scholarly journals Automated land cover change detection: the quest for meaningful high temporal time series extraction

2010 ◽  
Author(s):  
B.P. Salmon ◽  
J.C. Olivier ◽  
W. Kleynhans ◽  
K.J. Wessels ◽  
F. van den Bergh
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Land Cover Change Detection

scholarly journals Land Cover Change Detection Using Multiple Shape Parameters of Spectral and NDVI Curves

2018 ◽  
Vol 10 (8) ◽  
pp. 1251 ◽  
Author(s):  
Boyu Liu ◽  
Jun Chen ◽  
Jiage Chen ◽  
Weiwei Zhang
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Shape Features ◽  
Shape Parameters ◽  
Shape Information ◽  
Zero Crossing ◽  
Ndvi Time Series ◽  
Land Cover Change Detection

Spectral and NDVI values have been used to calculate the change magnitudes of land cover, but may result in many pseudo-changes because of inter-class variance. Recently, the shape information of spectral or NDVI curves such as direction, angle, gradient, or other mathematical indicators have been used to improve the accuracy of land cover change detection. However, these measurements, in terms of the single shape features, can hardly capture the complete trends of curves affected by the unsynchronized phenology. Therefore, the calculated change magnitudes are indistinct such that changes and no-changes have a low contrast. This problem has prevented traditional change detection methods from achieving a higher accuracy using bi-temporal images or NDVI time series. In this paper, a multiple shape parameters-based change detection method is proposed by combining the spectral correlation operator and the shape features of NDVI temporal curves (phase angle cumulant, baseline cumulant, relative cumulation rate, and zero-crossing rate). The change magnitude is derived by integrating all the inter-annual differences of these shape parameters. The change regions are discriminated by an automated threshold selection method known as histogram concavity analysis. The results showed that the mean differences in the change magnitudes of the proposed method between 2100 changed and 2523 unchanged pixels was 32%, the overall accuracy was approximately 88%, and the kappa coefficient was 0.76. A comparative analysis was conducted with bi-temporal image-based methods and NDVI time series-based methods, and we demonstrate that the proposed method is more effective and robust than traditional methods in achieving high-contrast change magnitudes and accuracy.

scholarly journals TRAJECTORY-BASED ANALYSIS OF URBAN LAND-COVER CHANGE DETECTION

2016 ◽  
Vol XLI-B7 ◽  
pp. 607-610 ◽  
Author(s):  
Y. H. Zhang ◽  
H. P. Liu
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Similarity Index ◽  
Urban Land ◽  
Urban Land Cover ◽  
Long Time ◽  
Urban Land Cover Change ◽  
Land Cover Change Detection

China have occurred unprecedented urban growth over the last two decades. It is reported that the level of China’s urbanization increased from 18 % in 1978 to 41 % in 2003, and this figure may exceed 65 % by 2050. The change detection of long time serious remote sensing images is the effective way to acquire the data of urban land-cover change to understand the pattern of urbanization. In this paper, we proposed the similarity index (SI) and apply it in long time series urban land-cover change detection. First of all, we built possible change trajectories in four times based on the normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI) that extracted from time series Landsat images. Secondly, we applied SI in similarity comparison between the observed change trajectory and the possible trajectories. Lastly, verifying the accuracy of the results. The overall accuracy in four periods is 85.7 % and the overall accuracy of each two years is about 90 % and kappa statistic is about 0.85. The results show that this method is effective for time series land-cover change detection.

scholarly journals Unsupervised Land Cover Change Detection: Meaningful Sequential Time Series Analysis

2011 ◽  
Vol 4 (2) ◽  
pp. 327-335 ◽  
Author(s):  
Brian P. Salmon ◽  
Jan Corne Olivier ◽  
Konrad J. Wessels ◽  
Waldo Kleynhans ◽  
Frans van den Bergh ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Analysis ◽  
Change Detection ◽  
Land Cover Change ◽  
Series Analysis ◽  
Land Cover Change Detection
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