Geomorphological patterns of remotely sensed methane hot spots in the Mackenzie Delta, Canada

Abstract We studied geomorphological controls on methane (CH4) hotspots in the Mackenzie Delta region in northern Canada using airborne imaging spectroscopy collected as part of the Arctic Boreal Vulnerability Experiment (ABoVE). Methane emissions hotspots were retrieved at ~25 m2 spatial resolution from a ~10,000 km2 AVIRIS-NG survey of the Mackenzie Delta acquired 31 July – 3 August 2017. Separating the region into the permafrost plateau and the lowland delta, we refined the domain wide power law of CH4 enhancements detected as a function of distance to standing water in different ecoregions. We further studied the spatial decay of the distance to water relationship as a function of land cover across the Delta. We show that geomorphology exerts a strong control on the spatial patterns of emissions at regional to sub-regional scales: compared to methane hotspots detected in the upland, we find that methane hotspots detected in the lowland have a more gradual power law curve indicating a weaker spatial decay with respect to distance from water. Spatial decay of CH4 hotspots in uplands is more than 2.5 times stronger than in lowlands, which is due to differences in topography and geomorphological influence on hydrology. We demonstrate that while the observed spatial distributions of CH4 follow expected trends in lowlands and uplands, these quantitatively complement knowledge from conventional wetland and freshwater CH4 mapping and modelling.

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High-Resolution Mapping of Redwood (Sequoia sempervirens) Distributions in Three Californian Forests

Remote Sensing ◽

10.3390/rs11030351 ◽

2019 ◽

Vol 11 (3) ◽

pp. 351 ◽

Cited By ~ 2

Author(s):

Emily Francis ◽

Gregory Asner

Keyword(s):

High Resolution ◽

Spatial Resolution ◽

Imaging Spectroscopy ◽

Training Data ◽

Spectroscopy Data ◽

Boosted Regression Tree ◽

High Resolution Mapping ◽

Resolution Mapping ◽

Redwood Forests ◽

Airborne Imaging

High-resolution maps of redwood distributions could enable strategic land management to satisfy diverse conservation goals, but the currently-available maps of redwood distributions are low in spatial resolution and biotic detail. Classification of airborne imaging spectroscopy data provides a potential avenue for mapping redwoods over large areas and with high confidence. We used airborne imaging spectroscopy data collected over three redwood forests by the Carnegie Airborne Observatory, in combination with field training data and application of a gradient boosted regression tree (GBRT) machine learning algorithm, to map the distribution of redwoods at 2-m spatial resolution. Training data collected from the three sites showed that redwoods have spectral signatures distinct from the other common tree species found in redwood forests. We optimized a gradient boosted regression model for high performance and computational efficiency, and the resulting model was demonstrably accurate (81–98% true positive rate and 90–98% overall accuracy) in mapping redwoods in each of the study sites. The resulting maps showed marked variation in redwood abundance (0–70%) within a 1 square kilometer aggregation block, which match the spatial resolution of currently-available redwood distribution maps. Our resulting high-resolution mapping approach will facilitate improved research, conservation, and management of redwood trees in California.

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High spatial resolution mapping of elevated atmospheric carbon dioxide using airborne imaging spectroscopy: Radiative transfer modeling and power plant plume detection

Remote Sensing of Environment ◽

10.1016/j.rse.2013.08.001 ◽

2013 ◽

Vol 139 ◽

pp. 116-129 ◽

Cited By ~ 30

Author(s):

Philip E. Dennison ◽

Andrew K. Thorpe ◽

Eric R. Pardyjak ◽

Dar A. Roberts ◽

Yi Qi ◽

...

Keyword(s):

Carbon Dioxide ◽

Power Plant ◽

Radiative Transfer ◽

Spatial Resolution ◽

Atmospheric Carbon Dioxide ◽

High Spatial Resolution ◽

Imaging Spectroscopy ◽

Resolution Mapping ◽

Airborne Imaging ◽

Radiative Transfer Modeling

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FlexBRDF: A Flexible BRDF Correction for Grouped Processing of Airborne Imaging Spectroscopy Flightlines

10.1002/essoar.10506891.1 ◽

2021 ◽

Author(s):

Natalie Queally ◽

Zhiwei Ye ◽

Ting Zheng ◽

Adam Chlus ◽

Fabian Schneider ◽

...

Keyword(s):

Imaging Spectroscopy ◽

Airborne Imaging

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Mapping tree species diversity of a tropical montane forest by unsupervised clustering of airborne imaging spectroscopy data

Ecological Indicators ◽

10.1016/j.ecolind.2015.12.026 ◽

2016 ◽

Vol 64 ◽

pp. 49-58 ◽

Cited By ~ 33

Author(s):

Elisa Schäfer ◽

Janne Heiskanen ◽

Vuokko Heikinheimo ◽

Petri Pellikka

Keyword(s):

Species Diversity ◽

Tree Species ◽

Imaging Spectroscopy ◽

Montane Forest ◽

Unsupervised Clustering ◽

Tropical Montane Forest ◽

Spectroscopy Data ◽

Tree Species Diversity ◽

Airborne Imaging

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Area and volume quantification of arctic thaw slumps using time-series of digital elevation models generated from radar interferometry

10.5194/egusphere-egu21-2280 ◽

2021 ◽

Author(s):

Philipp Bernhard ◽

Simon Zwieback ◽

Irena Hajnsek

Keyword(s):

Probability Density ◽

Power Law ◽

Volume Change ◽

Digital Elevation Models ◽

Probability Density Functions ◽

The Arctic ◽

Density Functions ◽

Digital Elevation ◽

Beta Coefficient ◽

Area And Volume

Vast areas of the Arctic host ice-rich permafrost, which is becoming increasingly vulnerable to terrain-altering thermokarst in a warming climate. Among the most rapid and dramatic changes are retrogressive thaw slumps. These slumps evolve by a retreat of the slump headwall during the summer months, making their change visible by comparing digital elevation models over time. In this study we use digital elevation models generated from single-pass radar TanDEM-X observations to derive volume and area change rates for retrogressive thaw slumps. At least three observations in the timespan from 2011 to 2017 are available with a spatial resolution of about 12 meter and a height sensitivity of about 0.5-2 meter. Our study regions include regions in Northern Canada (Peel Plateau/Richardson Mountains, Mackenzie River Delta Uplands, Ellesmere Island), Alaska (Noatak Valley) and Siberia (Yamal, Gydan, Taymyr, Chukotka) covering an area of 220.000 km2 with a total number of 1853 thaw slumps.In this presentation we will focus on the area and volume change rate probability density functions of the mapped thaw slumps in these study areas. For landslides in temperate climate zones the area and volume change probability density function typically follow a distribution that can be characterized by three quantities: A rollover point defined as the peak in the distribution, a cutoff-point indicating the transition to a power law scaling for large landslides and the exponential beta coefficient of this power law. Here we will show that thaw slumps across the arctic follow indeed such a distribution and that the obtained values for the rollover, cutoff and beta coefficient can be used to distinguish between regions. Furthermore we will elaborate on possible reason why arctic thaw slumps can be described by such probability density functions as well as analyzing the differences between regions. This characterization can be useful to further improve our understanding of thaw slump initiation, the investigation of the drivers of their evolution as well as for modeling future thaw slump activity.

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Imaging Spectroscopic Analysis of Biochemical Traits for Shrub Species in Great Basin, USA

Remote Sensing ◽

10.3390/rs10101621 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1621 ◽

Cited By ~ 3

Author(s):

Yi Qi ◽

Susan Ustin ◽

Nancy Glenn

Keyword(s):

Great Basin ◽

Land Surface ◽

Imaging Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Leaf Mass Per Area ◽

Shrub Species ◽

Biochemical Traits ◽

Airborne Imaging

The biochemical traits of plant canopies are important predictors of photosynthetic capacity and nutrient cycling. However, remote sensing of biochemical traits in shrub species in dryland ecosystems has been limited mainly due to the sparse vegetation cover, manifold shrub structures, and complex light interaction between the land surface and canopy. In order to examine the performance of airborne imaging spectroscopy for retrieving biochemical traits in shrub species, we collected Airborne Visible Infrared Imaging Spectrometer—Next Generation (AVIRIS-NG) images and surveyed four foliar biochemical traits (leaf mass per area, water content, nitrogen content and carbon) of sagebrush (Artemesia tridentata) and bitterbrush (Purshia tridentata) in the Great Basin semi-desert ecoregion, USA, in October 2014 and May 2015. We examined the correlations between biochemical traits and developed partial least square regression (PLSR) models to compare spectral correlations with biochemical traits at canopy and plot levels. PLSR models for sagebrush showed comparable performance between calibration (R2: LMA = 0.66, water = 0.7, nitrogen = 0.42, carbon = 0.6) and validation (R2: LMA = 0.52, water = 0.41, nitrogen = 0.23, carbon = 0.57), while prediction for bitterbrush remained a challenge. Our results demonstrate the potential for airborne imaging spectroscopy to measure shrub biochemical traits over large shrubland regions. We also highlight challenges when estimating biochemical traits with airborne imaging spectroscopy data.

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Ice Concentration Retrieval from the Analysis of Microwaves: A New Methodology Designed for the Copernicus Imaging Microwave Radiometer

Remote Sensing ◽

10.3390/rs12071060 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1060 ◽

Cited By ~ 1

Author(s):

Lise Kilic ◽

Catherine Prigent ◽

Filipe Aires ◽

Georg Heygster ◽

Victor Pellet ◽

...

Keyword(s):

Sea Ice ◽

Spatial Resolution ◽

Microwave Radiometer ◽

Optimal Estimation ◽

Arctic Sea Ice ◽

The Arctic ◽

Special Focus ◽

Polar Regions ◽

Sea Ice Concentration ◽

Ice Concentration

Over the last 25 years, the Arctic sea ice has seen its extent decline dramatically. Passive microwave observations, with their ability to penetrate clouds and their independency to sunlight, have been used to provide sea ice concentration (SIC) measurements since the 1970s. The Copernicus Imaging Microwave Radiometer (CIMR) is a high priority candidate mission within the European Copernicus Expansion program, with a special focus on the observation of the polar regions. It will observe at 6.9 and 10.65 GHz with 15 km spatial resolution, and at 18.7 and 36.5 GHz with 5 km spatial resolution. SIC algorithms are based on empirical methods, using the difference in radiometric signatures between the ocean and sea ice. Up to now, the existing algorithms have been limited in the number of channels they use. In this study, we proposed a new SIC algorithm called Ice Concentration REtrieval from the Analysis of Microwaves (IceCREAM). It can accommodate a large range of channels, and it is based on the optimal estimation. Linear relationships between the satellite measurements and the SIC are derived from the Round Robin Data Package of the sea ice Climate Change Initiative. The 6 and 10 GHz channels are very sensitive to the sea ice presence, whereas the 18 and 36 GHz channels have a better spatial resolution. A data fusion method is proposed to combine these two estimations. Therefore, IceCREAM will provide SIC estimates with the good accuracy of the 6+10GHz combination, and the high spatial resolution of the 18+36GHz combination.

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Spatial and Temporal Modelling of Biophysical Variables Within A High Arctic Wetland

10.32920/ryerson.14657343.v1 ◽

2021 ◽

Author(s):

Gillian Ramasay

Keyword(s):

Dynamic Systems ◽

Temporal Variability ◽

Hot Spots ◽

Carbon Flux ◽

Seasonal Changes ◽

High Arctic ◽

The Arctic ◽

Layer Depth ◽

Wetland Ecosystems ◽

Temporal Modelling

The Arctic has experienced greater climate warming in the last decade than anywhere else, potentially shifting its carbon status from a sink to a source. Increasing temperatures impact nival wetlands that rely on a strong hydrological input from melting perennial snowpacks. Soil moisture, soil temperature and active layer depth are key biophysical variables in predicting carbon flux trajectories in this environment. How these variables interact is crucial in delineating links between snowmelt and seasonal changes in wetland productivity. To date, there have been numerous studies that have examined these variables, but few have investigated the relationships between these biophysical variables and wetland thaw patterns at a high spatial and temporal scale. This study found a decrease in temporal variability and reduced interactions between variables as the wetland thawed as well as localized hot spots of increased values and an overall east to west trend across the site. This implies that Arctic wetland ecosystems are dynamic systems that reach a level of stability during peak growth. They also exhibit changeable spatial patterns that cannot be generalized.

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Noise-related roughness of railway wheel treads-full-scale testing of brake blocks

Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit ◽

10.1243/0954409001531342 ◽

2000 ◽

Vol 214 (2) ◽

pp. 63-77 ◽

Cited By ~ 11

Author(s):

M Petersson

Keyword(s):

Cast Iron ◽

Hot Spots ◽

Testing Machine ◽

Full Scale ◽

Spatial Distributions ◽

Material Transfer ◽

Railway Wheel ◽

Rolling Noise ◽

Brake Block ◽

Abrasive Property

Results from full-scale tread braking experiments on an inertia dynamometer (brake testing machine) are presented. Eighteen prototypes of brake blocks are investigated. Two braking characteristics relating to the influence of the blocks on the wheel tread are studied: generation of hot spots and generation of roughness (corrugation, waviness). Wheel tread temperatures are measured during braking using an infrared (IR) technique. The wheel roughness is measured after each brake cycle when the wheel has cooled down. A roughness indicator, RλCA, relates measured roughness to expected rolling noise as generated by the wheel in operation. A correlation between the spatial distributions of temperatures and roughnesses is normally found: stronger for cast iron blocks and composition blocks and weaker for sinter blocks. The cast iron blocks are found to produce high tread roughness levels, partly owing to material transfer from brake block to wheel tread. The composition blocks are found to result in lower roughness levels than the cast iron blocks. Finally, the sinter metal blocks are found to lead to the lowest roughness levels, a fact which is probably due to the abrasive property of these blocks. Friction coefficients during braking are also measured.

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Large-scale Arctic sea ice motion from Sentinel-1 and the RADARSAT Constellation Mission

10.5194/egusphere-egu21-514 ◽

2021 ◽

Author(s):

Stephen Howell ◽

Mike Brady ◽

Alexander Komarov

Keyword(s):

Sea Ice ◽

Spatial Resolution ◽

Temporal Resolution ◽

Large Scale ◽

Arctic Sea Ice ◽

The Arctic ◽

High Temporal Resolution ◽

Sar Images ◽

Sea Ice Extent ◽

Arctic Sea

As the Arctic&#8217;s sea ice extent continues to decline, remote sensing observations are becoming even more vital for the monitoring and understanding of this process.&#160; Recently, the sea ice community has entered a new era of synthetic aperture radar (SAR) satellites operating at C-band with the launch of Sentinel-1A in 2014, Sentinel-1B in 2016 and the RADARSAT Constellation Mission (RCM) in 2019. These missions represent a collection of 5 spaceborne SAR sensors that together can routinely cover Arctic sea ice with a high spatial resolution (20-90 m) but also with a high temporal resolution (1-7 days) typically associated with passive microwave sensors. Here, we used ~28,000 SAR image pairs from Sentinel-1AB together with ~15,000 SAR images pairs from RCM to generate high spatiotemporal large-scale sea ice motion products across the pan-Arctic domain for 2020. The combined Sentinel-1AB and RCM sea ice motion product provides almost complete 7-day coverage over the entire pan-Arctic domain that also includes the pole-hole. Compared to the National Snow and Ice Data Center (NSIDC) Polar Pathfinder and Ocean and Sea Ice-Satellite Application Facility (OSI-SAF) sea ice motion products, ice speed was found to be faster with the Senintel-1AB and RCM product which is attributed to the higher spatial resolution of SAR imagery. More sea ice motion vectors were detected from the Sentinel-1AB and RCM product in during the summer months and within the narrow channels and inlets compared to the NSIDC Polar Pathfinder and OSI-SAF sea ice motion products. Overall, our results demonstrate that sea ice geophysical variables across the pan-Arctic domain can now be retrieved from multi-sensor SAR images at both high spatial and temporal resolution.

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