Garrett L. Altmann

Extrapolating active layer thickness measurements across Arctic polygonal terrain using LiDAR and NDVI data sets.

Landscape attributes that vary with microtopography, such as active layer thickness (ALT), are labor intensive and difficult to document effectively through in situ methods at kilometer spatial extents, thus rendering remotely sensed methods desirable. Spatially explicit estimates of ALT can provide critically needed data for parameterization, initialization, and evaluation of Arctic terrestrial models. In this work, we demonstrate a new approach using high-resolution remotely sensed data for estimating centimeter-scale ALT in a 5 km2 area of ice-wedge polygon terrain in Barrow, Alaska. We use a simple regression-based, machine learning data-fusion algorithm that uses topographic and spectral metrics derived from multisensor data (LiDAR and WorldView-2) to estimate ALT (2 m spatial resolution) across the study area. Comparison of the ALT estimates with ground-based measurements, indicates the accuracy (r2 = 0.76, RMSE ±4.4 cm) of the approach. While it is generally accepted that broad climatic variability associated with increasing air temperature will govern the regional averages of ALT, consistent with prior studies, our findings using high-resolution LiDAR and WorldView-2 data, show that smaller-scale variability in ALT is controlled by local eco-hydro-geomorphic factors. This work demonstrates a path forward for mapping ALT at high spatial resolution and across sufficiently large regions for improved understanding and predictions of coupled dynamics among permafrost, hydrology, and land-surface processes from readily available remote sensing data.

Pathways and transformations of dissolved methane and dissolved inorganic carbon in Arctic tundra watersheds: Evidence from analysis of stable isotopes

Arctic soils contain a large pool of terrestrial C and are of interest due to their potential for releasing significant carbon dioxide (CO2) and methane (CH4) to the atmosphere. Due to substantial landscape heterogeneity, predicting ecosystem-scale CH4 and CO2 production is challenging. This study assessed dissolved inorganic carbon (DIC = Σ (total) dissolved CO2) and CH4 in watershed drainages in Barrow, Alaska as critical convergent zones of regional geochemistry, substrates, and nutrients. In July and September of 2013, surface waters and saturated subsurface pore waters were collected from 17 drainages. Based on simultaneous DIC and CH4 cycling, we synthesized isotopic and geochemical methods to develop a subsurface CH4 and DIC balance by estimating mechanisms of CH4 and DIC production and transport pathways and oxidation of subsurface CH4. We observed a shift from acetoclastic (July) toward hydrogenotropic (September) methanogenesis at sites located toward the end of major freshwater drainages, adjacent to salty estuarine waters, suggesting an interesting landscape-scale effect on CH4 production mechanism. The majority of subsurface CH4 was transported upward by plant-mediated transport and ebullition, predominantly bypassing the potential for CH4 oxidation. Thus, surprisingly, CH4 oxidation only consumed approximately 2.51 ± 0.82% (July) and 0.79 ± 0.79% (September) of CH4 produced at the frost table, contributing to <0.1% of DIC production. DIC was primarily produced from respiration, with iron and organic matter serving as likely e- acceptors. This work highlights the importance of spatial and temporal variability of CH4 production at the watershed scale and suggests broad scale investigations are required to build better regional or pan-Arctic representations of CH4 and CO2 production.

Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries

Abstract We present results from an ongoing effort to extend neuromimetic machine vision algorithms to multispectral data using adaptive signal processing combined with compressive sensing and machine learning techniques. Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and topographic/geomorphic characteristics. We use a Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labels are automatically generated using unsupervised clustering of sparse approximations (CoSA). We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska. We explore learning from both raw multispectral imagery and normalized band difference indices. We explore a quantitative metric to evaluate the spectral properties of the clusters in order to potentially aid in assigning land cover categories to the cluster labels. Our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.

Large uncertainty in permafrost carbon stocks due to hillslope soil deposits

Northern circumpolar permafrost soils contain more than a third of the global Soil Organic Carbon pool (SOC). The sensitivity of this carbon pool to a changing climate is a primary source of uncertainty in simulation-based climate projections. These projections, however, do not account for the accumulation of soil deposits at the base of hillslopes (hill-toes), and the influence of this accumulation on the distribution, sequestration, and decomposition of SOC in landscapes affected by permafrost. Here we combine topographic models with soil-profile data and topographic analysis to evaluate the quantity and uncertainty of SOC mass stored in perennially frozen hill-toe soil deposits. We show that in Alaska this SOC mass introduces an uncertainty that is >200% than state-wide estimates of SOC stocks (77 PgC), and that a similarly large uncertainty may also pertain at a circumpolar scale. Soil sampling and geophysical-imaging efforts that target hill-toe deposits can help constrain this large uncertainty.

Land cover classification in multispectral satellite imagery using sparse approximations on learned dictionaries

Techniques for automated feature extraction, including neuroscience-inspired machine vision, are of great interest for landscape characterization and change detection in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methodologies to the environmental sciences, using state-of-theart adaptive signal processing, combined with compressive sensing and machine learning techniques. We use a modified Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labels are automatically generated using CoSA: unsupervised Clustering of Sparse Approximations. We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska (USA). Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties (e.g., soil moisture and inundation), and topographic/geomorphic characteristics. In this paper, we explore learning from both raw multispectral imagery, as well as normalized band difference indexes. We explore a quantitative metric to evaluate the spectral properties of the clusters, in order to potentially aid in assigning land cover categories to the cluster labels.

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics to help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. Our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.

Change detection in Arctic satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

Advanced pattern recognition and computer vision algorithms are of great interest for landscape characterization, change detection, and change monitoring in satellite imagery, in support of global climate change science and modeling. We present results from an ongoing effort to extend neuroscience-inspired models for feature extraction to the environmental sciences, and we demonstrate our work using Worldview-2 multispectral satellite imagery. We use a Hebbian learning rule to derive multispectral, multiresolution dictionaries directly from regional satellite normalized band difference index data. These feature dictionaries are used to build sparse scene representations, from which we automatically generate land cover labels via our CoSA algorithm: Clustering of Sparse Approximations. These data adaptive feature dictionaries use joint spectral and spatial textural characteristics to help separate geologic, vegetative, and hydrologic features. Land cover labels are estimated in example Worldview-2 satellite images of Barrow, Alaska, taken at two different times, and are used to detect and discuss seasonal surface changes. Our results suggest that an approach that learns from both spectral and spatial features is promising for practical pattern recognition problems in high resolution satellite imagery.

Digital Elevation Model, 0.25 m, Barrow Environmental Observatory, Alaska, 2013

This 0.25m horizontal resolution digital elevation model, DEM, was developed from Airborne Laser Altimetry flown by Aerometric Inc, now known as Quantum Spatial, Inc. on 12 July, 2013. One Mission was flown and the data jointly processed with LANL personnel to produce a 0.25m DEM covering a region approximately 2.8km wide and 12.4km long extending from the coast above North Salt Lagoon to south of Gas Well Road. This DEM encompasses a diverse range of hydrologic, geomorphic, geophysical and biological features typical of the Barrow Peninsula. Vertical accuracy at the 95% confidence interval was computed as 0.143m. The coordinate system, datum, and geoid for this DEM are UTM Zone 4N, NAD83 (2011), NAVD88 (GEOID09).