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Woody Biomass Logistics [chapter]

Robert Keefe, Nathaniel Anderson, John Hogland, Ken Muhlenfeld
2014 Cellulosic Energy Cropping Systems  
The economics of using woody biomass as a fuel or feedstock for bioenergy applications is often driven by logistical considerations. Depending on the source of the woody biomass, the acquisition cost of the material is often quite low, sometimes near zero. However, the cost of harvesting, collection, processing, storage, and transportation from the harvest site to end users can be quite expensive. In many cases, the combined cost of logistics will exceed the delivered value of the resource by a
more » ... substantial margin. Therefore, it is highly important to the economic success of any bioenergy project that the logistics of bringing the woody biomass to the consuming facility be optimized to the greatest extent possible. Optimizing the logistics for woody biomass fuels and feedstocks can best be accomplished in the planning stages of the project. If the consuming facility is improperly located with respect to the geographic distribution of the woody biomass resource, the project will likely suffer a continuing economic burden in the form of excessive transportation costs. Furthermore, the design of any woody biomass-consuming operation is generally best served by providing for as much feedstock flexibility as the operation's core conversion technology permits. That is to say that a wider range of feedstock species, form, particle size, ash content, and moisture content will be preferable from an economic standpoint. Increased feedstock flexibility expands the usable resource base, which in turn will serve to reduce risk and uncertainty in feedstock supply. Diversified feedstock supply chains may also reduce procurement costs by avoiding competition for biomass with other users, such Cellulosic Energy Cropping Systems, First Edition. Edited by Douglas L. Karlen.
doi:10.1002/9781118676332.ch14 fatcat:5cjthrcunvdgdlniuhn2zfghay

Xylose fermentation

Norman D. Hinman, John D. Wright, William Hogland, Charles E. Wyman
1989 Applied Biochemistry and Biotechnology  
The economic impact of conversion of xylose to ethanol for a wood-to-ethanol plant was examined, and the maximum potential reduction in the price of ethanol from utilization of xylose is estimated to be $0.42 per gallon from a base case price of $1.65. The sensitivity of the price of ethanol to the yield, ethanol concentration and rate of the xylose fermentation was also examined, and the price of ethanol is most affected by changes in yield and ethanol concentration, with rate of lesser
more » ... nce. Current performances of various xylose conversion biocatalysts were analyzed, and C. shehatae and P. stipitis appear to be the best yeasts.
doi:10.1007/bf02936498 fatcat:ygnkqdy4cjh7leciwbumhxedkm

Mitigating the Impact of Field and Image Registration Errors through Spatial Aggregation

John Hogland, David L.R. Affleck
2019 Remote Sensing  
Remotely sensed data are commonly used as predictor variables in spatially explicit models depicting landscape characteristics of interest (response) across broad extents, at relatively fine resolution. To create these models, variables are spatially registered to a known coordinate system and used to link responses with predictor variable values. Inherently, this linking process introduces measurement error into the response and predictors, which in the latter case causes attenuation bias.
more » ... ugh simulations, our findings indicate that the spatial correlation of response and predictor variables and their corresponding spatial registration (co-registration) errors can have a substantial impact on the bias and accuracy of linear models. Additionally, in this study we evaluate spatial aggregation as a mechanism to minimize the impact of co-registration errors, assess the impact of subsampling within the extent of sample units, and provide a technique that can be used to both determine the extent of an observational unit needed to minimize the impact of co-registration and quantify the amount of error potentially introduced into predictive models.
doi:10.3390/rs11030222 fatcat:taco7sqrh5hllmigid23jlnjwe

21st Century Planning Techniques for Creating Fire-Resilient Forests in the American West

John Hogland, Christopher J. Dunn, James D. Johnston
2021 Forests  
monitoring program, the Forest Vegetation and Fuels (FVF) program, led by Forest Service managers, Oregon State University researchers, and the Blue Mountains Forest Partners, a stakeholder group based in John  ...  Department of Agriculture SDI = Suppression Difficulty Index PCL = Potential Control Locations GIS = Geographic Information System HVRAs = High Value Resources and assets stakeholder group based in John  ... 
doi:10.3390/f12081084 fatcat:v36efxpmjfbytlmizd6tbzawxm

Locating Forest Management Units Using Remote Sensing and Geostatistical Tools in North-Central Washington, USA

Palaiologos Palaiologou, Maureen Essen, John Hogland, Kostas Kalabokidis
2020 Sensors  
In this study, we share an approach to locate and map forest management units with high accuracy and with relatively rapid turnaround. Our study area consists of private, state, and federal land holdings that cover four counties in North-Central Washington, USA (Kittitas, Okanogan, Chelan and Douglas). This area has a rich history of landscape change caused by frequent wildfires, insect attacks, disease outbreaks, and forest management practices, which is only partially documented across
more » ... ips in an inconsistent fashion. To consistently quantify forest management activities for the entire study area, we leveraged Sentinel-2 satellite imagery, LANDFIRE existing vegetation types and disturbances, monitoring trends in burn severity fire perimeters, and Landsat 8 Burned Area products. Within our methodology, Sentinel-2 images were collected and transformed to orthogonal land cover change difference and ratio metrics using principal component analyses. In addition, the Normalized Difference Vegetation Index and the Relativized Burn Ratio index were estimated. These variables were used as predictors in Random Forests machine learning classification models. Known locations of forest treatment units were used to create samples to train the Random Forests models to estimate where changes in forest structure occurred between the years of 2016 and 2019. We visually inspected each derived polygon to manually assign one treatment class, either clearcut or thinning. Landsat 8 Burned Area products were used to derive prescribed fire units for the same period. The bulk of analyses were performed using the RMRS Raster Utility toolbar that facilitated spatial, statistical, and machine learning tools, while significantly reducing the required processing time and storage space associated with analyzing these large datasets. The results were combined with existing LANDFIRE vegetation disturbance and forest treatment data to create a 21-year dataset (1999–2019) for the study area.
doi:10.3390/s20092454 pmid:32357414 pmcid:PMC7249656 fatcat:2ra72x5xx5etvjr7sciobr6rdi

Risk Management and Analytics in Wildfire Response

Matthew P. Thompson, Yu Wei, David E. Calkin, Christopher D. O'Connor, Christopher J. Dunn, Nathaniel M. Anderson, John S. Hogland
2019 Current Forestry Reports  
Purpose of Review The objectives of this paper are to briefly review basic risk management and analytics concepts, describe their nexus in relation to wildfire response, demonstrate real-world application of analytics to support response decisions and organizational learning, and outline an analytics strategy for the future. Recent Findings Analytics can improve decision-making and organizational performance across a variety of areas from sports to business to real-time emergency response. A
more » ... k of robust descriptive analytics on wildfire incident response effectiveness is a bottleneck for developing operationally relevant and empirically credible predictive and prescriptive analytics to inform and guide strategic response decisions. Capitalizing on technology such as automated resource tracking and machine learning algorithms can help bridge gaps between monitoring, learning, and data-driven decision-making. Summary By investing in better collection, documentation, archiving, and analysis of operational data on response effectiveness, fire management organizations can promote systematic learning and provide a better evidence base to support response decisions. We describe an analytics management framework that can provide structure to help deploy analytics within organizations, and provide real-world examples of advanced fire analytics applied in the USA. To fully capitalize on the potential of analytics, organizations may need to catalyze cultural shifts that cultivate stronger appreciation for data-driven decision processes, and develop informed skeptics that effectively balance both judgment and analysis in decision-making. risk is a value-laden concept predicated on defined objectives and that objectives can be positively or negatively impacted (e.g., fire can enhance or degrade forest health). Risk management (RM)-a set of coordinated activities to direct and control an organization with regard to risk-has become somewhat of an organizing framework for wildfire management, with applications ranging from programmatic budgeting to fire prevention, fuel reduction, community planning, and broader topics such as performance, communication, and governance [1, 5-12, 13•]. Wildfire management is rich with opportunities to apply and refine RM acumen-organizations around the globe implement RM practices as a matter of routine. As one example, the Australasian Fire Authorities Council a d o p t e d I n t e r n a t i o n a l S t a n d a r d 3 1 0 0 0 R i s k management-principles and guidelines [1] as a guidepost for all firefighting operations [14] . As another, the USDA Forest Service describes RM as a required core competency for fire managers, and promulgates a RM protocol to guide assessment, analysis, communication, decisionmaking, review, and learning [15] . RM can help these organizations increase the likelihood of achieving objectives, establish a reliable basis for decision-making and planning, efficiently allocate and use resources, improve operational effectiveness and safety, and improve organizational learning [1-3]. Here, we limit our review to evaluating RM concepts and principles in the context of wildfire response, i.e., the development of a response strategy and its operational execution over the duration of an active fire incident from detection to containment. Strategies can range from full suppression to managing for ecosystem benefit, depending on a variety of factors like relevant policies, land ownership patterns, potential socioeconomic and ecological impacts, fire growth potential, and availability of resources. Important components of response strategies include mobilizing/demobilizing fire management resources, allocating and assigning resources to various tasks (e.g., line construction, structure protection, mop-up), and monitoring and updating strategies in response to changing conditions. As the complexity, duration, or size of fires increases, response strategies may increasingly entail blend direct and indirect tactics, mobilize a greater amount and diversity of ground and aerial resources, and require coordination of a wide variety of activities such as locating drop points and conducting burnout operations [16] . These incident response decisions can be complex, uncertain, time-pressured, and require balancing tradeoffs across many dimensions (e.g., fire impacts, suppression expenditures, and public and responder safety), which highlights the need for structured and timely decision support [3, [16] [17] [18] . Indeed, there is a
doi:10.1007/s40725-019-00101-7 fatcat:gb2fpq3kwzbf5plr5bgamjmafu

Linking Phenological Indices from Digital Cameras in Idaho and Montana to MODIS NDVI

Joseph St. Peter, John Hogland, Mark Hebblewhite, Mark Hurley, Nicole Hupp, Kelly Proffitt
2018 Remote Sensing  
Digital cameras can provide a consistent view of vegetation phenology at fine spatial and temporal scales that are impractical to collect manually and are currently unobtainable by satellite and most aerial based sensors. This study links greenness indices derived from digital images in a network of rangeland and forested sites in Montana and Idaho to 16-day normalized difference vegetation index (NDVI) from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). Multiple digital cameras
more » ... ere placed along a transect at each site to increase the observational footprint and correlation with the coarser MODIS NDVI. Digital camera phenology indices were averaged across cameras on a site to derive phenological curves. The phenology curves, as well as green-up dates, and maximum growth dates, were highly correlated to the satellite derived MODIS composite NDVI 16-day data at homogeneous rangeland vegetation sites. Forested and mixed canopy sites had lower correlation and variable significance. This result suggests the use of MODIS NDVI in forested sites to evaluate understory phenology may not be suitable. This study demonstrates that data from digital camera networks with multiple cameras per site can be used to reliably estimate measures of vegetation phenology in rangelands and that those data are highly correlated to MODIS 16-day NDVI.
doi:10.3390/rs10101612 fatcat:g24gyzhivfa6rescp5rnqiqw34

Estimating Forest Characteristics for Longleaf Pine Restoration Using Normalized Remotely Sensed Imagery in Florida USA

John Hogland, David L.R. Affleck, Nathaniel Anderson, Carl Seielstad, Solomon Dobrowski, Jon Graham, Robert Smith
2020 Forests  
Additionally, we would like to thank the three independent reviewers and Melissa Reynolds-Hogland. Their comments and suggestions helped to significantly improve this article.  ...  Using the variable selection procedure described by Hogland et al. [6] , EGAMs selected between four and twelve metrics out of the potential 68, at a significance threshold of α = 0.05.  ...  Using the variable selection procedure described by Hogland et al. [6] , EGAMs selected between four and twelve metrics out of the potential 68, at a significance threshold of α = 0.05.  ... 
doi:10.3390/f11040426 fatcat:hsgswwrumnhhbbwfagkrs2krjm

An Evaluation of Long-Term Capture Effects in Ursids: Implications for Wildlife Welfare and Research

Marc Cattet, John Boulanger, Gordon Stenhouse, Roger A. Powell, Melissa J. Reynolds-Hogland
2008 Journal of Mammalogy  
The need to capture wild animals for conservation, research, and management is well justified, but long-term effects of capture and handling remain unclear. We analyzed standard types of data collected from 127 grizzly bears (Ursus arctos) captured 239 times in western Alberta, Canada, 1999-2005, and 213 American black bears (U. americanus) captured 363 times in southwestern North Carolina, 1981Carolina, -2002, to determine if we could detect long-term effects of capture and handling, that is,
more » ... ffects persisting !1 month. We measured blood serum levels of aspartate aminotransferase (AST), creatine kinase (CK), and myoglobin to assess muscle injury in association with different methods of capture. Serum concentrations of AST and CK were above normal in a higher proportion of captures by leghold snare (64% of 119 grizzly bear captures and 66% of 165 black bear captures) than capture by helicopter darting (18% of 87 grizzly bear captures) or by barrel trap (14% of 7 grizzly bear captures and 29% of 7 black bear captures). Extreme AST values (.5 times upper reference limit) in 7 (6%) grizzly bears and 29 (18%) black bears captured by leghold snare were consistent with the occurrence of exertional (capture) myopathy. We calculated daily movement rates for 91 radiocollared grizzly bears and 128 radiocollared black bears to determine if our activities affected their mobility during a 100-day period after capture. In both species, movement rates decreased below mean normal rate immediately after capture (grizzly bears: " X ¼ 57% of normal, 95% confidence interval ¼ 45-74%; black bears: 77%, 64-88%) and then returned to normal in 3-6 weeks (grizzly bears: 28 days, 20-37 days; black bears: 36 days, 19-53 days). We examined the effect of repeated captures on age-related changes in body condition of 127 grizzly bears and 207 black bears and found in both species that age-specific body condition of bears captured !2 times (42 grizzly bears and 98 black bears) tended to be poorer than that of bears captured once only (85 grizzly bears and 109 black bears), with the magnitude of effect directly proportional to number of times captured and the effect more evident with age. Importantly, the condition of bears did not affect their probability of capture or recapture. These findings challenge persons engaged in wildlife capture to examine their capture procedures and research results carefully. Significant capture-related effects may go undetected, providing a false sense of the welfare of released animals. Further, failure to recognize and account for long-term effects of capture and handling on research results can potentially lead to erroneous interpretations.
doi:10.1644/08-mamm-a-095.1 fatcat:mkjqlbbuzzemrcxnkjq2zij2jy

Spatial and temporal quantification of forest residue volumes and delivered costs

Lucas A. Wells, Woodam Chung, Nathaniel M. Anderson, John S. Hogland
2016 Canadian Journal of Forest Research  
The classification is the result of a polytomous logistic regression scheme (Hogland et al. 2013 ) employing a collection of texture derivatives as explanatory variables.  ...  Stand characteristics Stand characteristics, including basal area, tree density, and aboveground biomass, are estimated for each treatment unit using the Forest Characteristics Model (FCM) (Hogland et  ... 
doi:10.1139/cjfr-2015-0451 fatcat:xfvr5yfwljcyxoeeexep3sjg4y

Fine Resolution Probabilistic Land Cover Classification of Landscapes in the Southeastern United States

Joseph St. Peter, John Hogland, Nathaniel Anderson, Jason Drake, Paul Medley
2018 ISPRS International Journal of Geo-Information  
Our classification approach follows the recommendation of Hogland et al.  ...  Our classification approach follows the recommendation of Hogland et al.  ...  John Hogland proposed and built the RMRS Raster Utility software used in this study. He also developed the SNN models, designed the case study and contributed to the writing of the manuscript.  ... 
doi:10.3390/ijgi7030107 fatcat:3poslzu3bzh7tlgffqjuw6c2ha

Using Forest Inventory Data with Landsat 8 Imagery to Map Longleaf Pine Forest Characteristics in Georgia, USA

John Hogland, Nathaniel Anderson, David L. R. Affleck, Joseph St. Peter
2019 Remote Sensing  
In previous work, Hogland [29] developed and evaluated ANR using Landsat Enhanced Thematic Mapper Plus imagery.  ...  In previous work, Hogland [29] developed and evaluated ANR using Landsat Enhanced Thematic Mapper Plus imagery.  ... 
doi:10.3390/rs11151803 fatcat:jmpvsrjzjvadlica5s45zpiwu4

Comparison of standard maximum likelihood classification and polytomous logistic regression used in remote sensing

John Hogland, Nedret Billor, Nathaniel Anderson
2013 European Journal of Remote Sensing  
Discriminant analysis, referred to as maximum likelihood classification within popular remote sensing software packages, is a common supervised technique used by analysts. Polytomous logistic regression (PLR), also referred to as multinomial logistic regression, is an alternative classification approach that is less restrictive, more flexible, and easy to interpret. To assess the utility of PLR in image classification, we compared the results of 15 classifications using independent validation
more » ... tasets, estimates of kappa and error, and a non-parametric analysis of variance derived from visually interpreted observations, Landsat Enhanced Thematic Mapper plus imagery, PLR, and traditional maximum likelihood classifications algorithms.
doi:10.5721/eujrs20134637 fatcat:nlxfl2eisjek7e7hznmer6s5dq

Improving Estimates of Natural Resources Using Model-Based Estimators: Impacts of Sample Design, Estimation Technique, and Strengths of Association

John Hogland, David L. R. Affleck
2021 Remote Sensing  
Natural resource managers need accurate depictions of existing resources to make informed decisions. The classical approach to describing resources for a given area in a quantitative manner uses probabilistic sampling and design-based inference to estimate population parameters. While probabilistic designs are accepted as being necessary for design-based inference, many recent studies have adopted non-probabilistic designs that do not include elements of random selection or balance and have
more » ... ed on models to justify inferences. While common, model-based inference alone assumes that a given model accurately depicts the relationship between response and predictors across all populations. Within complex systems, this assumption can be difficult to justify. Alternatively, models can be trained to a given population by adopting design-based principles such as balance and spread. Through simulation, we compare estimates of population totals and pixel-level values using linear and nonlinear model-based estimators for multiple sample designs that balance and spread sample units. The findings indicate that model-based estimators derived from samples spread and balanced across predictor variable space reduce the variability of population and unit-level estimators. Moreover, if samples achieve approximate balance over feature space, then model-based estimates of population totals approached simple expansion-based estimates of totals. Finally, in all comparisons made, improvements in estimation were achieved using model-based estimation over design-based estimation alone. Our simulations suggest that samples drawn from a probabilistic design, that are spread and balanced across predictor variable space, improve estimation accuracy.
doi:10.3390/rs13193893 fatcat:sumo6jwsrbbqhju4rsji5gzb4q

New Geospatial Approaches for Efficiently Mapping Forest Biomass Logistics at High Resolution over Large Areas

John Hogland, Nathaniel Anderson, Woodam Chung
2018 ISPRS International Journal of Geo-Information  
Author Contributions: John Hogland is the designer and programmer of the RMRS Raster Utility and proposed, built, and applied the Delivered Cost Tool in the study.  ... 
doi:10.3390/ijgi7040156 fatcat:2isptjzuurgvtcgovnl554o7dq
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