ỨNG DỤNG TƢ LIỆU ẢNH VIỄN THÁM VÀ CÔNG NGHỆ GIS THÀNH LẬP BẢN ĐỒ NGUY CƠ CHÁY RỪNG TỈNH ĐẮK LẮK

Forest fire is one of the disasters causing threats to the forests and the ecosystem and socio-economic aspects throught out the world. Forest fire also leads to an increase in green house gases emisstions. Air pollution due to smoke causes prolonged effects on human health such as respiratory and cardiovascular problems. Knowledge of flammable materials and their potential fire behavior in different forest types is essential in forest fire management. Remote Sensing and GIS can play an important role in detecting burnt forest and developing the spatial models to predict potential forest for fire risk. This study demonstrates the effective use of remote sensing imagery and geographic information system for establishing the forest fire hazard map at scale of 1:100.000 for Daklak province. Landsat ETM image captured in 2011 and Weighted Overlay tool in ArcGIS software were used in this study. Eight parameters of forest types, daily average temperature during  dry  season, daily average precipitation in dry season, daily average  wind  speed,  slope, terrain direction, distant  between  burned  field to forest  and  distant from  resident to forest  were  used  as main inputs in  GIS model. The study result shown that, the total fire area at low fire risk is 219,344 ha (accounting for 35.9% of total area of forest in Daklak province), medium fire risk is 130,207 ha (21.3%), high fire risk is 220,565 ha (36.1%) and very high fire risk is 41,488 ha (6.8%).ReferencesAmparo, A.B., Oscar, F.R., 2003. An intelligent system for forest fire risk prediction and fire fighting management in Galicia. Expert Systems with Applications 25(6), 545-554. Chuvieco, E., Congalton, R.G., 1989. Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing of the Environment 29, 147-159. Dong, X.U., 2005. Forest fire risk zone mapping from satellite images and GIS for Baihe Forestry Bureau, Jilin, China. Journal of Forestry Research 16(3), 169-174. Gholamreza, J.G., Bahram, G., Osman, M.D., 2012. Forest fire risk zone mapping form Geographic Information System in Northern Forests of Iran (Case study, Golestan province). International Journal of Agriculture and Crop Science 4(12), 818-824. Phạm Ngọc Hưng, 2001. Thiên tai khô hạn cháy rừng và giải pháp phòng cháy chữa cháy rừng ở Việt Nam. Nxb. Nông nghiệp, Hà Nội, 224tr. Phạm Ngọc Hưng, 2004. Quản lý lửa rừng ở Việt Nam. Nxb. Nghệ An, 231tr. Jaiswal, R.K., Mukherjee, S., Raju, D.K., Saxena, R., 2002. Forest fire risk zone mapping from satellite imagery and GIS. International Journal of Applied Earth Observation and Geoinformation 4, 1-10. Lazaros, S.I, Anastaios, K.P., Panagiotis, D.L., 2002. A computer-system that classifies the prefectures of Greece in forest fire risk zones using fuzzy sets [J]. Forest Policy and Economics 4, 43-54. Mariel, A., Marielle, J., 1996. Wildland fire risk mapping using a geographic information system and including satellite data: Example of "Les Maures" forest, south east of France [J]. EARSeL Advances in Remote Sensing 4(4), 49-56. Shailesh Nayak, Sisi Zlatanova, 2008. Remote sensing and GIS technologies for monitoring and prediction of disasters. Springer-Verlag Environment Science and Engineering, 127pp. William, A.T., Ilan, V., Hans, S., 2000. Using forest fire hazard modeling in multiple use forest management planning [J]. Forest Ecology and Management 134(2), 163-176. Bộ Nông nghiệp và Phát triển nông thôn, 2004. Cẩm nang ngành lâm nghiệp - Chương "Phòng cháy và chữa cháy rừng". Chương trình hỗ trợ ngành lâm nghiệp và đối tác, 89tr. Chi cục Kiểm lâm Đắk Lắk, 2012. Báo cáo chuyên đề "Thực trạng cháy rừng, nguyên nhân và các giải pháp phòng cháy chữa cháy rừng". Tài liệu lưu trữ tại Viện Địa lý, 27tr. Forest fire is one of the disasters causing threats to the forests and the ecosystem and socio-economic aspects throught out the world. Forest fire also leads to an increase in green house gases emisstions. Air pollution due to smoke causes prolonged effects on human health such as respiratory and cardiovascular problems. Knowledge of flammable materials and their potential fire behavior in different forest types is essential in forest fire management. Remote Sensing and GIS can play an important role in detecting burnt forest and developing the spatial models to predict potential forest for fire risk. This study demonstrates the effective use of remote sensing imagery and geographic information system for establishing the forest fire hazard map at scale of 1:100.000 for Daklak province. Landsat ETM image captured in 2011 and Weighted Overlay tool in ArcGIS software were used in this study. Eight parameters of forest types, daily average temperature  during  dry  season,  daily  average  precipitation  in  dry  season,  daily  average  wind  speed,  slope,  terrain direction,  distant  between  burned  field  to forest  and  distant from  resident to forest  were  used  as main  inputs in  GIS model. The study result shown that, the total fire area at low fire risk is 219,344 ha (accounting for 35.9% of total area of forest in Daklak province), medium fire risk is 130,207 ha (21.3%), high fire risk is 220,565 ha (36.1%) and very high fire risk is 41,488 ha (6.8%)

High-resolution SAR images for fire susceptibility estimation in urban forestry

We present an adaptive system for the automatic assessment of both physical and anthropic fire impact factors on periurban forestries. The aim is to provide an integrated methodology exploiting a complex data structure built upon a multi resolution grid gathering historical land exploitation and meteorological data, records of human habits together with suitably segmented and interpreted high resolution X-SAR images, and several other information sources. The contribution of the model and its novelty rely mainly on the definition of a learning schema lifting different factors and aspects of fire causes, including physical, social and behavioural ones, to the design of a fire susceptibility map, of a specific urban forestry. The outcome is an integrated geospatial database providing an infrastructure that merges cartography, heterogeneous data and complex analysis, in so establishing a digital environment where users and tools are interactively connected in an efficient and flexible way

Modeling Fire Danger in Galicia and Asturias (Spain) from MODIS images

Forest fires are one of the most dangerous natural hazards, especially when they are recurrent. In areas such as Galicia (Spain), forest fires are frequent and devastating. The development of fire risk models becomes a very important prevention task for these regions. Vegetation and moisture indices can be used to monitor vegetation status; however, the different indices may perform differently depending on the vegetation species. Eight different spectral indices were selected to determine the most appropriate index in Galicia. This study was extended to the adjacent region of Asturias. Six years of MODIS (Moderate Resolution Imaging Spectroradiometer) images, together with ground fire data in a 10 × 10 km grid basis were used. The percentage of fire events met the variations suffered by some of the spectral indices, following a linear regression in both Galicia and Asturias. The Enhanced Vegetation Index (EVI) was the index leading to the best results. Based on these results, a simple fire danger model was established, using logistic regression, by combining the EVI variation with other variables, such as fire history in each cell and period of the year. A seventy percent overall concordance was obtained between estimated and observed fire frequency

Design and conceptual development of a novel hybrid intelligent decision support system applied towards the prevention and early detection of forest fires

Forest fires have become a major problem that every year has devastating consequences at the environmental level, negatively impacting the social and economic spheres of the affected regions. Aiming to mitigate these terrible effects, intelligent prediction models focused on early fire detection are becoming common practice. Considering mainly a preventive approach, these models often use tools that indifferently apply statistical or symbolic inference techniques. However, exploring the potential for the hybrid use of both, as is already being done in other research areas, is a significant novelty with direct application to early fire detection. In this line, this work proposes the design, development, and proof of concept of a new intelligent hybrid system that aims to provide support to the decisions of the teams responsible for defining strategies for the prevention, detection, and extinction of forest fires. The system determines three risk levels: a general one called Objective Technical Fire Risk, based on machine learning algorithms, which determines the global danger of a fire in some area of the region under study, and two more specific others which indicate the risk over a limited area of the region. These last two risk levels, expressed in matrix form and called Technical Risk Matrix and Expert Risk Matrix, are calculated through a convolutional neural network and an expert system, respectively. After that, they are combined by means of another expert system to determine the Global Risk Matrix that quantifies the risk of fire in each of the study regions and generates a visual representation of these results through a color map of the region itself. The proof of concept of the system has been carried out on a set of historical data from fires that occurred in the Montesinho Natural Park (Portugal), demonstrating its potential utility as a tool for the prevention and early detection of forest fires. The intelligent hybrid system designed has demonstrated excellent predictive capabilities in such a complex environment as forest fires, which are conditioned by multiple factors. Future improvements associated with data integration and the formalization of knowledge bases will make it possible to obtain a standard tool that could be used and validated in real time in different forest areas

Knowledge-based approaches for river basin management

International audienceRare attempts to use knowledge technologies and other relevant approaches are found in the river basin management. Some applications of expert systems as well as utilization of soft computing techniques (as neural networks or genetic algorithms) are known in an experimental level. Knowledge management approaches still have not been used at all. In this paper we discuss knowledge-based approaches in the river basin management as a difficult yet important direction which could be proven to be helpful. We summarize the research done in the scope of the AQUIN project, one of first Czech knowledge management projects in the river basin management. The project was initiated by the water management company in Pilsen, where dispatchers make decisions about manipulations on the reservoir Nýrsko, the strategic source of drinking water for inhabitants of Pilsen. The project aim was to support dispatchers' decision making under a high degree of uncertainty or data shortage. The research is continued in the scope of a new project AQUINpro, planned for the period of 2006 to 2008

Comparative study on machine learning algorithms for early fire forest detection system using geodata

Forest fires have caused considerable losses to ecologies, societies and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, a competitive spatial prediction model for automatic early detection of wild forest fire using machine learning algorithms can be proposed. This model can help researchers to predict forest fires and identify risk zonas. System using machine learning algorithm on geodata will be able to notify in real time the interested parts and authorities by providing alerts and presenting on maps based on geographical treatments for more efficacity and analyzing of the situation. This research extends the application of machine learning algorithms for early fire forest prediction to detection and representation in geographical information system (GIS) maps

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

Wildfire Predictions: Determining Reliable Models using Fused Dataset

Wildfires are a major environmental hazard that causes fatalities greater than structural fire and other disasters Computerized models have increased the possibilities of predictions that enhanced the firefighting capabilities in U S While predictive models are faster and accurate it is still important to identify the right model for the data type analyzed The paper aims at understanding the reliability of three predictive methods using fused dataset Performances of these methods Support Vector Machine K-Nearest Neighbors and decision tree models are evaluated using binary and multiclass classifications that predict wildfire occurrence and its severity Data extracted from meteorological database and U S fire database are utilized to understand the accuracy of these models that enhances the discussion on using right model for dataset based on their size The findings of the paper include SVM as the best optimum models for binary and multiclass classifications on the selected fused datase

Review of the use of remote sensing for monitoring wildfire risk conditions to support fire risk assessment in protected areas

Fire risk assessment is one of the most important components in the management of fire that offers the framework for monitoring fire risk conditions. Whilst monitoring fire risk conditions commonly revolved around field data, Remote Sensing (RS) plays key role in quantifying and monitoring fire risk indicators. This study presents a review of remote sensing data and techniques for fire risk monitoring and assessment with a particular emphasis on its implications for wildfire risk mapping in protected areas. Firstly, we concentrate on RS derived variables employed to monitor fire risk conditions for fire risk assessment. Thereafter, an evaluation of the prominent RS platforms such as Broadband, Hyperspectral and Active sensors that have been utilized for wildfire risk assessment. Furthermore, we demonstrate the effectiveness in obtaining information that has operational use or immediate potentials for operational application in protected areas (PAs). RS techniques that involve extraction of landscape information from imagery were summarised. The review concludes that in practice, fire risk assessment that consider all variables/indicators that influence fire risk is impossible to establish, however it is imperative to incorporate indicators or variables of very high heterogeneous and “multi-sensoral or multivariate fire risk index approach for fire risk assessment in PA.Keywords: Protected Areas, Fire Risk conditions; Remote Sensing, Wildfire risk assessmen