Spatio-Temporal Patterns and Climate Variables Controlling of Biomass Carbon Stock of Global Grassland Ecosystems from 1982 to 2006

Xia, Jiangzhou; Liang, Shunlin; Yang, Chen; Xu, Wenfang; Yuan, Wenping

doi:10.3390/rs6031783

Cited by 73 publications

(52 citation statements)

References 45 publications

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“…Precipitation also significantly influences the spatial and temporal dynamics of NDVI in which the biomass accumulation follows the precipitation gradient [63]. The variation in vegetation productivity could be linked to either change in vegetation or variation in precipitation distribution.…”

Section: Ndvi Precipitation Correlation Analysismentioning

confidence: 99%

Identifying Categorical Land Use Transition and Land Degradation in Northwestern Drylands of Ethiopia

Zewdie

Csaplovics

2016

Remote Sensing

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Land use transition in dryland ecosystems is one of the major driving forces to landscape change that directly impacts the welfare of humans. In this study, the support vector machine (SVM) classification algorithm and cross tabulation matrix analysis are used to identify systematic and random processes of change. The magnitude and prevailing signals of land use transitions are assessed taking into account net change and swap change. Moreover, spatiotemporal patterns and the relationship of precipitation and the Normalized Difference Vegetation Index (NDVI) are explored to evaluate landscape degradation. The assessment showed that 44% of net change and about 54% of total change occurred during the study period, with the latter being due to swap change. The conversion of over 39% of woodland to cropland accounts for the existence of the highest loss of valuable ecosystem of the region. The spatial relationship of NDVI and precipitation also showed R 2 of below 0.5 over 55% of the landscape with no significant changes in the precipitation trend, thus representing an indicative symptom of land degradation. This in-depth analysis of random and systematic landscape change is crucial for designing policy intervention to halt woodland degradation in this fragile environment.

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Section: Ndvi Precipitation Correlation Analysismentioning

confidence: 99%

Identifying Categorical Land Use Transition and Land Degradation in Northwestern Drylands of Ethiopia

Zewdie

Csaplovics

2016

Remote Sensing

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“…Overall, the higher temperature, higher precipitation and lower elevation could result in the higher grassland biomass density in the southeast. Previous researches also suggested that carbon storage tended to accumulate in area where climatic factors were suitable (Gao et al, 2013a;Jiang et al, 2014;Kang et al, 2013;Mao et al, 2014;Wang et al, 2014;Xia et al, 2014). Moreover, anthropogenic factor (e.g., forage, fire, green project etc.)…”

Section: Spatial Distribution Of Grassland Biomass and Relative Uncermentioning

confidence: 99%

Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches

Jia

Liu

Yang

et al. 2016

Ecological Indicators

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a b s t r a c tAccurate estimation of grassland biomass and its dynamics are crucial not only for the biogeochemical dynamics of terrestrial ecosystems, but also for the sustainable use of grassland resources. However, estimations of grassland biomass on large spatial scale usually suffer from large variability and mostly lack quantitative uncertainty analyses. In this study, the spatial grassland biomass estimation and its uncertainty were assessed based on 265 field measurements and remote sensing data across Northern China during [2001][2002][2003][2004][2005]. Potential sources of uncertainty, including remote sensing data sources (DATsrc), model forms (MODfrm) and model parameters (biomass allocation, BMallo, e.g. root:shoot ratio), were determined and their relative contribution was quantified. The results showed that the annual grassland biomass in Northern China was 1268.37 ± 180.84 Tg (i.e., 532.02 ± 99.71 g/m 2 ) during 2001-2005, increasing from western to eastern area, with a mean relative uncertainty of 19.8%. There were distinguishable differences among the uncertainty contributions of three sources (BMallo > DATsrc > MODfrm), which contributed 52%, 27% and 13%, respectively. This study highlighted the need to concern the uncertainty in grassland biomass estimation, especially for the uncertainty related to BMallo.

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“…Grassland ecosystems accumulate up to 18% of the total global terrestrial carbon sink each year and play an important role in the global carbon cycle [3]. However, due to global warming and unreasonable human disturbance [4], they grazing regions from the NDVI data and then obtained grazing intensity through the grazing steppe area and annual sheep units [27].…”

Section: Introductionmentioning

confidence: 99%

Simulation of the Grazing Effects on Grassland Aboveground Net Primary Production Using DNDC Model Combined with Time-Series Remote Sensing Data—A Case Study in Zoige Plateau, China

Wang

Bian

2016

Remote Sensing

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Abstract:Measuring the impact of livestock grazing on grassland above-ground net primary production (ANPP) is essential for grass yield estimation and pasture management. However, since there is a lack of accurate and repeatable techniques to obtain the details of grazing locations and stocking rates at the regional scale, it is an extremely challenging task to study the influence of regional grazing on the grassland ANPP. Taking Zoige County as a case, this paper proposes an approach to quantify the spatial and temporal variation of grazing intensity and grazing period through time-series remote sensing data, simulated grassland ANPP through the denitrification and decomposition (DNDC) model, and then explores the impact of grazing on grassland ANPP. The result showed that the model-estimated ANPP while considering grazing had a significant relationship with the field-observed ANPP, with the coefficient of determination (R 2 ) of 0.75, root mean square error (RMSE) of 122.86 kgC/ha, and average relative error (RE) of 8.77%. On the contrary, if grazing activity was not considered in simulation, a large uncertainty was found when the model-estimated ANPP was compared with the field observation, showing R 2 of 0.4, RMSE of 211.51 kgC/ha, and average RE of 32.5%. For the whole area of Zoige County in 2012, the statistics of the estimation showed that the total regional ANPP was up to 3.815ˆ10 5 tC, while the total regional ANPP, without considering grazing, would be overestimated by 44.4%, up to 5.51ˆ10 5 tC. This indicates that the grazing parameters derived in this study could effectively improve the accuracy of ANPP simulation results. Therefore, it is feasible to combine time-series remote sensing data with the process model to simulate the grazing effects on grassland ANPP. However, some issues, such as selecting proper remote sensing data, improving the quality of model input parameters, collecting more field data, and exploring the data assimilation approaches, still should be considered in the future work.

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Spatio-Temporal Patterns and Climate Variables Controlling of Biomass Carbon Stock of Global Grassland Ecosystems from 1982 to 2006

Cited by 73 publications

References 45 publications

Identifying Categorical Land Use Transition and Land Degradation in Northwestern Drylands of Ethiopia

Identifying Categorical Land Use Transition and Land Degradation in Northwestern Drylands of Ethiopia

Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches

Simulation of the Grazing Effects on Grassland Aboveground Net Primary Production Using DNDC Model Combined with Time-Series Remote Sensing Data—A Case Study in Zoige Plateau, China

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