Remote Sensing of Large Wildfires: In the European Mediterranean Basin

ISBN-13: 9783642642845 / Angielski / Miękka / 2014 / 212 str.

Emilio Chuvieco

Opis

Forest fIres are considered a major environmental problem in many European Union Member States as well as in other parts of the world. According to a recent report of the European Commission, forest fues are a dominant feature of the landscapes of the fIve Southern European Member States - Portugal, Spain, France, Italy and Greece, and almost a half of the Community's forests have been classed as fue-risk areas. Protection from forest fues is an interdisciplinary endeavour, which needs to be addressed from both the technological and methodological point of view, and which necessitates a wide spectrum of various scientifIc disciplines. It also implies the solution of numerous practical problems (both of a generic nature and of a specifIc one) and the consideration of the characteristics of the specifIc areas in which fue fIghting is carried out. ScientifIc research is offering a major contribution to forest fIre fIghting. The European Commission (DG XII, Directorate General for Science, Research and Development) has supported since the 1980s a number of multinational research projects through its successive RTD programmes in the fIeld of the Environment.

Spis treści

1 Introduction.- 2 The role of fire In European Mediterranean ecosystems.- 2.1 Introduction.- 2.2 Fire history.- 2.2.1 Statistics.- 2.2.2 Land-use changes.- 2.2.3 Climate change.- 2.3 Fire effects on soils.- 2.4 Post-fire regeneration of vegetation.- 2.4.1 Plant traits.- 2.4.2 Environmental conditions.- 2.4.3 Prediction of long-term effects.- 2.5 Concluding remarks.- 3Short-term fire risk: foliage moisture content estimation from satellite data.- 3.1 The role of foliage moisture content in the short-term estimation of fire danger.- 3.2 The estimation of foliage moisture content.- 3.3 The effect of moisture content on reflectance and temperature.- 3.4 The use of low resolution data for foliage moisture estimation.- 3.5 Application of NOAA-AVHRR to FMC estimation.- 3.5.1 Study areas.- 3.5.2 Satellite data processing.- 3.5.3 Results on Chalkidiki study area.- 3.5.4 Results on Cabañeros study area.- 3.5.5 Results on Les Maures study area.- 3.5.6 Results on ONF land plots.- 3.5.7 Discussion.- 3.5.8 Conclusion.- 3.6 Foliage moisture assessment using high resolution data.- 4 Meteorological fire danger indices and remote sensing.- 4.1 Introduction.- 4.2 Processes and components embodied in fire danger indices.- 4.3 Meteorological fire danger indices.- 4.4 Large fire danger rating with meteorological indices in the European Mediterranean Basin.- 4.4.1 Databases and danger indices.- 4.4.2 Climatic stratification.- 4.4.3 Logistic regression.- 4.4.4 Assessment of the logistic model.- 4.5 Satellite data and meteorological danger indices.- 4.5.1 Satellite data and the logistic model for large fire danger rating….- 4.5.2 Estimation of long-term fire danger indices from satellite data…..- 5 Integrated fire risk mapping.- 5.1 Temporal and spatial scales in fire risk mapping.- 5.2 The use of GIS in fire risk assessment.- 5.2.1 Description of geographical variables of fire risk.- 5.2.2 Criteria to integrate forest fire danger variables.- 5.3 Analysis of long-term fire risk on a European level.- 5.3.1 Introduction.- 5.3.2 Selection of risk variables.- 5.3.3 Techniques to estimate large Fire occurrence.- 5.3.3.1 Logistic Regression.- 5.3.3.2 Linear Regression.- 5.3.3.3 Artificial Neural Networks.- 5.3.4 Conclusions.- 5.4 Examples of local-scale risk analysis.- 5.4.1 Proposal of a local-risk index.- 5.4.2 Application at local level.- 6 Fire detection and fire growth monitoring using satellite data.- 6.1 Introduction.- 6.2 Basis for fire detection from satellite data.- 6.3 General issues related to remote sensing of active fires.- 6.3.1 Temporal issues.- 6.3.2 Thermal sensitivity issues.- 6.3.3 Spatial issues.- 6.3.4 Other problems related with satellite fire observation and detection.- 6.4 Active fire detection with NOAA-AVHRR images.- 6.4.1 Channel 3 single threshold algorithms.- 6.4.2 Multi-channel threshold algorithms.- 6.4.3 Contextual algorithms.- 6.4.4 Sub-pixel fire detection algorithm.- 6.4.5 Additional issues.- 6.5 Fire growth monitoring using AVHRR images.- 6.6 Future systems.- 6.7 Conclusions.- 7 Spectral characterisation and discrimination of burnt areas.- 7.1 Introduction.- 7.2 Spectral properties of burnt areas.- 7.2.1 Visible (0.4 - 0.7 µm).- 7.2.2 Near-infrared (0.7 - 1.3µm).- 7.2.3 Mid-infrared (1.3 - 8.0 µm).- 7.2.4 Thermal infrared (8.0 - 14.0 µm).- 7.2.5 Microwave (> 1 mm).- 7.2.6 An overview of the characteristics of burnt surfaces using Landsat 5 TM imagery.- 7.2.6.1 Spectral properties and colour composites.- 7.2.6.2 Fire-induced spectral changes and vegetation recovery…..- 7.3 Conclusions.- 8 Regional-scale burnt area mapping in southern Europe using NOAA-AVHRR 1km dataµ.- 8.1 Introduction µ.- 8.2 Methods for burnt land mapping.- 8.3 Mapping burnt areas in southeµrn Europe from NOAA-AVHRR data.- 8.3.1 Data and methods.- 8.3.2 Results.- 8.4 Discussion and conclusions.- 9 Burnt land mapping at local scale.- 9.1 Introduction.- 9.2 Scale issues in burnt land mapping.- 9.3 Operational burnt land mapping in Mediterranean landscapes.- 9.3.1 Structure of the Mediterranean landscape.- 9.3.2 Methodological approaches for burnt land mapping.- 9.3.3 Advantages of using high resolution sensors.- 9.4 Techniques for burnt land mapping.- 9.4.1 Overview.- 9.4.2 Description of the techniques.- 9.4.2.1 Principal component analysis.- 9.4.2.2 Spectral mixture analysis.- 9.4.2.3 Logistic regression modeling.- 9.4.2.4 Intensity-Hue-Saturation transformation.- 9.4.2.5 Other techniques.- 9.5 Discrimination of damage intensities.- 9.5.1 Interest of discriminating damage intensities.- 9.5.2 Description of the techniques.- 9.5.2.1 Vegetation Indices thresholding.- 9.5.2.2 Unsupervised classification: segment-based classification.- 9.5.2.3 Supervised classification.- 9.6 Epilogue.- References.