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E-raamat: Remote Sensing Modeling and Applications to Wildland Fires

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  • Ilmumisaeg: 12-Dec-2014
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Keel: eng
  • ISBN-13: 9783642325304
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Remote Sensing Modeling and Applications to Wildland FiresSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 12-Dec-2014
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Keel: eng
  • ISBN-13: 9783642325304
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Scientists and managers alike need timely, cost-effective, and technically appropriate fire-related information to develop functional strategies for the diverse fire communities. "Remote Sensing Modeling and Applications to Wildland Fires" addresses wildland fire management needs by presenting discussions that link ecology and the physical sciences from local to regional levels, views on integrated decision support data for policy and decision makers, new technologies and techniques, and future challenges and how remote sensing might help to address them. While creating awareness of wildland fire management and rehabilitation issues, hands-on experience in applying remote sensing and simulation modeling is also shared. This book will be a useful reference work for researchers, practitioners and graduate students in the fields of fire science, remote sensing and modeling applications.

Professor John J. Qu works at the Department of Geography and GeoInformation Science at George Mason University (GMU), USA. He is the Founder and Director of the Environmental Science and Technology Center (ESTC) and EastFIRE Lab at GMU.

1 Introduction to Remote Sensing and Modeling Applications to Wildland Fires
1(10)
References
7(4)
2 Wildland Fire and Eastern States Diversity
11(8)
2.1 Introduction
11(1)
2.2 The Eastern United States
12(2)
2.3 Eastern United States Diversity
14(1)
2.4 A Fire Information Strategy for the Eastern States
14(2)
References
16(3)
3 Demographic Trends in the Eastern US and the Wildland Urban Interface: Implications for Fire Management
19(22)
3.1 Introduction
20(1)
3.2 Demographics
20(3)
3.3 The Wildland Urban Interface
23(8)
3.3.1 Georgia Case Study
27(4)
3.4 Implications for Managers
31(3)
3.5 Conclusion
34(1)
Acknowledgements
35(1)
References
35(6)
4 An Overview of NOAA's Fire Weather, Climate, and Air Quality Forecast Services
41(14)
4.1 NWS Fire Weather
42(1)
4.2 Products and Services
43(4)
4.3 Making Optimal Use of NWS Technology
47(2)
4.3.1 Digital Services
47(2)
4.4 NWS Climate Services
49(2)
4.4.1 Product Improvements
49(2)
4.5 National Air Quality Forecasting
51(2)
4.5.1 Planned Capabilities
51(2)
4.6 Summary
53(1)
References
54(1)
5 A Review of Wildland Fire and Air Quality Management
55(12)
5.1 Introduction
55(2)
5.1.1 Smoke Contributes to Air Pollution
55(2)
5.2 Regulatory Considerations Relating to Smoke
57(3)
5.2.1 Regional Haze Rule
57(2)
5.2.2 National Ambient Air Quality Standards for PM
59(1)
5.2.3 Managing Smoke from Wildfire
59(1)
5.3 A Review of the TASET Report---Tools Available to Manage Smoke
60(3)
5.4 Smoke Management---Programs and Systems
63(2)
5.4.1 Plan
64(1)
5.4.2 Do (Implement)
65(1)
5.4.3 Check (Evaluate)
65(1)
5.4.4 Act (Improve)
65(1)
5.5 Summary
65(1)
Acknowledgements
66(1)
References
66(1)
6 High-Resolution Numerical Models for Smoke Transport in Plumes from Wildland Fires
67(14)
6.1 Introduction
67(2)
6.2 Numerical Model
69(2)
6.3 Dynamical Properties of Simulated Plumes
71(7)
6.3.1 Mean Plume Trajectories
72(2)
6.3.2 Mean Plume Structure
74(2)
6.3.3 Turbulent Kinetic Energy (TKE)
76(2)
6.4 Summary and Conclusions
78(1)
Acknowledgements
78(1)
References
79(2)
7 Interaction between a Wildfire and the Sea-Breeze Front
81(18)
7.1 Introduction
82(4)
7.1.1 Sea-Breeze Structure and Characteristics
83(1)
7.1.2 Radar Observations of Smoke Plumes and the Sea-Breeze
84(1)
7.1.3 Effect of Sea-Breezes on Fires
85(1)
7.1.4 East Fork Fire
85(1)
7.2 Data and Methodology
86(3)
7.2.1 Case Study
86(1)
7.2.2 Idealized Numerical Simulations
87(2)
7.3 Case Study Analysis
89(5)
7.4 Numerical Simulations
94(2)
7.5 Summary and Conclusions
96(1)
Acknowledgments
96(1)
References
96(3)
8 Prescribed Fire and Air Quality in the American South: A Review of Conflicting Interests and a Technique for Incorporating the Land Manager into Regional Air Quality Modeling
99(18)
8.1 Introduction
100(1)
8.2 Conflicts over the Airshed of the American South
101(1)
8.3 Daysmoke
102(3)
8.4 SHRMC-4S
105(1)
8.5 Application
106(6)
8.5.1 Burn
106(1)
8.5.2 Daysmoke Simulation
107(2)
8.5.3 CMAQ Simulation
109(3)
8.6 Summary and Discussion
112(1)
Acknowledgements
113(1)
References
113(4)
9 Estimates of Wildland Fire Emissions
117(18)
9.1 Introduction
117(2)
9.2 Fire Emission Calculation
119(5)
9.2.1 Measurements
119(1)
9.2.2 Empirical relations
119(3)
9.2.3 Modeling
122(1)
9.2.4 Remote Sensing
122(2)
9.3 U.S. Fire Emissions
124(2)
9.3.1 Parameter Specifications
124(1)
9.3.2 Spatial Distribution
125(1)
9.3.3 Seasonal Distribution
126(1)
9.4 Uncertainties
126(2)
9.5 Summary and Perspective
128(2)
Acknowledgements
130(1)
References
130(5)
10 Integrating Remote Sensing and Surface Weather Data to Monitor Vegetation Phenology
135(12)
10.1 Introduction
135(1)
10.2 Methods
136(2)
10.2.1 System Introduction
136(1)
10.2.2 Surface Weather-Based Phenology Monitoring System
137(1)
10.3 Satellite-Derived Vegetation Index Data
138(2)
10.3.1 AVHRR Normalized Difference Vegetation Index (NDVI)
138(1)
10.3.2 Point Retrieval Interface
139(1)
10.3.3 PhenMon: The Phenology Monitoring System
139(1)
10.4 Results and Discussion
140(5)
10.4.1 Surface Observations Gridding System
140(1)
10.4.2 Growing Season Index
140(3)
10.4.3 AVHRR NDVI Data
143(1)
10.4.4 General Discussion
144(1)
Acknowledgements
145(1)
References
145(2)
11 Creating a Crosswalk of Vegetation Types and Fire Fuel Models for the National Park Service
147(14)
11.1 Introduction
147(1)
11.2 Digital Orthophoto Mosaics
148(3)
11.3 Formation-Level Vegetation Databases
151(1)
11.4 Fire Fuel Mapping
152(1)
11.5 Discussion
153(2)
Appendix A
155(1)
Appendix B
156(1)
Appendix C
157(1)
References
158(3)
12 Diurnal and Seasonal Cycles of Land Fires from TRMM Observations
161(20)
12.1 Introduction
161(2)
12.2 TSDIS Fire Algorithms
163(3)
12.3 TSDIS Fire Products
166(1)
12.4 Seasonal and Interannual Variability
167(4)
12.5 Diurnal and Seasonal Cycles
171(8)
12.5.1 Diurnal Cycle of TRMM Observation
171(4)
12.5.2 Seasonal Variation
175(4)
12.6 Summary
179(1)
References
179(2)
13 Fire Research in the New Jersey Pine Barrens
181(12)
13.1 Introduction
181(2)
13.2 Regional Fire Weather and Climate Modeling
183(4)
13.3 Fuel Mapping, Forest Biomass and Forest Dynamics
187(2)
13.4 Air Quality
189(1)
13.5 Conclusions
190(1)
References
190(3)
14 Dead Fuel Loads in North Carolina's Piedmont and Coastal Plain and a Small Scale Assessment of NFDRS Fuel Models
193(16)
14.1 Introduction
194(1)
14.2 Materials and Measures
195(4)
14.2.1 Site Descriptions
195(2)
14.2.2 Methods
197(2)
14.3 Results
199(4)
14.3.1 Dead Fine and Coarse Woody Fuel Load
199(1)
14.3.2 Total Dead (Woody, Litter and Duff) Fuel Load
200(2)
14.3.3 Comparison between Measured and NFDRS Dead Fuel Load Estimates
202(1)
14.4 Discussion and Conclusions
203(3)
14.4.1 Woody Fuel Load Variability
203(1)
14.4.2 Dead Fuel Load Variability
204(1)
14.4.3 Comparison between Measured and NFDRS Dead Fuel Load Estimates
204(2)
References
206(3)
15 Numerical Simulations of Grassland Fire Behavior from the LANL-FIRETEC and NIST-WFDS Models
209(18)
15.1 Introduction
209(1)
15.2 Overview of the FIRETEC and WFDS Numerical Models
210(2)
15.3 Overview of Grassland Fire Experiments
212(2)
15.4 Approach and Results
214(9)
15.4.1 Head Fire Spread Rate Dependence on Wind Speed in AU Grassland Fuel (WFDS only)
215(1)
15.4.2 Head Fire Spread Rate Dependence on the Head Fire Width in AU Grassland Fuel (WFDS only)
216(3)
15.4.3 Case Studies---Fire Perimeter in AU Grassland Fuel (WFDS only)
219(2)
15.4.4 Simulation of Tall Grass (FIRETEC and WFDS)
221(2)
15.5 Conclusions
223(1)
Acknowledgements
224(1)
References
224(3)
16 Physics-Based Modeling of Wildland-Urban Interface Fires
227(10)
16.1 Introduction
227(1)
16.2 WUI Fuels
228(3)
16.3 Fire Model
231(3)
16.4 Conclusions
234(1)
References
235(2)
17 Climate Change and Fire impacts on Ecosystem Critical Nitrogen Load
237(30)
17.1 Introduction
237(1)
17.2 Climate Change Impacts on Critical Loads
238(2)
17.2.1 Drought
238(1)
17.2.2 Climate Change Shifts in Water Availability
239(1)
17.2.3 Increased Air Temperature
240(1)
17.3 Fire Impacts on Critical Pollutant Loads
240(15)
17.3.1 Wildfire Impacts on Critical Loads
240(14)
17.3.2 Controlled Burn Impacts on Critical Loads
254(1)
17.4 Combined Impacts on Critical Pollutant Loads
255(2)
17.5 Conclusions and Future Research
257(1)
References
258(9)
18 Simulating Fire Spread with Landscape Level Edge Fuel Scenarios
267(14)
18.1 Introduction
268(2)
18.2 Methods
270(3)
18.2.1 Study Area
270(1)
18.2.2 Model Inputs
270(1)
18.2.3 Simulations
271(2)
18.3 Results
273(2)
18.4 Discussion
275(2)
Acknowledgements
277(1)
References
277(4)
19 The Need for Data Integration to Achieve Forest Sustainability: Modeling and Assessing the Impacts of Wildland Fire on Eastern Landscapes
281(12)
19.1 Introduction
281(3)
19.2 The Montreal Process
284(2)
19.3 Sustainable Forest Management (SFM)
286(1)
19.4 Northeastern Forests---an Example of Changing Conditions
287(1)
19.5 Modeling Landscape Conditions to Address Sustainable Forest Management
288(1)
19.6 Conclusions
289(1)
References
290(3)
20 Automated Wildfire Detection Through Artificial Neural Networks
293(12)
20.1 Introduction
294(1)
20.2 Data Archiving
294(1)
20.3 Preliminary Analysis
295(1)
20.4 Data Reduction
295(3)
20.5 Neural Network Architecture
298(2)
20.6 Training and Testing
300(1)
20.7 Classification and Analysis
300(3)
20.8 Conclusions
303(1)
Acknowledgements
303(1)
References
303(2)
21 Altered Disturbance Regimes: the Demise of Fire in the Eastern United States
305(18)
21.1 Introduction
305(3)
21.2 Methods
308(3)
21.3 Results and Discussion
311(12)
Acknowledgements
316(1)
Appendix A The Eastern Oak Story
316(1)
References
316(7)
22 Fire Spread Regulated by Weather, Landscape Structure, and Management in Wisconsin Oak-Dominated Forests and New Jersey Pinelands
323(18)
22.1 Introduction
324(1)
22.2 Methods and Materials
325(6)
22.2.1 Study Areas
325(2)
22.2.2 Study design
327(2)
22.2.3 Model Linkage and Applications
329(2)
22.3 Results
331(3)
22.4 Discussion
334(2)
22.5 Conclusions
336(5)
Acknowledgements
337(1)
References
337(4)
23 The GOFC-GOLD Fire Mapping and Monitoring Theme: Assessment and Strategic Plans
341
23.1 Introduction
343(2)
23.2 GOFC-GOLD Fire Goals and Current Implementation Status
345(17)
23.2.1 To Increase User Awareness by Providing an Improved Understanding of the Utility of Satellite Fire Products for Resource Management and Policy Within the United Nations and at Regional, National and Local Levels
345(2)
23.2.2 To Encourage the Development and Testing of Standard Methods for Fire Danger Rating Suited to Different Ecosystems and to Enhance Current Fire Early Warning Systems
347(2)
23.2.3 To Develop an Operational Global Geostationary Fire Network Providing Observations of Active Fires in Near Real Time
349(2)
23.2.4 To Establish Operational Polar Orbiters with Fire Monitoring Capability to Provide Operational Moderate Resolution Long-Term Global Fire Products and Enhanced Regional Products from Distributed Ground Stations to Meet User Requirements
351(1)
23.2.5 To Develop Long-Term Fire Data Records by Combining Data from Multiple Satellite Sources
352(2)
23.2.6 To Establish Operational Polar Orbiters with Fire Monitoring Capability to Provide Operational High Resolution Data Acquisition Allowing Fire Monitoring and Post-fire Assessments
354(2)
23.2.7 To Enhance Fire Product Use and Access by Developing Operational Multi-source Fire and GIS Data and Making These Available Over the Internet
356(2)
23.2.8 To Establish an Operational Network of Fire Validation Sites and Protocols, Providing Accuracy Assessment for Operational Products and a Testbed for New or Enhanced Products, Leading to Standard Products of Known Accuracy
358(1)
23.2.9 To Operationally Generate Fire Emission Product Suites of Known Accuracy Providing Annual and Near Real-Time Emission Estimates with Available Input Data Sets
359(3)
23.3 Example Contributory Activities from US Agencies
362(3)
23.3.1 NASA Wildfire Activities
362(1)
23.3.2 NOAA Wildfire Activities
363(1)
23.3.3 USDA Forest Service Wildfire Activities
364(1)
23.4 Conclusion
365
References
366
Dr. John J. Qu is a faculty member of the ESGS program at the school of Computational Sciences and is Technical Director of EastFIRE Lab at George Mason University. He is also with NASA Goddard Space Flight Center to support the NPOESS Preparatory Project (NPP) mission. His major research areas are satellite remote sensing, Earth systems sciences, fire science and GIS applications.

Dr. Menas Kafatos is Dean of the school of Computational Sciences (SCS), Director of the Center for Earth Observing and Space Research (CEOSR) and Professor of Interdisciplinary Science at George Mason University. He has published numerous books, and articles on computational science, astrophysics, Earth systems science, general relativity and the foundations of quantum theory.
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