基于GF-1/WFVNDVI时间序列数据的作物分类

归一化植被指数(normalized difference vegetation index,NDVI)时间序列已广泛应用于植被信息提取研究,然而目前NDVI时间序列的研究主要集中于中低分辨率遥感影像,从而影响了植被信息提取的精度。随着中国高分专项首颗卫星高分一号(GF-1)的发射,为高分辨率NDVI时间序列的构建提供了可能。该文尝试利用GF-1卫星16 m宽覆盖(wide field of view,WFV)影像,构建16 m分辨率NDVI时间序列,以河北省唐山市南部区域为研究区,开展作物分类研究。该文采用覆盖作物完整生长期的GF-1数据构建NDVI时间序列,避免了利用自然年(1-12月)数据构建NDVI时间序列的不足,有助于作物信息的提取。通过分析样地的NDVI时序曲线,发现GF-1/WFV NDVI时间序列能够清晰地区分不同作物的物候差异,捕捉作物特有的生长特性,而且能够识别研究区当年的作物种植模式。该文分别采用最大似然法、马氏距离、最小距离、神经网络分类、支持向量机(support vector machine,SVM)等分类方法,基于GF-1/WFV NDVI时间序列对研究区作物进行分类,研究结果表明SVM分类方法总体精度最高,达到96.33%。同时该文还采用时间序列谐波分析法(harmonic analysis of time series,HANTS)对NDVI时间序列进行了平滑处理,结果表明处理后的NDVI时间序列能更好地描述作物的物候特性,作物分类精度得到进一步提高。

Crop classification based on GF-1/WFV NDVI time series

Abstract: NDVI (Normalized Difference Vegetation Index) time series has been widely used in collecting vegetation information, while most of the present researches about NDVI time series are limited to moderate or low resolution remote sensing images, which affect the accuracy of vegetation information extraction. With the successful launch of the first satellite GF-1 of China High-resolution Earth Observation System, more opportunities have emerged for the construction of NDVI time series with high temporal and high spatial resolution. In this paper, we attempted to build 16 m resolution NDVI time series using images with wide field of view of GF-1 satellite. Different crops have different NDVI time sequence curves during the whole growth period. However, it should be noted that the same crop has a relatively stable growth process and pattern in the same area, which is the basis for the crop classification by using the time series data. While crop phenological characteristics vary largely during a growing cycle, they vary relatively smaller in the different growing cycles. Adopting data containing a complete crop growth cycle can contribute to the extraction of crop phenological information in the construction of NDVI time series. Furthermore, it can avoid the shortage of using data in a calendar year (January to December) to build NDVI time series. In order to carry out studies on crop classification based on GF-1/WFV NDVI time series data, Tangshan, which is located in Hebei Province, China, was chosen as the study area. Through the analysis of NDVI time series curves of samples, we can draw that NDVI time series was able to clearly distinguish crop phenological differences, capture the growth of crop specific features, and identify crop planting patterns in the study area. Irrigation period had the salient features different from that of upland crops before planting paddy rice, which formed the obvious differences compared with other crops. As far as winter wheat was concerned, its NDVI peak was the unique features different from others in overwintering stage. In addition, by analyzing NDVI time series curves in the study area, crop planting patterns can be summarized as follows: winter wheat and summer corn belonged to the planting patterns of two seasons a year, while the rice or peanuts was in a year planting patterns. Based on GF-1/WFV NDVI time series, maximum likelihood method, Mahalanobis distance, minimum distance, neural network, SVM classification methods were used to classify crop in the study area. The results demonstrated that SVM had the best classification accuracies compared to other classification methods, and its overall classification accuracy reached 96.33%. This research showed that GF-1/WFV NDVI with high resolution can be used for crop classification, and can be applied to large area crop classification of remote sensing due to the characteristics of wide coverage. Furthermore, the Harmonic Analysis of Time Series (HANTS) method was used for NDVI time series smoothing, and the results indicated that the processed NDVI time series can better represent different crop phenological characteristics. Then SVM method was used for classifying crop based on smoothed NDVI time series, and the overall classification accuracy was up to 97.57%, which was superior to the one based on the unsmoothed data. The study opens a new era for the domestic high resolution data on agricultural monitoring, and provides insightful reference for the study on the time series of remote sensing classification research.