Abstract
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.
Highlights
Land cover dynamics and their spatiotemporal information play a vital role in many scientific fields, such as natural resources management, environmental research, climate modeling, and earth biogeochemistry studies [1,2,3]
It is valuable to explore the potentiality of the shape of spectra and the NDVI temporal curve for obtaining more accurate change detection results
Inter-annual trends in the NDVI time series derived from remote sensing data can be used to distinguish between natural land cover variability and land cover change [20]
Summary
Land cover dynamics and their spatiotemporal information play a vital role in many scientific fields, such as natural resources management, environmental research, climate modeling, and earth biogeochemistry studies [1,2,3]. The algorithm known as spectral angle mapper (SAM) was developed to determine the spectral similarity between each pair of spectra treated as a vector [16] In this technique, the angles of two curves in multi-dimensional space are taken as a type of shape information to measure the difference of the spectra [17]. It uses the spectral gradient to quantitatively describe the shape of the spectrum of a given land cover type, and operates on the belief that gradient differences can compress the pseudo-changes more effectively than previous methods. This method detects change areas by considering the NDVI temporal curve shape and value differences simultaneously These approaches effectively reduce the influence of phenological differences between images from two different dates. Even if the shape features of a spectrum or an NDVI temporal curve such as angle, direction, and gradient have been utilized, some pseudo-changes cannot be reduced due to phenological difference. The chanagtherdesahnodldu. nchanged regions are acquired by comparing the change magnitude with a threshold
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