scholarly journals Is Alaska’s Yukon–Kuskokwim Delta Greening or Browning? Resolving Mixed Signals of Tundra Vegetation Dynamics and Drivers in the Maritime Arctic

2021 ◽  
Vol 25 (1) ◽  
pp. 76-93
Author(s):  
Gerald V. Frost ◽  
Uma S. Bhatt ◽  
Matthew J. Macander ◽  
Amy S. Hendricks ◽  
M. Torre Jorgenson
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Knowledge Exchange ◽  
Field Measurements ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Spring Sea Ice

Abstract Alaska’s Yukon–Kuskokwim Delta (YKD) is among the Arctic’s warmest, most biologically productive regions, but regional decline of the normalized difference vegetation index (NDVI) has been a striking feature of spaceborne Advanced High Resolution Radiometer (AVHRR) observations since 1982. This contrast with “greening” prevalent elsewhere in the low Arctic raises questions concerning climatic and biophysical drivers of tundra productivity along maritime–continental gradients. We compared NDVI time series from AVHRR, the Moderate Resolution Imaging Spectroradiometer (MODIS), and Landsat for 2000–19 and identified trend drivers with reference to sea ice and climate datasets, ecosystem and disturbance mapping, field measurements of vegetation, and knowledge exchange with YKD elders. All time series showed increasing maximum NDVI; however, whereas MODIS and Landsat trends were very similar, AVHRR-observed trends were weaker and had dissimilar spatial patterns. The AVHRR and MODIS records for time-integrated NDVI were dramatically different; AVHRR indicated weak declines, whereas MODIS indicated strong increases throughout the YKD. Disagreement largely arose from observations during shoulder seasons, when there is partial snow cover and very high cloud frequency. Nonetheless, both records shared strong correlations with spring sea ice extent and summer warmth. Multiple lines of evidence indicate that, despite frequent disturbances and high interannual variability in spring sea ice and summer warmth, tundra productivity is increasing on the YKD. Although climatic drivers of tundra productivity were similar to more continental parts of the Arctic, our intercomparison highlights sources of uncertainty in maritime areas like the YKD that currently, or soon will, challenge historical concepts of “what is Arctic.”

scholarly journals Changes in Vegetation Phenology and Productivity in Alaska Over the Past Two Decades

2020 ◽  
Vol 12 (10) ◽  
pp. 1546 ◽  
Author(s):  
Christopher Potter ◽  
Olivia Alexander
Keyword(s):  
Time Series ◽  
Coastal Plain ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Regional Scale ◽  
Data Sets ◽  
Vegetation Phenology ◽  
Vegetation Growth ◽  
The Past ◽  
Moderate Resolution Imaging Spectroradiometer

Understanding trends in vegetation phenology and growing season productivity at a regional scale is important for global change studies, particularly as linkages can be made between climate shifts and the vegetation’s potential to sequester or release carbon into the atmosphere. Trends and geographic patterns of change in vegetation growth and phenology from the MODerate resolution Imaging Spectroradiometer (MODIS) satellite data sets were analyzed for the state of Alaska over the period 2000 to 2018. Phenology metrics derived from the MODIS Normalized Difference Vegetation Index (NDVI) time-series at 250 m resolution tracked changes in the total integrated greenness cover (TIN), maximum annual NDVI (MAXN), and start of the season timing (SOST) date over the past two decades. SOST trends showed significantly earlier seasonal vegetation greening (at more than one day per year) across the northeastern Brooks Range Mountains, on the Yukon-Kuskokwim coastal plain, and in the southern coastal areas of Alaska. TIN and MAXN have increased significantly across the western Arctic Coastal Plain and within the perimeters of most large wildfires of the Interior boreal region that burned since the year 2000, whereas TIN and MAXN have decreased notably in watersheds of Bristol Bay and in the Cook Inlet lowlands of southwestern Alaska, in the same regions where earlier-trending SOST was also detected. Mapping results from this MODIS time-series analysis have identified a new database of localized study locations across Alaska where vegetation phenology has recently shifted notably, and where land cover types and ecosystem processes could be changing rapidly.

scholarly journals The potential of sea ice leads as a predictor for summer Arctic sea ice extent

2018 ◽  
Vol 12 (12) ◽  
pp. 3747-3757 ◽  
Author(s):  
Yuanyuan Zhang ◽  
Xiao Cheng ◽  
Jiping Liu ◽  
Fengming Hui
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Strong Relationship ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Ice Leads ◽  
Arctic Sea Ice Extent

Abstract. The Arctic sea ice extent throughout the melt season is closely associated with initial sea ice state in winter and spring. Sea ice leads are important sites of energy fluxes in the Arctic Ocean, which may play an important role in the evolution of Arctic sea ice. In this study, we examine the potential of sea ice leads as a predictor for summer Arctic sea ice extent forecast using a recently developed daily sea ice lead product retrieved from the Moderate-Resolution Imaging Spectroradiometer (MODIS). Our results show that July pan-Arctic sea ice extent can be predicted from the area of sea ice leads integrated from midwinter to late spring, with a prediction error of 0.28 million km2 that is smaller than the standard deviation of the observed interannual variability. However, the predictive skills for August and September pan-Arctic sea ice extent are very low. When the area of sea ice leads integrated in the Atlantic and central and west Siberian sector of the Arctic is used, it has a significantly strong relationship (high predictability) with both July and August sea ice extent in the Atlantic and central and west Siberian sector of the Arctic. Thus, the realistic representation of sea ice leads (e.g., the areal coverage) in numerical prediction systems might improve the skill of forecast in the Arctic region.

Arctic Ocean Sea Ice Area Extent Cyclicity and Non-Stationarity

2020 ◽  
Author(s):  
Reginald Muskett ◽  
Syun-Ichi Akasofu
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Wave Pattern ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Weather System ◽  
Sea Ice Extent ◽  
Temperature Variations ◽  
Arctic Sea ◽  
Arctic Temperature

<p>Arctic sea ice is a key component of the Arctic hydrologic cycle. This cycle is connected to land and ocean temperature variations and Arctic snow cover variations, spatially and temporally. Arctic temperature variations from historical observations shows an early 20th century increase (i.e. warming), followed by a period of Arctic temperature decrease (i.e. cooling) since the 1940s, which was followed by another period of Arctic temperature increase since the 1970s that continues into the two decades of the 21st century. Evidence has been accumulating that Arctic sea ice extent can experience multi-decadal to centennial time scale variations as it is a component of the Arctic Geohydrological System. </p><p><br>We investigate the multi-satellite and sensor daily values of area extent of Arctic sea ice since SMMR on Nimbus 7 (1978) to AMSR2 on GCOM-W1 (2019). From the daily time series we use the first year-cycle as a wave-pattern to compare to all subsequent years-cycles through April 2020 (in progress), and constitute a derivative time series. In this time series we find the emergence of a multi-decadal cycle, showing a relative minimum during the period of 2007 to 2014, and subsequently rising. This may be related to an 80-year cycle (hypothesis). The Earth’s weather system is principally driven the solar radiation and its variations. If the multi-decadal cycle in Arctic sea ice area extent that we interpret continues, it may be linked physically to the Wolf-Gleissberg cycle, a factor in the variations of terrestrial cosmogenic isotopes, ocean sediment layering and glacial varves, ENSO and Aurora.</p><p>Our hypothesis and results give more evidence that the multi-decadal variation of Arctic sea ice area extent is controlled by natural physical processes of the Sun-Earth system. </p>

scholarly journals A simple approach to providing a more consistent Arctic sea ice extent time series from the 1950s to present

2012 ◽  
Vol 6 (6) ◽  
pp. 1359-1368 ◽  
Author(s):  
W. N. Meier ◽  
J. Stroeve ◽  
A. Barrett ◽  
F. Fetterer
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Linear Trend ◽  
Statistical Significance ◽  
Recent Decade ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Combined Time Series

Abstract. Observations from passive microwave satellite sensors have provided a continuous and consistent record of sea ice extent since late 1978. Earlier records, compiled from ice charts and other sources exist, but are not consistent with the satellite record. Here, a method is presented to adjust a compilation of pre-satellite sources to remove discontinuities between the two periods and create a more consistent combined 59-yr time series spanning 1953–2011. This adjusted combined time series shows more realistic behavior across the transition between the two individual time series and thus provides higher confidence in trend estimates from 1953 through 2011. The long-term time series is used to calculate linear trend estimates and compare them with trend estimates from the satellite period. The results indicate that trends through the 1960s were largely positive (though not statistically significant) and then turned negative by the mid-1970s and have been consistently negative since, reaching statistical significance (at the 95% confidence level) by the late 1980s. The trend for September (when Arctic extent reaches its seasonal minimum) for the satellite period, 1979–2011 is −12.9% decade−1, nearly double the 1953–2011 trend of −6.8% decade−1 (percent relative to the 1981–2010 mean). The recent decade (2002–2011) stands out as a period of persistent decline in ice extent. The combined 59-yr time series puts the strong observed decline in the Arctic sea ice cover during 1979–2011 in a longer-term context and provides a useful resource for comparisons with historical model estimates.

scholarly journals Greening and Browning Trends across Peru’s Diverse Environments

2020 ◽  
Vol 12 (15) ◽  
pp. 2418 ◽  
Author(s):  
Molly H. Polk ◽  
Niti B. Mishra ◽  
Kenneth R. Young ◽  
Kumar Mainali
Keyword(s):  
Time Series ◽  
Protected Area ◽  
Landscape Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Elevation Gradient ◽  
Positive Trend ◽  
Scientific Legacy ◽  
Alexander Von Humboldt ◽  
Moderate Resolution Imaging Spectroradiometer

If he were living today, Alexander von Humboldt would be using current technology to evaluate change in the Andes. Inspired by von Humboldt’s scientific legacy and the 2019 celebrations of his influence, we utilize a Moderate Resolution Imaging Spectroradiometer (MODIS)time-series vegetation index to ask questions of landscape change. Specifically, we use an 18-year record of Normalized Difference Vegetation Index (NDVI) data as a proxy to evaluate landscape change in Peru, which is well known for its high biological and ecological diversity. Continent-level evaluations of Latin America have shown sites with a positive trend in NDVI, or “greening” and “browning”, a negative trend in NDVI that suggests biophysical or human-caused reductions in vegetation. Our overall hypothesis was that the major biomes in Peru would show similar NDVI change patterns. To test our expectations, we analyzed the NDVI time-series with Thiel-Sen regression and evaluated Peru overall, by protected area status, by biome, and by biome and elevation. Across Peru overall, there was a general greening trend. By protected area status, surprisingly, the majority of greening occurred outside protected areas. The trends were different by biome, but there were hotspots of greening in the Amazon, Andean Highlands, and Drylands where greening dominated. In the Tropical Subtropical Dry Broadleaf Forest biome, greening and browning signals were mixed. Greening trends varied across the elevation gradient, switching from greening, to browning, and then back to greening as elevation increased. By biome and elevation, the results were variable. We further explored biome-specific drivers of greening and browning drawing on high-resolution imagery, the literature, and field expertise, much as we imagine von Humboldt might have approached similar questions of landscape dynamism.

scholarly journals Comparison of Normalized Difference Vegetation Index Derived from Landsat, MODIS, and AVHRR for the Mesopotamian Marshes Between 2002 and 2018

2019 ◽  
Vol 11 (10) ◽  
pp. 1245 ◽  
Author(s):  
Reyadh Albarakat ◽  
Venkataraman Lakshmi
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Strong Positive Correlation ◽  
Statistical Correlations ◽  
Mesopotamian Marshes ◽  
Moderate Resolution ◽  
Satellite Sensors ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Linear Slope

The Mesopotamian marshes are a group of water bodies located in southern Iraq, in the shape of a triangle, with the cities Amarah, Nasiriyah, and Basra located at its corners. The marshes are appropriate habitats for a variety of birds and most of the commercial fisheries in the region. The normalized difference vegetation index (NDVI) has been derived using observations from various satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very-High-Resolution Radiometer (AVHRR), and Landsat over the Mesopotamian marshlands for the 17-year period between 2002 and 2018. We have chosen this time series (2002–2018) to monitor the change in vegetation of the study area since it is considered as a period of rehabilitation for the marshes (following a period when there was little to no water flowing into the marshes). Statistical analyses were performed to monitor the variability of the maximum biomass time (month of June). The results illustrated a strong positive correlation between the NDVI derived from Landsat, MODIS, and AVHRR. The statistical correlations were 0.79, 0.77, and 0.96 between Landsat and AVHRR, MODIS and AVHRR, and Landsat and MODIS, respectively. The linear slope of NDVI (Landsat, MODIS, and AVHRR) for each pixel over the period 2002–2018 displays a long-term trend of green biomass (NDVI) change in the study area, and the slope is slightly negative over most of the area. Slope values (−0.002 to −0.05) denote a slight decrease in the observed vegetation index over 17 years. The green biomass of the marshlands increased by 33.2% of the total area over 17 years. The areas of negative and positive slopes correspond to the same areas in slope map when calculated from Landsat, MODIS, and AVHRR, although they are different in spatial resolution (30 m, 1 km, and 5 km, respectively). The time series of the average NDVI (2002–2018) for three different sensors shows the highest and lowest NDVI values during the same years (for the month of June each year). The highest values were 0.19, 0.22, and 0.22 for Landsat, MODIS, and AVHRR, respectively, in 2006, and the lowest values were 0.09, 0.14, and 0.09 for Landsat, MODIS, and AVHRR, respectively, in 2003.

scholarly journals Vegetation phenology in Greenland and links to cryospheric change

2018 ◽  
Vol 59 (77) ◽  
pp. 59-68 ◽  
Author(s):  
Jeffery A. Thompson ◽  
Lora S. Koenig
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Greenland Ice Sheet ◽  
The Arctic ◽  
Growing Degree Days ◽  
Degree Days ◽  
Hydrologic Change ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cryospheric Change ◽  
Dry Out

ABSTRACTRecent greening of vegetation across the Arctic is associated with warming temperatures, hydrologic change and shorter snow-covered periods. Here we investigated trends for a subset of arctic vegetation on the island of Greenland. Vegetation in Greenland is unique due to its close proximity to the Greenland Ice Sheet and its proportionally large connection to the Greenlandic population through the hunting of grazing animals. The aim of this study was to determine whether or not longer snow-free periods (SFPs) were causing Greenlandic vegetation to dry out and become less productive. If vegetation was drying out, a subsequent aim of the study was to determine how widespread the drying was across Greenland. We utilized a 15-year time-series obtained by the MODerate Resolution Imaging Spectroradiometer (MODIS) to analyze the Greenland vegetation by deriving descriptors corresponding with the SFP, the number of cumulative growing degree-days and the time-integrated Normalized Difference Vegetation Index. While the productivity of most vegetated areas increased in response to longer growing periods, there were localized regions that exhibited signs consistent with the drying hypothesis. In these areas, vegetation productivity decreased in response to longer SFPs and more accumulated growing degree-days.

scholarly journals Spatio-temporal variability and decadal trends of snowmelt processes on Antarctic sea ice observed by satellite scatterometers

2019 ◽  
Author(s):  
Stefanie Arndt ◽  
Christian Haas
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Weddell Sea ◽  
Passive Microwave ◽  
The Arctic ◽  
Signal Frequency ◽  
Onset Date ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
Perennial Ice

Abstract. The timing and intensity of snowmelt processes on sea ice are key drivers determining the seasonal sea-ice energy and mass budgets. In the Arctic, satellite passive microwave and radar observations have revealed a trend towards an earlier snowmelt onset during the last decades, which is an important aspect of Arctic amplification and sea ice decline. Around Antarctica, snowmelt on perennial ice is weak and very different than in the Arctic, with most snow surviving the summer. Here we compile time series of snowmelt-onset dates on seasonal and perennial Antarctic sea ice from 1992 to 2014/15 using active microwave observations from European Remote Sensing Satellite (ERS-1/2), Quick Scatterometer (QSCAT) and Advanced Scatterometer (ASCAT) radar scatterometers. We define two snowmelt transition stages: A weak backscatter rise indicating the initial warming and metamorphism of the snowpack (pre-melt), followed by a rapid backscatter rise indicating the onset of thaw-freeze cycles (snowmelt). Results show large interannual variability with an average pre-melt onset date of 29 November and melt onset of 10 December, respectively, on perennial ice, without any significant trends over the study period, consistent with the small trends of Antarctic sea ice extent. There was a latitudinal gradient from early snowmelt onsets in mid-November in the northern Weddell Sea to late (end-December) or even absent snowmelt conditions in the southern Weddell Sea. We show that QSCAT Ku-band (13.4 GHz signal frequency) derived pre-melt and snowmelt onset dates are earlier by 25 and 11 days, respectively, than ERS and ASCAT C-band (5.6 GHz) derived dates. This offset has been considered when constructing the time series. Snowmelt onset dates from passive microwave observations (37 GHz) are later by 13 and 5 days than those from the scatterometers, respectively. Based on these characteristic differences between melt onset dates observed by different microwave wavelengths, we developed a conceptual model which illustrates how the evolution of seasonal snow temperature profiles affects different microwave bands with different penetration depths. These suggest that future multi-frequency active/passive microwave satellite missions could be used to resolve melt processes throughout the vertical snow column.

scholarly journals The potential of sea ice leads as a predictor for seasonal Arctic sea ice extent prediction

2018 ◽  
Author(s):  
Yuanyuan Zhang ◽  
Xiao Cheng ◽  
Jiping Liu ◽  
Fengming Hui
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Strong Relationship ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Ice Leads ◽  
Arctic Sea Ice Extent

Abstract. The Arctic sea ice extent throughout the melt season is closely associated with initial sea ice state in winter and spring. Sea ice leads are important sites of energy fluxes in the Arctic Ocean, which may play an important role in the evolution of Arctic sea ice. In this study, we examine the potential of sea ice leads as a predictor for seasonal Arctic sea ice extent forecast using a recently developed daily sea ice leads product retrieved from Moderate-Resolution Imaging Spectroradiometer. Our results show that July pan-Arctic sea ice extent can be accurately predicted from the area of sea ice leads integrated from mid-winter to late spring. However, the predictive skills for August and September pan-Arctic sea ice extent are very low. When the area of sea ice leads integrated in the Atlantic and central and west Siberian sector of the Arctic is used, it has a significantly strong relationship (high predictability) with both July and August sea ice extent in the Atlantic and central and west Siberian sector of the Arctic. Thus, the realistic representation of sea ice leads (e.g., the areal coverage) in numerical prediction systems might improve the skill of forecast in the Arctic region.

scholarly journals Generation and Evaluation of LAI and FPAR Products from Himawari-8 Advanced Himawari Imager (AHI) Data

2019 ◽  
Vol 11 (13) ◽  
pp. 1517 ◽  
Author(s):  
Yepei Chen ◽  
Kaimin Sun ◽  
Chi Chen ◽  
Ting Bai ◽  
Taejin Park ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climate Change Impacts ◽  
Field Measurements ◽  
Geostationary Satellite ◽  
Vegetation Phenology ◽  
Area Index ◽  
Spatial And Temporal Scales ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Modis Lai

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation are two of the essential biophysical variables used in most global models of climate, hydrology, biogeochemistry, and ecology. Most LAI/FPAR products are retrieved from non-geostationary satellite observations. Long revisit times and cloud/cloud shadow contamination lead to temporal and spatial gaps in such LAI/FPAR products. For more effective use in monitoring of vegetation phenology, climate change impacts, disaster trend etc., in a timely manner, it is critical to generate LAI/FPAR with less cloud/cloud shadow contamination and at higher temporal resolution—something that is feasible with geostationary satellite data. In this paper, we estimate the geostationary Himawari-8 Advanced Himawari Imager (AHI) LAI/FPAR fields by training artificial neural networks (ANNs) with Himawari-8 normalized difference vegetation index (NDVI) and moderate resolution imaging spectroradiometer (MODIS) LAI/FPAR products for each biome type. Daily cycles of the estimated AHI LAI/FPAR products indicate that these are stable at 10-min frequency during the day. Comprehensive evaluations were carried out for the different biome types at different spatial and temporal scales by utilizing the MODIS LAI/FPAR products and the available field measurements. These suggest that the generated Himawari-8 AHI LAI/FPAR fields were spatially and temporally consistent with the benchmark MODIS LAI/FPAR products. We also evaluated the AHI LAI/FPAR products for their potential to accurately monitor the vegetation phenology—the results show that AHI LAI/FPAR products closely match the phenological development captured by the MODIS products.

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