Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques

Fire in forested areas can be regarded as an environmental disaster which is triggered by either natural forces or anthropogenic activities. Fires are one of the major hazards in forested and grassland areas in the north of Iran. Control of fire is difficult, but it is feasible to map fire risk by geospatial technologies and thereby minimize the frequency of fire occurrences and damages caused by fire. The fire risk models provide a suitable concept to understand characterization of fire risk. Some models are map based, and they combine effectively different forest fire–causing variables with remote sensing data in a GIS environment for identifying and mapping forest fire risk. In this study, Structural Fire Index, Fire Risk Index, and a new index called Hybrid Fire Index were used to delineate fire risk in northeastern Iran that is subjected to frequent forest fire. Vegetation moisture, slope, aspect, elevation, distance from roads, and vicinity to settlements were used as the factors influencing accidental fire starts. These indices were set up by assigning subjective weight values to the classes of the layers based on their sensitivity ratio to fire. Hot spots data derived from MODIS satellite sensor were used to validate the indices. Assessment of the indices with receiver operating characteristic (ROC) curves shows that 76.7 % accuracy of the HFI outperformed the other two indices. According to the Hybrid Fire Index, 57.5 % of the study area is located under high-risk zone, 33 % in medium-risk zone, and the remaining 9.5 % area is located in low-risk zone.     

Land subsidence risk assessment using GIS fuzzy logic spatial modeling in Varamin aquifer,Iran

GeoJournal - The extent of the subsidence and the consequents damage to most of the residential and populated areas of Iran have made this phenomenon one of the most important natural hazards after...     

Flood risk index in data-scarce river basins using the AHP and GIS approach

Natural Hazards - The flood risk assessment study is an important factor in order to identify the critical or high-risk zone areas. This research intends to develop a flood risk index map of...     

A LabVIEW-based fire monitoring software using multi-criteria AHP approach for detecting geolocation of wildfire

Natural Hazards - High Conservation Value Forests or botanical parks are critical forested areas that need to be appropriately managed and protected against fire, as they contain large...     

Landslide susceptibility mapping using AHP and fuzzy methods in the Gilan province,Iran

GeoJournal - Landslides are natural destructive phenomena that can cause great damage to property and life loss. One of the fundamental proceedings to reduce the possible damage is identifying...     

Spatial assessment of the potential of groundwater quality using fuzzy AHP in GIS

In this study, fuzzy AHP method is used for extracting the water quality indicators based on the Schuler standard and World Health Organization (WHO) guidelines during a 20-year period. For this purpose, the best fit of the zoning model was performed. Furthermore, by comparing the standard errors, the continuous Raster layer was extracted from the important parameters used in generating the qualitative potential assessment index. The classified layer was generated by integrating continuous layers in the GIS environment and with the use of Python programming. The similarity of the outputs of both methods indicates the presence of large sections of aquifers in the middle and southwestern regions of Iran in the “temporarily drinkable” and “bad” classes. The calculations showed that the majority of aquifers that were located in the “inappropriate” class during the first 10 years fell to less valuable class types. Based on the results of the model, there is a direct correlation between the drop in water resources and the decline in the quality indices. In addition, in the Urmia and Bushehr coastal aquifers, due to excessive water withdrawal and salty water penetration, the quality of the table water is in critical condition. Based on the results of the research, the aquifers in the range of Zagros and Alborz mountains show the least change in water quality. The reason for this is the depth of the aquifer and the ability to recharge it.     

Application of a triangular fuzzy AHP approach for flood risk evaluation and response measures analysis

Flood risk evaluation and prediction represents an essential analytic step to coherently link flood control and disaster mitigation. The paper established a hybrid evaluation model based on fuzzy analytic hierarchy process (AHP) and triangular fuzzy number. It comprises flood risk evaluation and prediction to obtain risk factors ranking and comprehensive flood risk prediction, and then analyzed flood risk response measures. A case study is proposed entailing a flood risk evaluation and prediction in the Lower Yangtze River region. The evaluation results showed that the proposed evaluation and prediction model was capable of adequately representing the actual setting. In addition, a comparison with the previously described AHP and trapezoidal fuzzy AHP, and experimental results are encouraging, which fully demonstrates the effectiveness and superiority of the proposed model.     

Using AHP Method in GIS for PCDs Potential Mapping in Southeast of Iran

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A comparison of forest fire indices for predicting fire risk in contrasting climates in China

The relationships between fire danger indices and fire risk have been extensively studied in many regions of the world. This work uses partial effect analysis in semiparametric logistic regression models to assess the nonlinear relationships among location, day, altitude, fire danger indices, normalized difference vegetation index (NDVI), and fire ignition from 1996 to 2008 in four different climatic regions in China. The four regions are North China (NR), Northeast China (NE), Southeast China (SE), and Southwest China (SW). The three main results are as follows: First, different fire danger indices are selected as significant variables dependent on the region. The inter-regional difference could be partially explained by difference in local weather and vegetation conditions. Second, spatial location exerts highly significant effects in all four regions. NDVI values are selected as explained variable for NR, NE, and SE on fire ignitions. On a daily scale, altitude influences fire ignition for NR, SE, and SW. Third, the robustness of the probability models used in NE, SE, and SW is better than that in NR on a daily scale. The semiparametric logistic regression model used in this study is useful for assessing the ability of fire danger indices to estimate probabilities of fire ignition on a daily scale. This study encourages further research on assessing the predictive ability of fire danger indices developed at other temporal and spatial scales in China.     

Application of Bayesian networks for fire risk mapping using GIS and remote sensing data

This study estimates fire risk in Swaziland using geographic information system (GIS) and remote sensing data. Fire locations were identified in the study area from remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) active fire and burned area data for the period between April 2000 to December 2008 and January 2001 and December 2008, respectively. A total of thirteen biophysical and socio-economic explanatory variables were analyzed and processed using a Bayesian network (BN) and GIS to generate the fire risk maps. The interdependence of each of the factors was probabilistically determined using the expectation-maximization (EM) learning algorithm. The final probabilistic outputs were then used to classify the country into five fire risk zones for mitigation and management. Accuracy assessments and comparison of the fire risk maps indicate that the risk maps derived from the active fire and burned area data were 93.14 and 96.64% accurate, respectively, demonstrating sufficient agreement between the risk maps and the existing data. High fire risk areas are observed in the Highveld particularly plantation forests and grasslands and within the Lowveld sugarcane plantations. Land tenure and land cover are the dominant determinants of fire risk, the implications of which are discussed for fire management in Swaziland. Limitations of the data used and the modeling approach are also discussed including suggestions for improvements and future research.     

Forecasting areas vulnerable to forest conversion using artificial neural network and GIS (case study: northern Ilam forests,Ilam province,Iran)

Forest conversion due to illegal logging and agricultural expansion is a major problem that is hampering biodiversity conservation efforts in the Zagros region. Yet, areas vulnerable to forest conversion are unknown. This study aims to predict the spatial distribution of deforestation in western Iran. Landsat images dated 1988, 2001, and 2007 are classified in order to generate digital deforestation maps which locate deforestation and forest persistence areas. Meanwhile, in order to examine deforestation factors’ investigation, deforestation maps with physiographic and human spatial variables are entered into the model. Areas vulnerable to forest changes in the Zagros forest region are predicted by a multilayer perceptron neural network (MLPNN) with a Markov chain model. The results show that about 19,294 ha forest areas are deforested in the last 19 years. The predictive performance of the model appears successful, which is validated using the actual land cover map of the same year from Landsat data. The validated map is found to be 94 % accurate. The validation is also tested using the relative operating characteristic approach which yielded a value of 0.96. The model is then further extended to predict forest cover losses for 2020. The MLPNN approach was found to have a great potential to predict land use/land cover changes because it permits developing complex, nonlinear models.     

A new combined framework for sustainable development using the DPSIR approach and numerical modeling

Relying on the conceptual DPSIR framework and MODFLOW analysis,this study used a mixed approach to produce groundwater resource management solutions for the Najafabad area in central Iran.According to DPSIR results,agricultural activities put the highest pressure on groundwater resources in this region.The results showed the effectiveness of reducing water withdrawal over 30 years in maintaining the aquifer in a state of equilibrium.The best scenario consisted of cutting down extraction by 10% over the said period.Output maps of the water table rise at the Najafabad aquifer clearly showed that the groundwater management scenario involving a 10% reduction of water withdrawal was the most effective solution,as it would raise the water level by 6.7 m.Regarding other scenarios,reducing cultivated area by 20% was found to raise the water table by 5.03 m on average,while cutting down water withdrawal by 5% increased the water table by 3.6 m,and a 10% reduction of the cultivated area resulted in a 1.85 m rise.The combined model proposed here can be used for similar aquifers and can aid decision-makers and managers.     

Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS

Accurate estimates of wildfire probability and production of distribution maps are the first important steps in wildfire management and risk assessment. In this study, geographical information system (GIS)-automated techniques were integrated with the quantitative data-driven evidential belief function (EBF) model to predict spatial pattern of wildfire probability in a part of the Hyrcanian ecoregion, northern Iran. The historical fire events were identified using earlier reports and MODIS hot spot product as well as by carrying out multiple field surveys. Using the GIS-based EBF model, the relationships among existing fire events and various predictor variables predisposing fire ignition were analyzed. Model results were used to produce a distribution map of wildfire probability. The derived probability map revealed that zones of moderate, high, and very high probability covered nearly 60% of the landscape. Further, the probability map clearly demonstrated that the probability of a fire was strongly dependent upon human infrastructure and associated activities. By comparing the probability map and the historical fire events, a satisfactory spatial agreement between the five probability levels and fire density was observed. The probability map was further validated by receiver operating characteristic using both success rate and prediction rate curves. The validation results confirmed the effectiveness of the GIS-based EBF model that achieved AUC values of 84.14 and 81.03% for success and prediction rates, respectively.     

Soil erosion and sediment yield modeling using RS and GIS techniques: a case study, Iran

Water erosion is a serious and continuous environmental problem in many parts of the world. The need to quantify the amount of erosion, sediment delivery, and sediment yield in a spatially distributed form has become essential at the watershed scale and in the implementation of conservation efforts. In this study, an effort to predict potential annual soil loss and sediment yield is conducted by using the Revised Universal Soil Loss Equation (RUSLE) model with adaptation in a geographic information system (GIS). The rainfall erosivity, soil erosivity, slope length, steepness, plant cover, and management practice and conservation support practice factors are among the basic factors that are obtained from monthly and annual rainfall data, soil map of the region, 50-m digital elevation model, remote sensing (RS) techniques (with use of Normalized Difference Vegetation Index), and GIS, respectively. The Ilam dam watershed which is located southeast part of Ilam province in western Iran is considered as study area. The study indicates that the slope length and steepness of the RUSLE model are the most effective factors controlling soil erosion in the region. The mean annual soil loss and sediment yield are also predicted. Moreover, the results indicated that 45.25%, 12.18%, 12.44%, 10.79%, and 19.34% of the study area are under minimal, low, moderate, high, and extreme actual erosion risks, respectively. Since 30.13% of the region is under high and extreme erosion risk, adoption of suitable conservation measures seems to be inevitable. So, the RUSLE model integrated with RS and GIS techniques has a great potential for producing accurate and inexpensive erosion and sediment yield risk maps in Iran.     

GIS-based forest fire susceptibility modeling in Pauri Garhwal,India: a comparative assessment of frequency ratio,analytic hierarchy process and fuzzy modeling techniques

This study performs a comparative evaluation of Frequency Ratio (FR), Analytic Hierarchy Process (AHP), and Fuzzy AHP (FAHP) modeling techniques for forest fire susceptibility mapping in Pauri Garhwal, Uttarakhand, India. Locations of past forest fire events reported from November 2002 to July 2019 were collected from the Uttarakhand Forest Department and Forest Survey of India and combined with the ground observations obtained from the manual survey. Then, the locations were categorized into two groups of 70% (10,500 locations) and 30% (4500 locations), randomly, for training and validation purposes, respectively. Forest fire susceptibility mapping was performed on the basis of fourteen different topographic, biological, human-induced and climatic criteria such as Digital Elevation Model, Slope, Aspect, Curvature, Normalized Difference Vegetation Index, Normalized Difference Moisture Index, Topographic Wetness Index, Soil, Distance to Settlement, Distance to Road, Distance to Drainage, Rainfall, Temperature, and Wind Speed. The Receiver Operating Characteristic curve and the Area Under the Curve (AUC) were implemented for validation of the three achieved Forest Fire Susceptibility Maps. The AUC plot evaluation revealed that FAHP has a maximum prediction accuracy of 83.47%, followed by AHP (81.75%) and FR (77.21%). Thus, the map produced by FAHP exhibits the most satisfactory properties. Results and findings of this study will help in developing more efficient fire management strategies in both the open and the protected forest areas (Rajaji and Jim Corbett National Park) of the district.

     

Comparison of new individual and hybrid machine learning algorithms for modeling and mapping fire hazard: a supplementary analysis of fire hazard in different counties of Golestan Province in Iran

Natural Hazards - The forest fire hazard mapping using the accurate models in the fire-prone areas has particular importance to predict the future fire occurrence and allocate the resources for...     

Forest fire risk estimation in a typical temperate forest in Northeastern China using the Canadian forest fire weather index: case study in autumn 2019 and 2020

Natural Hazards - China's forest cover has increased by approximately 10% as a result of sustainable forest management since the late 1970s. The forest ecosystem area affected by fire is...     

Contribution of remote sensing and GIS to mapping the fire risk of Mediterranean forest case of the forest massif of Tlemcen (North-West Algeria)

Natural Hazards - Forest fires are one of the major environmental problems that cause harmful economic and ecological damage. The Algerian forest and in particular the Tlemcen forest massif where...     

Landslide susceptibility analysis with a bivariate approach and GIS in Northern Iran

Globally, landslides cause hundreds of billions of dollars in damage and hundreds of thousands of deaths and injuries each year. A landslide susceptibility map describes areas where landslides are likely to occur in the future by correlating some of the principal factors that contribute to landslides with the past distribution of landslides. A case study is conducted in the mountainous northern Iran. In this study, a landslide susceptibility map of the study area was prepared using bivariate method with the help of the geographic information system. Area density (bivariate) method was used to weight landslide-influencing data layers. An overlay analysis is carried out by evaluating the layers obtained according to their weight and the landslide susceptibility map is produced. The study area was classified into five hazard classes: very low, low, moderate, high, and very high. The percentage distribution of landslide susceptibility degrees was calculated. It was found that about 26% of the study area is classified as very high and high hazard classes.