scholarly journals A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales

2021 ◽  
Author(s):  
Frans-Jan W. Parmentier ◽  
Lennart Nilsen ◽  
Hans Tømmervik ◽  
Elisabeth J. Cooper
Keyword(s):  
Plant Communities ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Lapse ◽  
High Arctic ◽  
The Arctic ◽  
Vegetation Phenology ◽  
Additional Time ◽  
Svalbard Archipelago ◽  
Near Surface

Abstract. Near-surface remote sensing techniques are essential monitoring tools to provide spatial and temporal resolutions beyond the capabilities of orbital methods. This high level of detail is especially helpful to monitor specific plant communities and to accurately time the phenological stages of vegetation – which satellites can miss by days or weeks in frequently clouded areas such as the Arctic. In this paper, we describe a measurement network that is distributed across varying plant communities in the high Arctic valley of Adventdalen on the Svalbard archipelago, with the aim to monitor vegetation phenology. The network consists of ten racks equipped with sensors that measure NDVI (Normalized Difference Vegetation Index), soil temperature and moisture, as well as time-lapse RGB cameras. Three additional time-lapse cameras are placed on nearby mountain tops to provide an overview of the valley. The vegetation index GCC (Green Chromatic Channel) was derived from these RGB photos, which has similar applications as NDVI but at a fraction of the cost of NDVI imaging sensors. To create a robust timeseries for GCC, each set of photos was adjusted for unwanted movement of the camera with a stabilizing algorithm that enhances the spatial precision of these measurements. This code is available at https://doi.org/10.5281/zenodo.4554937 (Parmentier, 2021) and can be applied to time series obtained with other time-lapse cameras. This paper presents an overview of the data collection and processing, and an overview of the dataset which is available at https://doi.org/10.21343/kbpq-xb91 (Nilsen et al. 2021). In addition, we provide some examples of how this data can be used to monitoring different vegetation communities in the landscape.

scholarly journals A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales

2021 ◽  
Vol 13 (7) ◽  
pp. 3593-3606
Author(s):  
Frans-Jan W. Parmentier ◽  
Lennart Nilsen ◽  
Hans Tømmervik ◽  
Elisabeth J. Cooper
Keyword(s):  
Time Series ◽  
Plant Communities ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Lapse ◽  
The Arctic ◽  
Vegetation Phenology ◽  
Additional Time ◽  
Svalbard Archipelago ◽  
Near Surface

Abstract. Near-surface remote sensing techniques are essential monitoring tools to provide spatial and temporal resolutions beyond the capabilities of orbital methods. This high level of detail is especially helpful to monitor specific plant communities and to accurately time the phenological stages of vegetation – which satellites can miss by days or weeks in frequently clouded areas such as the Arctic. In this paper, we describe a measurement network that is distributed across varying plant communities in the high Arctic valley of Adventdalen on the Svalbard archipelago with the aim of monitoring vegetation phenology. The network consists of 10 racks equipped with sensors that measure NDVI (normalized difference vegetation index), soil temperature, and moisture as well as time-lapse RGB cameras (i.e. phenocams). Three additional time-lapse cameras are placed on nearby mountains to provide an overview of the valley. We derived the vegetation index GCC (green chromatic channel) from these RGB photos, which has similar applications as NDVI but at a fraction of the cost of NDVI imaging sensors. To create a robust time series for GCC, each set of photos was adjusted for unwanted movement of the camera with a stabilizing algorithm that enhances the spatial precision of these measurements. This code is available at https://doi.org/10.5281/zenodo.4554937 (Parmentier, 2021) and can be applied to time series obtained with other time-lapse cameras. This paper presents an overview of the data collection and processing and an overview of the dataset that is available at https://doi.org/10.21343/kbpq-xb91 (Nilsen et al., 2021). In addition, we provide some examples of how these data can be used to monitor different vegetation communities in the landscape.

scholarly journals MODIS and PROBA-V NDVI Products Differ when Compared with Observations from Phenological Towers at Four Tropical Dry Forests in the Americas

2019 ◽  
Vol 11 (19) ◽  
pp. 2316 ◽  
Author(s):  
J. Antonio Guzmán Q. ◽  
G. Arturo Sanchez-Azofeifa ◽  
Mário M. Espírito-Santo
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Southern Oscillation ◽  
Satellite Observations ◽  
Tropical Dry Forests ◽  
Vegetation Phenology ◽  
Wet Season ◽  
Dry Forests ◽  
Near Surface ◽  
Surface Observations

The Normalized Difference Vegetation Index (NDVI) is widely used to monitor vegetation phenology and productivity around the world. Over the last few decades, phenology monitoring at large scales has been possible due to the information and metrics derived from satellite sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Project for On-Board Autonomy–Vegetation (PROBA-V). However, due to their temporal and spatial resolution, adequate ground comparison is lacking. In this paper, we analyze how NDVI products from MODIS (Aqua and Terra) and PROBA-V predict vegetation phenology when compared with near-surface observations. We conduct this comparison at four tropical dry forests (TDFs) in the Americas. We undertake this study by comparing the following: (i) Dissimilarities of the standardized NDVI (NDVIS) using dynamic time warping, (ii) the differences of daily NDVIS between seasons and ENSO months using generalized linear models, and (iii) phenometrics derived from NDVI time series. Overall, our results suggest that NDVIS from satellite observations present DTW distances (dissimilarities) between 2.98 and 46.57 (18.91 ± 12.31) when compared with near-surface observations. Furthermore, NDVIS comparisons reveal that overall differences between satellite and near-surface observations are close to zero, but this tends to differ between seasons or when El Nino Southern Oscillation (ENSO) is present. Phenometrics comparisons show that metrics derived from satellite observations such as green-up, maturity, and start and end of the wet season strongly correlate with those from near-surface observations. In contrast, phenometrics that describe the day of the highest or lowest NDVI tend to be inconsistent with those from near-surface observations. All findings were observed independently of the NDVI source. Our results suggest that satellite-based NDVI products tend to be inconsistent descriptors of vegetation events on tropical deciduous forests in comparison with near-surface observations. These results reinforce the idea that satellite-based NDVI products should be used and interpreted with great caution and only in ecosystems with well-established knowledge of their vegetation phenology.

scholarly journals Geophysical imaging of permafrost in the SW Svalbard – the result of two high arctic expeditions to Spitsbergen

2020 ◽  
Author(s):  
Mariusz Majdanski ◽  
Artur Marciniak ◽  
Bartosz Owoc ◽  
Wojciech Dobiński ◽  
Tomasz Wawrzyniak ◽  
...  
Keyword(s):  
Surface Waves ◽  
Active Layer ◽  
High Arctic ◽  
The Arctic ◽  
Seismic Profiles ◽  
Frozen Ground ◽  
Near Surface ◽  
Seismic Processing ◽  
Reflection Seismic ◽  
Sea Coast

<p>The Arctic regions are the place of the fastest observed climate change. One of the indicators of such evolution are changes occurring in the glaciers and the subsurface in the permafrost. The active layer of the permafrost as the shallowest one is well measured by multiple geophysical techniques and in-situ measurements.</p><p>Two high arctic expeditions have been organized to use seismic methods to recognize the shape of the permafrost in two seasons: with the unfrozen ground (October 2017) and frozen ground (April 2018). Two seismic profiles have been designed to visualize the shape of permafrost between the sea coast and the slope of the mountain, and at the front of a retreating glacier. For measurements, a stand-alone seismic stations has been used with accelerated weight drop with in-house modifications and timing system. Seismic profiles were acquired in a time-lapse manner and were supported with GPR and ERT measurements, and continuous temperature monitoring in shallow boreholes.</p><p>Joint interpretation of seismic and auxiliary data using Multichannel analysis of surface waves, First arrival travel-time tomography and Reflection imaging show clear seasonal changes affecting the active layer where P-wave velocities are changing from 3500 to 5200 m/s. This confirms the laboratory measurements showing doubling the seismic velocity of water-filled high-porosity rocks when frozen. The same laboratory study shows significant (>10%) increase of velocity in frozen low porosity rocks, that should be easily visible in seismic.</p><p>In the reflection seismic processing, the most critical part was a detailed front mute to eliminate refracted arrivals spoiling wide-angle near-surface reflections. Those long offset refractions were however used to estimate near-surface velocities further used in reflection processing. In the reflection seismic image, a horizontal reflection was traced at the depth of 120 m at the sea coast deepening to the depth of 300 m near the mountain.</p><p>Additionally, an optimal set of seismic parameters has been established, clearly showing a significantly higher signal to noise ratio in case of frozen ground conditions even with the snow cover. Moreover, logistics in the frozen conditions are much easier and a lack of surface waves recorded in the snow buried geophones makes the seismic processing simpler.</p><p>Acknowledgements               </p><p>This research was funded by the National Science Centre, Poland (NCN) Grant UMO-2015/21/B/ST10/02509.</p>

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

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.

scholarly journals A trajectory analysis of atmospheric transport of black carbon aerosols to Canadian High Arctic in winter and spring (1990–2005)

2010 ◽  
Vol 10 (2) ◽  
pp. 2221-2244 ◽  
Author(s):  
L. Huang ◽  
S. L. Gong ◽  
S. Sharma ◽  
D. Lavoué ◽  
C. Q. Jia
Keyword(s):  
Regression Model ◽  
Black Carbon ◽  
North American ◽  
Emission Intensity ◽  
Atmospheric Transport ◽  
High Arctic ◽  
The Arctic ◽  
Canadian High Arctic ◽  
Near Surface ◽  
Annual Variations

Abstract. Black carbon (BC) particles accumulated in the Arctic troposphere and deposited over snow have significant effects on radiative forcing of the Arctic regional climate. Applying cluster analysis technique on 10-day backward trajectories, transport pathways affecting Alert (82.5° N, 62.5° W), Nunavut in Canada are identified in this work, along with the associated transport frequency. Based on the atmospheric transport frequency and the estimated BC emission intensity from surrounding regions, a linear regression model is constructed to investigate the inter-annual variations of BC observed at Alert in January and April, representative of winter and spring respectively, between 1990 and 2005. Strong correlations are found between BC concentrations predicted with the regression model and measured at Alert for both seasons (R2 equals 0.77 and 0.81 for winter and spring, respectively). Results imply that atmospheric transport and BC emission are the major contributors to the inter-annual variations in BC concentrations observed at Alert in the cold seasons for the 16-year period. Based on the regression model the relative contributions of regional BC emissions affecting Alert are attributed to the Eurasian sector, composed of the European Union and the former USSR, and the North American sector. Considering both seasons, the model suggests that Eurasia is the major contributor to the near-surface BC levels at the Canadian High Arctic site with an average contribution of over 85% during the 16-year period. In winter, the atmospheric transport of BC aerosols from Eurasia is found to be even more predominant with a multi-year average of 94%. The model estimates smaller contribution from the Eurasian sector in spring (70%) than that in winter. It is also found that the change in Eurasian contributions depends mainly on the reduction of emission intensity, while the changes in both emission and atmospheric transport contributed to the inter-annual variation of North American contributions.

scholarly journals Satellites reveal an increase in gross primary production in a greenlandic high arctic fen 1992–2008

2010 ◽  
Vol 7 (1) ◽  
pp. 1101-1129 ◽  
Author(s):  
T. Tagesson ◽  
M. Mastepanov ◽  
M. P. Tamstorf ◽  
L. Eklundh ◽  
P. Schubert ◽  
...  
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Gross Primary Production ◽  
Field Measurements ◽  
High Arctic ◽  
Research Area ◽  
Strong Increase ◽  
Co2 Uptake ◽  
Flux Measurements

Abstract. Arctic wetlands play a key role in the terrestrial carbon cycle. Recent studies have shown a greening trend and indicated an increase in CO2 uptake in boreal and sub- to low-arctic areas. Our aim was to combine satellite-based normalized difference vegetation index (NDVI) with ground-based flux measurements of CO2 to investigate a possible greening trend and potential changes in gross primary production (GPP) between 1992 and 2008 in a high arctic fen area. The study took place in Rylekaerene in the Zackenberg Research Area (74°28' N 20°34' W), located in the National park of North Eastern Greenland. We estimated the light use efficiency (ε) for the dominant vegetation types from field measured fractions of photosynthetic active radiation (FAPAR) and ground-based flux measurements of GPP. Measured FAPAR were correlated to satellite-based NDVI. The FAPAR-NDVI relationship in combination with ε was applied to satellite data to model GPP 1992–2008. The model was evaluated against field measured GPP. The model was a useful tool for up-scaling GPP and all basic requirements for the model were well met, e.g., FAPAR was well correlated to NDVI and modeled GPP was well correlated to field measurements. The studied high arctic fen area has experienced a strong increase in GPP between 1992 and 2008. The area has during this period also experienced a substantial increase in local air temperature. Consequently, the observed greening trend is most likely due to ongoing climatic change possibly in combination with CO2 fertilization, due to increasing atmospheric concentrations of CO2.

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 Contrasting molecular diversity and demography patterns in two intertidal amphipod crustaceans reflect Atlantification of High Arctic

2019 ◽  
Vol 166 (12) ◽  
Author(s):  
Michał Grabowski ◽  
Aleksandra Jabłońska ◽  
Agata Weydmann-Zwolicka ◽  
Mikhail Gantsevich ◽  
Petr Strelkov ◽  
...  
Keyword(s):  
Operational Taxonomic Unit ◽  
Molecular Study ◽  
High Arctic ◽  
The Arctic ◽  
Amphipod Species ◽  
Distribution Analysis ◽  
Svalbard Archipelago ◽  
Postglacial Expansion ◽  
Northern Atlantic ◽  
The Last Glacial

Abstract The distribution of two common intertidal amphipod species Gammarus oceanicus and Gammarus setosus was studied along the coast of Svalbard Archipelago. Genetic analysis showed geographical homogeneity of G. oceanicus with only one molecular operational taxonomic unit (MOTU) and much higher diversification of G. setosus (5 MOTUs) in the studied area. Only two MOTUs of G. setosus are widespread along the whole studied Svalbard coastline, whereas the remaining three MOTUs are present mainly along the northern and eastern parts of archipelago’s largest island, Spitsbergen. Distribution analysis indicates that the demographic and spatial expansion of G. oceanicus in the northern Atlantic has started already during the Last Glacial Maximum (LGM, ca. 18 ka), while G. setosus seems to be a long-persistent inhabitant of the Arctic, possibly even through the LGM, with slower distribution dynamics. Combining the results of our molecular study with previous field observations and the knowledge upon the direction of ocean currents around the Svalbard Archipelago, it can be assumed that G. oceanicus is a typical boreal Atlantic species that is still continuing its postglacial expansion northwards. In recent decades it colonized High Arctic due to the climate warming and has partly displaced G. setosus, that used to be the only common gammarid of the Svalbard intertidal zone.

scholarly journals Fire Recurrence and Normalized Difference Vegetation Index (NDVI) Dynamics in Brazilian Savanna

Fire ◽  
2018 ◽  
Vol 2 (1) ◽  
pp. 1 ◽  
Author(s):  
Níckolas Santana
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Trend Analysis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Management Policy ◽  
Brazilian Savanna ◽  
Vegetation Phenology ◽  
Modis Ndvi ◽  
Fire Recurrence

Fire is one of the main modeling agents of savanna ecosystems, affecting their distribution, physiognomy and species diversity. Changes in the natural fire regime on savannas cause disturbances in the structural characteristics of vegetation. Theses disturbances can be effectively monitored by time series of remote sensing data in different terrestrial ecosystems such as savannas. This study used trend analysis in NDVI (Normalized Difference Vegetation Index)–MODIS (Moderate Resolution Imaging Spectroradiometer) time series to evaluate the influence of different fire recurrences on vegetation phenology of the Brazilian savanna in the period from 2001 to 2016. The trend analysis indicated several factors responsible for changes in vegetation: (a) The absence of fire in savanna phytophysiognomies causes a constant increase in MODIS–NDVI, ranging from 0.001 to 0.002 per year, the moderate presence of fire in these areas does not cause significant changes, while the high recurrence results in decreases of MODIS–NDVI, ranging from −0.002 to −0.008 per year; (b) Forest areas showed a high decrease in NDVI, reaching up to −0.009 MODIS–NDVI per year, but not related to fire recurrence, indicating the high degradation of these phytophysiognomies; (c) Changes in vegetation are highly connected to the protection status of the area, such as areas of integral protection or sustainable use, and consequently their conservation status. Areas with greater vegetation conservation had more than 70% of positive changes in pixels with significant tendencies. Absence or presence of fire are the main agents of vegetation change in areas with lower anthropic influence. These results reinforce the need for a suitable fire management policy for the different types of Cerrado phytophysiognomies, in addition to highlighting the efficiency of remote sensing time series for evaluation of vegetation phenology.

scholarly journals The Use of Satellite Information (MODIS/Aqua) for Phenological and Classification Analysis of Plant Communities

Forests ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 561 ◽  
Author(s):  
Yulia Ivanova ◽  
Anton Kovalev ◽  
Oleg Yakubailik ◽  
Vlad Soukhovolsky
Keyword(s):  
Time Series ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Plant Communities ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Weather Conditions ◽  
Linear Discriminant ◽  
Canonical Correlations

Vegetation indices derived from remote sensing measurements are commonly used to describe and monitor vegetation. However, the same plant community can have a different NDVI (normalized difference vegetation index) depending on weather conditions, and this complicates classification of plant communities. The present study develops methods of classifying the types of plant communities based on long-term NDVI data (MODIS/Aqua). The number of variables is reduced by introducing two integrated parameters of the NDVI seasonal series, facilitating classification of the meadow, steppe, and forest plant communities in Siberia using linear discriminant analysis. The quality of classification conducted by using the markers characterizing NDVI dynamics during 2003–2017 varies between 94% (forest and steppe) and 68% (meadow and forest). In addition to determining phenological markers, canonical correlations have been calculated between the time series of the proposed markers and the time series of monthly average air temperatures. Based on this, each pixel with a definite plant composition can be characterized by only four values of canonical correlation coefficients over the entire period analyzed. By using canonical correlations between NDVI and weather parameters and employing linear discriminant analysis, one can obtain a highly accurate classification of the study plant communities.

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