Modeling of Alpine Grassland Cover Based on Unmanned Aerial Vehicle Technology and Multi-Factor Methods: A Case Study in the East of Tibetan Plateau, China

Meng, Baoping; Gao, Jinlong; Liang, Tiangang; Cui, Xia; Ge, J.; Yin, Jianpeng; Feng, Qisheng; Xie, Hongjie

doi:10.3390/rs10020320

Cited by 44 publications

(23 citation statements)

References 53 publications

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“…In each station, the monthly mean air temperature and cumulative precipitation were calculated. Anusplin software package was used to interpolate the station-specific data with thin plate smoothing spline interpolation method [29].…”

Section: Dem Soil and Meteorological Data Preprocessmentioning

confidence: 99%

“…Those features are suitable for monitoring of grassland AGB and its dynamic changes especially for large areas [24]. However, univariate parameter RS inversion models based on MODIS data have low accuracy and poor stability in alpine meadow grassland [23], [28], [29], because of the extensive spatial distribution, complex grass species and high spatial heterogeneity [14], [29], [24]. Hence, it is essential to explore a new grassland AGB monitoring method based on MODIS data.…”

Section: Introductionmentioning

confidence: 99%

“…Ali et al (2016) analyzed applicability of multiple linear regression (MLR), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) models, results showed that ANFIS has produced improved estimation of biomass as compared to the ANN and MLR [32]. Furthermore, additional critical factors like topographic, meteorological, soil and vegetation biophysical indicators are taken into construction of RS inversion model [20], [22], [24], [27], [29], [33]- [36]. Various form and structure of RS estimation models have been applied in AGB estimation study, i.e., multivariate parameter models include linear, logarithmic, power and reciprocal formula forms [23], [34], [35], multivariate non-parameter models include artificial neural network (BP-ANN), support vector machine (SVM) and random forest (RF) [20], [36]- [40], and so on.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Alpine Grassland Above Ground Biomass Based on Remote Sensing Data and Machine Learning Algorithm: A Case Study in East of the Tibetan Plateau, China

Meng

Liang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Effective and accurate assessment of grassland above-ground biomass (AGB) especially via remote sensing (RS), is crucial for forage-livestock balance and ecological environment protection of alpine grasslands. Because of complexity and extensive spatial distribution of natural grassland resources, the RS estimation models based on moderate resolution imaging spectroradiometer (MODIS) data exhibited low accuracy and poor stability. In this study, various methods for estimating the AGB of alpine grassland vegetation using MODIS vegetation indices were evaluated by combining with meteorology, soil, topography geography and in situ measured AGB data (during grassland growing season from 2011 to 2016) in Gannan region. Results show that (1) five out of ten factors (elevation, slope, aspect, topographic position, temperature, precipitation and the concentration of clay and sand in the soil) exert significant effects on grassland AGB, with R 2 0.04~0.39, and RMSE 859.68 kg/ha ~ 1075.09 kg/ha, respectively; (2) the accuracy and stability of AGB estimation model can be improved by constructing multivariate models, especially using multivariate non-parameter models; (3) the optimum estimation model is constructed on the basis of random forest algorithm (RF). Compared with univariate/multivariate parameter models, RMSE of RF model decreased 26.45 %-44.27 %. Meanwhile, RF models can explain 89.41 % variation in AGB during grass growing season. This study presented a more suitable RS inversion model integrated MODIS vegetation indices and other effect factors. Besides, the accuracy based on MODIS data were greatly improved. Thus, our study provides a scientific basis for effective and accurate estimating alpine grassland AGB.

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Section: Dem Soil and Meteorological Data Preprocessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling Alpine Grassland Above Ground Biomass Based on Remote Sensing Data and Machine Learning Algorithm: A Case Study in East of the Tibetan Plateau, China

Meng

Liang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

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show abstract

“…The NDVI uses red and infrared reflectance, which are sensitive to the soil background [26]. Studies have also shown that the NDVI could be saturated in grasslands with highly fractional vegetation cover [76]. As an improved vegetation index, EVI overcomes these shortcomings [26].…”

Section: Discussionmentioning

confidence: 99%

Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison

Zhu

et al. 2020

Sustainability

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While a number of machine learning (ML) models have been used to estimate RE, systematic evaluation and comparison of these models are still limited. In this study, we developed three traditional ML models and a deep learning (DL) model, stacked autoencoders (SAE), to estimate RE in northern China’s grasslands. The four models were trained with two strategies: training for all of northern China’s grasslands and separate training for the alpine and temperate grasslands. Our results showed that all four ML models estimated RE in northern China’s grasslands fairly well, while the SAE model performed best (R2 = 0.858, RMSE = 0.472 gC m−2 d−1, MAE = 0.304 gC m−2 d−1). Models trained with the two strategies had almost identical performances. The enhanced vegetation index and soil organic carbon density (SOCD) were the two most important environmental variables for estimating RE in the grasslands of northern China. Air temperature (Ta) was more important than the growing season land surface water index (LSWI) in the alpine grasslands, while the LSWI was more important than Ta in the temperate grasslands. These findings may promote the application of DL models and the inclusion of SOCD for RE estimates with increased accuracy.

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“…The results are shown in Tables 4 and 5, where Pn represents the average precipitation with a lead time n month, and Tn represents the average precipitation with a lead time n month. With reference to similar studies and the meteorological cycles [32][33][34][35], the maximum lead times were set to 6 months.…”

Section: Predictors Selectionmentioning

confidence: 99%

Modelling the Vegetation Response to Climate Changes in the Yarlung Zangbo River Basin Using Random Forest

Chi

Pang

Cui

et al. 2020

Water

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Vegetation coverage variation may influence watershed water balance and water resource availability. Yarlung Zangbo River, the longest river on the Tibetan Plateau, has high spatial heterogeneity in vegetation coverage and is the main freshwater resource of local residents and downstream countries. In this study, we proposed a model based on random forest (RF) to predict the Normalized Difference Vegetation Index (NDVI) of the Yarlung Zangbo River Basin and explore its relationship with climatic factors. High-resolution datasets of NDVI and monthly meteorological observation data from 2000 to 2015 were used to calibrate and validate the proposed model. The proposed model was then compared with artificial neural network and support vector machine models, and principal component analysis and partial correlation analysis were also used for predictor selection of artificial neural network and support vector machine models for comparative study. The results show that RF had the highest model efficiency among the compared models. The Nash–Sutcliffe coefficients of the proposed model in the calibration period and verification period were all higher than 0.8 for the five subzones; this indicated that the proposed model can successfully simulate the relationship between the NDVI and climatic factors. By using built-in variable importance evaluation, RF chose appropriate predictor combinations without principle component analysis or partial correlation analysis. Our research is valuable because it can be integrated into water resource management and elucidates ecological processes in Yarlung Zangbo River Basin.

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Modeling of Alpine Grassland Cover Based on Unmanned Aerial Vehicle Technology and Multi-Factor Methods: A Case Study in the East of Tibetan Plateau, China

Cited by 44 publications

References 53 publications

Modeling Alpine Grassland Above Ground Biomass Based on Remote Sensing Data and Machine Learning Algorithm: A Case Study in East of the Tibetan Plateau, China

Modeling Alpine Grassland Above Ground Biomass Based on Remote Sensing Data and Machine Learning Algorithm: A Case Study in East of the Tibetan Plateau, China

Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison

Modelling the Vegetation Response to Climate Changes in the Yarlung Zangbo River Basin Using Random Forest

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