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

doi:10.1016/S2095-3119(20)63556-0

摘要/Abstract

摘要：

植被覆盖度(FVC)是衡量作物生长状况的重要指标。在作物生长监测研究中，快速、准确地提取植被覆盖度非常重要。摄影法是目前应用最广泛的FVC提取方法，具有操作简单、提取精度高的优点。但是，当土壤湿度和采集时间不同时，提取结果的准确度较差。为了适应不同的植被覆盖度提取条件，本文提出了一种新的植被覆盖度提取方法，该方法采用密度峰值K-means (density peak K-means, DPK-means)算法从小麦归一化差值植被指数(NDVI)灰度图像中提取植被覆盖度。本研究以盆栽种植的杨麦4 (YF4)和田间种植的杨麦16 (Y16)为研究对象，使用三脚架搭载高光谱成像相机，在盆栽小麦冠层上方1m处采集不同土壤条件(干、湿)下小麦的地面高光谱图像。无人机搭载高光谱相机，在田间小麦冠层上方50m高空采集不同时期的冬小麦高光谱图像。分别采用像元二分法和DPK-means算法对小麦NDVI灰度图像中的植被像元和非植被像元进行分类，并对两种方法的提取效果进行了比较分析。结果表明，像素二分法提取的图像受到采集条件的影响较大，误差分布较为分散。DPK-means算法的提取效果受采集条件的影响较小，误差分布比较集中。干、湿土壤条件和不同时间条件下的误差绝对值分别为0.042和0.044，均方根误差(RMSE)分别为0.028和0.030,FVC拟合精度R²分别为0.87和0.93。本研究表明，在不同土壤和时间条件下，DPK-means算法比像元二分法能够获得更准确的结果，是一种准确、稳健的FVC提取方法。

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.

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

. [J]. Journal of Integrative Agriculture, 2021, 20(11): 2880-2891.

LIU Da-zhong, YANG Fei-fei, LIU Sheng-ping. Estimating wheat fractional vegetation cover using a density peak k-means algorithm based on hyperspectral image data[J]. Journal of Integrative Agriculture, 2021, 20(11): 2880-2891.

参考文献

Chen J, Yi S, Qin Y, Wang X. 2016. Improving estimates of fractional vegetation cover based on UAV in alpine grassland on the Qinghai–Tibetan Plateau. International Journal of Remote Sensing, 37, 1922–1936.
Ding Y, Zhang H, Zhao K, Zheng X. 2017. Investigating the accuracy of vegetation index-based models for estimating the fractional vegetation cover and the effects of varying soil backgrounds using in situ measurements and the PROSAIL model. International Journal of Remote Sensing, 38, 4206–4223.
Feng W, Yao X, Zhu Y, Tian Y C, Cao W X. 2008. Monitoring leaf nitrogen status with hyperspectral reflectance in wheat. European Journal of Agronomy, 28, 394–404.
Gnyp M L, Miao Y, Yuan F, Ustin S L, Yu K, Yao Y, Huang S, Bareth G. 2014. Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages. Field Crops Research, 155, 42–55.
Guo H, Jiapaer G, Bao A, Li X, Huang Y, Ndayisaba F, Meng F. 2017. Effects of the Tarim River’s middle stream water transport dike on the fractional cover of desert riparian vegetation. Ecological Engineering, 99, 333–342.
He R, Li H, Qiao X, Jiang J. 2018. Using wavelet analysis of hyperspectral remote-sensing data to estimate canopy chlorophyll content of winter wheat under stripe rust stress. International Journal of Remote Sensing, 39, 4059–4076.
Hill M J, Zhou Q, Sun Q, Schaaf C B, Palace M. 2017. Relationships between vegetation indices, fractional cover retrievals and the structure and composition of Brazilian Cerrado natural vegetation. International Journal of Remote Sensing, 38, 874–905.
Jia K, Liang S, Gu X, Baret F, Wei X, Wang X, Yao Y, Yang L, Li Y. 2016. Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data. Remote Sensing of Environment, 177, 184–191.
Jia K, Liang S, Liu S, Li Y, Xiao Z, Yao Y, Jiang B, Zhao X, Wang X, Xu S, Cui J. 2015. Global land surface fractional vegetation cover estimation using general regression neural networks from MODIS surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 53, 4787–4796.
Lamchin M, Park T, Lee J Y, Lee W K. 2015. Monitoring of vegetation dynamics in the Mongolia using MODIS NDVIs and their relationship to rainfall by natural zone. Journal of the Indian Society of Remote Sensing, 43, 325–337.
Li F, Chen W, Zeng Y, Zhao Q, Wu B. 2014. Improving estimates of grassland fractional vegetation cover based on a pixel dichotomy model: A case study in Inner Mongolia, China. Remote Sensing, 6, 4705–4722.
Li X, Wang H, Long H, Wei D, Bao Y. 2012. A model for the estimation of fractional vegetation cover based on the relationship between vegetation and soil moisture. International Journal of Remote Sensing, 33, 3580–3595.
Liang B, Liu S, Qu Y, Qu Y. 2015. Estimating fractional vegetation cover using the hand-held laser range finder: Method and validation. Remote Sensing Letters, 6, 20–28.
Likas A, Vlassis N, Verbeek J. 2003. The global k-means clustering algorithm. Pattern Recognition, 36, 451–461.
Liu H, Zhu H, Wang P. 2016. Quantitative modelling for leaf nitrogen content of winter wheat using UAV-based hyperspectral data. International Journal of Remote Sensing, 38, 2117–2134.
Liu Y, Mu X, Wang H, Yan G. 2012. A novel method for extracting green fractional vegetation cover from digital images. Journal of Vegetation Science, 23, 406–418.
Liu Y, Yin H P, Chai Y. 2016. An improved kernel k-means clustering algorithm. In: Proceedings of 2016 Chinese Intelligent Systems Conference. Springer, Singapore. pp. 275–280.
Martinelli F, Scalenghe R, Davino S, Panno S, Scuderi G, Ruisi P, Villa P, Stroppiana D, Boschetti M, Goulart L R, Davis C E, Dandekar A M. 2015. Advanced methods of plant disease detection. A review. Agronomy for Sustainable Development, 35, 1–25.
Martinuzzi S, Ramos-González O M, Muñoz-Erickson T A, Locke D H, Lugo A E, Radeloff V C. 2018. Vegetation cover in relation to socioeconomic factors in a tropical city assessed from sub-meter resolution imagery. Ecological Applications, 28, 681–693.
Mu X, Song W, Gao Z, McVicar T R, Donohue R J, Yan G. 2018. Fractional vegetation cover estimation by using multi-angle vegetation index. Remote Sensing of Environment, 216, 44–56.
Sadeghi-Tehran P, Virlet N, Sabermanesh K, Hawkesford M J. 2017. Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping. Plant Methods, 13, 103.
Singh C B, Jayas D S, Paliwal J, White N D G. 2009. Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging. Journal of Stored Products Research, 45, 151–158.
Smith K L, Steven M D, Colls J J. 2004. Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks. Remote Sensing of Environment, 92, 207–217.
Song W, Mu X, Ruan G, Gao Z, Li L, Yan G. 2017. Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method. International Journal of Applied Earth Observation and Geoinformation, 58, 168–176.
Song W, Mu X, Yan G, Huang S. 2015. Extracting the green fractional vegetation cover from digital images using a shadow-resistant algorithm (SHAR-LABFVC). Remote Sensing, 7, 10425–10443.
Torres-Sánchez J, Peña J M, de Castro A I, López-Granados F. 2014. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computers and Electronics in Agriculture, 103, 104–113.
Tzortzis G , Likas A. 2008. The global kernel k-means clustering algorithm. In: 2008 IEEE International Joint Conference on Neural Networks. IEEE, Hong Kong. pp. 1977–1984.
Wang Y, Wang D, Zhang G, Wang J. 2013. Estimating nitrogen status of rice using the image segmentation of G-R thresholding method. Field Crops Research, 149, 33–39.
Zhao J, Xie D, Mu X, Liu Y, Yan G. 2012. Accuracy evaluation of the ground-based fractional vegetation cover measurement by using simulated images. In: 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, Germany. pp. 3347–3350.
Zheng X, Sun Y, Qin Q, Ren H, Gao Z, Wu L, Meng Q, Wang J, Wang J. 2015. Bare soil moisture inversion model based on visible-shortwave infrared reflectance. Spectroscopy and Spectral Analysis, 35, 2113–2118. (in Chinese)
Zhou G, Liu S. 2015. Estimating ground fractional vegetation cover using the double-exposure method. International Journal of Remote Sensing, 36, 6085–6100.
Zhou Q, Robson M. 2001. Automated rangeland vegetation cover and density estimation using ground digital images and a spectral-contextual classifier. International Journal of Remote Sensing, 22, 3457–3470.
Zhou Q, Robson M, Pilesjo P. 1998. On the ground estimation of vegetation cover in Australian rangelands. International Journal of Remote Sensing, 19, 1815–1820.