scholarly journals Hydrometric measurements in peatland-dominated, discontinuous permafrost at Scotty Creek, Northwest Territories, Canada – Changing Cold Regions Network (CCRN) Special Observation and Analysis Period (SOAP)

2018 ◽  
Author(s):  
Kristine M. Haynes ◽  
Ryan F. Connon ◽  
William L. Quinton
Keyword(s):  
Land Cover ◽  
Northwest Territories ◽  
Land Cover Change ◽  
Water Levels ◽  
Data Repository ◽  
Permafrost Region ◽  
Permafrost Degradation ◽  
Cold Regions ◽  
Land Cover Types ◽  
Discontinuous Permafrost

Abstract. The discontinuous permafrost region of northwestern Canada is experiencing rapid warming resulting in dramatic land cover change from forested permafrost terrain to treeless wetlands. Extensive research has been conducted throughout this region to gain insight into how climate-induced land cover change will impact water resources and ecosystem function. This paper presents a hydrological and micrometeorological dataset collected in the Scotty Creek basin, Northwest Territories, Canada over the course of the Changing Cold Regions Network (CCRN) Special Observation and Analysis Period (SOAP) year of 1 October 2014 to 30 September 2015. Micrometeorological data collected from four stations located in land cover types representative of those comprising the Scotty Creek basin, including bog, channel fen, stable peat plateau and peat plateau undergoing rapid permafrost degradation and loss are presented. Monitored micrometeorological variables include incoming and outgoing shortwave and longwave radiation, air temperature, relative humidity, wind speed, precipitation (rain and snow) and snow depth. Deep ground temperatures (~ 1 to 10 m below the ground surface) from a channel fen as well as disturbed sites common to the basin including a seismic line and winter road are presented. Water levels were also monitored in the representative land cover types over this period. This dataset is available from the Wilfrid Laurier University Library Research Data Repository (https://doi.org/10.5683/SP/OQDRJG) and can be used in coordination with other hydrological and micrometeorological datasets, including those from the CCRN, to examine spatio-temporal effects of meteorological conditions on local hydrological responses across cold regions.

A thawing boreal peat landscape along the southern limit of permafrost presently is carbon neutral

2020 ◽  
Author(s):  
Oliver Sonnentag ◽  
Julien Fouché ◽  
Manuel Helbig ◽  
Gabriel Hould Gosselin ◽  
Matteo Detto ◽  
...  
Keyword(s):  
Land Cover ◽  
Eddy Covariance ◽  
Black Spruce ◽  
Mean Value ◽  
Permafrost Region ◽  
Southern Limit ◽  
Organic C ◽  
Land Cover Types ◽  
Ecosystem Carbon ◽  
Peat Plateaus

<p>Along the southern limit of permafrost in northwestern Canada rising air temperatures have caused widespread land cover changes at unprecedented rates. A prominent change includes thermokarst wetland expansion at the expense of black spruce-dominated boreal forest stands due to the permafrost thaw-induced collapse of peat plateaus. We present a multi-year (2013 – 2017) net ecosystem carbon (C) balance (NECB, g C m<sup>-2</sup>year<sup>-1</sup>) at Scotty Creek near Fort Simpson, NT. The highly fragmented study site is dominated by permafrost-free wetlands and forested permafrost peat plateaus. Eddy covariance  measurements of net ecosystem carbon dioxide (CO<sub>2</sub>) and methane (CH<sub>4</sub>) exchanges (2013 – 2017) are complemented by discharge (2014 – 2016) and water chemistry monitoring (2015 and 2016) at the outlets of three small headwater catchments (<0.5 km<sup>2</sup>) draining the eddy covariance footprint area. In addition to net ecosystem CO<sub>2</sub>and CH<sub>4</sub>exchanges, the NECB includes the export of dissolved C (DC) as the sum of inorganic and organic C (DIC and DOC), free CO<sub>2</sub>and CH<sub>4</sub>through runoff, and the estimated import of DOC through precipitation. We use absorbance spectroscopy for dissolved organic matter (DOM) characterization to distinguish different DOM sources among catchments and characteristic land cover types. Between 2013 and 2017, the NECB varied between a weak net C source (~16 ±5 g C m<sup>-2</sup>year<sup>-1</sup>) and sink (~-22 ±5 g C m<sup>-2</sup>year<sup>-1</sup>) in 2015 and 2013, respectively, with a mean value of -1 ±7 g C m<sup>-2</sup>year<sup>-1</sup>. The net C sink-source strength was largely controlled by variations in net CO<sub>2</sub>exchange, ranging between a weak net CO<sub>2 </sub>sink (~-29 ±3 g C m<sup>-2</sup>year<sup>-1</sup>) and source (~8 ±4 g C m<sup>-2</sup>year<sup>-1</sup>) in 2015 and 2013, respectively. In contrast, our study site was a persistent annual net CH<sub>4</sub>source (~8 ±1 g C m<sup>-2</sup>year<sup>-1</sup>). Compensated by the import of DOC through precipitation, DC exported from the three catchments was a negligible component of the NECB. There were no significant differences in DOC concentrations and absorbance indices among catchments, and thawed and frozen land cover types, overall illustrating high DOM aromaticity (SUVA<sub>254</sub>= 3.3 ± 0.6 L mg<sup>-1</sup>m<sup>-1</sup>) and high molecular weight (a254:a365 = 4.3 ± 0.3) characteristic for peatlands and peat-dominated landscapes outside the circumpolar permafrost region. We conclude that a rapidly thawing boreal peat landscape along the southern limit of permafrost presently appears to be C neutral.</p>

scholarly journals ANALYZING THE EFFECTS OF LAND COVER CHANGE ON SURFACE TEMPERATURE IN MOUNT MAKILING FOREST RESERVE (MMFR) AND ITS NEIGHBORING MUNICIPALITIES USING LANDSAT DATA

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 165-172
Author(s):  
V. K. M. Del Mundo ◽  
C. L. Tiburan Jr.
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Cover Change ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Centers ◽  
Forest Reserve ◽  
Land Cover Types ◽  
Rapid Economic Growth ◽  
Supervised Image Classification

Abstract. Land Surface Temperature (LST) is said to be affected by frequent changes in the land cover. Over the years, the immediate environs of Mount Makiling Forest Reserve (MMFR) have experienced such kind of change due to rapid economic growth of the area that also led to the expansion of urban centers. The study utilized Landsat imageries to determine the possible effects of land cover change on surface temperature using the integration of remote sensing and GIS technologies. Initially, the multispectral bands were radiometrically corrected using Dark Object Subtraction (DOS) while the thermal bands were corrected using Land Surface Emissivity (LSE). After these corrections were applied, the images were classified using supervised image classification technique where seven land cover types have been identified. The classified images were then validated using 200 reference data and this revealed an overall accuracy of 87.5% and 86.0% for the May 2003 and July 2015 images, respectively. Results showed that changes in land cover resulted to a significant change in Land Surface Temperature (LST). The LST in 2003 (16.49°C – 40.44°C) was found higher than that of 2015 which was observed between 13.35°C and 33.83°C only. The reason behind this is the increase in green spaces from 2003 to 2015. Among the major land cover types, forest lands exhibited the lowest mean surface temperature for both years having 27.27°C in 2003 and 21.35°C in 2015 while built-up areas had the highest surface temperature having 32.60°C in 2003 and 26.00°C in 2015.

scholarly journals Long-term climate-influenced land cover change in discontinuous permafrost peatland complexes

2021 ◽  
Vol 25 (6) ◽  
pp. 3301-3317
Author(s):  
Olivia Carpino ◽  
Kristine Haynes ◽  
Ryan Connon ◽  
James Craig ◽  
Élise Devoie ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Permafrost Zone ◽  
Permafrost Thaw ◽  
The North ◽  
Plateau Wetland ◽  
Discontinuous Permafrost ◽  
Advanced Stages ◽  
Induced Changes ◽  
Time Substitution

Abstract. The discontinuous permafrost zone is undergoing rapid transformation as a result of unprecedented permafrost thaw brought on by circumpolar climate warming. Rapid warming over recent decades has significantly decreased the area underlain by permafrost in peatland complexes. It has catalysed extensive landscape transitions in the Taiga Plains of northwestern Canada, transforming forest-dominated landscapes to those that are wetland dominated. However, the advanced stages of this landscape transition, and the hydrological and thermal mechanisms and feedbacks governing these environments, are unclear. This study explores the current trajectory of land cover change across a 300 000 km2 region of northwestern Canada's discontinuous permafrost zone by presenting a north–south space-for-time substitution that capitalizes on the region's 600 km latitudinal span. We combine extensive geomatics data across the Taiga Plains with ground-based hydrometeorological measurements collected in the Scotty Creek basin, Northwest Territories, Canada, which is located in the medial latitudes of the Taiga Plains and is undergoing rapid landscape change. These data are used to inform a new conceptual framework of landscape evolution that accounts for the observed patterns of permafrost thaw-induced land cover change and provides a basis for predicting future changes. Permafrost thaw-induced changes in hydrology promote partial drainage and drying of collapse scar wetlands, leading to areas of afforestation forming treed wetlands without underlying permafrost. Across the north–south latitudinal gradient spanning the Taiga Plains, relatively undisturbed forested plateau–wetland complexes dominate the region's higher latitudes, forest–wetland patchwork are most prevalent at the medial latitudes, and forested peatlands are increasingly present across lower latitudes. This trend reflects the progression of wetland transition occurring locally in the plateau–wetland complexes of the Scotty Creek basin and informs our understanding of the anticipated trajectory of change in the discontinuous permafrost zone.

scholarly journals Quantifying Short-Term Urban Land Cover Change with Time Series Landsat Data: A Comparison of Four Different Cities

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4319 ◽  
Author(s):  
Hongsheng Zhang ◽  
Ting Wang ◽  
Yuhan Zhang ◽  
Yiru Dai ◽  
Jiangjie Jia ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Urban Areas ◽  
Urban Land ◽  
Sao Paulo ◽  
São Paulo ◽  
Short Term ◽  
Land Cover Types ◽  
Urban Land Cover ◽  
Urban Land Cover Change

Short-term characteristics of urban land cover change have been observed and reported from satellite images, although urban landscapes are mainly influenced by anthropogenic factors. These short-term changes in urban areas are caused by rapid urbanization, seasonal climate changes, and phenological ecological changes. Quantifying and understanding these short-term characteristics of changes in various land cover types is important for numerous urban studies, such as urbanization assessments and management. Many previous studies mainly investigated one study area with insufficient datasets. To more reliably and confidently investigate temporal variation patterns, this study employed Fourier series to quantify the seasonal changes in different urban land cover types using all available Landsat images over four different cities, Melbourne, Sao Paulo, Hamburg, and Chicago, within a five-year period (2011–2015). The overall accuracy was greater than 86% and the kappa coefficient was greater than 0.80. The R-squared value was greater than 0.80 and the root mean square error was less than 7.2% for each city. The results indicated that (1) the changing periods for water classes were generally from half a year to one and a half years in different areas; and, (2) urban impervious surfaces changed over periods of approximately 700 days in Melbourne, Sao Paulo, and Hamburg, and a period of approximately 215 days in Chicago, which was actually caused by the unavoidable misclassification from confusions between various land cover types using satellite data. Finally, the uncertainties of these quantification results were analyzed and discussed. These short-term characteristics provided important information for the monitoring and assessment of urban areas using satellite remote sensing technology.

scholarly journals High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment

2018 ◽  
Vol 15 (6) ◽  
pp. 1663-1682 ◽  
Author(s):  
Matthias B. Siewert
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Random Forest ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Land Cover ◽  
Support Vector ◽  
Small Scale ◽  
Permafrost Degradation ◽  
Discontinuous Permafrost

Abstract. Soil organic carbon (SOC) stored in northern peatlands and permafrost-affected soils are key components in the global carbon cycle. This article quantifies SOC stocks in a sub-Arctic mountainous peatland environment in the discontinuous permafrost zone in Abisko, northern Sweden. Four machine-learning techniques are evaluated for SOC quantification: multiple linear regression, artificial neural networks, support vector machine and random forest. The random forest model performed best and was used to predict SOC for several depth increments at a spatial resolution of 1 m (1×1 m). A high-resolution (1 m) land cover classification generated for this study is the most relevant predictive variable. The landscape mean SOC storage (0–150 cm) is estimated to be 8.3 ± 8.0 kg C m−2 and the SOC stored in the top meter (0–100 cm) to be 7.7 ± 6.2 kg C m−2. The predictive modeling highlights the relative importance of wetland areas and in particular peat plateaus for the landscape's SOC storage. The total SOC was also predicted at reduced spatial resolutions of 2, 10, 30, 100, 250 and 1000 m and shows a significant drop in land cover class detail and a tendency to underestimate the SOC at resolutions  >  30 m. This is associated with the occurrence of many small-scale wetlands forming local hot-spots of SOC storage that are omitted at coarse resolutions. Sharp transitions in SOC storage associated with land cover and permafrost distribution are the most challenging methodological aspect. However, in this study, at local, regional and circum-Arctic scales, the main factor limiting robust SOC mapping efforts is the scarcity of soil pedon data from across the entire environmental space. For the Abisko region, past SOC and permafrost dynamics indicate that most of the SOC is barely 2000 years old and very dynamic. Future research needs to investigate the geomorphic response of permafrost degradation and the fate of SOC across all landscape compartments in post-permafrost landscapes.

scholarly journals High-resolution digital mapping of soil organic carbon in permafrost terrain using machine-learning: A case study in a sub-Arctic peatland environment

2017 ◽  
Author(s):  
Matthias B. Siewert
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Random Forest ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Land Cover ◽  
Support Vector ◽  
Small Scale ◽  
Permafrost Degradation ◽  
Discontinuous Permafrost

Abstract. Soil organic carbon (SOC) stored in northern peatlands and permafrost affected soils are key components in the global carbon cycle. I quantify SOC stocks in a sub-arctic mountainous peatland environment in the discontinuous permafrost zone in Abisko, northern Sweden. Four machine-learning techniques are evaluated: multiple linear regression, artificial neural networks, support vector machine and random forest. The random forest approach performed best and was used to predict SOC for several depth increments at a spatial resolution of 2 ×2 m. A high-resolution (1 × 1 m) land cover classification generated for this study is the most relevant predictive variable. The landscape mean SOC storage (0–150 cm) is estimated to 7.9 ± 8.0 kg C m−2 and the SOC stored in the top meter (0–100 cm) to 7.0 ± 6.3 kg C m−2. The predictive modeling highlights the relative importance of wetland areas and in particular peat plateaus for the landscape SOC storage. A surprising large number of small scale wetland areas are mapped forming very local hot-spots of SOC storage. The results show that robust SOC predictions are possible with the available methods and very high-resolution remote sensing data. Strong environmental gradients associated with land cover and permafrost distribution are the most challenging methodological aspect. However, in this study, at local, regional and circum-Arctic scale the main factor limiting robust, high-resolution SOC mapping efforts is the scarcity of soil pedon data from across the entire environmental space. For the Absiko region, past SOC and permafrost dynamics indicate that most of the SOC is barely 2000 years old and very dynamic in wetland areas with permafrost related landforms. Future research needs to investigate the geomorphic response of permafrost degradation and the fate of SOC across all landscape compartments in post-permafrost landscapes.

A study of decadal Land use-land cover change in Dehradun city.

2021 ◽  
Author(s):  
Gayatri Singh
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Water Levels ◽  
Physical Factors ◽  
Infrastructure Development ◽  
Land Use Land Cover ◽  
Detection Analysis ◽  
The Impact

<p>The present study is to quantify the spatial-temporal pattern of the Land Use/ Land Cover Change (LULCC) during a decade (i.e., 2010 to 2020) in the Dehradun city which is situated in the foothills of the Himalaya, using Landsat data. The study helps in identifying the major bio-physical factors governing LULCC through modern geospatial techniques. Change detection shows that the study area experienced an increase in its urban area from 2010 to 2020 and a comparatively decrease in cropland and forest area. This was due to an increase in its urban population, rapid increase in industrialization and tourism during the same period. The change detection analysis further shows that 2010-2020, associated with change in croplands, change in built-up, forest lands, change in water-bodies, water levels, and rainfall. With comparison of above results and collected socio-economic data in this region, the impact of changing land use & bio-physical/ economic factors on agricultural profitability were analyzed. The result of this study could thus lead to a detailed and lucid spatiotemporal assessment of the major bio-physical factors. It is expected that the study will help in facilitating better policy making and infrastructure development for industries and urbanization.<br><br></p>

scholarly journals Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine

2018 ◽  
Vol 10 (8) ◽  
pp. 1226 ◽  
Author(s):  
Kelsey E. Nyland ◽  
Grant E. Gunn ◽  
Nikolay I. Shiklomanov ◽  
Ryan N. Engstrom ◽  
Dmitry A. Streletskiy
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Land Cover Change ◽  
Environmental Modeling ◽  
Google Earth ◽  
The Arctic ◽  
Permafrost Degradation ◽  
Yenisei River ◽  
River Region ◽  
Google Earth Engine

Climate warming is occurring at an unprecedented rate in the Arctic due to regional amplification, potentially accelerating land cover change. Measuring and monitoring land cover change utilizing optical remote sensing in the Arctic has been challenging due to persistent cloud and snow cover issues and the spectrally similar land cover types. Google Earth Engine (GEE) represents a powerful tool to efficiently investigate these changes using a large repository of available optical imagery. This work examines land cover change in the Lower Yenisei River region of arctic central Siberia and exemplifies the application of GEE using the random forest classification algorithm for Landsat dense stacks spanning the 32-year period from 1985 to 2017, referencing 1641 images in total. The semiautomated methodology presented here classifies the study area on a per-pixel basis utilizing the complete Landsat record available for the region by only drawing from minimally cloud- and snow-affected pixels. Climatic changes observed within the study area’s natural environments show a statistically significant steady greening (~21,000 km2 transition from tundra to taiga) and a slight decrease (~700 km2) in the abundance of large lakes, indicative of substantial permafrost degradation. The results of this work provide an effective semiautomated classification strategy for remote sensing in permafrost regions and map products that can be applied to future regional environmental modeling of the Lower Yenisei River region.

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