Area extraction and spatiotemporal characteristics of winter wheat–summer maize in Shandong Province using NDVI time series

Autoři: Chao Dong aff001;  Gengxing Zhao aff002;  Yuanwei Qin aff003;  Hong Wan aff001
Působiště autorů: College of Information Science and Engineering, Shandong Agricultural University, Tai’an, Shan Dong, China aff001;  College of Resources and Environment, Shandong Agricultural University, Tai’an, Shan Dong, China aff002;  Center for Spatial Analysis, College of Atmospheric and Geographic Sciences, University of Oklahoma, Norman, Oklahoma, United States of America aff003
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226508


The use of remote sensing to rapidly and accurately obtain information on the spatiotemporal distribution of large-scale wheat and maize acreage is of great significance for improving the level of food production management and ensuring food security. We constructed a MODIS-NDVI time series dataset, combined linear interpolation and the Harmonic Analysis of Time Series algorithm to smooth the time series data curve, and classified the data with random forest algorithms. The results show that winter wheat–summer maize planting areas were mainly distributed in the western plains, southern region, and north-eastern part of the middle mountainous regions while the eastern hilly regions were less distributed and scattered. The winter wheat–summer maize planting areas in the study area continued to grow from 2004–2016, with the most significant growth in the northern part of the western plains and Yellow River Delta. The spatial planting probability reflected the planting core area and showed an intensive planting pattern. During the study period, the peak value and time for the NDVI of the winter wheat were significantly different and showed an increasing trend, while these parameters for the summer maize were relatively stable with little change. Therefore, we mapped a spatial distribution of the winter wheat and summer maize, using the time series data pre-processing synthesis and phenology curve random forest classification methods. Through precision analysis, we obtained satisfactory results, which provided a straightforward and efficient method to monitor the winter wheat and summer maize.

Klíčová slova:

Cereal crops – Crops – Data processing – Maize – Planting – Remote sensing – Statistical data – Wheat


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Článek vyšel v časopise


2019 Číslo 12