Spatio-temporal analysis of wildfire ignitions in the St Johns River Water Management District, Florida

2006 ◽  
Vol 15 (1) ◽  
pp. 87 ◽  
Author(s):  
Marc G. Genton ◽  
David T. Butry ◽  
Marcia L. Gumpertz ◽  
Jeffrey P. Prestemon
Keyword(s):  
Water Management ◽  
River Water ◽  
Spatial Scales ◽  
Temporal Structure ◽  
Temporal Analysis ◽  
Irregular Distribution ◽  
Point Patterns ◽  
North Eastern ◽  
Spatio Temporal ◽  
Fire Ignitions

We analyse the spatio-temporal structure of wildfire ignitions in the St Johns River Water Management District in north-eastern Florida. We show, using tools to analyse point patterns (e.g. the L-function), that wildfire events occur in clusters. Clustering of these events correlates with irregular distribution of fire ignitions, including lightning and human sources, and fuels on the landscape. In addition, we define a relative clustering index that summarizes the amount of clustering over various spatial scales. We carry our analysis in three steps: purely temporal, purely spatial, and spatio-temporal. Our results show that arson and lightning are the leading causes of wildfires in this region and that ignitions by railroad, lightning, and arson are spatially more clustered than ignitions by other accidental causes.

scholarly journals Rainfall spatio-temporal correlation and intermittency structure from micro-γ to meso-β scale in the Netherlands

2021 ◽  
Author(s):  
Thomas C. van Leth ◽  
Hidde Leijnse ◽  
Aart Overeem ◽  
Remko Uijlenhoet
Keyword(s):  
The Netherlands ◽  
Spatial Scales ◽  
Rainfall Variability ◽  
Temporal Structure ◽  
Temporal Correlation ◽  
Exponential Model ◽  
Rain Gauges ◽  
Wide Range ◽  
Spatio Temporal ◽  
Two Parameter

AbstractWe investigate the spatio-temporal structure of rainfall at spatial scales from 7m to over 200 km in the Netherlands. We used data from two networks of laser disdrometers with complementary interstation distances in two Dutch cities (comprising five and six disdrometers, respectively) and a Dutch nationwide network of 31 automatic rain gauges. The smallest aggregation interval for which raindrop size distributions were collected by the disdrometers was 30 s, while the automatic rain gauges provided 10-min rainfall sums. This study aims to supplement other micro-γ investigations (usually performed in the context of spatial rainfall variability within a weather radar pixel) with new data, while characterizing the correlation structure across an extended range of scales. To quantify the spatio-temporal variability, we employ a two-parameter exponential model fitted to the spatial correlograms and characterize the parameters of the model as a function of the temporal aggregation interval. This widely used method allows for a meaningful comparison with seven other studies across contrasting climatic settings all around the world. We also separately analyzed the intermittency of the rainfall observations. We show that a single parameterization, consisting of a two-parameter exponential spatial model as a function of interstation distance combined with a power-law model for decorrelation distance as a function of aggregation interval, can coherently describe rainfall variability (both spatial correlation and intermittency) across a wide range of scales. Limiting the range of scales to those typically found in micro-γ variability studies (including four of the seven studies to which we compare our results) skews the parameterization and reduces its applicability to larger scales.

scholarly journals New methods for retrieval of chlorophyll red fluorescence from hyper-spectral satellite instruments: simulations and application to GOME-2 and SCIAMACHY

2016 ◽  
Author(s):  
J. Joiner ◽  
Y. Yoshida ◽  
L. Guanter ◽  
E. M. Middleton
Keyword(s):  
Spatial Scales ◽  
Temporal Structure ◽  
Emission Feature ◽  
Ozone Monitoring Instrument ◽  
Additional Information ◽  
Time Space ◽  
Hyper Spectral ◽  
Spatio Temporal ◽  
Gas Measurements ◽  
Satellite Instruments

Abstract. Global satellite measurements of solar-induced fluorescence (SIF) from chlorophyll over land and ocean have proven useful for a number of different applications related to physiology, phenology, and productivity of plants and phytoplankton. Terrestrial chlorophyll fluorescence is emitted throughout the red and far-red spectrum, producing two broad peaks near 683 and 736 nm. From ocean surfaces, phytoplankton fluorescence emissions are entirely from the red region. Studies using satellite-derived SIF over land have focused almost exclusively on measurements in the far- red, since those are the most easily obtained with existing instrumentation. Here, we examine new ways to use existing hyper-spectral satellite data sets to retrieve red SIF over both land and ocean. Our approach offers noise reductions as compared with previously published solar line filling retrievals by making use of the oxygen (O2) γ-band that is not affected by SIF. The O2 γ-band in conjunction with solar Fraunhofer lines help to anchor the O2 B-band that provides additional information on red SIF. Biases due to instrumental artifacts that vary in time, space, and with instrument, must be addressed in order to obtain reasonable results. The satellite instruments that we use were designed to make atmospheric trace- gas measurements and are therefore not optimal for observing SIF; they have coarse spatial resolution and only moderate spectral resolution (∼0.5 nm). Nevertheless, these instruments offer a unique opportunity to compare red and far-red terrestrial SIF at regional spatial scales. Our eight year record of red SIF observations over land with the Global Ozone Monitoring Instrument 2 (GOME-2) allows for the first time reliable global mapping of monthly anomalies. These anomalies are shown to have similar spatio-temporal structure as those in the far-red, particularly for drought-prone regions. There is a somewhat larger percentage response in the red as compared with the far-red for these areas that are sensitive to soil moisture, although the differences are within the specified uncertainties that are dominated by systematic errors. We also demonstrate that high quality ocean fluorescence line height retrievals can be achieved with GOME-2 and similar instruments by utilizing the full complement of radiance measurements that span the red SIF emission feature.

scholarly journals Magnetic turbulent spectra in the magnetosheath: new insights

2004 ◽  
Vol 22 (6) ◽  
pp. 2283-2288 ◽  
Author(s):  
F. Sahraoui ◽  
G. Belmont ◽  
J. L. Pinçon ◽  
L. Rezeau ◽  
A. Balogh ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Maximum Growth ◽  
Temporal Analysis ◽  
Maximum Growth Rate ◽  
Magnetic Fluctuations ◽  
Pass Filter ◽  
Real Nature ◽  
Mirror Mode ◽  
Spatio Temporal ◽  
Turbulent Spectra

Abstract. The spectrum of the magnetic fluctuations measured by the Cluster satellites in the inner magnetosheath is investigated using the k-filtering technique. On a case study, it is shown first that the wave vectors calculated from the Flux Gate Magnetometer (FGM) data fit well with those determined from the Spatio-Temporal Analysis of Field Fluctuations (STAFF) data for their common range of frequency, which allows one to confirm that the high pass filter applied to STAFF data does not alter the spatial characteristics of its spectra. Both analyses confirm the dominance of the mirror mode for frequencies up to 1.4Hz. Furthermore, by comparing the experimental charateristics of the identified mirror mode to the prediction of the linear theory, it is shown that the predicted maximum growth rate is observed in the frequency range 0-0.15Hz, i.e. the FGM range. All the rest of the mirror mode, identified for higher frequencies is more likely to be a non linear extension of the most instable one. This cascade on the spatial scales is, in turn, observed in the satellite frame as a temporal spread due to Doppler shift. Further implications on the real nature of the observed spectrum are discussed.

scholarly journals Spatio-temporal analysis of flood data from South Carolina

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Haigang Liu ◽  
David B. Hitchcock ◽  
S. Zahra Samadi
Keyword(s):  
South Carolina ◽  
Temporal Structure ◽  
Daily Rainfall ◽  
Temporal Analysis ◽  
The United States ◽  
Environmental Data ◽  
Temporal Properties ◽  
Proximity Matrix ◽  
Spatio Temporal ◽  
Emergency Decision

AbstractTo investigate the relationship between flood gage height and precipitation in South Carolina from 2012 to 2016, we built a conditional autoregressive (CAR) model using a Bayesian hierarchical framework. This approach allows the modelling of the main spatio-temporal properties of water height dynamics over multiple locations, accounting for the effect of river network, geomorphology, and forcing rainfall. In this respect, a proximity matrix based on watershed information was used to capture the spatial structure of gage height measurements in and around South Carolina. The temporal structure was handled by a first-order autoregressive term in the model. Several covariates, including the elevation of the sites and effects of seasonality, were examined, along with daily rainfall amount. A non-normal error structure was used to account for the heavy-tailed distribution of maximum gage heights. The proposed model captured some key features of the flood process such as seasonality and a stronger association between precipitation and flooding during summer season. The model is able to forecast short term flood gage height which is crucial for informed emergency decision. As a byproduct, we also developed a Python library to retrieve and handle environmental data provided by some main agencies in the United States. This library can be of general usefulness for studies requiring rainfall, flow, and geomorphological information over specific areas of the conterminous US.

scholarly journals Multivariate spatio-temporal analysis of the global COVID-19 pandemic

2021 ◽  
Author(s):  
Wen Xiang ◽  
Ben Swallow
Keyword(s):  
Control Strategies ◽  
Temporal Structure ◽  
Temporal Trends ◽  
Virus Transmission ◽  
Temporal Analysis ◽  
Global Scale ◽  
Global Dynamics ◽  
Multivariate Statistical ◽  
Wide Range ◽  
Spatio Temporal

AbstractThe COVID-19 pandemic has caused significant mortality and disruption on a global scale not seen in living memory. Understanding the spatial and temporal vectors of transmission as well as similarities in the trajectories of recorded cases and deaths across countries can aid in understanding the benefit or otherwise of varying interventions and control strategies on virus transmission. It can also highlight emerging globa trends as they occur. Data on number of cases and deaths across the globe have been made available through a variety of databases and provide a wide range of opportunities for the application of multivariate statistical methods to extract information on similarity or difference from them. Here we conduct spatial and temporal multivariate statistical analyses of global COVID-19 cases and deaths for the period spanning January to August 2020, using a variety of distance based multivariate methods to cluster countries according to similar temporal trends in cases and deaths resulting from COVID-19. We also use novel air passenger data as a proxy for movement between countries. The air passenger movement can act as an important vector of transmission and thus scaling covariance matrices before conducting dimension reduction techniques can account for known structures in the data and help highlight important residual spatial and/or temporal trends that may then be attributable to the success of interventions or other cultural differences. Global temporal structure is found to be of significantly more importance than local spatial structure in terms of global dynamics. Our results highlight a significant global change in case and mortality daynamics from early-August, consistent in timing with the emergence of new strains with highger levels of transmission. We propose the methodology offers great potential in real-time analysis of complex, noisy spatio-temporal data and the extraction of emerging changes in pandemic dynamics that can support policy and decision makers.

scholarly journals Why Does It Always Rain on Me? A Spatio-Temporal Analysis of Precipitation in Austria

2016 ◽  
Vol 41 (1) ◽  
Author(s):  
Nikolaus Umlauf ◽  
Georg Mayr ◽  
Jakob Messner ◽  
Achim Zeileis
Keyword(s):  
Temporal Structure ◽  
Temporal Analysis ◽  
Weekend Effect ◽  
Quality Data ◽  
Data Set ◽  
Data Driven Approach ◽  
Spatio Temporal ◽  
Ordered Response ◽  
Additive Regression ◽  
Spatially Varying

It is popular belief that the weather is “bad” more frequently on weekends than on other days of the week and this is often perceived to be associated with an increased chance of rain. In fact, the meteorological literature does report some evidence for such human-induced weekly cycles although these findings are not undisputed. To contribute to this discussion, a modern data-driven approach using structured additive regression modelsis applied to a newly available high-quality data set for Austria. The analysis investigates how an ordered response of rain intensities is influenced by a (potential) weekend effect while adjusting for spatio-temporal structure using spatially varying effects of overall level and seasonality patterns. The underlying data are taken from the HOMSTART project which provides daily precipitation quantities over a period of more than 60 years and a dense netof more than 50 meteorological stations all across Austria.

scholarly journals Spatio-Temporal Analysis of Impervious Surface Expansion in the Qinhuai River Basin, China, 1988–2017

2021 ◽  
Vol 13 (22) ◽  
pp. 4494
Author(s):  
Shanshan Wang ◽  
Yingxia Pu ◽  
Shengfeng Li ◽  
Runjie Li ◽  
Maohua Li
Keyword(s):  
River Basin ◽  
Spatial Scales ◽  
Impervious Surface ◽  
Temporal Analysis ◽  
Impervious Surfaces ◽  
Continuous Change ◽  
Landsat Images ◽  
Rapid Urbanization ◽  
Spatio Temporal ◽  
Qinhuai River

Impervious surfaces are key indicators for urbanization monitoring and watershed degradation assessment over space and time. However, most empirical studies only extracted impervious surface from spatial, temporal or spectral perspectives, paying less attention to integrating multiple dimensions in acquiring continuous changes in impervious surfaces. In this study, we proposed a neighborhood-based spatio-temporal filter (NSTF) to obtain the continuous change information of impervious surfaces from multi-temporal Landsat images in the Qinhuai River Basin (QRB), Jiangsu, China from 1988–2017, based on the results from semi-automatic decision tree classification. Moreover, we used the expansion intensity index (EII) and the landscape extension index (LEI) to further characterize the spatio-temporal characteristics of impervious surfaces on different spatial scales. The preliminary results showed that the overall accuracies of the final classification were about 95%, with the kappa coefficients ranging between 0.9 and 0.96. The QRB underwent rapid urbanization with the percentage of the impervious surfaces increasing from 2.72% in 1988 to 25.6% in 2017. Since 2006, the center of urbanization expansion was shaped from the urban built-up areas of Nanjing and Jiangning to non-urban built-up areas of the Jiangning, Lishui, and Jurong districts. The edge expansion occupied 73% on average among the different landscape expansion types, greatly beyond outlying (12%) and infilling (15%). The window size in the NSTF has a direct impact on the subsequent analysis. Our research could provide decision-making references for future urban planning and development in the similar basins.

Spatio-temporal analysis of the Karun River water quality

2018 ◽  
Vol 12 (3) ◽  
pp. 208
Author(s):  
Kumars Ebrahimi ◽  
Mojtaba Moravej ◽  
Iman Karimirad
Keyword(s):  
Water Quality ◽  
River Water ◽  
Temporal Analysis ◽  
River Water Quality ◽  
Karun River ◽  
Spatio Temporal
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