Analysis of Reflected Spectral Signatures and Detection of Geophysical Disturbance Using Hyperspectral Imagery

Geophysical disturbances resulting from human activities often have significantconsequences for plants and animals, and even for entire ecosystems.Disturbances resulting from petroleum exploration and production activities can have long term impacts on soils, watersheds, rivers and lakes, vegetation, wildlife, and humans. These anthropogenic disturbances are frequently the result of hydrocarbon (oil) or produced water (brine) spills. Brine is usually produced simultaneously with oil or gas. The ability to detect brine spills with remote sensing techniques would be valuable to petroleum companies and industry regulators. The objectives of this research were to 1) determine if brine spills could be detected spectroscopically, 2) determine if spectral analysis could be performed using a statistical method to identify surface features quickly and easily from large imaging spectroscopy data sets without modeling and removing atmospheric effects or performing detailed spectral unmixing, 3) develop a spectral signature for brine spills which could be applied at other locations, and 4) determine if brine spills could be detected using substantially fewer spectral bands so that a smaller and cheaper instrument could be applied to detect these disturbances. Using hyperspectral image cubes acquired by NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over Osage County, Oklahoma, a multivariate statistical clustering technique successfully discerned well-documented brine disturbances on the Tallgrass Prairie Preserve, and the resulting brine spectral signature was applied to locate similar brine disturbances in surrounding image scenes. While validating the prediction results by visiting the site was outside the scope of this project, high resolution aerial photographs were used to assess the success of the predictions and attribute at least 40 of the 87 prediction regions to petroleum activities. While a number of false positives resulted from the analysis, many of these are easily discounted based on objects in the aerial photographs or explained by mineral/salt accumulation. In addition, four bands from the 224-band hyperspectral imagery were used to predict brine disturbances in one of the image cubes. Approximately 90% of the prediction regions detected in the original analysis—which used 187 of the 224 bands—were again detected using only four spectral bands

Report of the direct infrared sensors panel

The direct infrared sensors panel considered a wide range of options for technologies relevant to the science goals of the Astrotech 21 mission set. Among the technologies assessed are: large format arrays; photon counting detectors; higher temperature 1 to 10 micro-m arrays; impurity band conduction (IBC) or blocked impurity band (BIB) detectors; readout electronics; and adapting the Space Infrared Telescope Facility and Hubble Space Telescope. Detailed development plans were presented for each of these technology areas

Transit times and mean ages for nonautonomous and autonomous compartmental systems

We develop a theory for transit times and mean ages for nonautonomous compartmental systems. Using the McKendrick-von F\"orster equation, we show that the mean ages of mass in a compartmental system satisfy a linear nonautonomous ordinary differential equation that is exponentially stable. We then define a nonautonomous version of transit time as the mean age of mass leaving the compartmental system at a particular time and show that our nonautonomous theory generalises the autonomous case. We apply these results to study a nine-dimensional nonautonomous compartmental system modeling the terrestrial carbon cycle, which is a modification of the Carnegie-Ames-Stanford approach (CASA) model, and we demonstrate that the nonautonomous versions of transit time and mean age differ significantly from the autonomous quantities when calculated for that model

Deep Learning Approach for Mapping Arctic Vegetation using Multi-Sensor Remote Sensing Fusion

Land cover datasets are essential for modeling Arctic ecosystem structure and function and for understanding land–atmosphere interactions at high spatial resolutions. However, most Arctic land cover products are generated at a coarse resolution, and finding quality satellite remote sensing datasets to produce such maps is difficult due to cloud cover, polar darkness, and poor availability of high-resolution imagery. A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5 m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska. The datasets from hyperspectral, multispectral, synthetic aperture radar (SAR) platforms, and terrain datasets were fused together using unsupervised and supervised classification techniques over a 343 km2 region to generate high-resolution (5 m) vegetation type maps. A unsupervised technique was developed to classify high-dimensional remote sensing datasets into cohesive clusters and a quantitative technique to add supervision to the unlabeled clusters was employed, producing a fully labeled vegetation map. Deep neural networks (DNNs) were developed using multi-sensor remote sensing datasets to map vegetation distributions using the original labels and the labels produced by the unsupervised method for training [1]. Fourteen different combinations of remote sensing imagery were analyzed to explore the optimization of multi-sensor remote sensing fusion. To validate the resulting DNN-based vegetation maps, field vegetation observations were conducted at 30 plots during the summer of 2016 and developed vegetation maps were evaluated against them for accuracy. Our analysis showed that the DNN models based on hyperspectral EO-1 Hyperion, integrated with the other remote sensing data, provided the most accurate mapping of vegetation types, increasing the average validation score from 0.56 to 0.70 based on field observation-based vegetation. REFERENCES: 1. Langford, Z. L., Kumar, J., and Hoffman, F. M., "Convolutional Neural Network Approach for Mapping Arctic Vegetation Using Multi-Sensor Remote Sensing Fusion," 2017 IEEE International Conference on Data Mining Workshops (ICDMW), New Orleans, LA, 2017, pp. 322-331. doi: 10.1109/ICDMW.2017.4

Observational benchmarks inform representation of soil organic carbon dynamics in land surface models

Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Machine learning models can help identify dominant environmental controllers and establish their functional relationships with SOC stocks. The resulting knowledge can be integrated into ESMs to reduce uncertainty and improve predictions of SOC dynamics over space and time. In this study, we used a large number of SOC field observations (n=54 000), geospatial datasets of environmental factors (n=46), and two machine learning approaches (namely random forest, RF, and generalized additive modeling, GAM) to (1) identify dominant environmental controllers of global and biome-specific SOC stocks, (2) derive functional relationships between environmental controllers and SOC stocks, and (3) compare the identified environmental controllers and predictive relationships with those in models used in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Our results showed that the diurnal temperature, drought index, cation exchange capacity, and precipitation were important observed environmental predictors of global SOC stocks. While the RF model identified 14 environmental factors that describe climatic, vegetation, and edaphic conditions as important predictors of global SOC stocks (R2=0.61, RMSE = 0.46 kg m−2), current ESMs oversimplify the relationships between environmental factors and SOC, with precipitation, temperature, and net primary productivity explaining &gt; 96 % of the variability in ESM-modeled SOC stocks. Further, our study revealed notable disparities among the functional relationships between environmental factors and SOC stocks simulated by ESMs compared with observed relationships. To improve SOC representations in ESMs, it is imperative to incorporate additional environmental controls, such as the cation exchange capacity, and refine the functional relationships to align more closely with observations.</p

Ariel - Volume 4 Number 6

Editors David A. Jacoby Eugenia Miller Tom Williams Associate Editors Paul Bialas Terry Burt Michael Leo Gail Tenikat Editor Emeritus and Business Manager Richard J. Bonnano Movie Editor Robert Breckenridge Staff Richard Blutstein Mary F. Buechler J.D. Kanofsky Rocket Weber David Maye

Multicentury changes in ocean and land contributions to the climate-carbon feedback

Author Posting. © American Geophysical Union, 2015. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Global Biogeochemical Cycles 29 (2015): 744–759, doi:10.1002/2014GB005079.Improved constraints on carbon cycle responses to climate change are needed to inform mitigation policy, yet our understanding of how these responses may evolve after 2100 remains highly uncertain. Using the Community Earth System Model (v1.0), we quantified climate-carbon feedbacks from 1850 to 2300 for the Representative Concentration Pathway 8.5 and its extension. In three simulations, land and ocean biogeochemical processes experienced the same trajectory of increasing atmospheric CO2. Each simulation had a different degree of radiative coupling for CO2 and other greenhouse gases and aerosols, enabling diagnosis of feedbacks. In a fully coupled simulation, global mean surface air temperature increased by 9.3 K from 1850 to 2300, with 4.4 K of this warming occurring after 2100. Excluding CO2, warming from other greenhouse gases and aerosols was 1.6 K by 2300, near a 2 K target needed to avoid dangerous anthropogenic interference with the climate system. Ocean contributions to the climate-carbon feedback increased considerably over time and exceeded contributions from land after 2100. The sensitivity of ocean carbon to climate change was found to be proportional to changes in ocean heat content, as a consequence of this heat modifying transport pathways for anthropogenic CO2 inflow and solubility of dissolved inorganic carbon. By 2300, climate change reduced cumulative ocean uptake by 330 Pg C, from 1410 Pg C to 1080 Pg C. Land fluxes similarly diverged over time, with climate change reducing stocks by 232 Pg C. Regional influence of climate change on carbon stocks was largest in the North Atlantic Ocean and tropical forests of South America. Our analysis suggests that after 2100, oceans may become as important as terrestrial ecosystems in regulating the magnitude of the climate-carbon feedback.We are grateful for support from the U.S. Department of Energy Office of Science and the National Science Foundation (NSF). J.T.R. and F.H. received support from the Regional and Global Climate Modeling Program in the Climate and Environmental Sciences Division of the Biological and Environmental Research (BER) Program in the U.S. Department of Energy Office of Science. J.T.R., K.L., E.M., W.F., J.K.M., S.C.D., and N.N.M. received funding from the NSF project “Collaborative Research: Improved Regional and Decadal Predictions of the Carbon Cycle“ (AGS-1048827, AGS-1021776, and AGS-1048890). The Community Earth System Modeling project receives support from both NSF and BER.2015-12-0

Progress Report 2008: A Scalable and Extensible Earth System Model for Climate Change Science

This project employs multi-disciplinary teams to accelerate development of the Community Climate System Model (CCSM), based at the National Center for Atmospheric Research (NCAR). A consortium of eight Department of Energy (DOE) National Laboratories collaborate with NCAR and the NASA Global Modeling and Assimilation Office (GMAO). The laboratories are Argonne (ANL), Brookhaven (BNL) Los Alamos (LANL), Lawrence Berkeley (LBNL), Lawrence Livermore (LLNL), Oak Ridge (ORNL), Pacific Northwest (PNNL) and Sandia (SNL). The work plan focuses on scalablity for petascale computation and extensibility to a more comprehensive earth system model. Our stated goal is to support the DOE mission in climate change research by helping ... To determine the range of possible climate changes over the 21st century and beyond through simulations using a more accurate climate system model that includes the full range of human and natural climate feedbacks with increased realism and spatial resolution

The International Land Model Benchmarking (ILAMB) System: Design, Theory, and Implementation

The increasing complexity of Earth system models has inspired efforts to quantitatively assess model fidelity through rigorous comparison with best available measurements and observational data products. Earth system models exhibit a high degree of spread in predictions of land biogeochemistry, biogeophysics, and hydrology, which are sensitive to forcing from other model components. Based on insights from prior land model evaluation studies and community workshops, the authors developed an open source model benchmarking software package that generates graphical diagnostics and scores model performance in support of the International Land Model Benchmarking (ILAMB) project. Employing a suite of in situ, remote sensing, and reanalysis data sets, the ILAMB package performs comprehensive model assessment across a wide range of land variables and generates a hierarchical set of web pages containing statistical analyses and figures designed to provide the user insights into strengths and weaknesses of multiple models or model versions. Described here is the benchmarking philosophy and mathematical methodology embodied in the most recent implementation of the ILAMB package. Comparison methods unique to a few specific data sets are presented, and guidelines for configuring an ILAMB analysis and interpreting resulting model performance scores are discussed. ILAMB is being adopted by modeling teams and centers during model development and for model intercomparison projects, and community engagement is sought for extending evaluation metrics and adding new observational data sets to the benchmarking framework.Key PointThe ILAMB benchmarking system broadly compares models to observational data sets and provides a synthesis of overall performancePeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146994/1/jame20779_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146994/2/jame20779.pd

Representativeness-Based Sampling Network Design for the Arctic

Resource and logistical constraints limit the frequency and extent of environmental observations, particularly in the Arctic, necessitating the development of a systematic sampling strategy to maximize coverage and objectively represent environmental variability at desired scales. Required is a quantitative methodology for stratifying sampling domains, informing site selection, and determining the representativeness of measurement sites and networks. Multivariate spatiotemporal clustering was applied to down-scaled general circulation model results and data for the State of Alaska at 2 km ✕ 2 km resolution to define multiple sets of bioclimatic ecoregions across two decadal time periods. Maps of ecoregions for the present (2000–2009) and future (2090–2099) were produced, showing how combinations of 37 bioclimatic and permafrost characteristics are distributed and how they may shift in the future. Representative sampling locations are identified on present and future ecoregion maps. A representativeness metric was developed, and representativeness maps for eight candidate sampling locations were produced. This metric was used to characterize the environmental similarity of each site. This analysis provides model-inspired insights into optimal sampling strategies, offers a framework for up-scaling measurements, and provides a down-scaling approach for integration of models and measurements. These techniques can be applied at different spatial and temporal scales to meet the needs of individual measurement campaigns. More recently, we have extended this approach to investigate pan-Arctic and tropical forest representativeness, employing remote sensing and other data products, to quantify coverage of spatial heterogeneity from international monitoring and sampling efforts. New results describing global forest site constituency and Arctic sampling regimes will be presented