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Advances in Remote Sensing 2013年第2期2卷 181-192页

作者： mustafa mirik Sriroop Chaudhuri Brady Surber Srinivasulu Ale Robert James Ansley Texas A&M AgriLife Research (TAMAR) Vernon USA

Both temporal and spatial magnitude, structure, and distribution of rangeland aboveground biomass (AGB) are important inputs for many necessities, in particular for estimating terrestrial carbon amount, ecosystem productivity, climate change studies, and potential bioenergy uses. Much of the remote sensing research previously completed has focused on determining carbon stocks in forested ecosystems with little attention directed to estimate AGB amount in rangelands. Our objectives were to: 1) identify and delineate individual redberry juniper (Juniperus pinchotii) plants from surrounding live vegetation using the support vector machine method for classifying two-dimensional (2D) geospatial imagery with a 1-m spatial resolution at two sites;and 2) develop regression models relating imagery-derived and fieldmeasured single tree canopy area and diameter for dry AGB estimation. The regression results show that there were very close and significant relationships between field measured juniper plant AGB and canopy area derived from the image classification with r2 > 0.90. These results suggest that spectral reflectance recorded on 2D high resolution imagery is capable to assess and quantify AGB as a quick, repeatable, and unbiased method over large land areas.

关键词： Remote Sensing Geospatial Imagery Biomass Juniperus pinchoti Rangeland Bioenergy Terrestrial Carbon Budget

Co-Gasification of Mesquite and Coal Blend in an Updraft Fixed Bed Gasifier

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Journal of Sustainable Bioenergy Systems 2013年第3期3卷 234-241页

作者： Wei Chen Siva Sankar Thanapal Kalyan Annamalai Robert James Ansley mustafa mirik Department of Mechanical Engineering Texas A&M University College Station USA Texas AgriLife Research Vernon USA

In order to reduce the emission of NOx, SOx, and CO2 and mitigate the dependence on the fossil fuel, the use of renewable energy, especially the biomass energy, has been explored. Since most biomass fuels are hard to grind to the very fine size, gasification is the preferred technology of energy conversion. Updraft fixed bed gasification using partial oxidation process is adopted where air less than the stoichiometric quantity is admitted to oxidize the char to CO. The temperature profile within the bed reveals a characteristic temperature peak. The results reveal a correlation between the higher heating value (HHV) of producer gas and the peak gasification temperature (Tpeak). Coal with higher char content (~45%) is blended with biomass of low char content (~20%) in order to produce high quality gas. In this study, the Texas-based mesquite fuel was blended with Wyoming Power River Basin (PRB) coal with mass ratios of 100:0, 90:10, and 80:20 and fired downward into the gasifier. It was found that at a given mesquite to the coal mass ratio, the peak gasification temperature decreased with the increase in ER. With the increase of the coal ratio in the mesquite: coal blend and the peak temperature increased significantly;more combustible gases such as CO, CH4 were generated at the end of product gas, and the HHV of the product gas increased by 10%-20%.

关键词： Gasification Bioenergy Coal Heating Value Sustainability

Remote Monitoring of Wheat Streak Mosaic Progression Using Sub-Pixel Classification of Landsat 5 TM Imagery for Site Specific Disease Management in Winter Wheat

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Advances in Remote Sensing 2013年第1期2卷 16-28页

作者： mustafa mirik R. James Ansley Jacob A. Price Fekede Workneh Charles M. Rush Texas A&M AgriLife Research (TAMAR) Amarillo Boulevard West Amarillo USA Texas A&M AgriLife Research (TAMAR) Vernon USA

Wheat streak mosaic (WSM), caused by Wheat streak mosaic virus is a viral disease that affects wheat (Triticum aestivum L.), other grains, and numerous grasses over large geographical areas around the world. To improve disease management and crop production, it is essential to have adequate methods for monitoring disease epidemics at various scales and multiple times. Remote sensing has become an essential tool for monitoring and quantifying crop stress due to biotic and abiotic factors. The objective of our study was to explore the utility of Landsat 5 TM imagery for detecting, quantifying, and mapping the occurrence of WSM in irrigated commercial wheat fields. The infection and progression of WSM was biweekly assessed in the Texas Panhandle during the 2007-2008 crop years. Diseased-wheat was separated from uninfected wheat on the images using a sub-pixel classifier. The overall classification accuracies were >91% with kappa coefficient between 0.80 and 0.94 for disease detection were achieved. Omission errors varied between 2% and 14%, while commission errors ranged from 1% to 21%. These results indicate that the TM image can be used to accurately detect and quantify disease for site-specific WSM management. Remote detection of WSM using geospatial imagery may substantially improve monitoring, planning, and management practices by overcoming some of the shortcomings of the ground-based surveys such as observer bias and inaccessibility. Remote sensing techniques for accurate disease mapping offer a unique set of advantages including repeatability, large area coverage, and cost-effectiveness over the ground-based methods. Hence, remote detection is particularly and practically critical for repeated disease mo- nitoring and mapping over time and space during the course of a growing season.

关键词： Wheat Streak Mosaic Virus Viral Disease Remote Sensing of Disease Disease Mapping Wheat Disease Site-Specific Disease Management

Detecting Musk Thistle (Carduus nutans) Infestation Using a Target Recognition Algorithm

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Advances in Remote Sensing 2014年第3期3卷 95-105页

作者： mustafa mirik Yves Emendack Ahmed Attia Sriroop Chaudhuri Mimi Roy Georges F. Backoulou Song Cui Department of Crop Soil and Environmental Sciences Auburn University Auburn USA Middle Tennessee State University Murfreesboro USA Oklahoma State University 127 Noble Research Center Stillwater USA Texas A & M AgriLife Research and Extension Center Vernon USA

The outbreaks of invasive plant species can cause great ecological and agronomic problems through aggressively competing for environmental resources that could be otherwise utilized by other desirable species. Thus, it is crucial for detecting small infestations before they reach a significant extent that can cause ecological and economic damages over a large geological area. Remote sensing is a proven method for mapping invasion extent and pattern based on geospatial imagery and indicated great repeatability, large coverage area, and lower cost compared with traditional ground-based methods before. We investigated the feasibility and performances of adopting multispectral satellite imagery analyses for mapping infestation of musk thistle (Carduus nutans) on native grassland, crop field, and residential areas in early June using spectral angle mapper classifier. Our results showed an overall classification accuracy of 94.5%, indicating great potential of using moderate resolution multispectral satellite-based remote sensing techniques for musk thistle detection over a large spatial scale.

关键词： Accuracy Assessment Invasive Plant Weed Management Weed Infestation Remote Sensing Geospatial Data Nodding Thistle