智能手机原位牧草生物量估算

doi:10.3864/j.issn.0578-1752.2021.05.006

Abstract

Abstract:

【Objective】Biomass is the material and energy basis of grassland ecosystem and the most basic ecological parameter. In the past, the quantitative grassland biomass retrieval based on aerospace and aerial remote sensing was too specialized to be popularized among herders. Therefore, this paper proposed a method for estimating grassland biomass by using true color images taken on the phone near the ground, and constructing a grassland biomass estimation model, which provided a theoretical basis and technical support for herders to easily, quickly and non-destructively grasp the growth of grassland in their own pasture. 【Method】 Firstly, the feature sets of grassland biomass estimation were constructed based on vegetation index, texture features and combined vegetation index and texture features by using the ultra-high resolution true color images of mobile phones. Secondly, in order to prevent dimensional disaster caused by excessive feature extraction, this paper proposed a feature selection algorithm (XGB-SFS) that combined XGBoost and sequence forward selection to perform feature selection and optimal subset construction. Finally, random forest regression and leave-one-out cross-validation were used to compare the biomass estimation effects of different feature sets to build models, and analyze the role of different types of features and XGB-SFS algorithm in grassland AGB estimation.【Result】 (1) Compared with the model constructed by single-type features, the estimation model based on spatial texture features (R² = 0.76) was better than the estimation model based on spectral vegetation index (R² = 0.73), indicating that texture features had a certain role in the ultra-high-resolution grassland AGB estimation; (2) Compared with the model after feature selection, the combined spatial spectrum multi-type feature construction model was superior to any single-type feature model (R² = 0.83, RMSE = 127.57 g·m ^-2, MAE= 81.25 g·m ^-2), indicating that multi-type feature construction model could improve the accuracy of grassland AGB estimation to a certain extent. (3) Comparing the models building before and after feature selection, the model after feature selection by estimating the AGB effect was significantly better than the model without feature selection, and there was a high correlation between the selected features and grassland biomass, indicating that XGB-SFS could reduce the data dimension and improve the accuracy of grassland AGB estimation.【Conclusion】The ultra-high-resolution true color images of mobile phones could accurately estimate the grassland biomass. The XGB-SFS algorithm proposed in this paper could also select the features with high correlation with the grassland biomass from many features and improve the model estimation accuracy. Compared with the previous professional remote sensing quantitative inversion of grassland biomass, this method had the advantages of facing the public, low cost, and easy to use. The study combined the data collected on the phone with remote sensing and machine learning methods, which could open up new perspectives and support the development of agricultural informatization.

Key words: biomass, smart-phone, texture features, XGBoost, grassland

HaiYu TAO,AiWu ZHANG,HaiYang PANG,XiaoYan KANG. Smart-Phone Application in Situ Grassland Biomass Estimation[J].Scientia Agricultura Sinica, 2021, 54(5): 933-944.

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References 43

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植被指数 Vegetation index	计算公式 Formula	参考文献 Reference
R	r波段的DN值	—
G	g波段的DN值	—
B	b波段的DN值	—
r	R/(R+G+B)	[20]
g	G/(R+G+B)	[20]
b	B/(R+G+B)	[20]
ExG	2×g - b - r	[20]
GBRI	g/b	[21]
NPCI	(r - b) / (r + b)	[22]
NGBVI	(g - b) / (g +b)	[23]
VEGI	g/ ((r^0.67) ×b^0.33)	[24]
RGBVI	(g² - (b× r)) / (g² + (b ×r))	[25]
RGRI	r / g	[26]
RGMPI	r × g	[26]
RBRI	r / b	—
RBMPI	r × b	—
RBMI	r - b	—
RGMI	r - g	[20]
RGPI	r + g	[27]
RBPI	r + b	—
GBPI	g + b	[27]
GBMI	g - b	[20]
VDVI	(2×g - r - b) / (2×g+r+b)	[28]
VARI	(g - r / (g+r - b)	[29]

波段 Band	纹理特征 Texture features
R	MEA_R,VAR_R,HOM_R,CON_R,DIS_ R,ENT_R,SEC_R,COR_R
G	MEA_G,VAR_G,HOM_G,CON_G,DIS_ G,ENT_G,SEC_G,COR_G
B	MEA_B,VAR_B,HOM_B,CON_B,DIS_ B,ENT_B,SEC_B,COR_B

特征类型 Feature type	数量 Number	入选特征 Selected features
RGBVIs	14	RBMI, R, B, RGMI, NGBVI, RGPI, VDVI, RBPI, G, NPCI, b, RGMPI, GBRI, RBRI
Textures	12	VAR_B, VAR_R, CON_B, MEA_B, HOM_B, VAR_G, MEA_R, DIS_B, SEC_R, MEA_G, SEC_B, ENT_B
VI-Textures	11	R, VAR_G, VAR_B, G, VAR_R, RBRI, RBMI, RBMPI, MEA_G, SEC_B, MEA_B

Smart-Phone Application in Situ Grassland Biomass Estimation

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