Remote sensing classification method of vegetation dynamics based on time series Landsat image: a case of opencast mining area in China

Jiaxing Xu,Duo Jia,Gang Li,Hua Zhao,Pengcheng Yin

doi:10.1186/s13640-018-0360-0

Abstract

Time series remote sensing image is an important resource for dynamic monitoring of resources and environment, and its abundant time spectrum information can be used to characterize the dynamic change of vegetation coverage. This paper proposes a comprehensive clustering and pixel classification method for extracting the vegetation dynamics based on time series Landsat normalized difference vegetation index (NDVI). This method uses the time-division algorithm for fitting time-series NDVI firstly. And the Markov random field optimized (MRF) semi-supervised dynamic time warping (DTW) kernel fuzzy c-means clustering was constructed. Then the MRF-optimized semi-supervised DTW-kernel fuzzy c-means clustering was combined with the 1-nearest neighbor (1NN) DTW pixel classification to realize the extraction of vegetation dynamics. Shengli Opencast Coal Mine in The Xilin Gol Grassland was taken as the study area to analyze the applicability of the different classification methods. The results showed the fusion algorithm of the MRF-Semi-GDTW-FCM and 1NN-DTW generates accurate classification results with the overall accuracy of 93.8806% and Kappa coefficient of 0.9267, which were 1.7219, 0.0182, and 20.4080% and 0.2916 higher than the clustering and pixel classification, respectively. Experiments proof that the method proposed in this paper is not only simple but also accurate and effective.

Highlights

The study on changing processes of land use/land cover is helpful to reveal the response characteristics of environmental factors to environmental change and human intervention
Due to big amount of image time-series data, the time spectrum information is susceptible to noise interference, so that the data is subject to high uncertainty and is characterized in seasonality, space-time autocorrelation, etc. [16, 17], which limits the application of many time-series mathematical models, and the effective mining and classification methods of image time-series information is still faced with many challenges [18, 19]
Taking the Shengli Opencast Mining area as an example, this study is to investigate the consistency of normalized difference vegetation index (NDVI) time series constructed by different sensors of Landsat, the correction method of time-series image, and the extraction method of dynamic vegetation information of the opencast mining area based on the interannual Landsat NDVI time series

Summary

Introduction

The study on changing processes of land use/land cover is helpful to reveal the response characteristics of environmental factors to environmental change and human intervention. The time series of normalized difference vegetation index (NDVI), which are used to reflect the vegetation growth status, can effectively record their dynamic process, and their time spectrum meticulously depict the land-use evolution information and make it possible to. Verbesselt et al [9] proposed the breaks for additive season and trend algorithm (BFAST) for real-time remote sensing of ecological disturbance information. The extraction of existing time-series information is mostly based on the pixel time series [20], but further studies are required for the disturbance of vegetation phenological difference in different years, the statistical dependence of neighborhood pixel and category marking probability, the sensitivity of similarity measurement, and how to improve generalization ability of fuzzy classification

Methods

Results

Conclusion