Inversion of grassland aboveground biomass in Tianzhu Zangzu Autonomous County based on a machine learning algorithm

doi:10.11686/cyxb2021072

Abstract

Abstract:

Effective， accurate， and large-scale monitoring of grassland aboveground biomass （AGB） using machine learning algorithms is currently a very active field of research， but different machine learning algorithms vary greatly in performance depending on training samples and hyper-parameter settings. The research utilized grassland AGB data collected field， combined with remote sensing data， meteorological data， terrain data for the same period. Thirteen indicators with strong correlation with grassland AGB were selected as input variables for analysis using deep neural network （DNN）， random forest （RF）， gradient boosting regression tree （GBRT）， vector support machine （SVR）， artificial neural network （ANN） and Gaussian process regression （GPR） algorithms for AGB inversion. At the same time， the application potential of 6 models were evaluated from the aspects of model prediction accuracy， stability， sample sensitivity， and characteristics of AGB space-time changes in grasslands during the 2020 Tianzhu County growth season （April-September） and their response to climate. It was found that： 1） The multivariate performance of DNN in grassland AGB inversion was the best， but the stability was poor， and the sensitivity to samples was high； the comprehensive performance of GPR was inferior to DNN， and its stability and accuracy were good； the simulation accuracy of GBRT and RF was high， and its stability was poor； the accuracy of SVR and ANN was relatively poor and the stability of SVR was high， and the stability of ANN was poor. 2） The grassland AGB ranged from 50 to 250 g·m^-2. The spatial heterogeneity of AGB was large and variable over time. In general AGB showed a downward trend from northwest to southeast. 3） Except for temperate desert steppe， the AGB in mountain meadow， alpine meadow and temperate steppe species showed a significant positive correlation with air temperature. Precipitation had no obvious effect on AGB of alpine meadow， temperate grassland and mountain meadow， but had a great effect on AGB of temperate desert grassland. With decrease in precipitation， AGB tended to decrease. The above results provide technical information to support decisions on choice of method and parameter setting when remotely monitoring grassland biomass.

Key words: grassland biomass, machine learning, model performance, Tianzhu Zangzu Autonomous County

Ge-xia QIN, Jing WU, Chun-bin LI, Zhen-xia JI, Zheng-chao QIU, Ying LI. Inversion of grassland aboveground biomass in Tianzhu Zangzu Autonomous County based on a machine learning algorithm[J]. Acta Prataculturae Sinica, 2022, 31(4): 177-188.

Figures/Tables 10

Fig.1 Spatial distribution of grassland types and field sites in Tianzhu Zangzu Autonomous County

Fig.2 Photos of plot distribution and field measurement

Fig.3 Map of the measured data

Fig.4 Correlation coefficients between the AGB and the explanatory variables

Fig.5 The boxplot of model accuracy in the 30 repeated experiments

Table 1 Statistical table of R2， RMSE and MAErepeated 30 times

机器学习模型 Machine learning models	决定系数R²		均方根误差 RMSE		平均绝对误差 MAE
机器学习模型 Machine learning models	范围Range	平均Mean	范围Range	平均Mean	范围Range	平均Mean
DNN	0.80~0.88	0.85	25.43~32.30	32.30	4.23~6.14	5.63
GBRT	0.74~0.88	0.84	21.51~49.63	32.31	4.55~7.29	5.95
RF	0.75~0.88	0.84	30.41~43.48	37.21	4.55~6.63	5.58
SVR	0.77~0.84	0.80	39.55~49.24	46.01	6.33~7.92	7.46
ANN	0.74~0.87	0.81	38.04~54.19	45.99	4.95~9.36	6.54
GPR	0.76~0.88	0.83	32.60~51.03	39.72	4.94~7.14	6.02

Fig.6 Sensitivity of the machine learning models to the training sample size

Fig.7 Comprehensive comparison of six machine learning models with different metrics using a radar chart

Fig.8 Spatial distribution of AGB in grassland

Fig.9 Response mechanism of AGB in different grassland types to temperature and precipitation

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