Patterns of Arctic Tundra Greenness Based on Spatially Downscaled Solar-Induced Fluorescence

The climate change phenomenon occurring across the globe is having an increasingly alarming effect on Canada’s Arctic. Warming temperatures can have wide spanning impacts ranging from more rain and storm events, to increasing runoff, thawing permafrost, sea ice decline, melting glaciers, ecosystem disruption, and more. The purpose of this MRP was to assess the climate-induced landscape changes, including glacial loss and vegetation change, in Pond Inlet, Nunavut. A time series analysis was performed using the intervals 1989-1997, 1997-2005, and 2005-2016. The two methods for monitoring change were 1) the Normalized Difference Snow Index (NDSI) to detect glacial change, and 2) the Normalized Difference Vegetation Index (NDVI) to detect vegetation change, both utilizing threshold and masking techniques to increase accuracy. It was found that the percent of glacial loss and vegetation change in Pond Inlet had consistently increased throughout each time period. The area of glacial loss grew through each period to a maximum of 376 km2 of glacial loss in the last decade. Similarly, the area of the Arctic tundra that experienced vegetation change increased in each time period to a maximum of 660 km2 in the last decade. This vegetation change was characterized by overall increasing values of NDVI, revealing that many sections of the Arctic tundra in Pond Inlet were increasing in biomass. However, case study analysis revealed pixel clustering around the lower vegetation class thresholds used to classify change, indicating that shifts between these vegetation classes were likely exaggerated. Shifts between the higher vegetation classes were significant, and were what contributed to the most change in the last decade. The observations of higher glacial melt and increases in biomass are occurring in parallel with the increasing temperatures in Pond Inlet. Relevant literature in the Arctic agrees with the findings of this MRP that there are significant trends of glacial loss and vegetation greening and many studies attribute this directly to climate warming. The results of this study provide the necessary background with regards to landscape changes which could be used in future field studies investigating the climate induced changes in Pond Inlet. This study also demonstrates that significant landscape modifications have occurred in the recent decades and there is a strong need for continued research and monitoring of climate induced changes.

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Application of Sleator-Tarjan Dynamic Trees in a Monitoring System for the Arctic Region Based on Remote Sensing Data

Lecture Notes in Geoinformation and Cartography - Information Fusion and Geographic Information Systems (IF AND GIS 2013) ◽

10.1007/978-3-642-31833-7_9 ◽

2013 ◽

pp. 137-147

Author(s):

Philipp Galiano ◽

Mikhail Kharinov ◽

Sergey Vanurin

Keyword(s):

Remote Sensing ◽

Monitoring System ◽

Remote Sensing Data ◽

Arctic Region ◽

The Arctic ◽

Sensing Data ◽

Dynamic Trees ◽

The Arctic Region

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Disturbance Mapping in Arctic Tundra Improved by a Planning Workflow for Drone Studies: Advancing Tools for Future Ecosystem Monitoring

Remote Sensing ◽

10.3390/rs13214466 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4466

Author(s):

Isabell Eischeid ◽

Eeva M. Soininen ◽

Jakob J. Assmann ◽

Rolf A. Ims ◽

Jesper Madsen ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Predictive Ability ◽

Ground Cover ◽

Arctic Tundra ◽

The Arctic ◽

Ecosystem Monitoring ◽

Selection Of Variables ◽

State Changes ◽

Study Sites

The Arctic is under great pressure due to climate change. Drones are increasingly used as a tool in ecology and may be especially valuable in rapidly changing and remote landscapes, as can be found in the Arctic. For effective applications of drones, decisions of both ecological and technical character are needed. Here, we provide our method planning workflow for generating ground-cover maps with drones for ecological monitoring purposes. The workflow includes the selection of variables, layer resolutions, ground-cover classes and the development and validation of models. We implemented this workflow in a case study of the Arctic tundra to develop vegetation maps, including disturbed vegetation, at three study sites in Svalbard. For each site, we generated a high-resolution map of tundra vegetation using supervised random forest (RF) classifiers based on four spectral bands, the normalized difference vegetation index (NDVI) and three types of terrain variables—all derived from drone imagery. Our classifiers distinguished up to 15 different ground-cover classes, including two classes that identify vegetation state changes due to disturbance caused by herbivory (i.e., goose grubbing) and winter damage (i.e., ‘rain-on-snow’ and thaw-freeze). Areas classified as goose grubbing or winter damage had lower NDVI values than their undisturbed counterparts. The predictive ability of site-specific RF models was good (macro-F1 scores between 83% and 85%), but the area of the grubbing class was overestimated in parts of the moss tundra. A direct transfer of the models between study sites was not possible (macro-F1 scores under 50%). We show that drone image analysis can be an asset for studying future vegetation state changes on local scales in Arctic tundra ecosystems and encourage ecologists to use our tailored workflow to integrate drone mapping into long-term monitoring programs.

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Technology for developing a prototype of an information system for monitoring remote sensing data for the arctic region

Современное программирование ◽

10.36906/ap-2020/25 ◽

2021 ◽

Author(s):

M. Bizyukin ◽

◽

G. V. Abrahamyan

Keyword(s):

Remote Sensing ◽

Information System ◽

Russian Federation ◽

Remote Sensing Data ◽

Arctic Region ◽

Use Cases ◽

The Arctic ◽

Sensing Data ◽

The Russian Federation ◽

The Arctic Region

The work is aimed at developing a model of an information system for the analysis and monitoring of remote sensing data by the example of processing hyper- and multispectral satellite images, which are widely used to analyze the state of static and dynamic objects in the Arctic region of the Russian Federation. For automatic analysis and decryption of Arctic data in the development of the model, methods of high-performance computing, radiometric calibration, filtering and clustering of images, as well as intelligent data processing methods using deep learning convolutional neural networks were used. Object-oriented design and united modeling language notation were used to develop the model. A data-level model, a conceptual model of the structure of system modules, including a resource storage center, a resource and results management center, and a presentation-level interface have been developed. To develop a diagram of the use cases of the information system, the structure of actors, use cases and their interrelations were identified. The logical model of the information system was created based on a class diagram consisting of the Resource and Results Manager Center, Intellectual Information System, Functional Neural Modules packages. The practical significance of the study is due to the fact that the results obtained will allow the development of a prototype of an information system that can be used for effective monitoring of “useful data" of the Arctic region of the Russian Federation, as well as to automate the processes of analysis, updating, storage and processing of data from objects in various areas of the Arctic infrastructure.

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The Arctic Rain on Snow Study

10.5194/egusphere-egu21-13980 ◽

2021 ◽

Author(s):

Andrew Barrett ◽

Mark Serreze

Keyword(s):

Physical Environment ◽

Satellite Remote Sensing ◽

Arctic Region ◽

Meteorological Conditions ◽

Meteorological Station ◽

The Arctic ◽

Human Environment ◽

Antecedent Conditions ◽

Combining Data ◽

The Arctic Region

When rain falls on an existing cover of snow, followed by low temperatures, or falls as freezing rain, it can leave a hard crust. These Arctic rain on snow (ROS) events can profoundly influence the physical environment, animals, and human livelihoods. Impacts can be immediate (e.g., on human travel, herding, or harvesting) or evolve or accumulate, leading, for example, to massive starvation-induced die offs of reindeer, caribou and musk oxen. The international Arctic Rain on Snow Study (AROSS) will detect and catalogue ROS events, and study their impacts, addressing human-environment relationships, associated meteorological conditions, and challenges in their detection. We offer a path forward to anticipate and mitigate impacts through knowledge co-production. Although ROS events can be detected, and their intensity and trends across the Arctic region evaluated by combining data from satellite remote sensing, atmospheric reanalyses and meteorological station records, information most germane to impacts, such as the thickness of ice layers, how ice layers form within a snowpack, and antecedent conditions that can amplify impacts, can only be obtained through collaboration with local and Indigenous knowledge-holders.

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Remote sensing assessment of glacial loss and vegetation change from 1989 to 2016: Pond Inlet, Nunavut

10.32920/ryerson.14667960 ◽

2021 ◽

Author(s):

Ruby R. Pennell

Keyword(s):

Vegetation Change ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Relevant Literature ◽

Arctic Tundra ◽

The Arctic ◽

Landscape Changes ◽

Storm Events ◽

Time Period ◽

Induced Changes

The climate change phenomenon occurring across the globe is having an increasingly alarming effect on Canada’s Arctic. Warming temperatures can have wide spanning impacts ranging from more rain and storm events, to increasing runoff, thawing permafrost, sea ice decline, melting glaciers, ecosystem disruption, and more. The purpose of this MRP was to assess the climate-induced landscape changes, including glacial loss and vegetation change, in Pond Inlet, Nunavut. A time series analysis was performed using the intervals 1989-1997, 1997-2005, and 2005-2016. The two methods for monitoring change were 1) the Normalized Difference Snow Index (NDSI) to detect glacial change, and 2) the Normalized Difference Vegetation Index (NDVI) to detect vegetation change, both utilizing threshold and masking techniques to increase accuracy. It was found that the percent of glacial loss and vegetation change in Pond Inlet had consistently increased throughout each time period. The area of glacial loss grew through each period to a maximum of 376 km2 of glacial loss in the last decade. Similarly, the area of the Arctic tundra that experienced vegetation change increased in each time period to a maximum of 660 km2 in the last decade. This vegetation change was characterized by overall increasing values of NDVI, revealing that many sections of the Arctic tundra in Pond Inlet were increasing in biomass. However, case study analysis revealed pixel clustering around the lower vegetation class thresholds used to classify change, indicating that shifts between these vegetation classes were likely exaggerated. Shifts between the higher vegetation classes were significant, and were what contributed to the most change in the last decade. The observations of higher glacial melt and increases in biomass are occurring in parallel with the increasing temperatures in Pond Inlet. Relevant literature in the Arctic agrees with the findings of this MRP that there are significant trends of glacial loss and vegetation greening and many studies attribute this directly to climate warming. The results of this study provide the necessary background with regards to landscape changes which could be used in future field studies investigating the climate induced changes in Pond Inlet. This study also demonstrates that significant landscape modifications have occurred in the recent decades and there is a strong need for continued research and monitoring of climate induced changes.

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Positive response of tree productivity to warming is reversed by increased tree density at the Arctic tundra-taiga ecotone

Canadian Journal of Forest Research ◽

10.1139/cjfr-2020-0466 ◽

2021 ◽

Author(s):

Xanthe Walker ◽

Heather D. Alexander ◽

Logan Berner ◽

Melissa A Boyd ◽

Michael M. Loranty ◽

...

Keyword(s):

Tree Growth ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Arctic Tundra ◽

Tree Density ◽

The Arctic ◽

Growth Responses ◽

Annual Variations ◽

Stand Productivity ◽

Active Layers

The transition zone between the northern boreal forest and the arctic tundra, known as the tundra-taiga ecotone (TTE) has undergone rapid warming in recent decades. In response to this warming, tree density, growth, and stand productivity are expected to increase. Increases in tree density have the potential to negate the positive impacts of warming on tree growth through a reduction in the active layer and an increase in competitive interactions. We assessed the effects of tree density on tree growth and climate-growth responses of Cajander larch (Larix cajanderi) and on trends in the normalized difference vegetation index (NDVI) in the TTE of Northeast Siberia. We examined 19 mature forest stands that all established after a fire in 1940 and ranged in tree density from 300 to 37,000 stems ha-1. High density stands with shallow active layers had lower tree growth, higher stand productivity, and more negative growth responses to growing season temperatures compared to low density stands with deep active layers. Variation in stand productivity across the density gradient was not captured by Landsat derived NDVI, but NDVI did capture annual variations in stand productivity. Our results suggest that the expected increases in tree density following fires at the TTE may effectively limit tree growth and that NDVI is unlikely to capture increasing productivity associated with changes in tree density.

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Nuclear icebreaker fleet and power supply on the basis of floating power units

Voprosy regionalnoj ekonomiki ◽

10.21499/2078-4023-2018--4-110-113 ◽

2018 ◽

Vol 35 (4) ◽

pp. 110-113

Author(s):

V. A. Tupchienko ◽

H. G. Imanova

Keyword(s):

Foreign Policy ◽

Power Supply ◽

Nuclear Power ◽

Arctic Region ◽

Nuclear Industry ◽

The Arctic ◽

Far North ◽

The Arctic Region

The article deals with the problem of the development of the domestic nuclear icebreaker fleet in the context of the implementation of nuclear logistics in the Arctic. The paper analyzes the key achievements of the Russian nuclear industry, highlights the key areas of development of the nuclear sector in the Far North, and identifies aspects of the development of mechanisms to ensure access to energy on the basis of floating nuclear power units. It is found that Russia is currently a leader in the implementation of the nuclear aspect of foreign policy and in providing energy to the Arctic region.

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