scholarly journals A double peak in the seasonality of California's photosynthesis as observed from space

2019 ◽  
Author(s):  
Alexander J. Turner ◽  
Philipp Köhler ◽  
Troy S. Magney ◽  
Christian Frankenberg ◽  
Inez Fung ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Signal To Noise Ratio ◽  
Spatial Scales ◽  
Vegetation Indices ◽  
Temporal Patterns ◽  
Eof Analysis ◽  
Double Peak ◽  
Evergreen Forests ◽  
Spatio Temporal ◽  
Low Sensitivity

Abstract. Solar-Induced chlorophyll Fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and gross primary productivity (GPP). The recently launched TROPOspheric Monitoring Instrument (TROPOMI) features the required spectral resolution and signal-to-noise ratio to retrieve SIF from space. Here we present an oversampling and downscaling method to obtain 500-m spatial resolution SIF over California. We report daily values based on a 14-day window. TROPOMI SIF data show a strong correspondence with daily GPP estimates at AmeriFlux sites across multiple ecosystems in California. We find a linear relationship between SIF and GPP that is largely invariant across ecosystems with an intercept that is not significantly different from zero. Measurements of SIF from TROPOMI agree with MODIS vegetation indices (NDVI, EVI, and NIRv) at annual timescales but indicate different temporal dynamics at monthly and daily timescales. TROPOMI SIF data show a double peak in the seasonality of photosynthesis, a feature that is not present in the MODIS vegetation indices. The different seasonality in the vegetation indices may be due to a clear-sky bias in the vegetation indices, whereas SIF has a low sensitivity to clouds and can detect the downregulation of photosynthesis even when plants appear green. We further decompose the spatio-temporal patterns in the SIF data based on land cover. The double peak in the seasonality of California's photosynthesis is due to two processes that are out of phase: grasses, chaparral, and oak savanna ecosystems show an April maximum while evergreen forests peak in June. An empirical orthogonal function (EOF) analysis corroborates the phase offset and spatial patterns driving the double peak. The EOF analysis further indicates that two spatio-temporal patterns explain 84 % of the variability in the SIF data. Results shown here are promising for obtaining global GPP at sub-kilometer spatial scales and identifying the processes driving carbon uptake.

scholarly journals A double peak in the seasonality of California's photosynthesis as observed from space

2020 ◽  
Vol 17 (2) ◽  
pp. 405-422 ◽  
Author(s):  
Alexander J. Turner ◽  
Philipp Köhler ◽  
Troy S. Magney ◽  
Christian Frankenberg ◽  
Inez Fung ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Temporal Dynamics ◽  
Spatial Scales ◽  
Vegetation Indices ◽  
Spatiotemporal Patterns ◽  
Eof Analysis ◽  
Near Infrared Reflectance ◽  
Double Peak

Abstract. Solar-induced chlorophyll fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and gross primary productivity (GPP). The recently launched TROPOspheric Monitoring Instrument (TROPOMI) features the required spectral resolution and signal-to-noise ratio to retrieve SIF from space. Here, we present a downscaling method to obtain 500 m spatial resolution SIF over California. We report daily values based on a 14 d window. TROPOMI SIF data show a strong correspondence with daily GPP estimates at AmeriFlux sites across multiple ecosystems in California. We find a linear relationship between SIF and GPP that is largely invariant across ecosystems with an intercept that is not significantly different from zero. Measurements of SIF from TROPOMI agree with MODerate Resolution Imaging Spectroradiometer (MODIS) vegetation indices – the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation index (NIRv) – at annual timescales but indicate different temporal dynamics at monthly and daily timescales. TROPOMI SIF data show a double peak in the seasonality of photosynthesis, a feature that is not present in the MODIS vegetation indices. The different seasonality in the vegetation indices may be due to a clear-sky bias in the vegetation indices, whereas previous work has shown SIF to have a low sensitivity to clouds and to detect the downregulation of photosynthesis even when plants appear green. We further decompose the spatiotemporal patterns in the SIF data based on land cover. The double peak in the seasonality of California's photosynthesis is due to two processes that are out of phase: grasses, chaparral, and oak savanna ecosystems show an April maximum, while evergreen forests peak in June. An empirical orthogonal function (EOF) analysis corroborates the phase offset and spatial patterns driving the double peak. The EOF analysis further indicates that two spatiotemporal patterns explain 84 % of the variability in the SIF data. Results shown here are promising for obtaining global GPP at sub-kilometer spatial scales and identifying the processes driving carbon uptake.

scholarly journals Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China

2020 ◽  
Vol 17 (7) ◽  
pp. 2563 ◽  
Author(s):  
Wentao Yang ◽  
Min Deng ◽  
Chaokui Li ◽  
Jincai Huang
Keyword(s):  
Temporal Dynamics ◽  
Spatial Interaction ◽  
Temporal Trends ◽  
Temporal Patterns ◽  
Temporal Analysis ◽  
Hubei Province ◽  
Incidence Rates ◽  
County Level ◽  
Spatial Clusters ◽  
Spatio Temporal

Understanding the spatio-temporal characteristics or patterns of the 2019 novel coronavirus (2019-nCoV) epidemic is critical in effectively preventing and controlling this epidemic. However, no research analyzed the spatial dependency and temporal dynamics of 2019-nCoV. Consequently, this research aims to detect the spatio-temporal patterns of the 2019-nCoV epidemic using spatio-temporal analysis methods at the county level in Hubei province. The Mann–Kendall and Pettitt methods were used to identify the temporal trends and abrupt changes in the time series of daily new confirmed cases, respectively. The local Moran’s I index was applied to uncover the spatial patterns of the incidence rate, including spatial clusters and outliers. On the basis of the data from January 26 to February 11, 2020, we found that there were 11 areas with different types of temporal patterns of daily new confirmed cases. The pattern characterized by an increasing trend and abrupt change is mainly attributed to the improvement in the ability to diagnose the disease. Spatial clusters with high incidence rates during the period were concentrated in Wuhan Metropolitan Area due to the high intensity of spatial interaction of the population. Therefore, enhancing the ability to diagnose the disease and controlling the movement of the population can be confirmed as effective measures to prevent and control the regional outbreak of the epidemic.

scholarly journals Assessing spatio-temporal patterns of human-leopard interactions based on media reports in northwestern India

2021 ◽  
Vol 13 (6) ◽  
pp. 18453-18478
Author(s):  
Kaushal Chauhan ◽  
Arjun Srivathsa ◽  
Vidya Athreya
Keyword(s):  
Spatial Scales ◽  
Temporal Patterns ◽  
Media Content ◽  
Eastern Region ◽  
Large Carnivores ◽  
News Reports ◽  
Northwestern India ◽  
Human Use ◽  
Spatio Temporal ◽  
Media Reports

Large carnivores in human-use areas make for sensational print media content.  We used media reports to examine human-leopard interactions in Rajasthan, India.  We extracted news reports on leopard-related incidents from January 2016 to December 2018.  Incidents (n= 338) were categorized, mapped, and analysed to understand their nature and extent.  We found leopard-related news from 26 of 33 districts; a majority of these were in the eastern region of the State.  Most of the reported interactions appeared to be non-negative, despite losses to both leopards and people.  Our results provide a synthesis of spatio-temporal patterns of leopard-related incidents, which could help wildlife managers in better addressing negative interactions.  The study also demonstrates how news reports could be useful for examining human-wildlife interactions across large spatial scales.

scholarly journals Large scale spatio-temporal patterns of road development in the Amazon rainforest

2013 ◽  
Vol 41 (3) ◽  
pp. 253-264 ◽  
Author(s):  
SADIA E. AHMED ◽  
ROBERT M. EWERS ◽  
MATTHEW J. SMITH
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Temporal Patterns ◽  
Biodiversity Loss ◽  
Road Density ◽  
Road Development ◽  
Spatio Temporal ◽  
Dominant Direction ◽  
Dynamics Of Populations

SUMMARYThere is burgeoning interest in predicting road development because of the wide ranging important socioeconomic and environmental issues that roads present, including the close links between road development, deforestation and biodiversity loss. This is especially the case in developing nations, which are high in natural resources, where road development is rapid and often not centrally managed. Characterization of large scale spatio-temporal patterns in road network development has been greatly overlooked to date. This paper examines the spatio-temporal dynamics of road density across the Brazilian Amazon and assesses the relative contributions of local versus neighbourhood effects for temporal changes in road density at regional scales. To achieve this, a combination of statistical analyses and model-data fusion techniques inspired by studies of spatio-temporal dynamics of populations in ecology and epidemiology were used. The emergent development may be approximated by local growth that is logistic through time and directional dispersal. The current rates and dominant direction of development may be inferred, by assuming that roads develop at a rate of 55 km per year. Large areas of the Amazon will be subject to extensive anthropogenic change should the observed patterns of road development continue.

scholarly journals Ultrafast high-harmonic nanoscopy of magnetization dynamics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sergey Zayko ◽  
Ofer Kfir ◽  
Michael Heigl ◽  
Michael Lohmann ◽  
Murat Sivis ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Optical Switching ◽  
Temporal Dynamics ◽  
Spatial Scales ◽  
High Harmonic ◽  
Real Space ◽  
Local Magnetization ◽  
Magnetization Dynamics ◽  
Wide Range ◽  
Spatio Temporal

AbstractLight-induced magnetization changes, such as all-optical switching, skyrmion nucleation, and intersite spin transfer, unfold on temporal and spatial scales down to femtoseconds and nanometers, respectively. Pump-probe spectroscopy and diffraction studies indicate that spatio-temporal dynamics may drastically affect the non-equilibrium magnetic evolution. Yet, direct real-space magnetic imaging on the relevant timescales has remained challenging. Here, we demonstrate ultrafast high-harmonic nanoscopy employing circularly polarized high-harmonic radiation for real-space imaging of femtosecond magnetization dynamics. We map quenched magnetic domains and localized spin structures in Co/Pd multilayers with a sub-wavelength spatial resolution down to 16 nm, and strobosocopically trace the local magnetization dynamics with 40 fs temporal resolution. Our compact experimental setup demonstrates the highest spatio-temporal resolution of magneto-optical imaging to date. Facilitating ultrafast imaging with high sensitivity to chiral and linear dichroism, we envisage a wide range of applications spanning magnetism, phase transitions, and carrier dynamics.

Investigating spatio-temporal variability of soil moisture in a small farmland: from point to catchment scale

2020 ◽  
Author(s):  
Tailin Li ◽  
Nina Noreika ◽  
Jakub Jeřábek ◽  
Josef Krasa ◽  
David Zumr ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Temporal Variability ◽  
Temporal Dynamics ◽  
Anthropogenic Impacts ◽  
Temporal Patterns ◽  
Point Scale ◽  
Catchment Scale ◽  
Climate Conditions ◽  
Spatio Temporal ◽  
Hillslope Scale

<p>Many studies in recent years have focused on spatio-temporal variability of soil moisture and its value in hydrology and agriculture. The highly dynamic of soil moisture is controlled by soil properties, topography, landuse, climate conditions, and anthropogenic impacts. However, the understanding of soil moisture dynamics is limited by measurement restrictions. The aim of this study is to analyse spatio-temporal patterns of soil moisture using various soil moisture monitoring techniques and numerical modelling approaches that have been developed for application at differing scales at the Nucice experimental catchment (0.53 km<sup>2</sup>), which is located just outside of Prague, the Czech Republic.</p><p>The experimental catchment is dominated by agricultural activities. To identify spatio-temporal patterns in the catchment, we have implemented shallow soil moisture measurements at point-scale, hillslope-scale, and catchment-scale. We have deployed FDR (frequency domain reflectometry) sensors at different depths for point-scale measurements. The monitoring of hillslope-scale and catchment-scale have been mostly accomplished by field surveys with HydroSense II sensors. Subsequently, we have applied geostatistical analyses (Kriging and inverse distance weighting interpolation) for the measured soil moisture data to discover spatial patterns in soil moisture across the catchment. Besides, numerical models Hydrus (1D and 2D), MIKE-SHE, and SWAT have been set up at this study site. These models have been calibrated with event-based data and soil moisture measurements, which present a better image of the hydrological processes and spatio-temporal dynamics of soil moisture at various scales. The modelling outcomes have not only fit agreeably with the observed discharge and the temporal dynamics of soil moisture but have also identified wet zones along hillslopes.</p><p>Further research will intensify the soil moisture monitoring at the catchment-scale by using remote sensing and Comsic-ray soil moisture probes. Also, anthropogenic impacts (e.g. influence of wheel track) should be considered in the modelling approach. Ultimately, we should be able to understand and predict the spatio-temporal dynamics of soil moisture in small scale agricultural catchments under different climate conditions.</p><p>This research has been supported by project H2020 No. 773903 SHui, focused on water scarcity in European and Chinese cropping systems.</p>

scholarly journals The spatio-temporal distribution of COVID-19 infection in England between January and June 2020

2021 ◽  
Vol 149 ◽  
Author(s):  
Richard Elson ◽  
Tilman M. Davies ◽  
Iain R. Lake ◽  
Roberto Vivancos ◽  
Paula B. Blomquist ◽  
...  
Keyword(s):  
Urban Areas ◽  
Temporal Dynamics ◽  
Disease Risk ◽  
Spatial Scales ◽  
Temporal Distribution ◽  
Health Resources ◽  
Epidemiological Studies ◽  
Accurate Identification ◽  
Multiple Introductions ◽  
Spatio Temporal

Abstract The spatio-temporal dynamics of an outbreak provide important insights to help direct public health resources intended to control transmission. They also provide a focus for detailed epidemiological studies and allow the timing and impact of interventions to be assessed. A common approach is to aggregate case data to administrative regions. Whilst providing a good visual impression of change over space, this method masks spatial variation and assumes that disease risk is constant across space. Risk factors for COVID-19 (e.g. population density, deprivation and ethnicity) vary from place to place across England so it follows that risk will also vary spatially. Kernel density estimation compares the spatial distribution of cases relative to the underlying population, unfettered by arbitrary geographical boundaries, to produce a continuous estimate of spatially varying risk. Using test results from healthcare settings in England (Pillar 1 of the UK Government testing strategy) and freely available methods and software, we estimated the spatial and spatio-temporal risk of COVID-19 infection across England for the first 6 months of 2020. Widespread transmission was underway when partial lockdown measures were introduced on 23 March 2020 and the greatest risk erred towards large urban areas. The rapid growth phase of the outbreak coincided with multiple introductions to England from the European mainland. The spatio-temporal risk was highly labile throughout. In terms of controlling transmission, the most important practical application of our results is the accurate identification of areas within regions that may require tailored intervention strategies. We recommend that this approach is absorbed into routine surveillance outputs in England. Further risk characterisation using widespread community testing (Pillar 2) data is needed as is the increased use of predictive spatial models at fine spatial scales.

scholarly journals Multi-scale event synchronization analysis for unravelling climate processes: a wavelet-based approach

2017 ◽  
Vol 24 (4) ◽  
pp. 599-611 ◽  
Author(s):  
Ankit Agarwal ◽  
Norbert Marwan ◽  
Maheswaran Rathinasamy ◽  
Bruno Merz ◽  
Jürgen Kurths
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Real World ◽  
Temporal Dynamics ◽  
Multiple Scales ◽  
Temporal Patterns ◽  
Multi Scale ◽  
Main Drawback ◽  
Spatio Temporal ◽  
Linear Interactions

Abstract. The temporal dynamics of climate processes are spread across different timescales and, as such, the study of these processes at only one selected timescale might not reveal the complete mechanisms and interactions within and between the (sub-)processes. To capture the non-linear interactions between climatic events, the method of event synchronization has found increasing attention recently. The main drawback with the present estimation of event synchronization is its restriction to analysing the time series at one reference timescale only. The study of event synchronization at multiple scales would be of great interest to comprehend the dynamics of the investigated climate processes. In this paper, the wavelet-based multi-scale event synchronization (MSES) method is proposed by combining the wavelet transform and event synchronization. Wavelets are used extensively to comprehend multi-scale processes and the dynamics of processes across various timescales. The proposed method allows the study of spatio-temporal patterns across different timescales. The method is tested on synthetic and real-world time series in order to check its replicability and applicability. The results indicate that MSES is able to capture relationships that exist between processes at different timescales.

scholarly journals Dynamic spatio-temporal patterns of metapopulation occupancy in patchy habitats

2021 ◽  
Vol 8 (1) ◽  
pp. 201309
Author(s):  
L. E. Bertassello ◽  
E. Bertuzzo ◽  
G. Botter ◽  
J. W. Jawitz ◽  
A. F. Aubeneau ◽  
...  
Keyword(s):  
Habitat Suitability ◽  
Biological Diversity ◽  
Temporal Dynamics ◽  
Extinction Risk ◽  
Temporal Patterns ◽  
Suitable Habitat ◽  
Patch Occupancy ◽  
Patchy Habitats ◽  
Dynamic Stochastic ◽  
Spatio Temporal

Spatio-temporal dynamics in habitat suitability and connectivity among mosaics of heterogeneous wetlands are critical for biological diversity and species persistence in aquatic patchy landscapes. Despite the recognized importance of stochastic hydroclimatic forcing in driving wetlandscape hydrological dynamics, linking such effects to emergent dynamics of metapopulation poses significant challenges. To fill this gap, we propose here a dynamic stochastic patch occupancy model (SPOM), which links parsimonious hydrological and ecological models to simulate spatio-temporal patterns in species occupancy in wetlandscapes. Our work aims to place ecological studies of patchy habitats into a proper hydrologic and climatic framework to improve the knowledge about metapopulation shifts in response to climate-driven changes in wetlandscapes. We applied the dynamic version of the SPOM (D-SPOM) framework in two wetlandscapes in the US with contrasting landscape and climate properties. Our results illustrate that explicit consideration of the temporal dimension proposed in the D-SPOM is important to interpret local- and landscape-scale patterns of habitat suitability and metapopulation occupancy. Our analyses show that spatio-temporal dynamics of patch suitability and accessibility, driven by the stochasticity in hydroclimatic forcing, influence metapopulation occupancy and the topological metrics of the emergent wetlandscape dispersal network. D-SPOM simulations also reveal that the extinction risk in dynamic wetlandscapes is exacerbated by extended dry periods when suitable habitat decreases, hence limiting successful patch colonization and exacerbating metapopulation extinction risks. The proposed framework is not restricted only to wetland studies but could also be applied to examine metapopulation dynamics in other types of patchy habitats subjected to stochastic external disturbances.

scholarly journals Integrating spatial and temporal approaches to understanding species richness

2010 ◽  
Vol 365 (1558) ◽  
pp. 3633-3643 ◽  
Author(s):  
Ethan P. White ◽  
S. K. Morgan Ernest ◽  
Peter B. Adler ◽  
Allen H. Hurlbert ◽  
S. Kathleen Lyons
Keyword(s):  
Species Richness ◽  
Temporal Pattern ◽  
Temporal Dynamics ◽  
Rapid Development ◽  
Temporal Patterns ◽  
Spatial And Temporal Patterns ◽  
Future Directions ◽  
Spatial Gradients ◽  
Spatio Temporal ◽  
Richness Patterns

Understanding species richness patterns represents one of the most fundamental problems in ecology. Most research in this area has focused on spatial gradients of species richness, with a smaller area of emphasis dedicated to understanding the temporal dynamics of richness. However, few attempts have been made to understand the linkages between the spatial and temporal patterns related to richness. Here, we argue that spatial and temporal richness patterns and the processes that drive them are inherently linked, and that our understanding of richness will be substantially improved by considering them simultaneously. The species–time–area relationship provides a case in point: successful description of the empirical spatio-temporal pattern led to a rapid development and testing of new theories. Other areas of research on species richness could also benefit from an explicitly spatio-temporal approach, and we suggest future directions for understanding the processes common to these two traditionally isolated fields of research.

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