占比例呈现先增加后减少、总体上趋于增加的状态;而生态安全指数相对较高区域的面积所占比例呈现先减少后增加、总体上趋于减少的状态。     

Development of MODIS data-based algorithm for retrieving sea surface temperature in coastal waters

A new algorithm was developed for retrieving sea surface temperature (SST) in coastal waters using satellite remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua platform. The new SST algorithm was trained using the Artificial Neural Network (ANN) method and tested using 8 years of remote sensing data from MODIS Aqua sensor and in situ sensing data from the US coastal waters in Louisiana, Texas, Florida, California, and New Jersey. The ANN algorithm could be utilized to map SST in both deep offshore and particularly shallow nearshore waters at the high spatial resolution of 1 km, greatly expanding the coverage of remote sensing-based SST data from offshore waters to nearshore waters. Applications of the ANN algorithm require only the remotely sensed reflectance values from the two MODIS Aqua thermal bands 31 and 32 as input data. Application results indicated that the ANN algorithm was able to explaining 82–90% variations in observed SST in US coastal waters. While the algorithm is generally applicable to the retrieval of SST, it works best for nearshore waters where important coastal resources are located and existing algorithms are either not applicable or do not work well, making the new ANN-based SST algorithm unique and particularly useful to coastal resource management.     

Regional data refine local predictions: modeling the distribution of plant species abundance on a portion of the central plains

Species distribution models are frequently used to predict species occurrences in novel conditions, yet few studies have examined the consequences of extrapolating locally collected data to regional landscapes. Similarly, the process of using regional data to inform local prediction for species distribution models has not been adequately evaluated. Using boosted regression trees, we examined errors associated with extrapolating models developed with locally collected abundance data to regional-scale spatial extents and associated with using regional data for predictions at a local extent for a native and non-native plant species across the northeastern central plains of Colorado. Our objectives were to compare model results and accuracy between those developed locally and extrapolated regionally, those developed regionally and extrapolated locally, and to evaluate extending species distribution modeling from predicting the probability of presence to predicting abundance. We developed models to predict the spatial distribution of plant species abundance using topographic, remotely sensed, land cover and soil taxonomic predictor variables. We compared model predicted mean and range abundance values to observed values between local and regional. We also evaluated model prediction performance based on Pearson's correlation coefficient. We show that: (1) extrapolating local models to regional extents may restrict predictions, (2) regional data can help refine and improve local predictions, and (3) boosted regression trees can be useful to model and predict plant species abundance. Regional sampling designed in concert with large sampling frameworks such as the National Ecological Observatory Network may improve our ability to monitor changes in local species abundance.     

The Influence of Linear Elements on Plant Species Diversity of Mediterranean Rural Landscapes: Assessment of Different Indices and Statistical Approaches

This paper mainly aims to study the linear element influence on the estimation of vascular plant species diversity in five Mediterranean landscapes modeled as land cover patch mosaics. These landscapes have several core habitats and a different set of linear elements -habitat edges or ecotones, roads or railways, rivers, streams and hedgerows on farm land- whose plant composition were examined. Secondly, it aims to check plant diversity estimation in Mediterranean landscapes using parametric and non-parametric procedures, with two indices: Species richness and Shannon index.Land cover types and landscape linear elements were identified from aerial photographs. Their spatial information was processed using GIS techniques. Field plots were selected using a stratified sampling design according to relieve and tree density of each habitat type. A 50×20 m² multi-scale sampling plot was designed for the core habitats and across the main landscape linear elements. Richness and diversity of plant species were estimated by comparing the observed field data to ICE (Incidence-based Coverage Estimator) and ACE (Abundance-based Coverage Estimator) non-parametric estimators.The species density, percentage of unique species, and alpha diversity per plot were significantly higher (p < 0.05) in linear elements than in core habitats. ICE estimate of number of species was 32% higher than of ACE estimate, which did not differ significantly from the observed values. Accumulated species richness in core habitats together with linear elements, were significantly higher than those recorded only in the core habitats in all the landscapes. Conversely, Shannon diversity index did not show significant differences.     

Modeling Large Fire Frequency and Burned Area in Canadian Terrestrial Ecosystems with Poisson Models

Large wildland fires are major disturbances that strongly influence the carbon cycling and vegetation dynamics of Canadian boreal ecosystems. Although large wildland fires have recently received much scrutiny in scientific study, it is still a challenge for researchers to predict large fire frequency and burned area. Here, we use monthly climate and elevation data to quantify the frequency of large fires using a Poisson model, and we calculate the probability of burned area exceeding a certain size using a compound Poisson process. We find that the Poisson model simulates large fire occurrence well during the fire season (May through August) using monthly climate, and the threshold probability calculated by the compound Poisson model agrees well with historical records. Threshold probabilities are significantly different among different Canadian ecozones, with the Boreal Shield ecozone always showing the highest probability. The fire prediction model described in this study and the derived information will facilitate future quantification of fire risks and help improve fire management in the region.     

A Classification Procedure for Mapping Topo-climatic Conditions for Strategic Vegetation Planning

Environmental classification addresses issues involving the representation and analysis of continuous and variable ecological data. This study creates a methodology to define topo-climatic landscapes (TCL) in the north-west of Catalonia, which is situated in the north-east of the Iberian Peninsula. TCL provide data regarding the ecological behavior of a landscape in terms of its topography, physiognomy, and climate, which are the main drivers of an ecosystem. The variables selected are derived from a variety of different sources, such as remote sensing and climatic atlases. The methodology employed combines unsupervised iterative cluster classification with supervised fuzzy classification. Twenty eight TCL, which can be differentiated in terms of their vegetation physiognomy and vegetation altitudinal range type, were selected for the study area. Furthermore, a hierarchy among the TCL is established which permits the merging of clusters and allows for changes in thematic resolution. By using the topo-climatic landscape map, managers can identify patches with similar environmental conditions and at the same time assess the uncertainty involved in classification.