This paper uses multi-sensor remote sensing data to study the type and spatio-temporal variability of aerosols emitted from forest fires. The study is based on the Okefenokee Swamp fire that ravaged parts of Georgia and Florida between May and June of 2007. Moderate Resolution Imaging Spectroradiometer (MODIS) data is used to study the aerosol type and its spatial distribution. Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data is used to study the vertical distribution of aerosols. The results show that there is a high concentration of fine mode aerosols during the fire episode. It is also observed that the 24 hour averaged PM2.5 concentration was above unhealthy levels on several occasions, in some instances reaching values over 50µg/m3. The PM10 concentration on the other hand was below unhealthy levels although there were numerous instances of episodic non attainment of the PM10 air quality standard. The study shows that the vertical reach of the aerosol plume over the land ranged from 2 to 3 kilometers.
The retrieval of Live Fuel Moisture Content (LFMC) over fire prone grasslands is important for fire risk and drought assessment. Radiative transfer (RT) model based inversion of measured reflectances for retrievals of LFMC offers a promising method for estimating LFMC. This paper evaluates the extent to which inverse RT model based LFMC retrievals over grasslands can be improved by the use of prior information on soil moisture and LAI. However due to the uncertainty in the procedures used in obtaining the pre-retrieval information about LAI and soil moisture, the prior information is more likely to be in terms of an expected range for LAI and soil moisture rather than exact values. This study uses simulations from coupled soil-leaf-canopy radiative transfer models to investigate the extent to which such categorical prior information may reduce the uncertainty in LFMC retrievals. Results show that under the experimental conditions used in this study, prior information on LAI and soil moisture improves LFMC estimation on the average by about 2.3 to 3.4% (absolute LFMC) depending on the quality and accuracy of the prior information. This can be equivalent to a relative improvement of about 18-27%. This can be significant, since at the dry conditions represented by this study, when fire spread is highly sensitive to LFMC, such improvements in LFMC could considerably improve fire spread predictions and aid fire management decision making. Uncertainty analysis in terms of prediction intervals and standard deviation of errors also show that improvements are significant.
Research has shown that remote sensing in both the optical and microwave domain has the capability of estimating vegetation water content (VWC). Though lower in spatial resolution than MODIS optical bands, AMSR-E microwave measurements are typically less affected by clouds, water vapor, aerosol or solar illumination, making them complementary to MODIS real time measurements over regions of clouds and haze. In this study we explored a wavelet based approach for combining vegetation water content observations derived from higher spatial resolution MODIS and lower spatial resolution AMSR-E microwave measurements. Regression analysis between AMSR-E VWC and spatially aggregated MODIS NDII (Normalized Difference Infrared Index) was first used to scale MODIS NDII to MODIS VWC products. Our approach for combining information from the two sensors resorts to multiresolution wavelet decomposition of MODIS VWC into a set of detail images and a single approximation image at AMSR-E resolution. The substitution method of image fusion is then undertaken, in which the approximation image is replaced by AMSR-E VWC image, prior to using inverse wavelet transform to construct a merged VWC product. The merged VWC product thus has information from both MODIS and AMSR-E measurements. The technique is applied over low vegetation regions in Texas grasslands to obtain merged VWC products at intermediate resolutions of ~1.5km. Apart from offering a way to calibrate MODIS VWC content products to AMSR-E observations, the technique has the potential for downscaling AMSR-E VWC to higher spatial resolution over moderately cloudy or hazy regions where MODIS reflective bands become contaminated by the atmosphere. During such situations when contaminated MODIS signals cannot be used to obtain the wavelet detail images, MODIS detail images from a preceding time step is used to downscale the current AMSR-E VWC to higher resolutions. This approach of using detail images from the recent past would be justified if the detail images containing the high frequency components of the image change slowly. Correlation analysis of detail images from consecutive time steps shows that this is approximately true, at-least for the low spatial resolution detail images. Our approach yields accuracy of around 77% on the average over the selected study region and temporal period. This technique thus has the potential for ensuring the data continuity of high spatial resolution VWC products, a requirement essential for fire risk monitoring.
This paper surveys the potential use of hyperspectral imaging technology for standoff detection of chemical and biological agents in terrorism defense applications. In particular it focuses on the uses of hyperspectral imaging technology to detect and monitor chemical and biological attacks. In so doing it examines current technologies, their advantages and disadvantages, and investigates the possible role of hyperspectral imaging for homeland security applications. The study also addresses and provides applicable solutions for several of the potential challenges that currently create barriers to the full use of hyperspectral technology in the standoff detection of likely available chemical and biological agents.