Potential of hyperspectral data and machine learning algorithms to estimate the forage carbon-nitrogen ratio in an alpine grassland ecosystem of the Tibetan Plateau

2020 ◽  
Vol 163 ◽  
pp. 362-374 ◽  
Author(s):  
Jinlong Gao ◽  
Tiangang Liang ◽  
Jie Liu ◽  
Jianpeng Yin ◽  
Jing Ge ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tibetan Plateau ◽  
Learning Algorithms ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
The Tibetan Plateau ◽  
Grassland Ecosystem ◽  
Carbon Nitrogen Ratio ◽  
Nitrogen Ratio ◽  
Alpine Grassland Ecosystem

scholarly journals Plants and Microbes Mediate the Shift in Ecosystem Multifunctionality From Low to High Patterns Across Alpine Grasslands on the Tibetan Plateau

2021 ◽  
Vol 12 ◽  
Author(s):  
Yi Wang ◽  
Miao Liu ◽  
Youchao Chen ◽  
Tao Zeng ◽  
Xuyang Lu ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Correlation Coefficients ◽  
Terrestrial Ecosystems ◽  
Alpine Grassland ◽  
Future Climate Change ◽  
The Tibetan Plateau ◽  
Grassland Ecosystem ◽  
The Impact ◽  
Ecosystem Multifunctionality ◽  
Alpine Grassland Ecosystem

Both plant communities and soil microbes have been reported to be correlated with ecosystem multifunctionality (EMF) in terrestrial ecosystems. However, the process and mechanism of aboveground and belowground communities on different EMF patterns are not clear. In order to explore different response patterns and mechanisms of EMF, we divided EMF into low (<0) and high patterns (>0). We found that there were contrasting patterns of low and high EMF in the alpine grassland ecosystem on the Tibetan Plateau. Specifically, compared with low EMF, environmental factors showed higher sensitivity to high EMF. Soil properties are critical factors that mediate the impact of community functions on low EMF based on the change of partial correlation coefficients from 0 to 0.24. In addition, plant community functions and microbial biomass may mediate the shift of EMF from low to high patterns through the driving role of climate across the alpine grassland ecosystem. Our findings will be vital to clarify the mechanism for the stability properties of grassland communities and ecosystems under ongoing and future climate change.

scholarly journals Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data

2020 ◽  
Vol 9 (9) ◽  
pp. 507
Author(s):  
Sanjiwana Arjasakusuma ◽  
Sandiaga Swahyu Kusuma ◽  
Stuart Phinn
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Forest Height ◽  
Extreme Gradient Boosting

Machine learning has been employed for various mapping and modeling tasks using input variables from different sources of remote sensing data. For feature selection involving high- spatial and spectral dimensionality data, various methods have been developed and incorporated into the machine learning framework to ensure an efficient and optimal computational process. This research aims to assess the accuracy of various feature selection and machine learning methods for estimating forest height using AISA (airborne imaging spectrometer for applications) hyperspectral bands (479 bands) and airborne light detection and ranging (lidar) height metrics (36 metrics), alone and combined. Feature selection and dimensionality reduction using Boruta (BO), principal component analysis (PCA), simulated annealing (SA), and genetic algorithm (GA) in combination with machine learning algorithms such as multivariate adaptive regression spline (MARS), extra trees (ET), support vector regression (SVR) with radial basis function, and extreme gradient boosting (XGB) with trees (XGbtree and XGBdart) and linear (XGBlin) classifiers were evaluated. The results demonstrated that the combinations of BO-XGBdart and BO-SVR delivered the best model performance for estimating tropical forest height by combining lidar and hyperspectral data, with R2 = 0.53 and RMSE = 1.7 m (18.4% of nRMSE and 0.046 m of bias) for BO-XGBdart and R2 = 0.51 and RMSE = 1.8 m (15.8% of nRMSE and −0.244 m of bias) for BO-SVR. Our study also demonstrated the effectiveness of BO for variables selection; it could reduce 95% of the data to select the 29 most important variables from the initial 516 variables from lidar metrics and hyperspectral data.

CO2 processes in an alpine grassland ecosystem on the Tibetan Plateau

2003 ◽  
Vol 13 (4) ◽  
pp. 429-437 ◽  
Author(s):  
Pei Zhiyong ◽  
Ouyang Hua ◽  
Zhou Caiping ◽  
Xu Xingliang
Keyword(s):  
Tibetan Plateau ◽  
Alpine Grassland ◽  
The Tibetan Plateau ◽  
Grassland Ecosystem ◽  
Alpine Grassland Ecosystem

scholarly journals Soil Erosion Assessment of Alpine Grassland in the Source Park of the Yellow River on the Qinghai-Tibetan Plateau, China

2022 ◽  
Vol 9 ◽  
Author(s):  
Huilong Lin ◽  
Yuting Zhao
Keyword(s):  
Machine Learning ◽  
Soil Erosion ◽  
Tibetan Plateau ◽  
Variable Selection ◽  
Yellow River ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Yellow River ◽  
Optimal Model ◽  
Erosion Loss

The source park of the Yellow River (SPYR), as a vital ecological shelter on the Qinghai-Tibetan Plateau, is suffering different degrees of degradation and desertification, resulting in soil erosion in recent decades. Therefore, studying the mechanism, influencing factors and current situation of soil erosion in the alpine grassland ecosystems of the SPYR are significant for protecting the ecological and productive functions. Based on the 137Cs element tracing technique and machine learning algorithms, five strategic variable selection algorithms based on machine learning algorithms are used to identify the minimal optimal set and analyze the main factors that influence soil erosion in the SPYR. The optimal model for estimating soil erosion in the SPYR is obtained by comparisons model outputs between the RUSLE and machine learning algorithms combined with variable selection models. We identify the spatial distribution pattern of soil erosion in the study area by the optimal model. The results indicated that: (1) A comprehensive set of variables is more objective than the RUSLE model. In terms of verification accuracy, the simulated annealing -Cubist model (R = 0.67, RMSD = 1,368 t km–2⋅a–1) simulation results represents the best while the RUSLE model (R = 0.49, RMSD = 1,769 t⋅km–2⋅a–1) goes on the worst. (2) The soil erosion is more severe in the north than the southeast of the SPYR. The average erosion modulus is 6,460.95 t⋅km–2⋅a–1 and roughly 99% of the survey region has an intensive erosion modulus (5,000–8,000 t⋅km–2⋅a–1). (3) Total erosion loss is relatively 8.45⋅108 t⋅a–1 in the SPYR, which is commonly 12.64 times greater than the allowable soil erosion loss. The economic monetization of SOC loss caused by soil erosion in the entire research area was almost $47.90 billion in 2014. These results will help provide scientific evidences not only for farmers and herdsmen but also for environmental science managers and administrators. In addition, a new ecological policy recommendation was proposed to balance grassland protection and animal husbandry economic production based on the value of soil erosion reclassification.

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