A new approach to mapping permafrost and change incorporating uncertainties in ground conditions and climate projections

Zhang, Y.; Olthof, Ian; Fraser, Robert; Wolfe, S A

doi:10.5194/tc-8-2177-2014

Cited by 36 publications

(48 citation statements)

References 64 publications

(103 reference statements)

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“…S3c), resulting in an estimated SOC range from 21 to 69 kg C m −2 and a mean value of 45 kg C m −2 . This statistical distribution was similar to the OLT distribution observed from field sampling data in northern Canada (Zhang et al, 2014). Table 3.…”

Section: Modeled Alt Sensitivity To Landscape Heterogeneity Within Thsupporting

confidence: 83%

“…However, this process should have a relatively limited effect on the estimated soil carbon fraction due to general increases in soil bulk density and thus lower soil carbon concentration with depth (Hossain et al, 2015). Better information on the spatial and vertical distribution of SOC stocks would provide the single largest improvement in ALT accuracy, enabling more accurate predictions of permafrost active layer processes and climate feedbacks in regional and global carbon and climate models (Zhang et al, 2014;Mishra and Riley, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…2.3.1 Regional sensitivity analysis SOC fraction, soil moisture, and snow cover conditions are among the most important factors controlling permafrost active layer conditions at the landscape scale (Lawrence and Slater, 2008;Jafarov et al, 2012;Zhang et al, 2014;Yi et al, 2015). Therefore, model sensitivity analyses were conducted to investigate the ALT sensitivity to uncertainties in regional SOC fraction, soil moisture, and snow density (Fig.…”

Section: Model Sensitivity Analysismentioning

confidence: 99%

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Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska

Kimball

Chen

et al. 2018

The Cryosphere

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Abstract. An important feature of the Arctic is large spatial heterogeneity in active layer conditions, which is generally poorly represented by global models and can lead to large uncertainties in predicting regional ecosystem responses and climate feedbacks. In this study, we developed a spatially integrated modeling and analysis framework combining field observations, local-scale ( ∼ 50 m resolution) active layer thickness (ALT) and soil moisture maps derived from low-frequency (L + P-band) airborne radar measurements, and global satellite environmental observations to investigate the ALT sensitivity to recent climate trends and landscape heterogeneity in Alaska. Modeled ALT results show good correspondence with in situ measurements in higherpermafrost-probability (PP ≥ 70 %) areas (n = 33; R = 0.60; mean bias = 1.58 cm; RMSE = 20.32 cm), but with larger uncertainty in sporadic and discontinuous permafrost areas. The model results also reveal widespread ALT deepening since 2001, with smaller ALT increases in northern Alaska (mean trend = 0.32 ± 1.18 cm yr −1 ) and much larger increases (> 3 cm yr −1 ) across interior and southern Alaska. The positive ALT trend coincides with regional warming and a longer snow-free season (R = 0.60 ± 0.32). A spatially integrated analysis of the radar retrievals and model sensitivity simulations demonstrated that uncertainty in the spatial and vertical distribution of soil organic carbon (SOC) was the largest factor affecting modeled ALT accuracy, while soil moisture played a secondary role. Potential improvements in characterizing SOC heterogeneity, including better spatial sampling of soil conditions and advances in remote sensing of SOC and soil moisture, will enable more accurate predictions of active layer conditions and refinement of the modeling framework across a larger domain.

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Section: Modeled Alt Sensitivity To Landscape Heterogeneity Within Thsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Section: Model Sensitivity Analysismentioning

confidence: 99%

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Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska

Kimball

Chen

et al. 2018

The Cryosphere

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“…Spatial modeling of permafrost properties have largely been conducted at coarse-scale resolutions (N 2 km × 2 km) (e.g. Brown et al, 1997;Jafarov, Marchenko, & Romanovsky, 2012;Lawrence & Slater, 2005;Marchenko, Romanovsky, & Tipenko, 2008), which do not represent fine-scale differences in permafrost conditions and are unsuitable for landuse planning and environmental assessments (Zhang, Olthof, Fraser, & Wolfe, 2014). Furthermore, accuracy assessments conducted at coarse scales can be unreliable because of differences in scale between maps and field observations, and assessments tend to lack a diverse set of field observations for thorough validation and calibration.…”

Section: Introductionmentioning

confidence: 99%

Distribution of near-surface permafrost in Alaska: Estimates of present and future conditions

Pastick

Jorgenson²,

Wylie

et al. 2015

Remote Sensing of Environment

153

154

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High-latitude regions are experiencing rapid and extensive changes in ecosystem composition and function as the result of increases in average air temperature. Increasing air temperatures have led to widespread thawing and degradation of permafrost, which in turn has affected ecosystems, socioeconomics, and the carbon cycle of high latitudes. Here we overcome complex interactions among surface and subsurface conditions to map nearsurface permafrost through decision and regression tree approaches that statistically and spatially extend field observations using remotely sensed imagery, climatic data, and thematic maps of a wide range of surface and subsurface biophysical characteristics. The data fusion approach generated medium-resolution (30-m pixels) maps of near-surface (within 1 m) permafrost, active-layer thickness, and associated uncertainty estimates throughout mainland Alaska. Our calibrated models (overall test accuracy of~85%) were used to quantify changes in permafrost distribution under varying future climate scenarios assuming no other changes in biophysical factors. Models indicate that near-surface permafrost underlies 38% of mainland Alaska and that near-surface permafrost will disappear on 16 to 24% of the landscape by the end of the 21st Century. Simulations suggest that near-surface permafrost degradation is more probable in central regions of Alaska than more northerly regions. Taken together, these results have obvious implications for potential remobilization of frozen soil carbon pools under warmer temperatures. Additionally, warmer and drier conditions may increase fire activity and severity, which may exacerbate rates of permafrost thaw and carbon remobilization relative to climate alone. The mapping of permafrost distribution across Alaska is important for land-use planning, environmental assessments, and a wide-array of geophysical studies.

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“…In order to assess these impacts, accurate and efficient methods are required to measure Arctic vegetation changes over a range of spatial and temporal scales. Maps depicting the current distribution of northern vegetation are also needed as input to terrestrial ecosystem models (Euskirchen et al 2009) and permafrost models (Zhang et al 2014).…”

Section: Introductionmentioning

confidence: 99%

UAV photogrammetry for mapping vegetation in the low-Arctic

et al. 2016

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Plot-scale field measurements are necessary to monitor changes to tundra vegetation, which has a small stature and high spatial heterogeneity, while satellite remote sensing can be used to track coarser changes over larger regions. In this study, we explored the potential of unmanned aerial vehicle (UAV) photographic surveys to map low-Arctic vegetation at an intermediate scale. A multicopter was used to capture highly overlapping, subcentimetre photographs over a 2 ha site near Tuktoyaktuk, Northwest Territories. Images were processed into ultradense 3D point clouds and 1 cm resolution orthomosaics and vegetation height models using Structure-from-Motion (SfM) methods. Shrub vegetation heights measured on the ground were accurately represented using SfM point cloud data (r 2 = 0.96, SE = 8 cm, n = 31) and a combination of spectral and height predictor variables yielded an 11-class classification with 82% overall accuracy. Differencing repeat UAV surveys before and after manually trimming shrub patches showed that vegetation height decreases in trimmed areas (− 6.5 cm, SD = 21 cm). Based on these findings, we conclude that UAV photogrammetry provides a promising, cost-efficient method for high-resolution mapping and monitoring of tundra vegetation that can be used to bridge the gap between plot and satellite remote sensing measurements.Key words: unmanned aerial vehicle (UAV), unmanned aircraft system (UAS), Arctic, shrubs, vegetation, Structure-from-Motion, photogrammetry.Résumé : Des mesures sur des parcelles de terrain sont nécessaires afin de surveiller les changements de la végétation de la toundra, qui a une petite stature et une grande hétéro-généité spatiale, tandis que la télédétection par satellite peut être utilisée pour suivre les changements plus importants sur de plus vastes régions. Dans le cadre de cette étude, nous avons exploré les possibilités d'utiliser des relevés photographiques effectués au moyen d'un véhicule aérien sans pilote (UAV) afin de cartographier la végétation du BasArctique à une échelle intermédiaire. Un multicoptère a été utilisé pour prendre des photographies très chevauchantes subcentrimétriques sur un site de 2 ha près de Tuktoyaktuk, Territoires du Nord-Ouest. Des images ont été transformées en nuages de points en 3D et orthomosaïques en résolution 1 cm et modèles de la hauteur de la végétation en utilisant les méthodes de la structure par le mouvement « Structure-from-Motion » (SfM). Les hauteurs de la végétation arbustive mesurées sur le terrain ont été précisément représentées en utilisant les données SfM des nuages de points (r 2 = 0.96, erreur type = 8 cm, n = 31) et une combinaison de variables spectrales et prédictives de la hauteur ont donné une classification à 11 classes avec une exactitude générale de 82%. La différenciation des relevés d'UAV répétés pris avant et après la taille manuelle des parcelles d'arbustes a montré des diminutions de la hauteur de la végétation dans les zones où il y a eu la taille (−6.5 cm, écart type = 21 cm). Selon ces résult...

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A new approach to mapping permafrost and change incorporating uncertainties in ground conditions and climate projections

Cited by 36 publications

References 64 publications

Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska

Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska

Distribution of near-surface permafrost in Alaska: Estimates of present and future conditions

UAV photogrammetry for mapping vegetation in the low-Arctic

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