A Method for Ensemble Wildland Fire Simulation

An ensemble simulation system that accounts for uncertainty in long-range weather conditions and two-dimensional wildland fire spread is described. Fuel moisture is expressed based on the energy release component, a US fire danger rating index, and its variation throughout the fire season is modeled using time series analysis of historical weather data. This analysis is used to characterize the seasonal trend in ERC, autocorrelation of residuals, and daily standard deviation and stochastically generate artificial time series of afternoon fuel moisture. Daily wind speed and direction are sampled stochastically from joint probabilities of historical wind speed and direction for the date range of the fire simulation period. Hundreds or thousands of fire growth simulations are then performed using the synthetic fire weather sequences. The performance of these methods is evaluated in terms of the number of ensemble member simulations, one- versus two-dimensional fire spread simulations, and comparison with results from 91 fires occurring from 2007 to 2009. Simulations were found to be in consistent agreement with observations, but trends indicate that the ensemble average of simulated fire sizes were consistently larger than actual fires whereas the farthest extent burned by fires was underestimated.     

Soil Erosion Potential after Fire and Rehabilitation Treatments in Greece

Wildland fires are one of the more severe disturbances for natural ecosystems in the Mediterranean basin and can become a critical factor in the process of soil erosion. A quantitative assessment of soil erosion is needed in order to form an assessment on the extent and magnitude of post-fire soil erosion potential and to assess the effectiveness of the rehabilitation treatment. On 21 August 2006, a large wildland fire occurred in the Kassandra Peninsula in northern Greece, which burned one fifth of the Peninsula. After the fire, in order to protect the soil against erosion, the Forest Services applied a hillslope rehabilitation treatment of contour-felled logs and branch piles. In this paper, we report quantitative estimation of the wildland fire and erosion control treatment on soil erosion potential. The coupling of the Universal Soil Loss Equation and the Geographical Information Systems was implemented and the erosion potential was found to be 2.8 t/ha/year pre-fire, 29.5 t/ha/year post-fire, and 21.3 t/ha/year after rehabilitation treatment. The model can successfully contribute in the planning of the rehabilitation treatment but it cannot be used in the quantification of the soil loss after the application of the erosion control measures. The comparisons between the results of the three cases indicate the importance of the immediate erosion control measures in order to mitigate soil loss and restore the natural environment.     

Spatial Distribution of Forest Fires and Controlling Factors in Andhra Pradesh, India Using Spot Satellite Datasets

Fires are one of the major causes of forest disturbance and destruction in several dry deciduous forests of southern India. In this study, we use remote sensing data sets in conjunction with topographic, vegetation, climate and socioeconomic factors for determining the potential causes of forest fires in Andhra Pradesh, India. Spatial patterns in fire characteristics were analyzed using SPOT satellite remote sensing datasets. We then used nineteen different metrics in concurrence with fire count datasets in a robust statistical framework to arrive at a predictive model that best explained the variation in fire counts across diverse geographical and climatic gradients. Results suggested that, of all the states in India, fires in Andhra Pradesh constituted nearly 13.53% of total fires. District wise estimates of fire counts for Andhra Pradesh suggested that, Adilabad, Cuddapah, Kurnool, Prakasham and Mehbubnagar had relatively highest number of fires compared to others. Results from statistical analysis suggested that of the nineteen parameters, population density, demand of metabolic energy (DME), compound topographic index, slope, aspect, average temperature of the warmest quarter (ATWQ) along with literacy rate explained 61.1% of total variation in fire datasets. Among these, DME and literacy rate were found to be negative predictors of forest fires. In overall, this study represents the first statewide effort that evaluated the causative factors of fire at district level using biophysical and socioeconomic datasets. Results from this study identify important biophysical and socioeconomic factors for assessing ‘forest fire danger’ in the study area. Our results also identify potential ‘hotspots’ of fire risk, where fire protection measures can be taken in advance. Further this study also demonstrate the usefulness of best-subset regression approach integrated with GIS, as an effective method to assess ‘where and when’ forest fires will most likely occur.     

Long Term Fire Scar Monitoring with Remote Sensing in Northern Namibia: Relations Between Fire Frequency, Rainfall, Land Cover, Fire Management and Trees

A cost-effective method was developed to map fire scars on Quicklooks of Landat TM imagery. The method was compared with a full resolution Landsat image using visual interpretation and supervised classification using the Maximum Likelihood procedure, resulting in a high degree of agreement between methods. A long time series of fire scars was developed using all available Landsat Quicklooks between 1989 and 2001 for an area of 63000 sq km in north-east Namibia. Between 27 and 51% of the study area burned annually, while only 10% of the area did not burn between 1989 and 2001. Not-burned areas were mainly settled areas and permanent wetlands. 33% of the area burned between 5 and 7 times during the 13 years indicating a high frequency overall. Rainfall and livestock had little influence on burned areas. In 1996 formal fire management started in a portion of the study area consisting of building firebreaks and holding awareness programs. A comparison of burned areas before and after the intervention started allowed evaluating its effectiveness. The area where the formal fire management program was undertaken showed a significant decrease in burned area. It is suggested that awareness campaigns rather than firebreaks contributed to this decrease. Selected tree population data were compared with fire frequencies. Differences in tree occurrence, regeneration, and stem diameter distributions between low and high fire frequencies could be detected and explained with known responses of the species to fire. This suggests that the observed time series is representative of a long-term fire regime in the area.     

Decadal time-scale monitoring of forest fires in Similipal Biosphere Reserve,India using remote sensing and GIS

Analyzing the spatial extent and distribution of forest fires is essential for sustainable forest resource management. There is no comprehensive data existing on forest fires on a regular basis in Biosphere Reserves of India. The present work have been carried out to locate and estimate the spatial extent of forest burnt areas using Resourcesat-1 data and fire frequency covering decadal fire events (2004–2013) in Similipal Biosphere Reserve. The anomalous quantity of forest burnt area was recorded during 2009 as 1,014.7 km². There was inconsistency in the fire susceptibility across the different vegetation types. The spatial analysis of burnt area shows that an area of 34.2 % of dry deciduous forests, followed by tree savannah, shrub savannah, and grasslands affected by fires in 2013. The analysis based on decadal time scale satellite data reveals that an area of 2,175.9 km² (59.6 % of total vegetation cover) has been affected by varied rate of frequency of forest fires. Fire density pattern indicates low count of burnt area patches in 2013 estimated at 1,017 and high count at 1,916 in 2004. An estimate of fire risk area over a decade identifies 12.2 km² is experiencing an annual fire damage. Summing the fire frequency data across the grids (each 1 km²) indicates 1,211 (26 %) grids are having very high disturbance regimes due to repeated fires in all the 10 years, followed by 711 grids in 9 years and 418 in 8 years and 382 in 7 years. The spatial database offers excellent opportunities to understand the ecological impact of fires on biodiversity and is helpful in formulating conservation action plans.     

Fire risk evaluation using multicriteria analysis—a case study

Forest fires are one of the major causes of ecological disturbance and environmental concerns in tropical deciduous forests of south India. In this study, we use fuzzy set theory integrated with decision-making algorithm in a Geographic Information Systems (GIS) framework to map forest fire risk. Fuzzy set theory implements classes or groupings of data with boundaries that are not sharply defined (i.e., fuzzy) and consists of a rule base, membership functions, and an inference procedure. We used satellite remote sensing datasets in conjunction with topographic, vegetation, climate, and socioeconomic datasets to infer the causative factors of fires. Spatial-level data on these biophysical and socioeconomic parameters have been aggregated at the district level and have been organized in a GIS framework. A participatory multicriteria decision-making approach involving Analytical Hierarchy Process has been designed to arrive at a decision matrix that identified the important causative factors of fires. These expert judgments were then integrated using spatial fuzzy decision-making algorithm to map the forest fire risk. Results from this study were quite useful in identifying potential “hotspots” of fire risk, where forest fire protection measures can be taken in advance. Further, this study also demonstrates the potential of multicriteria analysis integrated with GIS as an effective tool in assessing “where and when” forest fires will most likely occur.     

中国秸秆焚烧的遥感监测与分析

用最近四年的MODIS火点数据,通过相关处理和分析,得出了全国秸秆焚烧状况的时空分布规律。结果表明,秸秆焚烧最严重的区域一直分布在淮河流域和陕西关中平原,这是秸秆禁烧工作的重点;南部各省秸秆焚烧点密度较小,比较分散,造成的危害相对较小。秸秆焚烧的主要时间,北方集中在夏秋季节,主要是小麦、玉米、油菜等的秸秆;南方集中在冬春季节,主要是水稻、甘蔗、油菜等的秸秆。秸秆焚烧的时空分布规律在最近几年基本无大的变化,秸秆焚烧面积在2002年~2005年期间逐年上升。文章结论反映了各地执行秸秆禁烧政策的效果,可为相关政府部门进行秸秆禁烧工作提供依据。     

Estimating the probability of wildfire occurrence in Mediterranean landscapes using Artificial Neural Networks

Wildfires are a major disturbance in the Mediterranean Basin and an ecological factor that constantly alters the landscape. In this context, it is crucial to understand where wildfires are more likely to occur as well as the drivers guiding them in complex landscapes such as the Mediterranean area. The objectives of this study are to estimate wildfire probability occurrence as a function of biophysical and human-related drivers, to provide an assessment of the relative impact of each driver and analyze the performance of machine learning techniques compared to traditional regression modeling. By employing an Artificial Neural Network model and fire data (2004–2012), we estimated wildfire probability across two geographical regions covering most of the Italian territory: Alpine and subalpine region and Insular and peninsular region. The high classification accuracy (0.68 for the Alpine and subalpine region and 0.76 for the Insular and peninsular region) and good performances of the technique (AUC values of 0.82 and 0.76, respectively) suggest that our model can be used in the areas studied to assess wildfire probability occurrence. We compared our model with a logistic function, which showed a weaker predictive power (AUC values of 0.78 for the Alpine and subalpine region and 0.65 for the Insular and peninsular region) compared to the Artificial Neural Network. In addition, we assessed the importance of each variable by isolating it in the model. The importance of an individual variable differed between the two regions, underscoring the high diversity of wildfire occurrence drivers in Mediterranean landscapes. Results show that in the Alpine and subalpine region, the presence of forest is the most important variable, while climate resulted as being the most important variable in the Insular and peninsular region. The majority of areas recently affected by large wildfires in both regions have been correctly classified by the ANN model as ‘high fire probability’. Hence, the use of an Artificial Neural Network is efficient and robust for understanding the probability of wildfire occurrence in Italy and other similar complex landscapes.     

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.     

Heat wave hazard classification and risk assessment using artificial intelligence fuzzy logic

The average summer temperatures as well as the frequency and intensity of hot days and heat waves are expected to increase due to climate change. Motivated by this consequence, we propose a methodology to evaluate the monthly heat wave hazard and risk and its spatial distribution within large cities. A simple urban climate model with assimilated satellite-derived land surface temperature images was used to generate a historic database of urban air temperature fields. Heat wave hazard was then estimated from the analysis of these hourly air temperatures distributed at a 1-km grid over Athens, Greece, by identifying the areas that are more likely to suffer higher temperatures in the case of a heat wave event. Innovation lies in the artificial intelligence fuzzy logic model that was used to classify the heat waves from mild to extreme by taking into consideration their duration, intensity and time of occurrence. The monthly hazard was subsequently estimated as the cumulative effect from the individual heat waves that occurred at each grid cell during a month. Finally, monthly heat wave risk maps were produced integrating geospatial information on the population vulnerability to heat waves calculated from socio-economic variables.     

Greenhouse gas emissions from vegetation fires in Southern Africa

Methane (CH₄), carbon monoxide (CO), nitrogen oxides (NO_x), volatile organic carbon, and aerosols emitted as a result of the deliberate or accidental burning of natural vegetation constitute a large component of the greenhouse gas emissions of many African countries, but the data needed for calculating these emissions by the IPCC methodology is sparse and subject to estimation errors. An improved procedure for estimating emissions from fires in southern Africa has been developed. The proposed procedure involves reclassifying existing vegetation maps into one of eleven broad, functional vegetation classes. Fuel loads are calculated within each 0.5 × 0.5° cell based on empirical relationships to climate data for each class. The fractional area of each class that burns is estimated by using daily low-resolution satellite fire detection, which is calibrated against a subsample of pre- and post-fire high-resolution satellite images. The emission factors that relate the quantity of gas released to the mass of fuel burned are based on recent field campaigns in Africa and are related to combustion efficiency, which is in turn related to the fuel mix. The emissions are summed over the 1989 fire season for Africa south of the equator. The estimated emissions from vegetation burning in the subcontinent are 0.5 Tg CH₄, 14.9 Tg CO, 1.05 Tg NO_x, and 1.08 Tg of particles smaller than 2.5µm. The 324 Tg CO₂ emitted is expected to be reabsorbed in subsequent years. These estimates are smaller than previous estimates.     

Assessing the risk to firefighters from chemical vapors and gases during vehicle fire suppression

Despite the frequent occurrence of vehicle fires, very few studies investigating firefighters' potential inhalation exposures during vehicle fire suppression have been conducted. In this paper, we present an assessment of firefighters' health risk from vehicle fire suppression that accounts for the mixture of gases and vapors likely to be found in these fires. Summa canisters were used to collect emissions from the engine and cabin fires of a single vehicle and were analyzed for 75 volatile organic compounds (VOCs). Firefighters' breathing zone concentrations (BZCs) of aromatic hydrocarbons, aldehydes, isocyanates, and carbon monoxide were measured during the suppression of three vehicle fires. The Summa canister and BZC data were used to develop a simple model for predicting BZCs for the compounds that were not measured in the firefighters' breathing zones. Hazard quotients (HQs) were calculated by dividing the predicted and measured BZCs by the most conservative short-term exposure limits (STELs) or ceiling limits. Hazard indices (HIs) were determined by adding HQs for compounds grouped by the target organ for acute health effects. Any HIs above unity represented unacceptable risks. According to this mixture analysis, the estimated 95(th) percentile of the exposure distribution for the study population represents ≥ 9.2 times the acceptable level of risk to the respiratory tract and eyes. Furthermore, chemicals known or reasonably anticipated to be human carcinogens contributed to > 45% of these HIs. While STELs are not usually based on carcinogenicity, maintaining exposures below STELs may protect individuals from the biological stress that could result from short-term exposures to carcinogens over time. Although vehicle fires are suppressed quickly (<10 min), this assessment suggests that firefighters have the potential to be overexposed to acute toxins during vehicle fire suppression and should therefore wear self-contained breathing apparatus at all times during vehicle fire response.     

Pre- and post-fire pollutant loads in an urban fringe watershed in Southern California

Post-fire runoff has the potential to be a large source of contaminants to downstream areas. However, the magnitude of this effect in urban fringe watersheds adjacent to large sources of airborne contaminants is not well documented. The current study investigates the impacts of wildfire on stormwater contaminant loading from the upper Arroyo Seco watershed, burned in 2009. This watershed is adjacent to the Greater Los Angeles, CA, USA area and has not burned in over 60 years. Consequently, it acts as a sink for regional urban pollutants and presents an opportunity to study the impacts of wildfire. Pre- and post-fire storm samples were collected and analyzed for basic cations, trace metals, and total suspended solids. The loss of vegetation and changes in soil properties from the fire greatly increased the magnitude of storm runoff, resulting in sediment-laden floods carrying high concentrations of particulate-bound constituents. Post-fire concentrations and loads were up to three orders of magnitude greater than pre-fire values for many trace metals, including lead and cadmium. A shift was also observed in the timing of chemical delivery, where maximum suspended sediment, trace metal, and cation concentrations coincided with, rather than preceded, peak discharge in the post-fire runoff, amplifying the fire’s impacts on mass loading. The results emphasize the importance of sediment delivery as a primary mechanism for post-fire contaminant transport and suggest that traditional management practices that focus on treating only the early portion of storm runoff may be less effective following wildfire. We also advocate that watersheds impacted by regional urban pollutants have the potential to pose significant risk for downstream communities and ecosystems after fire.     

Assessment of Wildfire Hazards with a Semiparametric Spatial Approach

Rural households in agricultural economies are vulnerable to several environmental risks such as fires, floods, and droughts that may affect their productivity in whole or in part. These hazards are especially relevant in developing countries where farmers have few or no access to traditional risk-transfer techniques, such as insurance and finance, and where low governmental investments in rural infrastructure, risk assessment techniques, or early warning systems makes the aftermath of such hazards more expensive and results in slower recovery for those who are affected. In this paper, we use historical satellite data (Terra) of burned areas in South America to fit a semiparametric spatial model, based on kernel smoothing and on a nonlinear relationship between average time between events and damage, to assess the environmental hazard affecting the land’s productivity. The results were twofold: first, we were able to develop a spatial assessment of fire hazard, and second, we were able to evaluate how much a farmer may lose in terms of productivity per hectare due to the environmental hazard. The methodology may be easily adapted to other world regions; to different environmental hazards such as floods, windbreak, windthrow, or related land-use changes; or to integrate various environmental hazards simultaneously, as long as they can be monitored via remote sensing (e.g., satellite imagery, aerial photographs, etc).     

A GIS-based decision support system for determining the shortest and safest route to forest fires: a case study in Mediterranean Region of Turkey

The ability of firefighting vehicles and staff to reach a fire area as quickly as possible is critical in fighting against forest fires. In this study, a Geographical Information System-based decision support system was developed to assist fire managers in determining the fastest and the safest or more reliable access routes from firefighting headquarters to fire areas. The decision support system was tested in the Kahramanmaras Forestry Regional Directoratein the Mediterranean region of Turkey. The study area consisted of forested lands which had been classified according to fire sensitivity. The fire response routing simulations considered firefighting teams located in 20 firefighting headquarter locations. The road network, the locations of the firefighting headquarters, and possible fire locations were mapped for simulation analysis. In alternative application simulations, inaccessible roads which might be closed due to fire or other reasons were indicated in the network analysis so that the optimum route was not only the fastest but also the safest and most reliable path. The selection of which firefighting headquarters to use was evaluated by considering critical response time to potential fire areas based on fire sensitivity levels. Results indicated that new firefighting headquarters should be established in the region in order to provide sufficient firefighting response to all forested lands. In addition, building new fire access roads and increasing the design speed on current roads could also increase firefighting response capabilities within the study area.     

Habitat monitoring and conservation prioritisation of protected areas in Western Ghats,Kerala, India

Spatially explicit approach is essential to prioritise the ecosystems for biodiversity conservation. In the present study, the conservation status of 20 protected areas of the Western Ghats of Kerala, India, was analysed based on long-term changes in forests (1975–1985–1995–2005–2013), landscape level changes in fragmentation and forest fires (2005–2015). This study has shown that a significant forest loss occurred in protected areas before declaration. Idukki is one of the major protected areas which showed a drastic reduction (18.83%) in its forest cover. During 1985–1995, Periyar tiger reserve had lost 24.19 km² core 3 forest area followed by Peppara (18.54 km²), Parambikulam (17.93 km²), Chimmony (17.71 km²), Peechi-Vazhani (12.31 km²) and Neyyar (11.67 km²). An area of 71.33 km² of the protected area was affected by fires in 2014. Overall protected area-wise decadal analysis indicates Periyar has the highest number of fire incidences followed by Wayanad, Kurinjimala, Silent Valley and Eravikulam. Disturbances in the form of fires and fragmentation still exist and may have significant conservation threat to flora and fauna. Among protected areas, many are having a probability to go under threat or dynamic stage. Chinnar, Thattekkad and Kurinjimala sanctuaries are representing high levels of vulnerability, or they are near to decline stage. Habitat level monitoring of the anthropogenic disturbances can be efficiently useful for the strategic conservation planning. The present study has provided geospatial database on spatial patterns of deforestation, fragmentation and forest fires in protected areas of Kerala. Conservation prioritization approach based on these parameters will be useful for the strategic planning in the state of Kerala.     

Analysing Seasonal Differences between a Soil Water Balance Model and in Situ Soil Moisture Measurementsat Nine Locations Across Europe

We compared soil moisture from the soil water balance model for European Water Accounting (swbEWA) with in situ observations from nine locations in three European climatic zones (continental, Mediterranean and maritime temperate), for different periods between 2003 and 2011. Despite the simplicity of the swbEWA model, the patterns of temporal changes in soil moisture content are well represented at all locations. Annual averages show that the model overestimates the soil moisture content, and that overestimations are the smallest when measurements are obtained from more than one depth. These results suggest that the relationship between simulated and observed soil moisture also depends on the number of measurements and the depth over which they are taken. In the continental climate, where snow cover and frozen soil influence soil moisture, we observe higher root mean square error values in winter months. However, in the Mediterranean and maritime temperate climates, we do not observe clear common seasonal patterns in the soil moisture profile, which makes it difficult to relate the model’s accuracy to climate. With the percentage of correctness and probability of detection measures, we tested the model performance in simulating dry versus non-dry events. The percentage of the correctly classified dry and non-dry events is higher than 84 % at all locations, whereas the probability to detect dry events is significantly lower, exceeding 50 % at only four out of nine stations. The frequency distribution of consecutive days with dry soil (CDDS) confirms the model performance: higher number of short dry periods (with less than 20 days of soil moisture near wilting point) are reproduced and observed in continental climates, whereas long dry periods (longer than 50 days) are noted in the Mediterranean climate. Overall, the statistical measures suggest that the model produces the highest accuracy in summer months at the stations in continental climates, whereas in the Mediterranean climate, the accuracy is slightly higher in the colder seasons.     

Generating Synthetic Rainfall on Various Timescales—Daily, Monthly and Yearly

The main objective of this paper is to present a model for generating synthetic rainfall totals on various timescales to be applicable for a variety of uses. Many large-scale ecological and water resource models require daily, monthly and yearly rainfall data as input to the model. As historical data provides only one realisation, synthetic generated rainfall totals are needed to assess the impact of rainfall variability on water resources systems (Srikanthan, In: MODSIM2005, Melbourne, Dec. 2005, pp. 1915–1921, 2005). Thus, our preferred model should simulate rainfall for yearly, monthly and daily periods. We believe that, for water supply issues, no higher resolution is needed, although higher resolution would be useful in models designed to measure the risk of local flooding. The critical factors are daily, monthly and yearly totals and daily, monthly and yearly variation. A model for generating yearly totals will be described using traditional time series methods. This model, along with a similarly constructed daily generation model by Piantadosi et al. (A New Model for Correlated Daily Rainfall, 2008), will be cascaded to start with a synthetic yearly total, then generate a synthetic sequence of monthly totals (through selection from a large number of realisations) that match the yearly total, and subsequently perform a similar operation for sequences of daily totals to match the required monthly totals. We present a new model for the generation of synthetic monthly rainfall data, which we demonstrate for Parafield in Adelaide, South Australia. The rainfall for each month of the year is modelled as a non-negative random variable from a mixed distribution with either a zero outcome or a strictly positive outcome. We use maximum likelihood to find parameters for both the probability of a zero outcome and the gamma distribution that best matches the observed probability density for the strictly positive outcomes. We describe a new model that generates correlated monthly rainfall totals using a diagonal band copula with a single parameter to generate lag-1 correlated random numbers. Our model preserves the marginal monthly distributions and, hence, also preserves the monthly and yearly means. We show that, for Parafield, the correlation between rainfall totals for successive months is not significant, and so, it is reasonable to assume independence. This is, however, not true for daily rainfall. We describe a new model that generates correlated daily rainfall totals using a diagonal band copula with a single parameter to generate lag-1 correlated random numbers. This is an extended version of a paper presented at the 17th Biennial Congress on Modelling and Simulation, Christchurch, New Zealand, December 2007.     

Characteristics and driving factors of marsh changes in Zhalong wetland of China

Zhalong National Nature Reserve in the northeast of China is a large wetland and a habitat of hundreds species of fauna and flora. The rare red-crowned crane is one kind of endangered birds in it. Recently, Zhalong wetland is shrinking and it encounters many problems including occasional fires, bad water quality, human activities, etc. In order to find out a proper way to protect and restore the wetland, this study, using a geographic information system, the global positioning system and remote sensing techniques, analyses the spatial characteristics of the changes in marsh landscape pattern and examines the driving factors for these changes. Data sources include 8 Landsat Thematic Mapper satellite images of Zhalong area in the period of 1986–2002 and the investigation information on site. Based on the analysis of changes of marsh area and annual precipitation during the 16 years, it is found that there is a close correlation between annual precipitation and marsh area. It means that climate is one of driving factors of marsh pattern changes. To understand influences of other kinds of land uses on marsh spatial distribution in Zhalong wetland, this paper analyses the relationship between marsh and different kinds of land uses, such as water surface, residential area, farmland, salina land and grass land, respectively. According to the patch analysis theory, a fragmental index and a fractal dimension of the marsh are calculated with perimeter-area method. The results indicate that the marsh pattern is affected by human activities significantly. In addition, the location alteration of marsh centroid point over the 16 years is studied. The movement trace of marsh centroid point is concerned with different hydrological situation in different areas of the wetland. In summary the characteristics of the marsh landscape pattern evolution during the 16 years are affected by multiple driving factors. The main driving factors are climate, human activities, distribution of other kinds of land uses and hydrological situation in different areas.     

Characterizing spatiotemporal non-stationarity in vegetation dynamics in China using MODIS EVI dataset

This paper evaluated the spatiotemporal non-stationarity in the vegetation dynamic based on 1-km resolution 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) datasets in China during 2001–2011 through a wavelet transform method. First, it revealed from selected pixels that agricultural crops, natural forests, and meadows were characterized by their distinct intra-annual temporal variation patterns in different climate regions. The amplitude of intra-annual variability generally increased with latitude. Second, parameters calculated using a per-pixel strategy indicated that the natural forests had the strongest variation pattern from seasonal to semiannual scales, and the multiple-cropping croplands typically showed almost equal variances distributed at monthly, seasonal, and semiannual scales. Third, spatiotemporal non-stationarity induced from cloud cover was also evaluated. It revealed that the EVI temporal profiles were significantly distorted with regular summer cloud cover in tropical and subtropical regions. Nevertheless, no significant differences were observed from those statistical parameters related to the interannual and interannual components between the de-clouded and the original MODIS EVI datasets across the whole country. Finally, 12 vegetation zones were proposed based on spatiotemporal variability, as indicated by the magnitude of interannual and intra-annual dynamic components, normalized wavelet variances of detailed components from monthly to semiannual scale, and proportion of cloud cover in summer. This paper provides insightful solutions for addressing spatiotemporal non-stationarity by evaluating the magnitude and frequency of vegetation variability using monthly, seasonal, semiannual to interannual scales across the whole study area.