scholarly journals TIME SERIES CHANGE ANALYSIS OF THE OPTIMUM LOCATION PATTERN OF FACILITIES AND DWELLINGS FOCUSING ON FREQUENCY IN USE OF CONVENIENCE FACILITIES THAT CHANGES WITH AGE

2014 ◽  
Vol 79 (697) ◽  
pp. 737-744 ◽  
Author(s):  
Yushi NOGUCHI ◽  
Tohru YOSHIKAWA
Keyword(s):  
Time Series ◽  
Optimum Location ◽  
Change Analysis ◽  
Location Pattern

Change detection matrix for multitemporal filtering and change analysis of SAR and PolSAR image time series

2015 ◽  
Vol 107 ◽  
pp. 64-76 ◽  
Author(s):  
Thu Trang Lê ◽  
Abdourrahmane M. Atto ◽  
Emmanuel Trouvé ◽  
Akhmad Solikhin ◽  
Virginie Pinel
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Change Analysis

Application of Genetic Algorithms to Optimum Location Pattern of Parking Lots

1994 ◽  
Vol 29 (0) ◽  
pp. 307-312
Author(s):  
Toru Tamura ◽  
Yuzo Masuya ◽  
Kazuo Saito
Keyword(s):  
Genetic Algorithms ◽  
Optimum Location ◽  
Location Pattern ◽  
Parking Lots

scholarly journals Regime change analysis of interval-valued time series with an application to PM10

2015 ◽  
Vol 146 ◽  
pp. 337-346 ◽  
Author(s):  
Carmela Cappelli ◽  
Pierpaolo D'Urso ◽  
Francesca Di Iorio
Keyword(s):  
Time Series ◽  
Regime Change ◽  
Change Analysis ◽  
Interval Valued

scholarly journals INFLUENCE OF SPATIAL AND TEMPORAL RESOLUTION ON TIME SERIES-BASED COASTAL SURFACE CHANGE ANALYSIS USING HOURLY TERRESTRIAL LASER SCANS

2021 ◽  
Vol V-2-2021 ◽  
pp. 137-144
Author(s):  
K. Anders ◽  
L. Winiwarter ◽  
H. Mara ◽  
R. C. Lindenbergh ◽  
S. E. Vos ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Input Data ◽  
Laser Scanning ◽  
Poor Performance ◽  
Computational Effort ◽  
Spatial Extent ◽  
Change Analysis ◽  
Surface Change ◽  
Natural Surface

Abstract. Near-continuously acquired terrestrial laser scanning (TLS) data contains valuable information on natural surface dynamics. An important step in geographic analyses is to detect different types of changes that can be observed in a scene. For this, spatiotemporal segmentation is a time series-based method of surface change analysis that removes the need to select analysis periods, providing so-called 4D objects-by-change (4D-OBCs). This involves higher computational effort than pairwise change detection, and efforts scale with (i) the temporal density of input data and (ii) the (variable) spatial extent of delineated changes. These two factors determine the cost and number of Dynamic Time Warping distance calculations to be performed for deriving the metric of time series similarity. We investigate how a reduction of the spatial and temporal resolution of input data influences the delineation of twelve erosion and accumulation forms, using an hourly five-month TLS time series of a sandy beach. We compare the spatial extent of 4D-OBCs obtained at reduced spatial (1.0 m to 15.0 m with 0.5 m steps) and temporal (2 h to 96 h with 2 h steps) resolution to the result from highest-resolution data. Many change delineations achieve acceptable performance with ranges of ±10 % to ±100 % in delineated object area, depending on the spatial extent of the respective change form. We suggest a locally adaptive approach to identify poor performance at certain resolution levels for the integration in a hierarchical approach. Consequently, the spatial delineation could be performed at high accuracy for specific target changes in a second iteration. This will allow more efficient 3D change analysis towards near-realtime, online TLS-based observation of natural surface changes.

Multiscale framework for rapid change analysis from SAR image time series: Case study of flood monitoring in the central coast regions of Vietnam

2022 ◽  
Vol 269 ◽  
pp. 112837
Author(s):  
Thu Trang Lê ◽  
Jean-Luc Froger ◽  
Dinh Ho Tong Minh
Keyword(s):  
Time Series ◽  
Rapid Change ◽  
Sar Image ◽  
Change Analysis ◽  
Central Coast ◽  
Flood Monitoring

scholarly journals Remote Sensing of Mangroves and Estuarine Communities in Central Queensland, Australia

2020 ◽  
Vol 12 (1) ◽  
pp. 197
Author(s):  
Debbie Chamberlain ◽  
Stuart Phinn ◽  
Hugh Possingham
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Great Barrier Reef ◽  
National Park ◽  
Landsat 8 ◽  
Barrier Reef ◽  
Change Analysis ◽  
Estuarine Wetland ◽  
Open Forest

Great Barrier Reef catchments are under pressure from the effects of climate change, landscape modifications, and hydrology alterations. With the use of remote sensing datasets covering large areas, conventional methods of change detection can expose broad transitions, whereas workflows that excerpt data for time-series trends divulge more subtle transformations of land cover modification. Here, we combine both these approaches to investigate change and trends in a large estuarine region of Central Queensland, Australia, that encompasses a national park and is adjacent to the Great Barrier Reef World Heritage site. Nine information classes were compiled in a maximum likelihood post classification change analysis in 2004–2017. Mangroves decreased (1146 hectares), as was the case with estuarine wetland (1495 hectares), and saltmarsh grass (1546 hectares). The overall classification accuracies and Kappa coefficient for 2004, 2006, 2009, 2013, 2015, and 2017 land cover maps were 85%, 88%, 88%, 89%, 81%, and 92%, respectively. The cumulative area of open forest, estuarine wetland, and saltmarsh grass (1628 hectares) was converted to pasture in a thematic change analysis showing the “from–to” change. We generated linear regression relationships to examine trends in pixel values across the time series. Our findings from a trend analysis showed a decreasing trend (p value range = 0.001–0.099) in the vegetation extent of open forest, fringing mangroves, estuarine wetlands, saltmarsh grass, and grazing areas, but this was inconsistent across the study site. Similar to reports from tropical regions elsewhere, saltmarsh grass is poorly represented in the national park. A severe tropical cyclone preceding the capture of the 2017 Landsat 8 Operational Land Imager (OLI) image was likely the main driver for reduced areas of shoreline and stream vegetation. Our research contributes to the body of knowledge on coastal ecosystem dynamics to enable planning to achieve more effective conservation outcomes.

scholarly journals A Novel Method for the Homogenization of Daily Temperature Series and Its Relevance for Climate Change Analysis

2010 ◽  
Vol 23 (19) ◽  
pp. 5325-5331 ◽  
Author(s):  
Andrea Toreti ◽  
Franz G. Kuglitsch ◽  
Elena Xoplaki ◽  
Jürg Luterbacher ◽  
Heinz Wanner
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Eastern Mediterranean ◽  
Higher Order ◽  
Temperature Time Series ◽  
Daily Temperature ◽  
Change Analysis ◽  
Higher Order Moments ◽  
The Impact ◽  
Temperature Time

Abstract Instrumental daily series of temperature are often affected by inhomogeneities. Several methods are available for their correction at monthly and annual scales, whereas few exist for daily data. Here, an improved version of the higher-order moments (HOM) method, the higher-order moments for autocorrelated data (HOMAD), is proposed. HOMAD addresses the main weaknesses of HOM, namely, data autocorrelation and the subjective choice of regression parameters. Simulated series are used for the comparison of both methodologies. The results highlight and reveal that HOMAD outperforms HOM for small samples. Additionally, three daily temperature time series from stations in the eastern Mediterranean are used to show the impact of homogenization procedures on trend estimation and the assessment of extremes. HOMAD provides an improved correction of daily temperature time series and further supports the use of corrected daily temperature time series prior to climate change assessment.

Forest Spatial Information Generate and Forest Change Analysis using Time Series Aerial Photographic Image Database

2019 ◽  
Vol 27 (2) ◽  
pp. 31-41
Author(s):  
Dong Gook Lee ◽  
Kyeong Deok Kim ◽  
Sung Ryong Yang ◽  
Hyun Jik Lee
Keyword(s):  
Time Series ◽  
Spatial Information ◽  
Image Database ◽  
Forest Change ◽  
Change Analysis ◽  
Photographic Image
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