Satellite remote sensing of active wildfires in Alaska's boreal forest

Thesis (Ph.D.) University of Alaska Fairbanks, 2017This research addresses improvements to the detection and characterization of active wildfires in Alaska with satellite-based sensors. The VIIRS I-band Fire Detection Algorithm for High Latitudes (VIFDAHL) was developed and evaluated against existing active fire products from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). This new algorithm is based on VIIRS 375 m spatial resolution imagery and was tuned using fires in Alaska's boreal forest. It provides improved fire detection of low-intensity fires, especially during daytime and at sensor zenith angles smaller than approximately 50° off nadir. Low-intensity active fires, which represent residual combustion present after the passage of a high-intensity fire front, are not very well detected by existing active fire products. A second topic was fire remote sensing with ~30 m resolution imaging spectrometer (or hyperspectral instrument), the Hyperion sensor on NASA's EO-1 spacecraft, which was in use from 2000 to 2016. Hyperion had a much higher spectral resolution than VIIRS or MODIS, but no repeat imagery of the same active fire was available in Alaska. The investigation relied on absorption and emission features in the radiance spectra acquired at every pixel location. Three fire detection methods were evaluated using archived Hyperion data from three fires in interior Alaska from 2004 and 2009: A version of the Hyperspectral Fire Detection Algorithm (HFDI) produced excellent active fire maps; an approach that relies on a shortwave infrared carbon dioxide absorption feature and associated Continuum Interpolated Band Ratio (CO₂ CIBR) proved to be useful, but was affected by sensor noise and clouds; finally, a potassium emission feature from biomass burning was not detectable in the Hyperion data. Fire temperatures were determined using the Hyperion shortwave infrared spectra between 1400 nm and 2400 nm. The temperatures of active fire, the corresponding partial pixel areas, and the pixel areas occupied by unburned and already-burned vegetation, respectively, were modeled within each fire pixel. A model with two reflected background components and two temperature endmembers, applied to the same three study scenes, yielded an excellent fit to Hyperion spectral radiance data. Fire temperatures ranged from approximately 500-600 K to approximately 800-900 K. The retrieved lower fire temperatures are within the range of smoldering combustion; high-temperature values are limited by Hyperion's saturation behavior. High-temperature fire occupying 0.2% of a pixel (2 m²) was detectable. Sub-pixel fire area and temperature were also retrieved using VIIRS 750 m (M-band) data, with comparable results. Uncertainties were evaluated using a Monte Carlo simulation. This work offers insight into the sensitivity of fire detection products to time of day (largely due to overpass timing), spatial distribution over the study area (largely due to orbital properties) and sensor zenith angle. The results are relevant for sensor and algorithm design regarding the use of new multi- and hyperspectral sensors for fire science in the northern high latitudes. Data products resulting from this research were designed to be suitable for supporting fire management with an emphasis on real-time applications and also address less time-sensitive questions such as retrievals of fire temperature and time series of fire evolution.Chapter 1: General Introduction -- 1.1 Fires in the boreal forest -- 1.2 Satellite remote sensing of active fires -- 1.3 Objectives and structure of this dissertation -- References. Chapter 2: Detecting high and low-intensity fires in Alaska using VIIRS I-band data: An improved operational approach for high latitudes -- Abstract -- 2.1 Introduction -- 2.2 Global active fire products: a brief review -- 2.3 Wildfire study areas -- 2.3.1 Willow: Sockeye fire, June 2015 -- 2.3.2 Yukon-Koyukuk: multiple wildfires, July 2015 -- 2.3.3 Eagle: early-season wildfires, May 2015 -- 2.3.4 Northern Koyukuk: multiple large fires, July 2016 -- 2.4 Data -- 2.4.1 Global MODIS and VIIRS I-band products -- 2.4.2 VIIRS Sensor Data Record (SDR) data -- 2.4.3 Fire location and perimeter data -- 2.4.4 Landsat 8 imagery -- 2.4.5 Evaluation of operational MODIS and VIIRS I-band products -- 2.4.6 VIIRS I-band Fire Detection Algorithm for High Latitudes (VIFDAHL) -- 2.4.7 Validation using Landsat -- 2.5 Results -- 2.5.1 Exploratory data analysis of operational MODIS and VIIRS I-band fire detection datasets -- 2.5.2 VIIRS I-band Fire Detection Algorithm for High Latitudes (VIFDAHL) -- 2.6 Discussion and conclusions -- 2.7 Acknowledgements -- References. Chapter 3: Fire detection and temperature retrieval using EO-1 Hyperion data over selected Alaskan boreal fires -- Abstract -- 3.1 Introduction -- 3.2 Study Areas -- 3.3 Data -- 3.3.1 The Hyperion sensor on EO-1 -- 3.3.2 Hyperion scenes -- 3.4 Methods -- 3.4.1 Fire-related feature extraction -- 3.4.2 Fire detection -- 3.4.3 MODTRAN for atmospheric correction -- 3.4.4 Temperature retrieval -- 3.5 Results -- 3.5.1 Fire detection and comparative analysis -- 3.5.2 Temperature retrieval -- 3.6 Discussion -- 3.7 Conclusions, recommendations, and future work -- 3.7 Conclusions, recommendations, and future work -- 3.8 Acknowledgements -- References. Chapter 4: Sensitivity considerations in fire detection and sub-pixel fire temperature retrieval with Suomi-NPP VIIRS -- Abstract -- 4.1 Introduction -- 4.2 Study area and data used -- 4.3 Methods -- 4.3.1 Fire detection -- 4.3.2 Sensor angle statistics -- 4.3.3 Temperature retrieval -- 4.3.4 Atmospheric correction -- 4.3.5 Uncertainty estimation -- 4.4 Results and discussion -- 4.4.1 Zenith angle dependency of fire detection -- 4.4.2 Fire temperature and partial pixel area retrieval -- 4.5 Conclusions -- References. Chapter 5: General Conclusion -- References. Appendix A: Coal-Fire Hazard Mapping in High-Latitude Coal Basins: A Case Study from Interior Alaska -- A.1 High latitude coal fires -- A.1.1 Introduction -- A.1.2 Alaskan Context -- A.2 Case Study from Interior Alaska -- A.2.1 Introduction -- A.2.2 Study Area -- A.2.3 Data -- A.2.4 Data Processing -- A.2.5 Results -- A.2.6 Discussion -- A.2.7 Conclusions -- A.2.8 Acknowledgements -- A.2.9 Important Terms -- References

Emerging Anthropogenic Influences on the Southcentral Alaska Temperature and Precipitation Extremes and Related Fires in 2019

The late-season extreme fire activity in Southcentral Alaska during 2019 was highly unusual and consequential. Firefighting operations had to be extended by a month in 2019 due to the extreme conditions of hot summer temperature and prolonged drought. The ongoing fires created poor air quality in the region containing most of Alaska’s population, leading to substantial impacts to public health. Suppression costs totaled over $70 million for Southcentral Alaska. This study’s main goals are to place the 2019 season into historical context, provide an attribution analysis, and assess future changes in wildfire risk in the region. The primary tools are meteorological observations and climate model simulations from the NCAR CESM Large Ensemble (LENS). The 2019 fire season in Southcentral Alaska included the hottest and driest June–August season over the 1979–2019 period. The LENS simulation analysis suggests that the anthropogenic signal of increased fire risk had not yet emerged in 2019 because of the CESM’s internal variability, but that the anthropogenic signal will emerge by the 2040–2080 period. The effect of warming temperatures dominates the effect of enhanced precipitation in the trend towards increased fire risk.The National Science Foundation (#OIA-1753748), the State of Alaska, the United States Geological Survey (G17AC00363), and the Alaska Climate Adaptation Science Center (G17AC00213) provided support for this study. NOAA supported this work through grants #NA16OAR4310162 (R.T., J.E.W., A.Y.) and #NA16OAR4310142 (U.S.B., P.A.B.)Ye

Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest