scholarly journals Seasonal to interannual variability of vegetation and fires at SAFARI 2000 sites inferred from advanced very high resolution radiometer time series data

2003 ◽  
Vol 108 (D13) ◽  
pp. n/a-n/a ◽  
Author(s):  
A. Anyamba ◽  
C. O. Justice ◽  
C. J. Tucker ◽  
Robert Mahoney
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Interannual Variability ◽  
Time Series Data ◽  
Series Data ◽  
Very High

scholarly journals BUILDING CHANGE DETECTION IN VERY HIGH RESOLUTION SATELLITE STEREO IMAGE TIME SERIES

2016 ◽  
Vol III-7 ◽  
pp. 149-155 ◽  
Author(s):  
J. Tian ◽  
R. Qin ◽  
D. Cerra ◽  
P. Reinartz
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Time Series Data ◽  
Satellite Image ◽  
Stereo Image ◽  
Series Data ◽  
Change Analysis ◽  
Stereo Imagery ◽  
Increasing Demand ◽  
Very High

There is an increasing demand for robust methods on urban sprawl monitoring. The steadily increasing number of high resolution and multi-view sensors allows producing datasets with high temporal and spatial resolution; however, less effort has been dedicated to employ very high resolution (VHR) satellite image time series (SITS) to monitor the changes in buildings with higher accuracy. In addition, these VHR data are often acquired from different sensors. The objective of this research is to propose a robust time-series data analysis method for VHR stereo imagery. Firstly, the spatial-temporal information of the stereo imagery and the Digital Surface Models (DSMs) generated from them are combined, and building probability maps (BPM) are calculated for all acquisition dates. In the second step, an object-based change analysis is performed based on the derivative features of the BPM sets. The change consistence between object-level and pixel-level are checked to remove any outlier pixels. Results are assessed on six pairs of VHR satellite images acquired within a time span of 7 years. The evaluation results have proved the efficiency of the proposed method.

scholarly journals BUILDING CHANGE DETECTION IN VERY HIGH RESOLUTION SATELLITE STEREO IMAGE TIME SERIES

2016 ◽  
Vol III-7 ◽  
pp. 149-155
Author(s):  
J. Tian ◽  
R. Qin ◽  
D. Cerra ◽  
P. Reinartz
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Time Series Data ◽  
Satellite Image ◽  
Stereo Image ◽  
Series Data ◽  
Change Analysis ◽  
Stereo Imagery ◽  
Increasing Demand ◽  
Very High

There is an increasing demand for robust methods on urban sprawl monitoring. The steadily increasing number of high resolution and multi-view sensors allows producing datasets with high temporal and spatial resolution; however, less effort has been dedicated to employ very high resolution (VHR) satellite image time series (SITS) to monitor the changes in buildings with higher accuracy. In addition, these VHR data are often acquired from different sensors. The objective of this research is to propose a robust time-series data analysis method for VHR stereo imagery. Firstly, the spatial-temporal information of the stereo imagery and the Digital Surface Models (DSMs) generated from them are combined, and building probability maps (BPM) are calculated for all acquisition dates. In the second step, an object-based change analysis is performed based on the derivative features of the BPM sets. The change consistence between object-level and pixel-level are checked to remove any outlier pixels. Results are assessed on six pairs of VHR satellite images acquired within a time span of 7 years. The evaluation results have proved the efficiency of the proposed method.

scholarly journals Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China

2020 ◽  
Vol 12 (19) ◽  
pp. 3120
Author(s):  
Luojia Hu ◽  
Nan Xu ◽  
Jian Liang ◽  
Zhichao Li ◽  
Luzhen Chen ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Mangrove Forest ◽  
Time Series Data ◽  
Landsat Imagery ◽  
Google Earth ◽  
Series Data ◽  
National Scale ◽  
Google Earth Engine ◽  
Sentinel 2

A high resolution mangrove map (e.g., 10-m), including mangrove patches with small size, is urgently needed for mangrove protection and ecosystem function estimation, because more small mangrove patches have disappeared with influence of human disturbance and sea-level rise. However, recent national-scale mangrove forest maps are mainly derived from 30-m Landsat imagery, and their spatial resolution is relatively coarse to accurately characterize the extent of mangroves, especially those with small size. Now, Sentinel imagery with 10-m resolution provides an opportunity for generating high-resolution mangrove maps containing these small mangrove patches. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and/or Sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features of random forest to classify mangroves in China. We found that Sentinel-2 (F1-Score of 0.895) is more effective than Sentinel-1 (F1-score of 0.88) in mangrove extraction, and a combination of SAR and MSI imagery can get the best accuracy (F1-score of 0.94). The 10-m mangrove map was derived by combining SAR and MSI data, which identified 20003 ha mangroves in China, and the area of small mangrove patches (<1 ha) is 1741 ha, occupying 8.7% of the whole mangrove area. At the province level, Guangdong has the largest area (819 ha) of small mangrove patches, and in Fujian, the percentage of small mangrove patches is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest map is expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of the mangrove forest.

scholarly journals Missing data imputation of high‐resolution temporal climate time series data

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
E Afrifa‐Yamoah ◽  
U. A. Mueller ◽  
S. M. Taylor ◽  
A. J. Fisher
Keyword(s):  
Time Series ◽  
Missing Data ◽  
High Resolution ◽  
Time Series Data ◽  
Series Data ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Climate Time Series

scholarly journals Probing the size of a magnetosphere of a young solar-like star

2008 ◽  
Vol 4 (S259) ◽  
pp. 413-414
Author(s):  
Catrina M. Hamilton ◽  
C. M. Johns-Krull ◽  
R. Mundt ◽  
W. Herbst ◽  
J. N. Winn
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Binary System ◽  
Line Profile ◽  
Main Sequence ◽  
Time Series Data ◽  
Young Stars ◽  
Series Data ◽  
Accretion Flows ◽  
Magnetospheric Accretion

AbstractWe have obtained high resolution spectra of the pre-main sequence binary system KH 15D (V582 Mon) while the star is fully visible, fully occulted, and during several ingress and egress events over the course of five contiguous observing seasons. The Hα line profile is a standard probe of the magnetospheric accretion flows on young stars such as KH 15D. We use these time series data to map out the size of the magnetosphere and find that it changes size from one observing season to the next.

scholarly journals Contextualizing time-series data: quantification of short-term regional variability in the San Pedro Channel using high-resolution in situ glider data

2018 ◽  
Vol 15 (20) ◽  
pp. 6151-6165 ◽  
Author(s):  
Elizabeth N. Teel ◽  
Xiao Liu ◽  
Bridget N. Seegers ◽  
Matthew A. Ragan ◽  
William Z. Haskell ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Water Column ◽  
Time Series Data ◽  
Series Data ◽  
Regional Variability ◽  
Modes Of Variability ◽  
Subsurface Chlorophyll Maximum ◽  
San Pedro

Abstract. Oceanic time series have been instrumental in providing an understanding of biological, physical, and chemical dynamics in the oceans and how these processes change over time. However, the extrapolation of these results to larger oceanographic regions requires an understanding and characterization of local versus regional drivers of variability. Here we use high-frequency spatial and temporal glider data to quantify variability at the coastal San Pedro Ocean Time-series (SPOT) site in the San Pedro Channel (SPC) and provide insight into the underlying oceanographic dynamics for the site. The dataset could be described by a combination of four water column profile types that typified active upwelling, a surface bloom, warm-stratified low-nutrient conditions, and a subsurface chlorophyll maximum. On weekly timescales, the SPOT station was on average representative of 64 % of profiles taken within the SPC. In general, shifts in water column profile characteristics at SPOT were also observed across the entire channel. On average, waters across the SPC were most similar to offshore profiles, suggesting that SPOT time series data would be more impacted by regional changes in circulation than local coastal events. These results indicate that high-resolution in situ glider deployments can be used to quantify major modes of variability and provide context for interpreting time series data, allowing for broader application of these datasets and greater integration into modeling efforts.

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