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At least 127 records · Page 7
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NASA NTRS2021

Observations of Greenhouse Gases as Climate Indicators

Humans have significantly altered the energy balance of the Earth’s climate system mainly not only by extracting and burning fossil fuels but also by altering the biosphere and using halocarbons. The 3rd US National Climate Assessment pointed to a need for a system of indicators of climate and global change based on long-term data that could be used to support assessments and this led to the development of the National Climate Indicators System (NCIS). Here we identify a representative set of key atmospheric indicators of changes in atmospheric radiative forcing due to greenhouse gases (GHGs), and we evaluate atmospheric composition measurements, including non-CO2GHGs for use as climate change indicators in support of the US National Climate Assessment. GHG abundances and their changes over time can provide valuable information on the success of climate mitigation policies, as well as insights into possible carbon-climate feedback processes that may ultimately affect the success of those policies. To ensure that reliable information for assessing GHG emission changes can be provided on policy-relevant scales, expanded observational efforts are needed. Furthermore, the ability to detect trends resulting from changing emissions requires a commitment to supporting long-term observations. Long-term measurements of greenhouse gases, aerosols, and clouds and related climate indicators used with a dimming/brightening index could provide a foundation for quantifying forcing and its attribution and reducing error in existing indicators that do not account for complicated cloud processes.

greenhouse gases↗
NASA NTRS2016

Disturbance and the Carbon Balance of US Forests: A Quantitative Review of Impacts from Harvests, Fires, Insects, and Droughts

Disturbances are a major determinant of forest carbon stocks and uptake. They generally reduce land carbon stocks but also initiate a regrowth legacy that contributes substantially to the contemporary rate of carbon stock increase in US forestlands. As managers and policy makers increasingly look to forests for climate protection and mitigation, and because of increasing concern about changes in disturbance intensity and frequency, there is a need for synthesis and integration of current understanding about the role of disturbances and other processes in governing forest carbon cycle dynamics, and the likely future of this and other sinks for atmospheric carbon. This paper aims to address that need by providing a quantitative review of the distribution, extent and carbon impacts of the major disturbances active in the US. We also review recent trends in disturbances, climate, and other global environmental changes and consider their individual and collective contributions to the US carbon budget now and in the likely future. Lastly, we identify some key challenges and opportunities for future research needed to improve current understanding, advance predictive capabilities, and inform forest management in the face of these pressures. Harvest is found to be the most extensive disturbance both in terms of area and carbon impacts, followed by fire, windthrow and bark beetles, and lastly droughts. Collectively these lead to the gross loss of about 200 Tg C y(exp -1) in live biomass annually across the conterminous US. At the same time, the net change in forest carbon stocks is positive (190 Tg C y(exp -1)), indicating not only forest resilience but also an apparently large response to growth enhancements such as fertilization by CO2 and nitrogen. Uncertainty about disturbance legacies, disturbance interactions, likely trends, and global change factors make the future of the US forest carbon sink unclear. While there is scope for management to enhance carbon sinks in US forests, tradeoffs with other values and uses are likely to significantly limit practical implementation. Continued and expanded remote sensing and field-based monitoring capabilities and manipulative experimentation are needed to improve understanding of the US forest carbon sink, and assess how disturbance processes are responding to the pressures of global environmental change. In addition, continued development and application of holistic, decision support tools that consider a range of forest values are needed to enable managers and policy makers to use the best available information for guiding forest resources now and into the future.

Williams, Christopher A.↗
NASA NTRS2020

Historical and Future Changes in Air Pollutants from CMIP6 Models

Poor air quality is currently responsible for large impacts on human health across the world. In addition, the air pollutants ozone (O3) and particulate matter less than 2.5 µm in diameter (PM2.5) are also radiatively active in the atmosphere and can influence Earth's climate. It is important to understand the effect of air quality and climate mitigation measures over the historical period and in different future scenarios to ascertain any impacts from air pollutants on both climate and human health. The Coupled Model Intercomparison Project Phase 6 (CMIP6) presents an opportunity to analyse the change in air pollutants simulated by the current generation of climate and Earth system models that include a representation of chemistry and aerosols (particulate matter). The shared socio-economic pathways (SSPs) used within CMIP6 encompass a wide range of trajectories in precursor emissions and climate change, allowing for an improved analysis of future changes to air pollutants. Firstly, we conduct an evaluation of the available CMIP6 models against surface observations of O3 and PM2.5. CMIP6 models consistently overestimate observed surface O3 concentrations across most regions and in most seasons by up to 16 ppb, with a large diversity in simulated values over Northern Hemisphere continental regions. Conversely, observed surface PM2.5 concentrations are consistently underestimated in CMIP6 models by up to 10 µg m−3, particularly for the Northern Hemisphere winter months, with the largest model diversity near natural emission source regions. The biases in CMIP6 models when compared to observations of O3 and PM2.5 are similar to those found in previous studies. Over the historical period (1850–2014) large increases in both surface O3 and PM2.5 are simulated by the CMIP6 models across all regions, particularly over the mid to late 20th century, when anthropogenic emissions increase markedly. Large regional historical changes are simulated for both pollutants across East and South Asia with an annual mean increase of up to 40 ppb for O3 and 12 µg m−3 for PM2.5. In future scenarios containing strong air quality and climate mitigation measures (ssp126), annual mean concentrations of air pollutants are substantially reduced across all regions by up to 15 ppb for O3 and 12 µg m−3 for PM2.5. However, for scenarios that encompass weak action on mitigating climate and reducing air pollutant emissions (ssp370), annual mean increases in both surface O3 (up 10 ppb) and PM2.5 (up to 8 µg m−3) are simulated across most regions, although, for regions like North America and Europe small reductions in PM2.5 are simulated due to the regional reduction in precursor emissions in this scenario. A comparison of simulated regional changes in both surface O3 and PM2.5 from individual CMIP6 models highlights important regional differences due to the simulated interaction of aerosols, chemistry, climate and natural emission sources within models. The projection of regional air pollutant concentrations from the latest climate and Earth system models used within CMIP6 shows that the particular future trajectory of climate and air quality mitigation measures could have important consequences for regional air quality, human health and near-term climate. Differences between individual models emphasise the importance of understanding how future Earth system feedbacks influence natural emission sources, e.g. response of biogenic emissions under climate change.

Air pollutants↗
NASA NTRS2022

What Does Global Land Climate Look Like at 2°C Warming?

Constraining an increase in global mean temperature below 2°C compared to pre-industrial levels is critical to limiting dangerous and cascading impacts of anthropogenic climate change. Understanding future climatic changes and their spatial heterogeneity at 2°C warming is thus important for policy makers to prepare actionable adaptation and mitigation plans by identifying where and to what extent lives and livelihoods will be impacted. This study uses the recently released NASA Earth eXchange Global Daily Downscaled Projections (NEX-GDDP) CMIP6 data to provide a broad overview of projected changes in six key climate variables and two climate impact indicators at a time when warming exceeds 2°C. Analysis of global mean temperature changes indicates the 2040s as the decade when most CMIP6 models reach 2°C warming with respect to a pre-industrial period (1850–1900). During the 2040s, we find that global mean temperature, precipitation, relative humidity, downwelling shortwave and longwave radiation, and wind speed over land under the high emission scenario are projected to change by +2.8°C, +22.4 mm/year, −0.73%, −2.23 , +15.9 W/m 2 , and −0.04 m/s, respectively. Many of the future changes are expected to exacerbate climate impacts including heat stress and fire danger. Our analysis shows geographic patterns of policy-relevant climatic changes, as parts of the globe will experience significant climate impacts even if the goal to keep warming below 2°C goal is achieved. Our results highlight the urgent need for further studies focused on identifying key hotspots and advancing region-specific actionable adaptation and mitigation plans.

GDDP↗
NASA NTRS2024

Harmonizing Food Systems Emissions Accounting for More Effective Climate Action

Food systems—encompassing activities in food production, land-use change, supply chains and waste management—contribute significantly to climate change. Recent estimates indicate that food systems produce over 30% of annual anthropogenic greenhouse gas (GHG) emissions (about 20% of CO 2 , 50% of CH 4 , and 75% of N 2 O), with the Intergovernmental Panel on Climate Change (IPCC) estimating a notably broad range of 23%–42% of global GHG emissions. This paper synthesizes current research on the contributions of food systems to climate change, highlights challenges in quantifying their impact and proposes a harmonized accounting framework for more effective climate action. We recommend that an expert committee aligned with the IPCC develop guidance for food systems emissions accounting in four key areas, including: (1) defining system boundaries and nomenclature; (2) developing protocols to allocate broader sectoral emissions to food systems; (3) prioritizing critical areas for research into activity data and emissions factors; and (4) developing a balanced framework for evaluating the impact of mitigation interventions in light of other food systems imperatives. The committee should be integrated into two key international policy processes—the United Nations Framework Convention on Climate Change and the United Nations Food Systems Summit—to support coordinated action towards global net-zero goals. Guidance from the committee could significantly improve the ability of governments, companies, and researchers to estimate, report, monitor and ultimately reduce the climate impacts of food systems.

GHG accounting↗
NASA NTRSDate not supplied

Milwaukee Urban Development: Assessing the Drivers of Urban Flood Vulnerability in Milwaukee using the Integrated Valuation of Ecosystem Services and Tradeoffs Urban Flood Risk Mitigation Model (InVEST)

Milwaukee County has experienced an increase in flooding due to climate change and urbanization. The frequency and severity of flooding vary spatially due to differences in land cover, surface permeability, and infrastructure. Marginalized communities tend to experience disproportionately high flooding and damage due to infrastructural inequalities and limited access to resources. To quantify these differences, we used the Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model to calculate and create maps of runoff retention, nominal flood depth, and economic damage to buildings in Milwaukee. Our model inputs included land cover, surface permeability, and rainfall. To inform our precipitation inputs, we used NASA’s Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) and National Weather Service (NWS) data. We assessed the relationship between flood risk and social and environmental spatial data including redlining, racial demographics, greenspace, and community resilience. The data demonstrate that flood risk is higher in historically redlined neighborhoods, majority Hispanic and Black census block groups, areas that lack parks and trees, and areas of low community resilience as measured by the Census Bureau’s Community Resilience Estimates (CRE). These findings will support our partners, Groundwork Milwaukee and Groundwork USA, in their efforts to promote the equitable distribution of resources and support environmental health in urban spaces. The end products of this project provide our partners with tools to assess urban flooding vulnerability, guide future intervention projects, quantify the effects of environmental injustice, and improve stakeholder access to data.

Madeleine Tango↗
NASA NTRS1991

Countermeasures for mitigating the effects of global environment changes

Environmental countermeasures for preventing the negative effects of global climate change and ozone depletion are discussed with special emphasis on the possibilities of space-based actions. Among the programs addressed are the Mission to Planet Earth, the Solar Power Satellite (and linkage to the Space Exploration Initiative), and proposed projects such as a lunar-based power generator that utilizes He-3 as a fusion fuel when combined with deuterium. The concept of regional working groups is proposed for initiating the programs for effective countermeasures.

Jenkins, Lyle M.↗
NASA NTRSDate not supplied

Development of Thermostable Carbonic Anhydrases Using Structure-Guided Recombination for Use in CO2 Removal Systems on Spacecraft

Carbon capture and storage has been a research area of great interest in recent years as a method for mitigation of CO2 emissions, due to the effects of climate change. The development of technologies for the efficient capture of CO2 are also of great interest for human spaceflight applications. One of the most promising technologies in this area is CO2 scrubbing using liquid amines, unfortunately, liquid amines with low heats of desorption tend to have slow CO2 binding kinetics. One potential solution to this problem is to use the enzyme carbonic anhydrase (CA) to enhance the kinetics of CO2 binding to liquid amines, allowing the overall process to be more energy efficient. Interest in using carbonic anhydrase as a biocatalyst has led to a number of efforts to improve the thermostability and solvent tolerance of several distinct carbonic anhydrase enzymes. In the work described here, we screened through a diverse set of natural carbonic anhydrases to identify candidates for protein engineering aimed at increased stability and activity in various liquid amines. We then used SCHEMA structure-guided recombination to develop a set of chimeric carbonic anhydrases with high thermostability and activity. These chimeras were used as the starting points for further protein engineering work targeting activity in liquid amine systems. Our ultimate goal is to test the engineered enzymes in a liquid amine system for cabin air revitalization on ISS or other spacecraft.

Life Support, Carbon dioxide, Carbonic anhydrase, ↗
NASA NTRSDate not supplied

InVEST Urban Development: Incorporating Earth Observation Data into the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model Python API

Urban flooding poses as one of the biggest issues for cities today, as its impacts are amplified by both climate change and urbanization. The Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation (UFRM) model, which benefits from its simplicity and robustness, is commonly used in NASA DEVELOP projects for disaster mitigation, urban planning, and environmental justice issues. While InVEST UFRM model was able to produce the surface water runoff and retention map sufficient for the scopes of past projects, the model accuracy and spatial variability need improvement. Since the current InVEST UFRM model employs constant rainfall depth for all pixels in the area of interest (AOI), the model suffers from inaccurately estimating rainfall depth, runoff volume, and flood depth. Therefore, we adapted the model so that satellite-based precipitation raster datasets (i.e., Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [GPM IMERG]) can be used instead of a single constant value. We simulated the flood events on August 21st and August 22nd, 2017 in Wyandotte County, Kansas using both our modified and the original InVEST UFRM model and then compared the results after incorporating the rainfall raster into the model. Areas with developed land on the land use map predicted moderate to high flood volume in the original volume regardless of the actual amount of precipitation. The modified model considered the rainfall depth’s spatial variation achieving less overestimation of flood runoff and volume at low-to-moderate rainfall area.

Son Do↗
NASA NTRS2023

InVEST Urban Development: Incorporating Earth Observation Data into the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model Python API

Urban flooding poses as one of the biggest issues for cities today as its impacts are amplified by both climate change and urbanization. The Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation (UFRM) model, which benefits from its simplicity and robustness, is commonly used in NASA DEVELOP projects for disaster mitigation, urban planning, and environmental justice issues. While the InVEST UFRM model was able to produce the surface water runoff and retention map sufficient for the scopes of past projects, the model’s accuracy and spatial variability need improvement. Since the current InVEST UFRM model employs constant rainfall depth for all pixels in the area of interest (AOI), the model suffers from inaccurately estimating rainfall depth, runoff volume, and flood depth. Therefore, we adapted the model so that satellite-based precipitation raster datasets (i.e., Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [GPM IMERG]) can be used instead of a single constant value. We simulated the flood events on August 21st and August 22nd, 2017, in Wyandotte County, Kansas using both our modified and the original InVEST UFRM model and then compared the results after incorporating rainfall raster into the model. Areas with developed land on the land use map predicted moderate to high flood volume in the original volume regardless of the actual amount of precipitation. The modified model considered the rainfall depth’s spatial variation achieving less overestimation of flood runoff and volume at low-to-moderate rainfall area.

Urban flooding↗
NASA NTRS2021

Context and future directions for integrating forest carbon into sub-national climate mitigation planning in the RGGI region of the U.S.

International frameworks for climate mitigation that build from national actions have been developed under the United National Framework Convention on Climate Change and advanced most recently through the Paris Climate Agreement. In parallel, sub-national actors have set greenhouse gas (GHG) reduction goals and developed corresponding climate mitigation plans. Within the U.S., multi-state coalitions have formed to facilitate coordination of related science and policy. Here, utilizing the forum of the NASA Carbon Monitoring System’s Multi-State Working Group, we collected and reviewed climate mitigation plans for 11 states in the Regional Greenhouse Gas Initiative region of the Eastern U.S. For each state we reviewed the (a) policy framework for climate mitigation, (b) GHG reduction goals, (c) inclusion of forest activities in the state’s climate action plan, (d) existing science used to quantify forest carbon estimates, and (e) stated needs for forest carbon monitoring science. Across the region, we found important differences across all categories. While all states have GHG reduction goals and framework documents, nearly three-quarters of all states do not account for forest carbon when planning GHG reductions; those that do account for forest carbon use a variety of scientific methods with various levels of planning detail and guidance. We suggest that a common, efficient, standardized forest carbon monitoring system would provide important benefits to states and the geographic region as a whole. In addition, such a system would allow for more effective transparency and progress tracking to support state, national, and international efforts to increase ambition and implementation of climate goals.

Rachel L Lamb↗
NASA NTRS2015

Enhanced Weathering Strategies for Stabilizing Climate and Averting Ocean Acidification

Chemical breakdown of rocks, weathering, is an important but very slow part of the carbon cycle that ultimately leads to CO2 being locked up in carbonates on the ocean floor. Artificial acceleration of this carbon sink via distribution of pulverized silicate rocks across terrestrial landscapes may help offset anthropogenic CO2 emissions. We show that idealized enhanced weathering scenarios over less than a third of tropical land could cause significant drawdown of atmospheric CO2 and ameliorate ocean acidification by 2100. Global carbon cycle modelling driven by ensemble Representative Concentration Pathway (RCP) projections of twenty-first-century climate change (RCP8.5, business-as-usual; RCP4.5, medium-level mitigation) indicates that enhanced weathering could lower atmospheric CO2 by 30-300 ppm by 2100, depending mainly on silicate rock application rate (1 kg or 5 kg m(exp -2) yr (exp -1)) and composition. At the higher application rate, end-of-century ocean acidification is reversed under RCP4.5 and reduced by about two-thirds under RCP8.5. Additionally, surface ocean aragonite saturation state, a key control on coral calcification rates, is maintained above 3.5 throughout the low latitudes, thereby helping maintain the viability of tropical coral reef ecosystems. However, we highlight major issues of cost, social acceptability, and potential unanticipated consequences that will limit utilization and emphasize the need for urgent efforts to phase down fossil fuel emissions.

Taylor, Lyla L.↗
NASA NTRS2015

Enhanced Weathering Strategies for Stabilizing Climate and Averting Ocean Acidification - Supplementary Information

Chemical breakdown of rocks, weathering, is an important but very slow part of the carbon cycle that ultimately leads to CO2 being locked up in carbonates on the ocean floor. Artificial acceleration of this carbon sink via distribution of pulverized silicate rocks across terrestrial landscapes may help offset anthropogenic CO2 emissions. We show that idealized enhanced weathering scenarios over less than a third of tropical land could cause significant drawdown of atmospheric CO2 and ameliorate ocean acidification by 2100. Global carbon cycle modelling driven by ensemble Representative Concentration Pathway (RCP) projections of twenty-first-century climate change (RCP8.5, business-as-usual; RCP4.5, medium-level mitigation) indicates that enhanced weathering could lower atmospheric CO2 by 30-300 ppm by 2100, depending mainly on silicate rock application rate (1 kg or 5 kg m(exp. -2) yr (exp -1)) and composition. At the higher application rate, end-of-century ocean acidification is reversed under RCP4.5 and reduced by about two-thirds under RCP8.5. Additionally, surface ocean aragonite saturation state, a key control on coral calcification rates, is maintained above 3.5 throughout the low latitudes, thereby helping maintain the viability of tropical coral reef ecosystems. However, we highlight major issues of cost, social acceptability, and potential unanticipated consequences that will limit utilization and emphasize the need for urgent efforts to phase down fossil fuel emissions.

Taylor, Lyla L.↗
NASA NTRS2023

Potentially Underestimated Gas Flaring Activities—A New Approach to Detect Combustion Using Machine Learning and NASA’s Black Marble Product Suite

Monitoring changes in greenhouse gas (GHG) emission is critical for assessing climate mitigation efforts towards the Paris Agreement goal. A crucial aspect of science-based GHG monitoring is to provide objective information for quality assurance and uncertainty assessment of the reported emissions. Emission estimates from combustion events (gas flaring and biomass burning) are often calculated based on activity data (AD) from satellite observations, such as those detected from the visible infrared imaging radiometer suite (VIIRS) onboard the Suomi-NPP and NOAA-20 satellites. These estimates are often incorporated into carbon models for calculating emissions and removals. Consequently, errors and uncertainties associated with AD propagate into these models and impact emission estimates. Deriving uncertainty of AD is therefore crucial for transparency of emission estimates but remains a challenge due to the lack of evaluation data or alternate estimates. This work proposes a new approach using machine learning (ML) for combustion detection from NASA's Black Marble product suite and explores the assessment of potential uncertainties through comparison with existing detections. We jointly characterize combustion using thermal and light emission signals, with the latter improving detection of probable weaker combustion with less distinct thermal signatures. Being methodologically independent, the differences in ML-derived estimates with existing approaches can indicate the potential uncertainties in detection. The approach was applied to detect gas flares over the Eagle Ford Shale, Texas. We analyzed the spatio-temporal variations in detections and found that approximately 79.04% and 72.14% of the light emission-based detections are missed by ML-derived detections from VIIRS thermal bands and existing datasets, respectively. This improvement in combustion detection and scope for uncertainty assessment is essential for comprehensive monitoring of resulting emissions and we discuss the steps for extending this globally.

gas flaring↗
NASA NTRSDate not supplied

Novel Observing Strategies (NOS) and Earth System Digital Twins (ESDT) for Disaster Resilience

"Earth systems have now been observed continuously for more than 50 years, not only from space but also from airplanes, balloons and in-situ sensors. With the addition of commercial remote sensing providers, many more Internet-of-Things sensors and new NASA and international observatories being planned, these incredible amounts of data will soon be augmented by even larger amounts of diverse data and therefore will become more and more difficult to access, integrate, understand and utilize. At the same time, because of climate change and its impacts, in order to predict, manage, and mitigate the effects of extreme science events and disasters, the information produced by all of this data needs to be optimized, organized and analyzed in such a way that it can be utilized by traditional as well as many new non-traditional users. This calls for the development of observing systems that are agile, coordinated and responsive to events of interest, and for information systems to be dynamic, interactive and fast. With these objectives in mind, the Advanced Information Systems Technology (AIST) Program has been developing technologies that will allow for the development of Novel Observing Strategies (NOS) in a distributed, coordinated fashion (i.e., similar to an “Internet-of-Earth-Things”), as well as for the development of novel information systems or Earth System Digital Twins (ESDT) that will build a digital replica of the past and current states of the Earth systems, will derive forecasts of future states under nominal assumptions and will also offer the capability to investigate many hypothetical evolution scenarios under varying impact assumptions. Both NOS and ESDT will be facilitated by the unprecedented advances of Artificial Intelligence technologies, especially Machine Learning (ML), for extracting relevant information from these large amounts of data, for fusing and assimilating diverse data together and for running complex models faster. Together NOS and ESDT will help build the capabilities needed to anticipate, predict and mitigate the effects of future disasters."

Earth Science Remote Sensing; Information Systems↗
NASA NTRS2022

Embedding Climate Change in Urban Planning and Urban Design in New York City

Confronting the challenges of a rapidly urbanizing world threatened by climate change requires expanding the traditional influence and capabilities of urban planning and urban design, integrating climate science, natural systems and compact urban form to configure dynamic, desirable and healthy communities. Cost-effective planning and design measures that help mitigate emissions while bringing adaptive benefits should be prioritized. The chapter draws from the publication Climate Change and Cities (Cambridge University Press 2018) by the Urban Climate Change Research Network (UCCRN). The two-phase New York City case study by the Urban Design Climate Lab at the New York Institute of Technology and a team of international urban design climate experts illustrates how this emerging expertise can be replicated and implemented worldwide. Its focus on configuring people-centered public spaces that enhance energy efficiency and improve public health draws from four urban climate factors: improving efficiency of urban systems, both in energy and transportation; optimizing the form and layout of urban districts to enhance ventilation; promoting appropriate building materials with high reflectivity; and increasing green and blue urban infrastructure. The chapter highlights a set of tools and methods to measure success.

Resiliency↗
NASA NTRSDate not supplied

Mapping Forest Carbon Stocks to Understand Carbon Implications of Treatment on Wildfire for the Calwood Fire, Boulder County Colorado

Recent, record-breaking wildfire activity in the western U.S. illustrates the need for fire mitigation, such as forest fuels reduction treatments. Forests serve as crucial carbon sinks that combat the increasing effects of climate change, but fuels reduction treatments may remove carbon from forested systems. As a result, forest managers need to find a balance between fire mitigation and carbon preservation. This project partnered with Boulder County Parks and Open Space (BCPOS) and the University of Colorado, Denver to investigate the 2020 Cal-Wood fire in Boulder County, Colorado. Using remote sensing data from Landsat 8 Operational Land Imager, Shuttle Radar Topography Mission, Sentinel-2 MultiSpectral Imagery, and LiDAR, we mapped post-fire forest carbon pools and compared these values with values derived from field measurements. The analysis suggested that fuels reduction treatments did not reduce carbon loss in the presence of wildfire enough to clearly distinguish post-fire carbon between treated and untreated areas. However, the final carbon maps provide BCPOS and researchers with an opportunity to explore carbon estimation models based on remote sensing data as well as a framework to evaluate fuels reduction treatment effectiveness and impact on forest carbon stocks for future wildfire events.

Sarah Hettema↗
NASA NTRS2010

The Use of Remote Sensing Data for Advancing America's Energy Policy

After briefly reviewing America's Energy Policy laid out by the Obama Administration, we outline how a Global Carbon Observing System designed to monitor Carbon from space can provide the necessary data and tools to equip decision makers with the knowledge necessary to formulate effective energy use and practices policy. To stabilize greenhouse gas emissions in the atmosphere in a manner that it does not interfere with the Earth's climate system (which is one of the goals of United Nations Framework for Convention on Climate Change) requires vastly improved prediction of the atmospheric carbon dioxide (CO2) concentrations. This in torn requires a robust understanding of the carbon exchange mechanisms between atmosphere, land, and oceans and a clear understanding of the sources and sinks (i.e. uptake and storage) of CO2. We discuss how the Carbon Observing System from space aids in better understanding of the connection between the carbon cycle and climate change and provides more accurate predictions of atmospheric CO2 concentration. It also enables implementation of greenhouse gas (GHG) mitigation policies such as cap and trade programs, international climate treaties, as well as formulation of effective energy use policies.

Valinia, Azita↗