Satellite Image Sequences Research Articles

Compression of Satellite Imagery Sequences Using Wavelet for Detection of Natural Disaster

Indonesia, geographically and geologically, is potentially encounter natural disasters. One of the tools used to early detect disaster is sensor of ocean waves change, but it has drawbacks including the time difference between information/warnings obtained with the disaster event is very short, less than 30 minutes. The faster detector is required, so the time difference will be longer. For example, early detection of natural disasters information system, which can be made with the pattern recognition of satellite imagery sequences of before and during natural disaster images. This study was conducted to determine the right wavelet to compress the satellite image sequences. The compressed images will be used to perform the pattern recognition of natural disaster using artificial neural network. This study use satellite imagery sequences of tornadoes and hurricanes. The eight wavelets used are Haar, Coiflet 1, Coiflet 3, Symlet 2, Symlet 5, 1 AJS, AJS 2, and AJS 3. The test results are then compared with the compression ratio. Result of this study are the comparison of wavelet used to compress satellite imagery sequences, which is save the storage space, access time, processing time and delivery time.

The severe thunderstorm of 5th April, 2010 at Guwahati airport : An observational study

During the afternoon of 5th April, 2010, a thunderstorm swept across Guwahati Airport (Lat. 26º26´, Long 91º35´) and neighborhood from northwest direction. Strong squally winds (reaching up to 49 knots) and high intensity rain (11mm in 15 minutes) were registered accompanying the storm. One person was killed by the falling tree due to squally winds and several others were injured by the event. The observed evolution of temperature, humidity, wind and pressure at Guwahati Airport, as well as the sequence of satellite and radar images, revealed the presence and movement of convective cells. An observational analysis of the event has been given in this paper. The aim of the study is to contribute to the characterization of these events by analyzing the observational information available. The diagnosis is aimed at helping forecasters to identify this kind of organized deep convective events and being able to issue timely warnings. The synoptic scenario shows warm and moist advection from the Bay of Bengal in low levels over Northeastern region of India and an upper-level north-south trough running from Sub-Himalayan West Bengal to north Orissa. This situation is known to be favorable for development of severe convection over Northeastern region of India during pre-monsoon season.

Stochastic Level Set Dynamics to Track Closed Curves Through Image Data

We introduce a stochastic filtering technique for the tracking of closed curves from image sequence. For that purpose, we design a continuous-time dynamics that allows us to infer inter-frame deformations. The curve is defined by an implicit level-set representation and the stochastic dynamics is expressed on the level-set function. It takes the form of a stochastic partial differential equation with a Brownian motion of low dimension. The evolution model we propose combines local photometric information, deformations induced by the curve displacement and an uncertainty modeling of the dynamics. Specific choices of noise models and drift terms lead to an evolution law based on mean curvature as in classic level set methods, while other choices yield new evolution laws. The approach we propose is implemented through a particle filter, which includes color measurements characterizing the target and the background photometric probability densities respectively. The merit of this filter is demonstrated on various satellite image sequences depicting the evolution of complex geophysical flows.

Surface trajectories of oil transport along the Northern Coastline of the Gulf of Mexico

After the destruction of the Deepwater Horizon drilling platform during the spring of 2010, the northern Gulf of Mexico was threatened by an oil spill from the Macondo well. Emergency responders were concerned about oil transport in the nearshore, where it threatened immediately the fishing waters and coastline from Louisiana to Florida. In this region, oil movement was influenced by a continental shelf with varying width, the protruding Mississippi River delta, the marshes and bayou of southern Louisiana, and the shallow sounds and barrier islands that protect the coastline. Transport forecasts require physics-based computational models and high-resolution meshes that represent the circulation in deep water, on the continental shelf, and within the complex nearshore environment.This work applies the coupled SWAN+ADCIRC model on a high-resolution computational mesh to simulate the current velocity field on the continental shelf, nearshore and marsh areas during the time that oil was visible on the surface of the Gulf. The SWAN+ADCIRC simulations account for the influence of tides, riverine discharge, winds and wind-driven waves. A highly efficient Lagrangian particle transport model is employed to simulate the surface trajectories of the oil. The transport model accounts for dispersion and advection by wind and currents. Transport is evaluated using 2-week long sequences of satellite images. During both periods, the SWAN+ADCIRC current fields alone appeared to be more successful moving the oil than when direct wind forcing was included. In addition, hypothetical oil transport is considered during two hurricane scenarios. Had a hurricane significantly impacted the northern Gulf while the spill was active, depending on the track of the storm relative to the spill location, oil would have moved farther into the marshes of southern Louisiana or farther along the shelf toward Texas than actually occurred during the spill.

A Source Apportionment Approach to Study the Evolution of Convective Cells: An Application to the Nowcasting of Convective Weather Systems

A new algorithm is developed, based on Source Apportionment (SA) technique for tracking and nowcasting of Mesoscale Convective Systems (MCS) using satellite image sequences. The algorithm does not use the conventional threshold method for identifying convective regions, instead it introduces neighbourhood search criteria to select contiguous pixels. This offers better opportunity for data mining, and inherently provides an automatic method for tracking. The nowcasting scheme is based on growth curve model fitting and extrapolation using time varying coefficients. The methodology proposed in the paper is demonstrated for the life cycle of a MCS. The algorithm is also tested on an ensemble set comprising of thermal infrared image sequences acquired from Kalpana-1 satellite. Error statistics and skill score analysis indicate the operational feasibility of the proposed algorithm.

Solitary wave behavior in sand dunes observed from space

[1] Although the dynamics of individual barchan dunes are well understood, their interactions are the subject of ongoing scientific interest and debate. Numerical and analog model predictions of shape-preserving binary dune collisions have been hard to test due to the long timescales over which such processes typically occur. This paper documents ten binary dune collisions in a 45-year time sequence of satellite images from the Bodele Depression in Chad. The observations confirm that when two barchan dunes collide, a transfer of mass occurs so that one dune appears to travel through the other unscathed, like a solitary wave.

The Descending Stratified Flow and Internal Hydraulic Jump in the Lee of the Sierras

AbstractCross-barrier density differences and westerly flow established a descending stratified flow across the Sierra Nevada (United States) on 9–10 April 2006. Downslope flow and an internal hydraulic jump occurred only when the potential temperature of the westerly descending flow was at least as cold as the existing upvalley-flowing valley air mass. The onset was observed in sequences of visible satellite images and with weather stations. The University of Wyoming King Air flew through the stratified flow and imaged the structure of the internal hydraulic jump with its cloud radar. Shear-layer instabilities, which first developed near the jump face, grew and paired downstream, mixing the internal hydraulic jump layer. A single wave response to the downslope flow and internal hydraulic jump was observed aloft, but only after the downslope flow had become established.

Extrapolation of Sequence of Geostationary Satellite Images for Weather Nowcasting

In this letter, a novel scheme for image sequence extrapolation is proposed and demonstrated, particularly for the purpose of near-real-time weather nowcasting during satellite launches. The highlight of this model is its ability to produce a sequence of simulated satellite images extended in time scale, which is very important for forecasting the evolution of a meteorological system. For this, three different models based on spatio-temporal autoregressive technique, discrete Fourier transform, and hybrid approach are developed and tested on an extensive data set of satellite image sequences covering different meteorological conditions.

An Ensemble Kalman Filter and Smoother for Satellite Data Assimilation

This paper proposes a methodology for combining satellite images with advection-diffusion models for interpolation and prediction of environmental processes. We propose a dynamic state-space model and an ensemble Kalman filter and smoothing algorithm for on-line and retrospective state estimation. Our approach addresses the high dimensionality, measurement bias, and nonlinearities inherent in satellite data. We apply the method to a sequence of SeaWiFS satellite images in Lake Michigan from March 1998, when a large sediment plume was observed in the images following a major storm event. Using our approach, we combine the images with a sediment transport model to produce maps of sediment concentrations and uncertainties over space and time. We show that our approach improves out-of-sample RMSE by 20%–30% relative to standard approaches. This article has supplementary material online.

Summer diatom and dinoflagellate blooms in Lisbon Bay from 2002 to 2005: Pre-conditions inferred from wind and satellite data

Sea surface temperature (SST) and chlorophyll ( Chl) maps derived from satellite data, together with wind and phytoplankton cell concentration time-series, are used to characterize the wind forcing and the spatio-temporal SST/ Chl variability for periods preceding the summer (June–September) diatom and dinoflagellate blooms. The blooms (concentrations of cells higher than 200 × 10 3 cells l - 1 ) were identified in the weekly samples collected at the long-term phytoplankton study site in Lisbon Bay, from 2002 to 2005. It is shown that bloom detection at the site occurs either during upwelling events, i.e. when offshore northerly wind speed is greater than 5 m s - 1 for more than 5 days, or up to 4 days after wind relaxation. However, no direct relationship was found between the blooms and either the intensity or duration of the wind pulses. The satellite image sequences show that the Chl patterns in Lisbon Bay are strongly linked to the orientation and branching of the upwelling filament rooted at Cape Roca and the westward progression of the warm-core cyclones shed from the slope off Setúbal Bay. High Chl values are typically maintained throughout the upwelling cycle along the inshore boundary of the Roca filament, covering the whole bay during the intense wind phase. During the relaxation phase, there is a Chl increase concurrent with the contraction of the high Chl area to the northern part of the bay, associated with an inshore poleward current. It is proposed that the earlier setup of the offshore stratification conditions and the shedding of a slope eddy favored the bloom of dinoflagellates in July 2004, while the stable position of the main circulation features in July 2002 favored a diatom bloom. The results add further evidence that, in summer, the ocean circulation in the embayments south Cape Roca is dominated by the upwelling dynamics and plays a key role in the development of phytoplankton blooms off central Portugal.

Multisatellite observations and numerical simulation of an along‐coast cumulus cloud line induced by sea‐breeze circulation

A coastal cumulus cloud‐line formation along the east coast of the USA was observed on a National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellite (POES) Advanced Very High Resolution Radiometer (AVHRR) satellite image from 17 August 2001. The cloud line starts to form at about 16:00 UTC (local 12:00 noon) and follows the coastline from Florida to North Carolina. The length and width of the cloud line are about 850 km and 8.5 km, respectively. A 15‐min interval sequence of NOAA Geostationary Operational Environmental Satellite (GOES) images shows that the cloud line maintains the shape of the coastline and penetrates inland for more than 20 km over the next 6‐h timespan. Model simulation with actual atmospheric conditions as inputs shows that the cloud line is formed near the land–sea surface temperature (SST) gradient. The synoptic flow at all model levels is in the offshore direction prior to 16:00 UTC whereas low‐level winds (below 980 hPa) reverse direction to blow inland after 16:00 UTC. This reversal is due to the fact that local diurnal heating over the land takes place on shorter time‐scales than over the ocean. The vertical wind at these levels becomes stronger as the land–SST increases during the summer afternoon, and the leading edge of the head of the inland wind ascends from 920 hPa to about 850 hPa in the 3 h after 16:00 UTC. Model simulation and satellite observations show that the cloud line becomes very weak after 21:00 UTC when the diurnal heating decreases.

Pressure image assimilation for atmospheric motion estimation

The complexity of the laws of dynamics governing 3-D atmospheric flows associated with incomplete and noisy observations make the recovery of atmospheric dynamics from satellite image sequences very difficult. In this paper, we address the challenging problem of estimating physical sound and time-consistent horizontal motion fields at various atmospheric depths for a whole image sequence. Based on a vertical decomposition of the atmosphere, we propose a dynamically consistent atmospheric motion estimator relying on a multilayer dynamic model. This estimator is based on a weak constraint variational data assimilation scheme and is applied on noisy and incomplete pressure difference observations derived from satellite images. The dynamic model is a simplified vorticity-divergence form of a multilayer shallow-water model. Average horizontal motion fields are estimated for each layer. The performance of the proposed technique is assessed using synthetic examples and using real world meteorological satellite image sequences. In particular, it is shown that the estimator enables exploiting fine spatio-temporal image structures and succeeds in characterizing motion at small spatial scales.

Three-Dimensional Motion Estimation of Atmospheric Layers From Image Sequences

In this paper, we address the problem of estimating 3-D motions of a stratified atmosphere from satellite image sequences. The analysis of 3-D atmospheric fluid flows associated with incomplete observation of atmospheric layers due to the sparsity of cloud systems is very difficult. This makes the estimation of dense atmospheric motion field from satellite image sequences very difficult. The recovery of the vertical component of fluid motion from a monocular sequence of image observations is a very challenging problem for which no solution exists in the literature. Based on a physically sound vertical decomposition of the atmosphere into cloud layers of different altitudes, we propose here a dense motion estimator dedicated to the extraction of 3-D wind fields characterizing the dynamics of a layered atmosphere. Wind estimation is performed over the complete 3-D space, using a multilayer model describing a stack of dynamic horizontal layers of evolving thickness, interacting at their boundaries via vertical winds. The efficiency of our approach is demonstrated on synthetic and real sequences.

Pressure image assimilation for atmospheric motion estimation

The complexity of the laws of dynamics governing 3-D atmospheric flows associated with incomplete and noisy observations make the recovery of atmospheric dynamics from satellite image sequences very difficult. In this paper, we address the challenging problem of estimating physical sound and time-consistent horizontal motion fields at various atmospheric depths for a whole image sequence. Based on a vertical decomposition of the atmosphere, we propose a dynamically consistent atmospheric motion estimator relying on a multilayer dynamic model. This estimator is based on a weak constraint variational data assimilation scheme and is applied on noisy and incomplete pressure difference observations derived from satellite images. The dynamic model is a simplified vorticity-divergence form of a multilayer shallow-water model. Average horizontal motion fields are estimated for each layer. The performance of the proposed technique is assessed using synthetic examples and using real world meteorological satellite image sequences. In particular, it is shown that the estimator enables exploiting fine spatio-temporal image structures and succeeds in characterizing motion at small spatial scales.

Use of the ASTER satellite images for evaluation of structural changes in the Popocatépetl volcano related to microseismicity

Mexico is one of the most volcanically active regions in North America. Volcanic activity in central Mexico is associated with the subduction of the Cocos and Rivera plates beneath the North American plate. Periods of enhanced microseismic activity, associated with the volcanic activity of the Popocatépetl volcano are compared with periods, during which the microseismic activity was low. We detected systematical changes in the number of lineaments, associated with the microseismic activity due to lineament analysis of a temporal sequence of high resolution satellite images of the Popocatépetl volcano, provided by the ASTER/VNIR instrument. The Lineament Extraction and Stripes Statistic Analysis (LESSA) software package was used for the lineament extraction. In the future it would allow develop a methodology for detection of possible elevation of pressure in volcano edifice.

Layered Estimation of Atmospheric Mesoscale Dynamics From Satellite Imagery

In this paper, we address the problem of estimating mesoscale dynamics of atmospheric layers from satellite image sequences. Due to the great deal of spatial and temporal distortions of cloud patterns and because of the sparse 3-D nature of cloud observations, standard dense-motion field-estimation techniques used in computer vision are not well adapted to satellite images. Relying on a physically sound vertical decomposition of the atmosphere into layers, we propose a dense-motion estimator dedicated to the extraction of multilayer horizontal wind fields. This estimator is expressed as the minimization of a global function including data and spatio-temporal smoothness terms. A robust data term relying on the integrated-continuity equation mass-conservation model is proposed to fit sparse-transmittance observations related to each layer. A novel spatio-temporal smoother derived from large eddy prediction of a shallow-water momentum-conservation model is used to build constraints for large-scale temporal coherence. These constraints are combined in a global smoothing framework with a robust second-order smoother, preserving divergent and vorticity structures of the flow. For optimization, a two-stage motion estimation scheme is proposed to overcome multiresolution limitations when capturing the dynamics of mesoscale structures. This alternative approach relies on the combination of correlation and optical-flow observations in a variational context. An exhaustive evaluation of the novel method is first performed on a scalar image sequence generated by direct numerical simulation of a turbulent 2-D flow. By qualitative comparisons, the method is then assessed on a METEOSAT image sequence.

Retrieving ocean surface current by 4-D variational assimilation of sea surface temperature images

In this article we propose a new method to estimate ocean mesoscale structures of the surface current velocity by processing sea surface satellite images. Assuming that the intensity level can be described by a transport–diffusion equation, the proposed approach is based on variational assimilation of image observations within a simple transport–diffusion model. This approach permits to retrieve the current velocity field from a sequence of satellite images. Results of processing synthetic data and real NOAA-AVHRR satellite images are presented and commented.

The Pixel Value Data Approach for Rainfall Forecasting Based on GOES-9 Satellite Image Sequence Analysis

The Pixel Value Data Approach for Rainfall Forecasting Based on GOES-9 Satellite Image Sequence Analysis

Curvelet-Based Snake for Multiscale Detection and Tracking of Geophysical Fluids

Detection and target tracking have an application to many scientific problems. The approach developed in this paper is motivated by the applications of detection and tracking characteristic deformable structures in geophysical fluids. We develop an integrated detection and tracking method of geophysical fluids based on a discrete curvelet representation of the information characterizing the targets. Curvelets are in some sense geometric wavelets, allowing an optimal sparse representation of two-dimensional piecewise continuous objects with C 2-singularities. The proposed approach first identifies a consistent vortex by a curvelet-based gradient-vector-flow snake and then establishes the motion correspondence of the snaxels between successive time frames by a constructed so-called semi-T or comp-T multiscale motion-estimation method based on the geometric wavelets. Furthermore, a combination of total-variation regularization and cycle-spinning techniques effectively removes false matches and improves significantly the estimation. Numerical experiments at each stage demonstrate the performance of the proposed tracking methodology for temporal oceanographic satellite image sequences corrupted by noise, with weak edges and submitted to large deformations, in comparison to conventional methods

Winter‐spring fronts in Taiwan Strait

Fine structures of surface fronts during winter‐spring in the Taiwan Strait and their evolution over time are studied using satellite and in situ data. At times, different water masses are interleaved into each other. The northward intruding warm water bifurcates into several branches, causing complicated multiple cross strait fronts with maximum surface temperature gradients at the fronts to be greater than 2°C/km. The mean surface velocity of the fronts estimated from a sequence of satellite images is between 0.15 and 0.3 m/s during the February–March 2001 frontal event. The speed appears to have accelerated during the latter stages from 0.2 m/s during March 2–4 to 0.3 m/s during March 4–6. The total northward transport associated with the passage of the fronts is estimated to be 0.6–1 Sv between February 16 and March 6, 2001. The timescale of the passage of the fronts through the entire Taiwan Strait is therefore at least four weeks. Hydrographic observations in February–March of 1998 and 2000 also demonstrate similar frontal events with strong vertical gradients of water temperature and salinity on the western side of the strait. The 1998 data also show clearly that the low temperature and low salinity Zhemin Coastal Current separates from the coast and enters into the middle of the strait, a feature consistent with the satellite observations.