基于高光谱图像数据的密度峰值 k 均值算法估算小麦植被覆盖度

摘要：植被覆盖度（（ FVC）是衡量作物长势的重要参数，在作物生长监测研究中，快速准确地提取FVC非常重要。照相 法作为应用最广泛的FVC提取方法，具有操作简单、提取精度高的优点，但当土壤水分和采集时间发生变化时，提取结 果的准确性较低。为了适应FVC提取的各种情况，本研究提出了一种新的FVC提取方法，利用密度峰值k均值（（ DPK- means）算法从小麦归一化植被指数（（ NDVI）灰度图像中提取FVC。本研究以盆栽扬辐麦4号（（ YF4）和大田扬麦16号 （（Y16）为研究材料。使用安装在三脚架上的高光谱成像相机，在盆栽小麦冠层上方1 m处采集不同土壤条件下（（干、湿） 冬小麦地面高光谱图像。利用搭载高光谱相机的无人机在田间小麦冠层上方50 m处采集不同生长阶段的冬小麦无人机高 光谱图像。分别采用像素二分法和DPK-means算法对小麦NDVI灰度图像中的植被像素和非植被像素进行分类，并对2 种方法的提取效果进行对比分析。结果表明：像素二分法提取受采集条件影响较大且误差分布较为分散，DPK-means算 法的提取效果受采集条件影响较小，误差分布较为集中。土壤干燥、土壤潮湿条件下以及不同时间条件下，误差绝对值 分别为0.042和0.044，均方根误差（（RMSE）分别为0.028和0.030，FVC的拟合精度R2分别为0.87和0.93。研究发现， DPK-means算法在各种土壤和时间条件下都能比像素二分法获得更准确的结果，是一种准确、稳健的FVC提取方法。

关键词：植被覆盖度，K均值算法，NDVI，植被指数，小麦

期刊：Journal of Integrative Agriculture

DOI：10.1016/S2095-3119(20)63556-0

设备：GaiaSky

Estimating wheat fractional vegetation cover using a density peak k-means algorithm based on hyperspectral image data

Abstract：Fractional vegetation cover (FVC) is an important parameter to measure crop growth. In studies of crop growth monitoring, it is very important to extract FVC quickly and accurately. As the most widely used FVC extraction method, the photographic method has the advantages of simple operation and high extraction accuracy. However, when soil moisture and acquisition times vary, the extraction results are less accurate. To accommodate various conditions of FVC extraction, this study proposes a new FVC extraction method that extracts FVC from a normalized difference vegetation index (NDVI) greyscale image of wheat by using a density peak k-means (DPK-means) algorithm. In this study, Yangfumai 4 (YF4) planted in pots and Yangmai 16 (Y16) planted in the field were used as the research materials. With a hyperspectral imaging camera mounted on a tripod, ground hyperspectral images of winter wheat under different soil conditions (dry and wet) were collected at 1 m above the potted wheat canopy. Unmanned aerial vehicle (UAV) hyperspectral images of winter wheat at various stages were collected at 50 m above the field wheat canopy by a UAV equipped with a hyperspectral camera. The pixel dichotomy method and DPK-means algorithm were used to classify vegetation pixels and non-vegetation pixels in NDVI greyscale images of wheat, and the extraction effects of the two methods were compared and analysed. The results showed that extraction by pixel dichotomy was influenced by the acquisition conditions and its error distribution was relatively scattered, while the extraction effect of the DPK-means algorithm was less affected by the acquisition conditions and its error distribution was concentrated. The absolute values of error were 0.042 and 0.044, the root mean square errors (RMSE) were 0.028 and 0.030, and the fitting accuracy R2 of the FVC was 0.87 and 0.93, under dry and wet soil conditions and under various time conditions, respectively. This study found that the DPK-means algorithm was capable of achieving more accurate results than the pixel dichotomy method in various soil and time conditions and was an accurate and robust method for FVC extraction.

Keywords：：fractional vegetation cover, k-means algorithm, NDVI, vegetation index, wheat