Multi-factor modeling of above-ground biomass in alpine grassland: A case study in the Three-River Headwaters Region, China

Liang, Tiangang; Yang, Shuai; Feng, Qisheng; Liu, Baokang; Huang, Xiaodong; Xie, Hongjie

doi:10.1016/j.rse.2016.08.014

Cited by 113 publications

(85 citation statements)

References 34 publications

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“…We found the gaps between potential and actual productivity differed regionally, which may reflect differences in alpine grassland types that are dominated by different plant species [37,40]. Similarly, Liang et al [41] found that alpine grassland biomass in the pastoral area of southern Qinghai Province, a region in the central-eastern Qinghai-Tibetan Plateau, shows considerable spatial heterogeneity because of the geographical, topographical, climatic and biophysical limitations. In this study, NPP gap was included in the assessment framework for evaluating the relative contributions of climate change and grazing activities.…”

Section: Discussionsupporting

confidence: 52%

Identifying the Relative Contributions of Climate and Grazing to Both Direction and Magnitude of Alpine Grassland Productivity Dynamics from 1993 to 2011 on the Northern Tibetan Plateau

Feng

Zhang

et al. 2017

Remote Sensing

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Alpine grasslands on the Tibetan Plateau are claimed to be sensitive and vulnerable to climate change and human disturbance. The mechanism, direction and magnitude of climatic and anthropogenic influences on net primary productivity (NPP) of various alpine pastures remain under debate. Here, we simulated the potential productivity (with only climate variables being considered as drivers; NPP P ) and actual productivity (based on remote sensing dataset including both climate and anthropogenic drivers; NPP A ) from 1993 to 2011. We denoted the difference between NPP P and NPP A as NPP pc to quantify how much forage can be potentially consumed by livestock. The actually consumed productivity (NPP ac ) by livestock were estimated based on meat production and daily forage consumption per standardized sheep unit. We hypothesized that the gap between NPP pc and NPP ac (NPP gap ) indicates the direction of vegetation dynamics, restoration or degradation. Our results show that growing season precipitation rather than temperature significantly relates with NPP gap , although warming was significant for the entire study region while precipitation only significantly increased in the northeastern places. On the Northern Tibetan Plateau, 69.05% of available alpine pastures showed a restoration trend with positive NPP gap , and for 58.74% of alpine pastures, stocking rate is suggested to increase in the future because of the positive mean NPP gap and its increasing trend. This study provides a potential framework for regionally regulating grazing management with aims to restore the degraded pastures and sustainable management of the healthy pastures on the Tibetan Plateau.

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Section: Discussionsupporting

confidence: 52%

Identifying the Relative Contributions of Climate and Grazing to Both Direction and Magnitude of Alpine Grassland Productivity Dynamics from 1993 to 2011 on the Northern Tibetan Plateau

Feng

Zhang

et al. 2017

Remote Sensing

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“…Elevation was regarded as a key topographical factor influencing the growth of vegetation [58]. The elevation data were acquired from the Shuttle Radar Topography Mission with a spatial resolution of 30 m.…”

Section: Climate and Topography Datamentioning

confidence: 99%

Migration of Rural Residents to Urban Areas Drives Grassland Vegetation Increase in China’s Loess Plateau

et al. 2019

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Human activities are critical factors influencing ecosystem sustainability. However, knowledge on regarding the mechanisms underlying the response of vegetation dynamics to human activities remains limited. To detect the driving factors and their individual contribution to the grassland vegetation dynamics in China’s Loess Plateau, a structural equation model (SEM) and a principal component regression model were built. The SEM showed that population change and urbanization, temperature and humidity, and agriculture and economy accounted for 62.5%, 31.2%, and 7.7%, respectively, of the overall impact directly affecting grassland vegetation dynamics. Furthermore, the principal component regression model demonstrated that the effects of the urbanization rate on the grassland above-ground biomass exceeded those of the other factors. The agriculture population had the maximum negative effect on grassland area. The higher the urbanization rate means the higher the number of residents migrates from rural to urban areas. Following this argument, the disturbances of human activities to grassland vegetation were expected to gradually decrease in rural areas, where the vast majority of the Loess Plateau is located. The migration of rural residents to urban areas promoted the increase in biomass and areas of grassland vegetation. Our findings suggest that the effect of urbanization should be considered when assessing vegetation change.

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“…(3) incorporate new remote sensing observation techniques (e.g., hyperspectral imagery and the UAV remote sensing technique) and strengthen research on the spectral characteristics of the grassland vegetation community and the applications of the narrow band remote sensing vegetation index in monitoring grassland AGB [47,48]; and (4) construct multi-factor grassland AGB estimation models based on statistical analysis and machine learning techniques. These multiple factors include climatic factors (e.g., sunlight, temperature and rainfall), soil factors (e.g., soil nutrients, soil structure and fertility), biological factors (e.g., grassland type, species richness and distribution of malignant weeds) and management factors (e.g., pasture, fencing enclosures and rotational grazing) [49][50][51]. For example, Li et al (2013) used neural networks to build an AGB model based on multiple MODIS vegetation indices and showed higher accuracy than a model based on a single index, by decreased RMSE of 433 kg/ha and increased R 2 of 0.35 [49].…”

Section: Limitations and Prospects Of Remote Sensing Monitoring Biomassmentioning

confidence: 99%

“…For example, Li et al (2013) used neural networks to build an AGB model based on multiple MODIS vegetation indices and showed higher accuracy than a model based on a single index, by decreased RMSE of 433 kg/ha and increased R 2 of 0.35 [49]. A multi-factor model by Liang et al (2016) showed decreased RMSE by 14.5% as compared with the optimum single-factor model [50]. Diouf et al (2016) studied the semi-arid grassland in the Sahel region and indicated that a combined photosynthetic radiation and meteorological data model had better performance (R 2 = 0.69 and RMSE = 483 kg DW/ha) than the single-factor model of photosynthetic radiation or meteorological data (R 2 = 0.63 and 0.55 and RMSE = 550 kg DW/ha and 585 kg DW/ha, respectively) [51].…”

Section: Limitations and Prospects Of Remote Sensing Monitoring Biomassmentioning

confidence: 99%

Evaluation of Remote Sensing Inversion Error for the Above-Ground Biomass of Alpine Meadow Grassland Based on Multi-Source Satellite Data

Meng

Liang

et al. 2017

Remote Sensing

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It is not yet clear whether there is any difference in using remote sensing data of different spatial resolutions and filtering methods to improve the above-ground biomass (AGB) estimation accuracy of alpine meadow grassland. In this study, field measurements of AGB and spectral data at Sangke Town, Gansu Province, China, in three years (2013)(2014)(2015) are combined to construct AGB estimation models of alpine meadow grassland based on these different remotely-sensed NDVI data: MODIS, HJ-1B CCD of China and Landsat 8 OLI (denoted as NDVI MOD , NDVI CCD and NDVI OLI , respectively). This study aims to investigate the estimation errors of AGB from the three satellite sensors, to examine the influence of different filtering methods on MODIS NDVI for the estimation accuracy of AGB and to evaluate the feasibility of large-scale models applied to a small area. The results showed that: (1) filtering the MODIS NDVI using the Savitzky-Golay (SG), logistic and Gaussian approaches can reduce the AGB estimation error; in particular, the SG method performs the best, with the smallest errors at both the sample plot scale (250 m × 250 m) and the entire study area (33.9% and 34.9%, respectively); (2) the optimum estimation model of grassland AGB in the study area is the exponential model based on NDVI OLI , with estimation errors of 29.1% and 30.7% at the sample plot and the study area scales, respectively; and (3) the estimation errors of grassland AGB models previously constructed at different spatial scales (the Tibetan Plateau, Gannan Prefecture and Xiahe County) are higher than those directly constructed based on the small area of this study by 11.9%-36.4% and 5.3%-29.6% at the sample plot and study area scales, respectively. This study presents an improved monitoring algorithm of alpine natural grassland AGB estimation and provides a clear direction for future improvement of the grassland AGB estimation and grassland productivity from remote sensing technology.

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Multi-factor modeling of above-ground biomass in alpine grassland: A case study in the Three-River Headwaters Region, China

Cited by 113 publications

References 34 publications

Identifying the Relative Contributions of Climate and Grazing to Both Direction and Magnitude of Alpine Grassland Productivity Dynamics from 1993 to 2011 on the Northern Tibetan Plateau

Identifying the Relative Contributions of Climate and Grazing to Both Direction and Magnitude of Alpine Grassland Productivity Dynamics from 1993 to 2011 on the Northern Tibetan Plateau

Migration of Rural Residents to Urban Areas Drives Grassland Vegetation Increase in China’s Loess Plateau

Evaluation of Remote Sensing Inversion Error for the Above-Ground Biomass of Alpine Meadow Grassland Based on Multi-Source Satellite Data

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