基于多生育期NDVI变化特征的冬小麦覆盖度提取

Extraction of Winter Wheat Cover Based on NDVI Variation Characteristics over Multiple Growth Stage

摘要:

植被覆盖度是反映地表植被分布的重要参数，遥感影像中非植被背景的剔除对于基于遥感影像的早期作物产量预测、长势监测等具有重要意义。本文利用无人机遥感系统获取冬小麦返青期、拔节期、抽穗期、灌浆期NDVI（Normalized difference vegetation index）影像及拔节期、抽穗期、灌浆期与返青期冬小麦NDVI差值影像，基于最大类间方差法（Otsu）对各生育期冬小麦植被及非植被NDVI进行分割（NDVI-OTSU植被提取方法），并基于Otsu对冬小麦各生育期NDVI差值影像进行初步分割，进行掩膜等提取后利用Otsu进行再分割，进而实现对各生育期冬小麦植被及非植被NDVI的分割（NDVID-OTSU植被提取方法）。对比2种方法提取作物植被效果，结果表明：对于NDVI-OTSU植被提取方法，作物植被各生育期提取误差为16.68%、7.24%、7.40%、10.79%，各生育期提取精度由高到低为拔节期、抽穗期、灌浆期、返青期。NDVID-OTSU植被提取方法各生育期提取误差分别为6.44%、3.53%、1.36%、4.15%，精度均高于基于NDVI-OTSU的植被提取方法，各生育期植被提取精度由高到低依次为抽穗期、拔节期、灌浆期、返青期。非植被背景剔除后，各生育期NDVI与冬小麦产量相关性进一步提高，其中返青期提升幅度最大。在低植被覆盖度时，植被覆盖度与NDVI及冬小麦产量相关性较好，在高植被覆盖度时，相关性变差，出现严重饱和现象。基于NDVID-OTSU的作物植被提取为作物早期长势监测等提供了一种快速、有效的方法。

Abstract:

Vegetation coverage is an important parameter reflecting the distribution of vegetation on the ground surface, and the removal of non-vegetation background from remote sensing images is of great significance for early crop yield prediction and growth monitoring based on remote sensing images. UAV remote sensing system was used to obtain the remote sensing image information of winter wheat at the greening, jointing, heading, and filling stages, and calculated to get the NDVI images of farmland in each growth stage of winter wheat and the difference images of NDVI of farmland at the jointing, heading, and filling stage with the greening stage. The first method: the NDVI of the winter wheat canopy and non-canopy layer at each growth stage was segmented based on the Otsu (NDVI-OTSU-based vegetation extraction method). The second method: preliminary segmentations of NDVI difference images at each growth stage were carried out based on the Otsu, and the mask extractions was performed and then re-segmentation was carried out by using the Otsu, which in turn realized the segmentation of the NDVI of canopy and non-canopy layers of winter wheat at each growth stage (NDVID-OTSU-based vegetation extraction method). Finally, the effectiveness of the two methods for extracting crop vegetation was investigated. The results showed that for the NDVI-OTSU vegetation extraction method, the extraction errors of crop vegetation at each growth stage were 16.68%, 7.24%, 7.40% and 10.79%, respectively. The extraction precision of each growth stage was as follows in descending order: jointing stage, heading stage, filling stage, and greening stage. For each growth stage, the extraction errors of the multi-growth stage NDVID-OTSU vegetation extraction method were 6.44%, 3.53%, 1.36% and 4.15%, respectively, which were higher than those of the NDVI-OTSU vegetation extraction method, respectively. The vegetation extraction accuracies of each growth stage in descending order were as follows: heading stage, jointing stage, filling stage, and greening stage. The correlation between NDVI and winter wheat yield at each growth stage was further improved after the non-vegetation background was removed, with the greatest enhancement of NDVI at the greening stage. The correlations between vegetation coverage and NDVI, winter wheat yield were good at low vegetation coverage, and poor at high vegetation coverage, with severe saturation. Crop vegetation extraction based on the multi-growth stage NDVID-OTSU can provide a fast and effective method for crop early growth monitoring and so on.