A robust approach for phenological change detection within satellite image time series

2011 ◽  
Author(s):  
Jan Verbesselt ◽  
Martin Herold ◽  
Rob Hyndman ◽  
Achim Zeileis ◽  
Darius Culvenor
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Satellite Image ◽  
Robust Approach

scholarly journals Change Detection within Remotely Sensed Satellite Image Time Series via Spectral Analysis

2020 ◽  
Vol 12 (23) ◽  
pp. 4001
Author(s):  
Ebrahim Ghaderpour ◽  
Tijana Vujadinovic
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Change Detection ◽  
Satellite Image ◽  
Remotely Sensed ◽  
Stationary Time Series ◽  
Atmospheric Effects ◽  
Landsat 8 ◽  
Temporal Segmentation ◽  
Jump Detection

Jump or break detection within a non-stationary time series is a crucial and challenging problem in a broad range of applications including environmental monitoring. Remotely sensed time series are not only non-stationary and unequally spaced (irregularly sampled) but also noisy due to atmospheric effects, such as clouds, haze, and smoke. To address this challenge, a robust method of jump detection is proposed based on the Anti-Leakage Least-Squares Spectral Analysis (ALLSSA) along with an appropriate temporal segmentation. This method, namely, Jumps Upon Spectrum and Trend (JUST), can simultaneously search for trends and statistically significant spectral components of each time series segment to identify the potential jumps by considering appropriate weights associated with the time series. JUST is successfully applied to simulated vegetation time series with varying jump location and magnitude, the number of observations, seasonal component, and noises. Using a collection of simulated and real-world vegetation time series in southeastern Australia, it is shown that JUST performs better than Breaks For Additive Seasonal and Trend (BFAST) in identifying jumps within the trend component of time series with various types. Furthermore, JUST is applied to Landsat 8 composites for a forested region in California, U.S., to show its potential in characterizing spatial and temporal changes in a forested landscape. Therefore, JUST is recommended as a robust and alternative change detection method which can consider the observational uncertainties and does not require any interpolations and/or gap fillings.

scholarly journals INTRODUCING AUTOMATIC SATELLITE IMAGE PROCESSING INTO LAND COVER MAPPING BY PHOTO-INTERPRETATION OF AIRBORNE DATA

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 29-34
Author(s):  
H. Costa ◽  
P. Benevides ◽  
F. Marcelino ◽  
M. Caetano
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Land Use Changes ◽  
Satellite Image ◽  
Landsat 8 ◽  
Photo Interpretation ◽  
Time Production ◽  
Mainland Portugal ◽  
Land Cover Maps

Abstract. A series of five land cover maps, widely known as COS (Carta de Uso e Ocupação do Solo), have been produced since 1990 for mainland Portugal. Previous to 2015, all maps were produced through photo-interpretation of orthophotos. Land cover and land use changes were detected through comparison of previous and recent orthophotos, which were used for map updating, thereby producing a new map. The remaining areas of no change were preserved across the maps for consistency. Despite the value of the maps produced, the method is very time-consuming and limited to the single-date reference of the orthophotos. From 2015 onwards, a new approach was adopted for map production. Photo-interpretation of orthophoto maps is still the basis of mapping, but assisted by products derived from satellite data. The goals are three-fold: (i) cut time production, (ii) increase map accuracy, and (iii) further detail the nomenclature. The last map published (COS 2015) benefited from change detection and classification analyses of Landsat data, namely for guiding the photo-interpretation in forest, shrublands, and mapping annual agriculture. Time production and map error have been reduced comparing to previous maps. The new 2018 map, currently in production, further explores this approach. Landsat 8 time series of 2015–2018 are used for change detection in vegetation based on NDVI differencing, thresholding and clustering. Sentinel-2 time series of 2017–2018 are used to classify Autumn/Winter crops and Spring/Summer crops based on NDVI temporal profiles and classification rules. Benefits and pitfalls of the new mapping approach are presented and discussed.

A land cover-driven approach for fitting satellite image time series in a change detection context

2020 ◽  
Author(s):  
Yady Tatiana Solano-Correa ◽  
Khatereh Meshkini ◽  
Francesca Bovolo ◽  
Lorenzo Bruzzone
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Satellite Image

scholarly journals Phenological change detection while accounting for abrupt and gradual trends in satellite image time series

2010 ◽  
Vol 114 (12) ◽  
pp. 2970-2980 ◽  
Author(s):  
Jan Verbesselt ◽  
Rob Hyndman ◽  
Achim Zeileis ◽  
Darius Culvenor
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Satellite Image
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