Abandoned Land Identification and Spatial Pattern Analysis Based on Time-Series Remote Sensing

Abstract

With rapid urbanization and the structural transformation of economic development, cultivated land abandonment is becoming more serious, posing great threats and challenges to the security of food production. The rapid and accurate monitoring of cultivated land abandonment is of great value for ensuring regional and national food security. In view of the urgent need for abandoned land monitoring, we proposed a method to identify abandoned land based on the temporal features of optical time series data. First, the NDVI time series of cultivated lands was constructed using multitemporal Sentinel-2 data. The constructed NDVI time series was preprocessed to filter out outliers among the observations. Subsequently, the amplitude feature of the NDVI time series was extracted for cultivated land. We acquired the amplitudes of the abandoned and non-abandoned land samples and generated statistics for the range of amplitudes. We then extracted the optimal threshold of NDVI amplitude for separating abandoned land from non-abandoned land using two steps: initialization and optimization of the threshold. In the initialization step, the maximum NDVI amplitude among the abandoned land samples was set as the initial threshold. In the optimization step, the threshold was decreased in step of 0.01, and the F1-score was calculated iteratively to determine the optimal threshold. With a decrease in the threshold when the F1-score reached its highest value, the corresponding threshold was set as the optimal threshold. Accordingly, the rule for abandoned land recognition was constructed; namely, when the NDVI amplitude was lower than the optimal threshold, cultivated land was classified as abandoned land. Finally, based on the mapping results, landscape pattern indices were calculated to analyze the landscape pattern characteristics of abandoned lands. The proposed method was validated in Potou District, Zhanjiang City, Guangdong Province, China. The main results of the study were as follows: 1) Comparing the NDVI time series curves of abandoned and non-abandoned lands, we found that the NDVI time series curves of abandoned land changed gently, whereas the NDVI time series of non-abandoned land changed with great fluctuation due to the phenological process of crop growth. 2) By iteratively searching for the optimal NDVI amplitude, the optimal segmentation threshold of NDVI amplitude was 0.42 for distinguishing abandoned lands from non-abandoned lands in Potou. We used 100 abandoned and non-abandoned land samples to validate their accuracy. By using this optimal threshold, the overall recognition accuracy of abandoned land reached 91.83%, and the overall extraction accuracy of non-abandoned land reached 90.20%. 3) The calculation results of the landscape pattern index of abandoned lands revealed that these are generally small and have irregular shapes. Abandoned lands were scattered throughout Potou. This study demonstrates that the proposed method can effectively identify abandoned land using the amplitude of NDVI time series. This method can produce highly reliable abandoned land mapping and shows good potential for large-scale agricultural applications.

Keywords

• abandoned land
• remote sensing
• ndvi time series
• amplitude
• spatial pattern
• zhanjiang city