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首页> 外文期刊>Remote Sensing >Mapping Rice Cropping Systems in Vietnam Using an NDVI-Based Time-Series Similarity Measurement Based on DTW Distance
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Mapping Rice Cropping Systems in Vietnam Using an NDVI-Based Time-Series Similarity Measurement Based on DTW Distance

机译：基于DTW距离的基于NDVI的时间序列相似性测绘在越南的水稻种植系统

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摘要

Normalized Difference Vegetation Index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data has been widely used in the fields of crop and rice classification. The cloudy and rainy weather characteristics of the monsoon season greatly reduce the likelihood of obtaining high-quality optical remote sensing images. In addition, the diverse crop-planting system in Vietnam also hinders the comparison of NDVI among different crop stages. To address these problems, we apply a Dynamic Time Warping (DTW) distance-based similarity measure approach and use the entire yearly NDVI time series to reduce the inaccuracy of classification using a single image. We first de-noise the NDVI time series using S-G filtering based on the TIMESAT software. Then, a standard NDVI time-series base for rice growth is established based on field survey data and Google Earth sample data. NDVI time-series data for each pixel are constructed and the DTW distance with the standard rice growth NDVI time series is calculated. Then, we apply thresholds to extract rice growth areas. A qualitative assessment using statistical data and a spatial assessment using sampled data from the rice-cropping map reveal a high mapping accuracy at the national scale between the statistical data, with the corresponding R 2 being as high as 0.809; however, the mapped rice accuracy decreased at the provincial scale due to the reduced number of rice planting areas per province. An analysis of the results indicates that the 500-m resolution MODIS data are limited in terms of mapping scattered rice parcels. The results demonstrate that the DTW-based similarity measure of the NDVI time series can be effectively used to map large-area rice cropping systems with diverse cultivation processes.
机译：源自中等分辨率成像光谱仪（MODIS）时间序列数据的归一化植被指数（NDVI）已广泛用于作物和水稻分类领域。季风季节的阴雨天气特征极大地降低了获得高质量光学遥感图像的可能性。此外，越南多样化的农作物种植系统也阻碍了不同作物阶段之间NDVI的比较。为了解决这些问题，我们应用了基于动态时间规整（DTW）距离的相似性度量方法，并使用整个NDVI年度时间序列来减少使用单个图像进行分类的不准确性。我们首先使用基于TIMESAT软件的S-G滤波对NDVI时间序列进行消噪。然后，根据田间调查数据和Google Earth样本数据，建立了水稻生长的标准NDVI时间序列基础。构造每个像素的NDVI时间序列数据，并计算与标准水稻生长NDVI时间序列的DTW距离。然后，我们应用阈值来提取水稻生长区域。使用统计数据进行的定性评估和使用水稻种植地图的采样数据进行的空间评估显示，在全国范围内，统计数据之间的制图准确性很高，相应的R 2高达0.809;但是，由于每个省的水稻种植面积减少，地图绘制的水稻精度在省级范围内下降。结果分析表明，在绘制散布的稻米地块方面，分辨率为500 m的MODIS数据有限。结果表明，基于DTW的NDVI时间序列相似性度量可以有效地用于绘制具有不同栽培过程的大面积稻作系统。

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