Front Range Wildland Fires: Evaluating the Efficacy of Remote Sensing Imagery in Monitoring Forest Fuels Treatment Methods
Explore the source record for details and available documents.
SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Explore the source record for details and available documents.
While the relationships between dry land surface conditions, heat, and aridity have been well-established, few studies have addressed whether global warming will affect the ability of wet conditions to moderate high temperatures and atmospheric aridity. Using Coupled Model Intercomparison Project Phase 6 models, we demonstrate that absolute changes in the monthly maximum temperature distribution during Central North American summers strongly outweigh the historical cooling effect of high precipitation and soil moisture conditions. Although wet conditions nearly always prevent concurrent extreme temperatures in the baseline period, these conditions are 40%–48% and 96%–98% less effective at 1 and 2° of global warming, respectively. However, high precipitation and soil moisture partially retain the ability to constrain concurrent high vapor pressure deficit conditions below historical thresholds at 1–2° of warming. Our results highlight the growing vulnerability of Central North America to warmer temperatures and drier atmospheric conditions, even during periods of high precipitation and soil moisture.
Plan execution in unknown environments poses a number of challenges: uncertainty in domain modeling, stochasticity at execution time, and the presence of exogenous events. These challenges motivate an integrated approach to planning and execution that is able to respond intelligently to variation. We examine this problem in the context of the Europa Lander mission concept, and evaluate a planning and execution framework that responds to feedback and task failure using two techniques: flexible execution and replanning with plan optimization. We develop a theoretical framework to estimate gains from these techniques, and we compare these predictions to empirical results generated in simulation. These results indicate that an integrated approach to planning and execution leveraging flexible execution, replanning, and utility maximization shows significant promise for future tightly-constrained space missions that must address significant uncertainty.
Plan execution in unknown environments poses a number of challenges: uncertainty in domain modeling, stochasticity at execution time, and the presence of exogenous events. These challenges motivate an integrated approach to planning and execution that is able to respond intelligently to variation. We examine this problem in the context of the Europa Lander mission concept, and propose a planning and execution framework that responds to feedback and task failure using two techniques: flexible execution and replanning with plan optimization. We develop a theoretical framework to predict the value of each of these techniques, and we compare these predictions to empirical results generated in simulation. We demonstrate that an integrated approach to planning and execution that is grounded in flexible execution, replanning, and utility maximization will be an enabling technology for future tightly-constrained planetary surface missions.
Plan execution in unknown environments poses a number of challenges: uncertainty in domain modeling, stochasticity at execution time, and the presence of exogenous events. These challenges motivate an integrated approach to planning and execution that is able to respond intelligently to variation. We examine this problem in the context of the Europa Lander mission concept, and propose a planning and execution framework that responds to feedback and task failure using two techniques: flexible execution and replanning with plan optimization. We develop a theoretical framework to predict the value of each of these techniques, and we compare these predictions to empirical results generated in simulation. We demonstrate that an integrated approach to planning and execution that is grounded in flexible execution, replanning, and utility maximization will be an enabling technology for future tightly-constrained planetary surface missions.
Over the last several decades, wildfire frequency and severity in forested areas along Colorado’s Front Range have increased due to a buildup of fuels. This has led to an increase in forest treatments, as well as an increased need to evaluate the success of these treatments. Remote sensing products offer an efficient and cost-effective way to monitor forest treatments; however, not all remote sensing products and analysis techniques have been explored by Coloradan land managers. Specifically, project partners at the Colorado State Forest Service (CSFS) and the Colorado Forest Restoration Institute (CFRI) were interested in using an effective and streamlined method of mapping canopy cover to better monitor forest treatment success. To support their needs, the NASA DEVELOP Front Range Wildland Fires team explored National Agricultural Imagery Program (NAIP) imagery at different spatial resolutions and numbers of training points with NASA’s Shuttle Radar Topography Mission (SRTM) Data Elevation Model (DEM) as a predictor in addition to NAIP imagery spectral predictors. From this analysis, we created classified canopy cover rasters, and compared accuracy metrics across model iterations. We also determined that the best performing model, with an overall accuracy of 0.900 uses 2021 NAIP imagery at 2-meter resolution, 800 training points, 200 testing points, does not use topographic predictors, and reclassifies shadow pixels via a pre-selected NDVI threshold.
Human exploration of the space environment has been a topic of interest for decades; however, the threat to physiological health posed by ionizing radiation and microgravity effects remains a concern. Previous research utilizing combined hindlimb unloading and irradiation to simulate spaceflight effects found that a dried plum diet can mitigate adverse effects of simulated spaceflight on skeletal health. Though the mechanism is not fully understood we believe this protective effect is due to its high antioxidant capacity and polyphenolic content. This study seeks to extend these findings to investigate if dried plum is an effective countermeasure in other tissues relating to aging and neurotoxicity. Male C57BL/6 J mice were assigned to one of eight groups (n = 10/group) and were fed either a controlled diet or dried plum diet, underwent normal loading or hindlimb unloading as well as exposure 2 Gy gamma radiation or sham exposure. At the end of the experiment, mice were euthanized, and RNA was extracted from brain and bone marrow tissue and PCR was employed to analyze significant changes in gene expression profiles. Analysis of five house-keeping genes revealed genes B2M (neurotoxicity) and GUSB (aging) to be the most stable genes across all treatments and were used to find the fold change within our genes of interest. Through this, and analysis via ANOVA, we were able to identify genes with significant altered expression levels within brain and bone marrow tissues related to neurotoxicity and aging. Considering previous research, we hypothesize that the dried plum diet’s antioxidative properties ameliorated the oxidative stress from irradiation and microgravity via altered levels of gene expression. Identified expression changes will provide insights into molecular mechanisms behind the observed protective effects. These findings present potential avenues for future interventions to alleviate negative impacts of spaceflight on astronaut health.
In the literature, dry land surface conditions possess a well-established association with extreme heat and high atmospheric aridity, especially in regions that experience strong land-atmosphere feedbacks. However, few studies have examined whether global warming will impact the ability of wet conditions to constrain high temperatures and atmospheric aridity occurrences. Here we investigate how wet conditions, which we define using precipitation and soil moisture, constrain high temperature and high vapor pressure deficit during the warm season over the Central Plains in North America at different global warming levels (0-3 degrees K). Using a CMIP6 multi-model ensemble, we show that absolute changes in the maximum temperature distribution outweigh the historical cooling effect of high precipitation or soil moisture conditions, reducing the probability of non-extreme maximum temperatures under global warming. Strikingly, the conditional likelihood of non-extreme maximum temperatures falls from 97-98% at baseline conditions to 50-58% at 1 degree of global warming. Once 2 degrees of warming is reached, there is very little probability of constraining extreme maximum temperatures. On the other hand, high precipitation and high soil moisture are able to better retain the ability to constrain concurrent high vapor pressure deficit conditions – moving from 99% at baseline conditions to 81-91% at 1 degree of warming and 26-42% at 2 degrees of warming. With our results, we find that the extent of this retention is dependent on how individual models represent relative changes in the VPD distribution as temperatures warm. Overall, our results highlight the growing vulnerability of the Central North American region to warmer temperatures and drier atmospheric conditions, even during periods of high precipitation and soil moisture.
Human exploration of the space environment has been a topic of interest for decades; however, the threat to physiological health posed by ionizing radiation and microgravity effects remains a concern. Previous research utilizing combined hindlimb unloading and irradiation to simulate spaceflight effects found that a dried plum diet can mitigate adverse effects of simulated spaceflight on skeletal health. Though the mechanism is not fully understood we believe this protective effect is due to its high antioxidant capacity and polyphenolic content. This study seeks to extend these findings to investigate if dried plum is an effective countermeasure in other tissues relating to aging and neurotoxicity. Male C57BL/6 J mice were assigned to one of eight groups (n = 10/group) and were fed either a controlled diet or dried plum diet, underwent normal loading or hindlimb unloading as well as exposure 2 Gy gamma radiation or sham exposure. At the end of the experiment, mice were euthanized, and RNA was extracted from brain and bone marrow tissue and PCR was employed to analyze significant changes in gene expression profiles. Analysis of five house-keeping genes revealed genes B2M (neurotoxicity) and GUSB (aging) to be the most stable genes across all treatments and were used to find the fold change within our genes of interest. Through this, and analysis via ANOVA, we were able to identify genes with significant altered expression levels within brain and bone marrow tissues related to neurotoxicity and aging. Considering previous research, we hypothesize that the dried plum diet’s antioxidative properties ameliorated the oxidative stress from irradiation and microgravity via altered levels of gene expression. Identified expression changes will provide insights into molecular mechanisms behind the observed protective effects. These findings present potential avenues for future interventions to alleviate negative impacts of spaceflight on astronaut health.
The objective of this document is to assess the value of collision on launch assessment (COLA, often referred to as “launch COLA”) practices and provide recommendations regarding its implementation for NASA robotic missions. The scope of this effort is limited to launch COLA screens against catalog objects that are either spacecraft or debris. No modifications to manned safety launch COLA practices are considered in this effort. An assessment of the value of launch COLA can be broken down into two fundamental questions: 1) Does collision during launch represent a significant risk to either the payload being launched or the space environment? 2) Can launch collision mitigation be performed in a manner that provides meaningful risk reduction at an acceptable level of operational impact? While it has been possible to piece together partial answers to these questions for some time, the first attempt to comprehensively address them is documented in reference (a), Launch COLA Operations: an Examination of Data Products, Procedures, and Thresholds, Revision A. This report is the product of an extensive study that addressed fundamental technical questions surrounding launch collision avoidance analysis and practice. The results provided in reference (a) will be cited throughout this document as these two questions are addressed. The premise of this assessment is that in order to conclude that launch COLA is a value-added activity, the answer to both of these questions must be affirmative. A “no” answer to either of these questions points toward the conclusion that launch COLA provides little or no risk mitigation benefit. The remainder of this assessment will focus on addressing these two questions.
Astronauts are subject to unique stressors during spaceflight, leading to changes in their cellular function. However, neither astronauts nor model organisms respond the same to spaceflight, and research implicates a contribution of omics components in differential responses. Understanding how gene expression affects astronaut health is critical for the success of long-term space missions, prompting interest in developing personalized predictive models leveraging artificial intelligence (AI) and machine learning (ML) techniques. Developing such models requires extensive data, which is challenging to obtain and share. This study explores the use of conditional variational autoencoders (CVAEs) to synthetically generate single-nuclei RNA-seq (snRNA-seq) data. CVAEs build on standard variational autoencoders (VAEs) by conditioning data generation on covariates like sample identity and mission parameters, enhancing the relevance of generated data for specific contexts. For our work, we built two CVAEs with varying degrees of sparsity to optimize both interpretability and generative power. We train and validate models on existing snRNA-seq data collected from the brain tissue of mice subjected to spaceflight conditions and their ground control counterparts. We evaluate model performance using statistical tests and visualizations to compare synthetic data to real data. We aim to demonstrate that these prototype CVAE architectures could be used in future space biology work and that this is a method worth further exploring.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Abstract not provided.
If history repeats itself, lighting 20 years from now will be substantially different than what it is today. Twenty years ago, LEDs were a novelty, not quite ready for use in architectural lighting systems. A little more than 20 years before that, rare-earth tri-phosphor T8 fluorescent lamps were beginning to replace broadband halophosphate T12 fluorescent lamps, which themselves became the predominant source of lumens in the United States around the middle of the last century. At each step in this evolution, new lighting metrics have been developed to address differences in performance, and they in turn guided technology development. For example, in the 1960s, it was CRI and CCT, and more recently it has been LM-79 and TM-30.
Abstract not provided.
Explore the source record for details and available documents.
Explore the source record for details and available documents.