Towards a Real-Time Data Driven Wildland Fire Model

A wildland fire model based on semi-empirical relations for the spread rate of a surface fire and post-frontal heat release is coupled with the Weather Research and Forecasting atmospheric model (WRF). The propagation of the fire front is implemented by a level set method. Data is assimilated by a morphing ensemble Kalman filter, which provides amplitude as well as position corrections. Thermal images of a fire will provide the observations and will be compared to a synthetic image from the model state.Comment: 5 pages, 4 figure

Environmental applications of remote sensing

This article may also be accessed from the publisher\u27s website at http://www.svifsi.ch/revue/pages/issues/n004/no004.html Remote sensing is routinely used for understanding many aspects of the earth environment that are important to sustainability. Remote sensing is used in weather forecasting and global climate studies, natural hazard analysis, crop condition and yield prediction, and forestry applications, for example. The techniques and hardware used to obtain the remotely sensed data for these applications are as widely varying as the applications themselves. Remote imaging systems may collect spectral data of reflected sunlight, emitted thermal or microwave radiation, or reflected radar signals to provide the desired information on the current status of the environment. These data can be collected from the air or from space and may be useful in the form of numerical data or in the form of an image. The goal of this paper is to examine the current state of the art in transforming remotely sensed image data into more useful information by integration with predictive models

Determining improvements in Landsat spectral sampling for inland water quality monitoring

Inland waters are optically complex and provide an ongoing challenge to effective water quality monitoring through remote sensing. Imaging satellites with spectral sampling designed for this task often have coarse spatial resolutions, preventing any capture of information from small lakes. Medium resolution satellite systems such as Landsat 8 have the appropriate spatial resolution and sensitivity required to resolve these waterbodies, but the spectral sampling is not optimal. This work uses system simulation to explore potential changes to Landsat spectral sampling to determine if its ability to monitor inland waters could be improved. The HydroLight and MODTRAN radiative transfer models are used for simulation in a Look Up Table and spectrum matching approach to provide maximum flexibility intesting spectral sampling scenarios. To isolate the testing to the impacts of spectral sampling, all simulations were performed based on the known system noise characteristics of Landsat 8. Spectral sampling changes tested include the addition of yellow and red edge spectral bands as well as conversion to an imaging spectrometer. Simulated spectra of inland waters undergoing a cyanobacteria bloom, including atmospheric effects and sensor noise, were implemented with the Look-Up-Table retrieval process to extract estimated concentrations of waterbody components. The retrieval accuracy of each potential system is compared to that of a modeled Landsat 8 baseline. All potential systems show an increase of retrieval accuracy over the baseline. The best performing system design is an imaging spectrometer, followed by the addition of both a yellow and red edge band simultaneously, and the addition of either band individually. Testing also demonstrates that resampling an imaging spectrometer with 20 nm spectral resolution to the Landsat 8 band responses produces outputs matching those available from Landsat 8. Our results indicate that future Landsat missions should aim to add as much spectral sampling as is feasible, while maintaining at least the same sensitivity. The minimum change to improve water quality monitoring capability is the addition of a red edge spectral band

Glint Correction of Unmanned Aerial System Imagery

Glint in aquatic imagery captured by Unmanned Aerial Systems (UAS) is a limiting factor when performing spectral analysis. It cannot be corrected by methods developed for space-based imaging systems, meaning new approaches are required. Two processes using in-situ radiometric data were developed augmenting an established method for removing atmospheric effects from imagery, the Empirical Line Method (ELM), to remove glint from multispectral UAS imagery. The results of this correction showed good agreement with in-situ spectroradiometer measurements and similar accuracy to atmospherically compensated satellite measurements. The Root-Mean-Square Error of the UAS retrieved remote sensing reflectance was as low as 0.0004 sr -1 and outperformed the traditional ELM

Automated Extraction of Fire Line Parameters from Multispectral Infrared Images

Remotely sensed infrared images are often used to assess wildland ¯re conditions. Separately, ¯re propagation models are in use to forecast future conditions. In the Dynamic Data Driven Application System (DDDAS) concept, the ¯re propagation model will react to the image data, which should produce more accurate predictions of ¯re propagation. In this study we describe a series of image processing tools that can be used to extract ¯re propagation parameters from multispectral infrared images so that the parameters can be used to drive a ¯re propagation model built upon the DDDAS concept. The method is capable of automatically determining the ¯re perimeter, active ¯re line, and ¯re propagation direction. A multi-band image gradient calculation, the Normalized Di®erence Vegetation Index, and the Normalized Di®erence Burn Ratio along with several standard image processing techniques are used to identify and constrain the ¯re propagation parameters. These ¯re propagation parameters can potentially be used within the DDDAS modeling framework for model update and adjustment

An Automatic Statistical Segmentation Algorithm for Extraction of Fire and Smoke Regions

Estimation of the extent and spread of wildland fires is an important application of high spatial resolution multispectral images. This work addresses a fuzzy segmentation algorithm to map fire extent, active fire front, hot burn scar, and smoke regions based on a statistical model. The fuzzy results are useful data sources for integrated fire behavior and propagation models built using Dynamic Data Driven Applications Systems (DDDAS) concepts that use data assimilation techniques which require error estimates or probabilities for the data parameters. The Hidden Markov Random Field (HMRF) model has been used widely in image segmentation, but it is assumed that each pixel has a particular class label belonging to a prescribed finite set. The mixed pixel problem can be addressed by modeling the fuzzy membership process as a continuous Multivariate Gaussian Markov Random Field. Techniques for estimating the class membership and model parameters are discussed. Experimental results obtained by applying this technique to two Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images show that the proposed methodology is robust with regard to noise and variation in fire characteristics as well as background. The segmentation results of our algorithm are compared with the results of a K-means algorithm, an Expectation Maximization (EM) algorithm (which is very similar to the Fuzzy C-Means Clustering algorithm with entropy regularization), and an MRF-MAP algorithm. Our fuzzy algorithm achieves more consistent segmentation results than the comparison algorithms for these test images with the added advantage of simultaneously providing a proportion or error map needed for the data assimilation problem

A wildland fire model with data assimilation

A wildfire model is formulated based on balance equations for energy and fuel, where the fuel loss due to combustion corresponds to the fuel reaction rate. The resulting coupled partial differential equations have coefficients that can be approximated from prior measurements of wildfires. An ensemble Kalman filter technique with regularization is then used to assimilate temperatures measured at selected points into running wildfire simulations. The assimilation technique is able to modify the simulations to track the measurements correctly even if the simulations were started with an erroneous ignition location that is quite far away from the correct one.Comment: 35 pages, 12 figures; minor revision January 2008. Original version available from http://www-math.cudenver.edu/ccm/report

A Comparison of White Matter Brain Differences in Monolingual and Highly Proficient Multilingual Speakers.

Language processing relies on the communication between brain regions that is achieved through several white matter tracts, part of the dorsal, ventral, and medial pathways involved in language processing and control (Coggins et al., 2004; Friederici & Gierhan, 2013; Hickok & Poeppel, 2007; Luk et al., 2011). While changes in white matter tract morphology have been reported as a function of second language learning in bilinguals, little is known about changes that may be present in multilanguage users. Here we investigate white matter morphometry in a group of highly proficient multilinguals, (individuals with proficiency in four or more languages), compared to a group of monolinguals. White matter morphometry was quantified using a fixel-based analysis (Raffelt et al., 2015; Raffelt et al., 2017; Tournier et al., 2007). Higher fiber cross-section and lower fiber density values were observed for the multilinguals, in the dorsal pathways (superior longitudinal fasciculus and arcuate fasciculus) and the ventral pathway, including the inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, and the uncinate fasciculus. Segments of the corpus callosum, the fornix, and the cortico-spinal tract showed decreases in all three morphometry measures for multilinguals. The findings suggest differential efficiencies in neural communication between domain-specific language regions and domain-general cognitive processes underlying multilingual language use. We discuss the results in relation to the bilingual Anterior to Posterior and Subcortical Shift (BAPSS) hypothesis (Grundy et al., 2017) and the Dynamic Restructuring Model (Pliatsikas, 2020)

Simulation of Internet of Things Water Management for Efficient Rice Irrigation in Rwanda

The central role of water access for agriculture is a clear challenge anywhere in the world and particularly in areas with significant seasonal variation in rainfall such as in Eastern and Central Africa. The combination of modern sensor technologies, the Internet, and advanced irrigation equipment combined in an Internet of Things (IoT) approach allow a relatively precise control of agricultural irrigation and creating the opportunity for high efficiency of water use for agricultural demands. This IoT approach can thereby increase the resilience of agricultural systems in the face of complex demands for water use. Most previous works on agricultural IoT systems are in the context of countries with higher levels of economic development. However, in Rwanda, with a low level of economic development, the advantages of efficient water use from the application of IoT technology requires overcoming constraints such as lack of irrigation control for individual farmers, lack of access to equipment, and low reliability of power and Internet access. In this work, we describe an approach for adapting previous studies to the Rwandan context for rice (Oryza sativa) farming with irrigation. The proposed low cost system would automatically provide irrigation control according to seasonal and daily irrigational needs when the system sensors and communications are operating correctly. In cases of system component failure, the system switches to an alternative prediction mode and messages farmers with information about the faults and realistic irrigation options until the failure is corrected. We use simulations to demonstrate, for the Muvumba Rice Irrigation Project in Northeast Rwanda, how the system would respond to growth stage, effective rainfall, and evapotranspiration for both correct operation and failure scenarios

Writing Early Ireland: A Panel Discussion

In past decades, early Irish literature has received relatively little scholarly attention. However, works such as the Tain Bo Cuailnge (The Cattle Raid of Cooley), Edmund Spenser’s Book Five of The Faerie Queene, and Spenser’s A View of the State of Ireland provide unique and important representations of early Irish culture. In this five-person panel, we will examine these works and our collective analyses of Irish cultural and literary representations within them. Specifically, we will critique problems of colonialism and portrayals of gender, and we will affirm the importance of the landscape and of literary intersections with archaeology and history. By doing so, we will shed light on an underrepresented aspect of literary history and explore how these writings can reshape modern perspectives of early Ireland.https://digitalcommons.morris.umn.edu/urs_2019/1003/thumbnail.jp