Abstract
Leaf area index (LAI) is a key parameter for describing vegetation structures and is closely associated with vegetative photosynthesis and energy balance. The accurate retrieval of LAI is important when modeling biophysical processes of vegetation and the productivity of earth systems. The Random Forests (RF) method aggregates an ensemble of decision trees to improve the prediction accuracy and demonstrates a more robust capacity than other regression methods. This study evaluated the RF method for predicting grassland LAI using ground measurements and remote sensing data. Parameter optimization and variable reduction were conducted before model prediction. Two variable reduction methods were examined: the Variable Importance Value method and the principal component analysis (PCA) method. Finally, the sensitivity of RF to highly correlated variables was tested. The results showed that the RF parameters have a small effect on the performance of RF, and a satisfactory prediction was acquired with a root mean square error (RMSE) of 0.1956. The two variable reduction methods for RF prediction produced different results; variable reduction based on the Variable Importance Value method achieved nearly the same prediction accuracy with no reduced prediction, whereas variable reduction using the PCA method had an obviously degraded result that may have been caused by the loss of subtle variations and the fusion of noise information. After removing highly correlated variables, the relative variable importance remained steady, and the use of variables selected based on the best-performing vegetation indices performed better than the variables with all vegetation indices or those selected based on the most important one. The results in this study demonstrate the practical and powerful ability of the RF method in predicting grassland LAI, which can also be applied to the estimation of other vegetation traits as an alternative to conventional empirical regression models and the selection of relevant variables used in ecological models.
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Claims 7
Claim · model-written summary
Random Forests predicted grassland LAI with a satisfactory root mean square error of 0.1956.
Verbatim
e0“a satisfactory prediction was acquired with a root mean square error (RMSE) of 0.1956”
t0“Estimating grassland LAI”
t1“in the meadow steppe of Hulunber, China”
Claim · model-written summary
Variable reduction based on the Variable Importance Value method gave nearly the same prediction accuracy as no reduction.
Verbatim
e0“variable reduction based on the Variable Importance Value method achieved nearly the same prediction accuracy with no reduced prediction”
t0“Estimating grassland LAI”
Claim · model-written summary
Variable reduction using the PCA method gave a clearly degraded result compared with the Variable Importance Value method.
Verbatim
e0“whereas variable reduction using the PCA method had an obviously degraded result that may have been caused by the loss of subtle variations and the fusion of noise information”
e1“Two variable reduction methods were examined: the Variable Importance Value method”
t0“Estimating grassland LAI”
Claim · model-written summary
The degraded PCA result may have been caused by loss of subtle variations and fusion of noise information.
Verbatim
e0“whereas variable reduction using the PCA method had an obviously degraded result that may have been caused by the loss of subtle variations and the fusion of noise information”
t0“Estimating grassland LAI”
Claim · model-written summary
RF parameters had a small effect on the performance of RF.
Verbatim
e0“the RF parameters have a small effect on the performance of RF”
t0“Estimating grassland LAI”
Claim · model-written summary
After highly correlated variables were removed, relative variable importance remained steady.
Verbatim
e0“After removing highly correlated variables, the relative variable importance remained steady”
t0“Estimating grassland LAI”
Claim · model-written summary
Variables selected from the best-performing vegetation indices performed better than all vegetation indices or variables selected by the most important one.
Verbatim
e0“the use of variables selected based on the best-performing vegetation indices performed better than the variables with all vegetation indices or those selected based on the most important one”
t0“Estimating grassland LAI”