Downloads & Free Reading Options - Results

Fire in the urban/wildland interface: a landscape modeling approach

We present here a model of carrier distribution and transport in semiconductor alloys accounting for quantum localization effects in disordered materials. This model is based on the recent development of a mathematical theory of quantum localization which introduces for each type of carrier a spatial function called \emph{localization landscape}. These landscapes allow us to predict the localization regions of electron and hole quantum states, their corresponding energies, and the local densities of states. We show how the various outputs of these landscapes can be directly implemented into a drift-diffusion model of carrier transport and into the calculation of absorption/emission transitions. This creates a new computational model which accounts for disorder localization effects while also capturing two major effects of quantum mechanics, namely the reduction of barrier height (tunneling effect), and the raising of energy ground states (quantum confinement effect), without having to solve the Schr\"odinger equation. Finally, this model is applied to several one-dimensional structures such as single quantum wells, ordered and disordered superlattices, or multi-quantum wells, where comparisons with exact Schr\"odinger calculations demonstrate the excellent accuracy of the approximation provided by the landscape theory.

We present here a model of carrier distribution and transport in semiconductor alloys accounting for quantum localization effects in disordered materials. This model is based on the recent development of a mathematical theory of quantum localization which introduces for each type of carrier a spatial function called \emph{localization landscape}. These landscapes allow us to predict the localization regions of electron and hole quantum states, their corresponding energies, and the local densities of states. We show how the various outputs of these landscapes can be directly implemented into a drift-diffusion model of carrier transport and into the calculation of absorption/emission transitions. This creates a new computational model which accounts for disorder localization effects while also capturing two major effects of quantum mechanics, namely the reduction of barrier height (tunneling effect), and the raising of energy ground states (quantum confinement effect), without having to solve the Schr\"odinger equation. Finally, this model is applied to several one-dimensional structures such as single quantum wells, ordered and disordered superlattices, or multi-quantum wells, where comparisons with exact Schr\"odinger calculations demonstrate the excellent accuracy of the approximation provided by the landscape theory.

Landscape pattern is an important driver in ecosystem dynamics and can control system-level functions such as nutrient cycling, connectivity, biodiversity, carbon sequestration, etc. Advances in remote sensing and Geographic Information Systems (GIS) have led to increased capability for quantifying landscape pattern, which is essential for relating spatial patterns to ecological processes. This study analyzes GIS and remote sensing data from two time periods, 2006 and 2010, over an approximately 2-km2 area in coastal Southwest Florida, to develop and examine landscape metrics with the goal of obtaining a better understanding of the factors that influence landscape changes observed as a result of the 2004 and 2005 hurricanes. Results are summarized by the change detection statistics and the landscape metrics, illustrating how quantitative measures can be applied to land cover data and assess both general land cover characteristics and underlying structure, aggregation, and shape characteristics. The landscape metrics analyses provided important indicators regarding the nature of the landscape changes and, thus, revealed important clues about the underlying ecological processes shaping them. This study represents an important first step in understanding how landscape metrics can be developed and in examining their potential use for linking spatial process to ecological pattern. More importantly, it also sets the stage for future research that will relate these findings to long-term ecological modeling and apply them to Engineering With Nature projects and Regional Sediment Management initiatives.

Landscape pattern is an important driver in ecosystem dynamics and can control system-level functions such as nutrient cycling, connectivity, biodiversity, carbon sequestration, etc. Advances in remote sensing and Geographic Information Systems (GIS) have led to increased capability for quantifying landscape pattern, which is essential for relating spatial patterns to ecological processes. This study analyzes GIS and remote sensing data from two time periods, 2006 and 2010, over an approximately 2-km2 area in coastal Southwest Florida, to develop and examine landscape metrics with the goal of obtaining a better understanding of the factors that influence landscape changes observed as a result of the 2004 and 2005 hurricanes. Results are summarized by the change detection statistics and the landscape metrics, illustrating how quantitative measures can be applied to land cover data and assess both general land cover characteristics and underlying structure, aggregation, and shape characteristics. The landscape metrics analyses provided important indicators regarding the nature of the landscape changes and, thus, revealed important clues about the underlying ecological processes shaping them. This study represents an important first step in understanding how landscape metrics can be developed and in examining their potential use for linking spatial process to ecological pattern. More importantly, it also sets the stage for future research that will relate these findings to long-term ecological modeling and apply them to Engineering With Nature projects and Regional Sediment Management initiatives.

This report describes the Carnegie Mellon Software Engineering Institute (SEI) Assurance Modeling Framework. It also discusses an initial piloting of the framework to prove its value and insights gained from that piloting for the adoption of selected assurance solutions. The SEI is developing a way to model key aspects of assurance to accelerate the adoption of assurance solutions within operational set-tings for the U. S. Department of Defense (DoD) and other government organizations. As part of that undertaking, SEI researchers have developed an Assurance Modeling Framework to build a profile for an assurance capability area such as vulnerability management with-in an assurance quality such as security. The profile consists of many views developed using selected methods and models. From the analysis of these views, inefficiencies and candidate improvements for assurance adoption can be identified.

My long-term goals are to a) develop a mechanistic understanding of submarine debris flows and associated turbidity currents and b) incorporate this understanding into the broader context of the evolution of the morphology of the continental slope. Objectives can be enumerated as follows: 1) Determine the mechanisms of submarine debris flow runout. 2) Characterize the deposits of submarine debris flows. 3) Characterize and describe tendencies for transition to turbidity currents. 4) Develop models for predicting runout and deposition of submarine debris flows. 5) Extract the essentials of submarine debris flow behavior for incorporation into models of continental slope dynamics, morphology and stratigraphy.

"Fifty Years of Landscape Modeling" is an article from The Art World, Volume 1 . View more articles from The Art World . View this article on JSTOR . View this article's JSTOR metadata . You may also retrieve all of this items metadata in JSON at the following URL: https://archive.org/metadata/jstor-25587704

"Fifty Years of Landscape Modeling" is an article from The Art World, Volume 1 . View more articles from The Art World . View this article on JSTOR . View this article's JSTOR metadata . You may also retrieve all of this items metadata in JSON at the following URL: https://archive.org/metadata/jstor-25587704

Scientific literature till date can be thought of as a partially revealed landscape, where scholars continue to unveil hidden knowledge by exploring novel research topics. How do scholars explore the scientific landscape , i.e., choose research topics to work on? We propose an agent-based model of topic mobility behavior where scholars migrate across research topics on the space of science following different strategies, seeking different utilities. We use this model to study whether strategies widely used in current scientific community can provide a balance between individual scientific success and the efficiency and diversity of the whole academic society. Through extensive simulations, we provide insights into the roles of different strategies, such as choosing topics according to research potential or the popularity. Our model provides a conceptual framework and a computational approach to analyze scholars' behavior and its impact on scientific production. We also discuss how such an agent-based modeling approach can be integrated with big real-world scholarly data.

Urban areas are sources of land use change and CO2 emissions that contribute to global climate change. Despite this, assessments of urban vegetation carbon stocks often fail to identify important landscape-scale drivers of variation in urban carbon, especially the potential effects of landscape structure variables at different spatial scales. We combined field measurements with Light Detection And Ranging (LiDAR) data to build high-resolution models of woody plant aboveground carbon across the urban portion of Brisbane, Australia, and then identified landscape scale drivers of these carbon stocks. First, we used LiDAR data to quantify the extent and vertical structure of vegetation across the city at high resolution (5 × 5 m). Next, we paired this data with aboveground carbon measurements at 219 sites to create boosted regression tree models and map aboveground carbon across the city. We then used these maps to determine how spatial variation in land cover/land use and landscape structure affects these carbon stocks. Foliage densities above 5 m height, tree canopy height, and the presence of ground openings had the strongest relationships with aboveground carbon. Using these fine-scale relationships, we estimate that 2.2 ± 0.4 TgC are stored aboveground in the urban portion of Brisbane, with mean densities of 32.6 ± 5.8 MgC ha− 1 calculated across the entire urban land area, and 110.9 ± 19.7 Mg C ha− 1 calculated within treed areas. Predicted carbon densities within treed areas showed strong positive relationships with the proportion of surrounding tree cover and how clumped that tree cover was at both 1 km2 and 1 ha resolutions. Our models predict that even dense urban areas with low tree cover can have high carbon densities at fine scales. We conclude that actions and policies aimed at increasing urban carbon should focus on those areas where urban tree cover is most fragmented.

Urban areas are sources of land use change and CO2 emissions that contribute to global climate change. Despite this, assessments of urban vegetation carbon stocks often fail to identify important landscape-scale drivers of variation in urban carbon, especially the potential effects of landscape structure variables at different spatial scales. We combined field measurements with Light Detection And Ranging (LiDAR) data to build high-resolution models of woody plant aboveground carbon across the urban portion of Brisbane, Australia, and then identified landscape scale drivers of these carbon stocks. First, we used LiDAR data to quantify the extent and vertical structure of vegetation across the city at high resolution (5 × 5 m). Next, we paired this data with aboveground carbon measurements at 219 sites to create boosted regression tree models and map aboveground carbon across the city. We then used these maps to determine how spatial variation in land cover/land use and landscape structure affects these carbon stocks. Foliage densities above 5 m height, tree canopy height, and the presence of ground openings had the strongest relationships with aboveground carbon. Using these fine-scale relationships, we estimate that 2.2 ± 0.4 TgC are stored aboveground in the urban portion of Brisbane, with mean densities of 32.6 ± 5.8 MgC ha− 1 calculated across the entire urban land area, and 110.9 ± 19.7 Mg C ha− 1 calculated within treed areas. Predicted carbon densities within treed areas showed strong positive relationships with the proportion of surrounding tree cover and how clumped that tree cover was at both 1 km2 and 1 ha resolutions. Our models predict that even dense urban areas with low tree cover can have high carbon densities at fine scales. We conclude that actions and policies aimed at increasing urban carbon should focus on those areas where urban tree cover is most fragmented.

This article is from International Journal of Health Geographics , volume 13 . Abstract Background: Predictive models of malaria vector larval habitat locations may provide a basis for understanding the spatial determinants of malaria transmission. Methods: We used four landscape variables (topographic wetness index [TWI], soil type, land use-land cover, and distance to stream) and accumulated precipitation to model larval habitat locations in a region of western Kenya through two methods: logistic regression and random forest. Additionally, we used two separate data sets to account for variation in habitat locations across space and over time. Results: Larval habitats were more likely to be present in locations with a lower slope to contributing area ratio (i.e. TWI), closer to streams, with agricultural land use relative to nonagricultural land use, and in friable clay/sandy clay loam soil and firm, silty clay/clay soil relative to friable clay soil. The probability of larval habitat presence increased with increasing accumulated precipitation. The random forest models were more accurate than the logistic regression models, especially when accumulated precipitation was included to account for seasonal differences in precipitation. The most accurate models for the two data sets had area under the curve (AUC) values of 0.864 and 0.871, respectively. TWI, distance to the nearest stream, and precipitation had the greatest mean decrease in Gini impurity criteria in these models. Conclusions: This study demonstrates the usefulness of random forest models for larval malaria vector habitat modeling. TWI and distance to the nearest stream were the two most important landscape variables in these models. Including accumulated precipitation in our models improved the accuracy of larval habitat location predictions by accounting for seasonal variation in the precipitation. Finally, the sampling strategy employed here for model parameterization could serve as a framework for creating predictive larval habitat models to assist in larval control efforts.

Produced, recorded and mixed by Minuit de Lacroix in a cold storage space of site. Tijuana, México. Edited and designed at the BreedFactor Lab, Tijuana, México. Suzie Metzler: Classical and Spanish guitars on 1 and 13. Recorded in Oackland, California. Photography and sleeve design: M. Late night interior graphic design: Mae Wahbe.

Produced, recorded and mixed by Minuit de Lacroix in a cold storage space of site. Tijuana, México. Edited and designed at the BreedFactor Lab, Tijuana, México. Suzie Metzler: Classical and Spanish guitars on 1 and 13. Recorded in Oackland, California. Photography and sleeve design: M. Late night interior graphic design: Mae Wahbe.

Research agenda for integrated landscape modeling

Research agenda for integrated landscape modeling

Research agenda for integrated landscape modeling

Research agenda for integrated landscape modeling

This article is from PLoS ONE , volume 9 . Abstract Cattle ticks are distributed worldwide and affect animal health and livestock production. White tailed deer (WTD) sustain and spread cattle tick populations. The aim of this study was to model the efficacy of anti-tick vaccination of WTD to control tick infestations in the absence of cattle vaccination in a territory where both host species coexist and sustain cattle tick populations. Agent-based models that included land cover/landscape properties (patch size, distances to patches) and climatic conditions were built in a GIS environment to simulate WTD vaccine effectiveness under conditions where unvaccinated cattle shared the landscape. Published and validated information on tick life cycle was used to build models describing tick mortality and developmental rates. Data from simulations were applied to a large territory in northeastern Mexico where cattle ticks are endemic and WTD and cattle share substantial portions of the habitat. WTD movements were simulated together with tick population dynamics considering the actual landscape and climatic features. The size of the vegetation patches and the distance between patches were critical for the successful control of tick infestations after WTD vaccination. The presence of well-connected, large vegetation patches proved essential for tick control, since the tick could persist in areas of highly fragmented habitat. The continued application of one yearly vaccination on days 1-70 for three years reduced tick abundance/animal/patch by a factor of 40 and 60 for R. annulatus and R. microplus, respectively when compared to non-vaccinated controls. The study showed that vaccination of WTD alone during three consecutive years could result in the reduction of cattle tick populations in northeastern Mexico. Furthermore, the results of the simulations suggested the possibility of using vaccines to prevent the spread and thus the re-introduction of cattle ticks into tick-free areas.

Perma.cc archive of https://www.researchgate.net/publication/33692301_Landscape_modeling_and_spatial_optimization_of_watershed_interventions_to_reduce_phosphorus_load_to_surface_waters_using_a_process-oriented_and_participatory_research_approach_a_case_study_in_the_St_A created on 2016-06-11 17:54:25+00:00.

Landscape analysis in ecosystem management: modeling process and pattern (Part 1) - U.S. Forest Service 1996 - BMRI-V10-A - Pacific Northwest Research Station - Blue Mountain Natural Resources Institute. November 14, 1996. Disturbance by fire and insects leaves a pattern of change on the landscape that affects all plants and animals. Steve Garman argues that if managers can design their management schemes to match the pattern of natural disturbance, we will produce a healthier ecosystem over the long run.

U.S. Forest Service BMRI-V10-A Landscape analysis in ecosystem management: modeling process and pattern (Part 1) Pacific Northwest Research Station - Blue Mountain Natural Resources Institute. November 14, 1996. Disturbance by fire and insects leaves a pattern of change on the landscape that affects all plants and animals. Steve Garman argues that if managers can design their management schemes to match the pattern of natural disturbance, we will produce a healthier ecosystem over the long run.

Evaluating and modeling the spatiotemporal pattern of regional-scale salinized land expansion in highly sensitive shoreline landscape of southeastern Iran 作者： Mohammad, SHAFIEZADEH Hossein, MORADI Sima, FAKHERAN 作者单位： 1. Department of Natural Resources, Isfahan University of Technology, Isfahan 8415683111, Iran 通讯作者： Hossein, MORADI Email:[email protected] 提交时间： 2018-10-29 摘要: Taking an area of about 2.3×104 km2 of southeastern Iran, this study aims to detect and predict regional-scale salt-affected lands. Three sets of Landsat images, each set containing 4 images for 1986, 2000, and 2015 were acquired as the main source of data. Radiometric, atmospheric and cutline blending methods were used to improve the quality of images and help better classify salinized land areas under the support vector machine method. A set of landscape metrics was also employed to detect the spatial pattern of salinized land expansion from 1986 to 2015. Four factors including distance to sea, distance to sea water channels, slope, and elevation were identified as the main contributing factors to land salinization. These factors were then integrated using the multi-criteria evaluation (MCE) procedure to generate land sensitivity map to salinization and also to calibrate the cellular-automata (CA) Markov chain (CA-Markov) model for simulation of salt-affected lands up to 2030, 2040 and 2050. The results of this study showed a dramatic dispersive expansion of salinized land from 7.7 % to 12.7% of the total study area from 1986 to 2015. The majority of areas prone to salinization and the highest sensitivity of land to salinization was found to be in the southeastern parts of the region. The result of the MCE-informed CA-Markov model revealed that 20.3% of the study area is likely to be converted to salinized lands by 2050. The findings of this research provided a view of the magnitude and direction of salinized land expansion in a past-to-future time period which should be considered in future land development strategies. soil salinization remote sensing CA-Markov salt land expansion southeastern Iran 分类： 地球科学 >> 地球科学史 引用： ChinaXiv:201810.00181 (或此版本 ChinaXiv:201810.00181V1 ) doi:10.12074/201810.00181 CSTR:32003.36.ChinaXiv.201810.00181.V1 推荐引用方式： Mohammad, SHAFIEZADEH,Hossein, MORADI,Sima, FAKHERAN.(2018).Evaluating and modeling the spatiotemporal pattern of regional-scale salinized land expansion in highly sensitive shoreline landscape of southeastern Iran.干旱区科学.[ChinaXiv:201810.00181] 版本历史 [V1] 2018-10-29 19:23:15 ChinaXiv:201810.00181V1 下载全文

This article is from PLoS ONE , volume 9 . Abstract Extrapolating landscape regression models for use in assessing vector-borne disease risk and other applications requires thoughtful evaluation of fundamental model choice issues. To examine implications of such choices, an analysis was conducted to explore the extent to which disparate landscape models agree in their epidemiological and entomological risk predictions when extrapolated to new regions. Agreement between six literature-drawn landscape models was examined by comparing predicted county-level distributions of either Lyme disease or Ixodes scapularis vector using Spearman ranked correlation. AUC analyses and multinomial logistic regression were used to assess the ability of these extrapolated landscape models to predict observed national data. Three models based on measures of vegetation, habitat patch characteristics, and herbaceous landcover emerged as effective predictors of observed disease and vector distribution. An ensemble model containing these three models improved precision and predictive ability over individual models. A priori assessment of qualitative model characteristics effectively identified models that subsequently emerged as better predictors in quantitative analysis. Both a methodology for quantitative model comparison and a checklist for qualitative assessment of candidate models for extrapolation are provided; both tools aim to improve collaboration between those producing models and those interested in applying them to new areas and research questions.

The Department of Defense and the Department of Energy are stewards of millions of acres of land and the cultural resources they contain. Federal regulations require that DoD and DoE installations and facilities accomplish their respective missions in compliance with cultural resource laws. Compliance with Executive Order 11593, as codified in amendments to the National Historic Preservation Act of 1966 (NHPA), requires complete inventories of all historic properties on federally controlled lands. Additional legislation expands the compliance and stewardship roles of DoD and DoE in regard to historic preservation. These acts include the Archaeological Resources Protection Act (ARPA), the National Environmental Policy Act (NEPA), the Native American Graves Protection and Repatriation Act (NAGPRA), the American Indian Religious Freedom Act (AIRFA), and related Federal legislation. Because of the protection from commercial exploitation, DoD- and DoE-administered lands (both cantonment and training/testing areas) contain some of the nation's most significant prehistoric archeological sites (e.g., the Yuchi Town village site at Fort Benning, GA; the Pendejo Cave site at Fort Bliss, NM; the Santa Elena site at the Marine Corps Recruit Depot, Parris Island, SC), as well as Native American and Native Hawaiian sacred sites and Traditional Cultural Properties (TCPs). At many Army installations across the nation, cantonment areas are known to contain nationally significant archeological sites associated with the history of the military. Historic preservation legislation and Army regulations require complete inventories and significance evaluations of all historic properties on Federally owned or administered lands.

Erosion on military lands limits Army training activities, and may cause environmental problems both at training sites and downstream. Thus, remedial soil and land conservation plans are vitally important to stabilize erosion and use the land more safely for Army activities while complying with environmental regulations and standards. The channel-hillslope integrated landscape development (CHILD) model is a dynamic, physically based, spatially distributed numerical framework for modeling soil erosion and landscape evolution, originally developed at MIT. Potential uses of CHILD include understanding the sensitivity of earth surface to changes in climate, land cover conditions and anthropogenic disturbances. In this paper, we present recent advances in the CHILD model with example numerical simulations. These are the effects of vegetation-erosion coupling on the short and long-term response of erosion rates and landscape topography, and modeling gully development by plunge-pool erosion at the gully head and widening by bank failures. CHILD simulations using a dynamic plant growth component show vegetation loss as an important factor accelerating erosion rates in decadal time scales. Over the long term, vegetation may change the dominant erosion process on the landscape, create episodicity in the sediment yields, and alter the visual appearance of landforms. Landscapes modeled using the new gully erosion module in CHILD produce landforms that compare well with the observed gullies in the Army training areas in Fort Carson and Pinon Canyon Maneuver Sites in Colorado. These results show potential applicability of the model for short and long-term land management purposes in the army lands.

This article is from PLoS Computational Biology , volume 10 . Abstract Viral immune evasion by sequence variation is a major hindrance to HIV-1 vaccine design. To address this challenge, our group has developed a computational model, rooted in physics, that aims to predict the fitness landscape of HIV-1 proteins in order to design vaccine immunogens that lead to impaired viral fitness, thus blocking viable escape routes. Here, we advance the computational models to address previous limitations, and directly test model predictions against in vitro fitness measurements of HIV-1 strains containing multiple Gag mutations. We incorporated regularization into the model fitting procedure to address finite sampling. Further, we developed a model that accounts for the specific identity of mutant amino acids (Potts model), generalizing our previous approach (Ising model) that is unable to distinguish between different mutant amino acids. Gag mutation combinations (17 pairs, 1 triple and 25 single mutations within these) predicted to be either harmful to HIV-1 viability or fitness-neutral were introduced into HIV-1 NL4-3 by site-directed mutagenesis and replication capacities of these mutants were assayed in vitro. The predicted and measured fitness of the corresponding mutants for the original Ising model (r = −0.74, p = 3.6×10−6) are strongly correlated, and this was further strengthened in the regularized Ising model (r = −0.83, p = 3.7×10−12). Performance of the Potts model (r = −0.73, p = 9.7×10−9) was similar to that of the Ising model, indicating that the binary approximation is sufficient for capturing fitness effects of common mutants at sites of low amino acid diversity. However, we show that the Potts model is expected to improve predictive power for more variable proteins. Overall, our results support the ability of the computational models to robustly predict the relative fitness of mutant viral strains, and indicate the potential value of this approach for understanding viral immune evasion, and harnessing this knowledge for immunogen design.

The presence of brown-headed cowbirds (BHCO) affects local songbird populations, including breeding populations of black-capped vireos (BCVI) and golden-cheeked warblers (GCWA), across large distances on Fort Hood, Texas. Due to the impact of BHCO parasitism on these two endangered species, Fort Hood personnel have conducted a BHCO control program since 1991, which includes live-trapping and shooting individual BHCO throughout the breeding season. The objective of this study was to develop a model that simulates BHCO breeding and feeding habitats across the Fort Hood landscape, daily BHCO movement behavior, and the placement and management of BHCO traps. The model generates predictions of sites most likely to receive frequent visitation by feeding BHCOs, and compares the capture success among different trapping strategies. Output suggests that the model is fairly stable to variation in several input variables, and the model accurately captures current knowledge about BHCO movement and locations on Fort Hood. Ongoing efforts will include improved mapping of nearby feeding habitat across the installation boundary, and validation of BCHO visitation output.