Forest Change Analysis Using Time-Series Landsat Observations

2011 ◽  
pp. 339-366
Keyword(s):  
Time Series ◽  
Forest Change ◽  
Change Analysis

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

scholarly journals Land Cover-Specific Local Incidence Angle Correction: A Method for Time-Series Analysis of Forest Ecosystems

2021 ◽  
Vol 13 (9) ◽  
pp. 1743
Author(s):  
Daniel Paluba ◽  
Josef Laštovička ◽  
Antonios Mouratidis ◽  
Přemysl Štych
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Forest Type ◽  
Incidence Angle ◽  
Correction Method ◽  
Google Earth ◽  
Forest Change ◽  
Corine Land Cover ◽  
Angle Correction ◽  
Wide Range

This study deals with a local incidence angle correction method, i.e., the land cover-specific local incidence angle correction (LC-SLIAC), based on the linear relationship between the backscatter values and the local incidence angle (LIA) for a given land cover type in the monitored area. Using the combination of CORINE Land Cover and Hansen et al.’s Global Forest Change databases, a wide range of different LIAs for a specific forest type can be generated for each scene. The algorithm was developed and tested in the cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, Shuttle Radar Topography Mission (SRTM) digital elevation model, and CORINE Land Cover and Hansen et al.’s Global Forest Change databases. The developed method was created primarily for time-series analyses of forests in mountainous areas. LC-SLIAC was tested in 16 study areas over several protected areas in Central Europe. The results after correction by LC-SLIAC showed a reduction of variance and range of backscatter values. Statistically significant reduction in variance (of more than 40%) was achieved in areas with LIA range >50° and LIA interquartile range (IQR) >12°, while in areas with low LIA range and LIA IQR, the decrease in variance was very low and statistically not significant. Six case studies with different LIA ranges were further analyzed in pre- and post-correction time series. Time-series after the correction showed a reduced fluctuation of backscatter values caused by different LIAs in each acquisition path. This reduction was statistically significant (with up to 95% reduction of variance) in areas with a difference in LIA greater than or equal to 27°. LC-SLIAC is freely available on GitHub and GEE, making the method accessible to the wide remote sensing community.

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

Classification of annual non-stand replacing boreal forest change in Canada using Landsat time series: a case study in northern Ontario

2016 ◽  
Vol 8 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Oumer S. Ahmed ◽  
Michael A. Wulder ◽  
Joanne C. White ◽  
Txomin Hermosilla ◽  
Nicholas C. Coops ◽  
...  
Keyword(s):  
Time Series ◽  
Boreal Forest ◽  
Forest Change ◽  
Northern Ontario

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.

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