Scientia Agricultura Sinica ›› 2021, Vol. 54 ›› Issue (5): 933-944.doi: 10.3864/j.issn.0578-1752.2021.05.006


Smart-Phone Application in Situ Grassland Biomass Estimation

HaiYu TAO(),AiWu ZHANG(),HaiYang PANG,XiaoYan KANG   

  1. Center for Geographic Environment Research and Education, Capital Normal University/Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048
  • Received:2020-05-26 Accepted:2020-07-30 Online:2021-03-01 Published:2021-03-09
  • Contact: AiWu ZHANG;


【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

Fig. 1

Study area a: Location of the study area; b: Experiment design; c: Quadrat image by smartphone"

Table 1

Formula of vegetation index in this study"

Vegetation index
R r波段的DN
G g波段的DN
B b波段的DN
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/ ((r0.67) ×b0.33) [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 (g - r - b) / (2×g+r+b) [28]
VARI (g - r / (g+r - b) [29]

Table 2

Texture features in this study"

Texture features

Fig. 2

Technique flow chart"

Fig. 3

The relationship between XGB-SFS optimal number of features and prediction accuracy R2"

Table 3

The selected features of XGB-SFS algorithm"

Feature type
Selected features

Fig. 4

Absolute values of Pearson correlation coefficients of selected features and biomass"

Fig. 5

Comparison of measured and predicted values in six experiments (a) RGBVIs, (b)Textures, (c)VI-Textures, (d)Selected RGBVIs, (e)Selected Textures, (f)Selected VI-Textures. The same as below"

Fig. 6

The relationship between modeling accuracy and the number of features in six experiments"

Fig. 7

VIs + Textures feature model inversion map and interpolation map based on XGB-SFS feature selection"

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