Increased Ecosystem Carbon Storage between 2001 and 2019 in the Northeastern Margin of the Qinghai-Tibet Plateau

Wei, Peijie; Chen, Shengyun; Wu, Minghui; Jia, Yinglan; Xu, Haojie; Liu, Deming

doi:10.3390/rs13193986

Cited by 17 publications

(12 citation statements)

References 49 publications

(71 reference statements)

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“…However, research also indicates that LUCC of terrestrial ecosystems has become an important source of carbon emissions, which is second only to the burning of fossil fuel 8 , 9 . Research on changes in carbon storage based on LUCC has become one of the main methods for monitoring ecosystem service function 10 , 11 .…”

Section: Introductionmentioning

confidence: 99%

“…The InVEST model provides new technology for conducting spatial expression, dynamic analysis, and quantitative evaluation of ecosystem service function 14 . More importantly, the InVEST model can easily be used to assess the impact of climate and LUCC changes on ecosystem carbon storage 11 . Changes in LUCC alter the structure (biomass, species composition) and function (energy balance, carbon cycle, biodiversity) of the ecosystem.…”

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal evolution and driving factors of carbon storage in the Western Sichuan Plateau

Xiang

Wang

Tan

et al. 2022

Sci Rep

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The carbon sequestration function of the ecosystem is one of the most important functions of ecosystem service, and it of great significance to study the spatio-temporal differentiation of carbon storage for promoting regional sustainable development. Ecosystems on the Western Sichuan Plateau are highly variable, but its spatio-temporal differentiation and driving factors are not yet clear. In this study, on the basis of land use monitoring data, meteorological and demographic data interpreted from Landsat remote sensing image, and through GIS analysis tools, the carbon storage module of InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model was used to estimate carbon storage and geodetector was used to detect the driving factors of carbon storage spatial differentiation. The results show that: (1) The carbon storage increased to 1.2455 × 1010 t from 1.2438 × 1010 t in the past 20 years, the ecosystem developed in a healthy way overall. (2) Carbon storage show High-High and Low-Low aggregation characteristics, but the area decreased by 1481.81 km2 and 311.11 km2 respectively, and the spatial cluster effect gradually weakened. (3) HAI is the leading factor causing the spatio-temporal differentiation of regional carbon storage, followed by temperature and NDVI; the interaction between factors significantly enhances the spatial differentiation of carbon storage, indicating that the change of carbon storage is the result of the joint action of natural and socioeconomic factors. The results of the study provide some theoretical basis for the development of differentiated ecological regulation models and strategies, and help to promote high-quality regional development.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatio-temporal evolution and driving factors of carbon storage in the Western Sichuan Plateau

Xiang

Wang

Tan

et al. 2022

Sci Rep

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“…Many studies have utilised the InVEST model to evaluate the status of C reserves in regional ecosystems. For example, Wei et al (2021) performed a systematic study on C stocks in the northeastern Qinghai-Tibet Plateau (QTP) (P. Wei et al 2021). However, this only re ected the changes in regional ecological C stocks and it was lack of basis for predicting future trends in C stocks or the future management of C pools and policy formulation.…”

Section: Deng Et Al 2016)mentioning

confidence: 99%

“…LUCC can also alter the process of C cycling by changing the structure and function of ecosystems (P. Wei et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal variation and future multi-scenario simulation of carbon storage in Bailong River Basin using GeoSOS-FLUS and InVEST models

Wang

Zhang

Ding

et al. 2023

Preprint

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By providing a scientific foundation for managing regional ecosystem carbon (C) pools, research on the spatial distribution characteristics of regional C stocks can assist in the development of policies on C emissions reduction and sequestration enhancement. Using the GeoSOS-FLUS and InVEST models and explorations of the Bailong River Basin in the past 20 years, the influence of three future scenarios of land use change—natural development (ND), ecological protection (EP) and arable land protection (ALP)—on C storage was modelled. Between 2000 and 2020, there was a gradual increase in C storage in the BRB with a total increase of 5.58 Tg (3.19%), showing notable spatial heterogeneity. The increase in C storage was attributed to land use conversion among woodland, arable land and grassland, with the conversion between woodland and arable land being the primary factor contributing to the increase in C storage. By 2050, C storage under the EP, ALP and NP scenarios was 183.915, 183.108 and 183.228 Tg, respectively. In 2050, C storage under the EP scenario increased by 0.37% compared with that in 2020, and decreased by 0.07% and 0.005% under the ALP and NP scenarios, respectively. In contrast to the other scenarios, the EP scenario prioritised the protection of the woodland and grassland C sinks, which has significant implications for future planning.

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“…Establishing relationships between VIs and recorded AGB is the most common and direct approach for regional grassland AGB assessment. Many studies have reported that there is a strong correlation between the VIs, including normalized difference vegetation index (NDVI; Wei et al, 2021), enhanced vegetation index (EVI; Ge et al, 2018), soil adjusted vegetation index (SAVI; Ren et al, 2018), ratio vegetation index (RVI; Guerschman et al, 2009) and biomass in varies satellite images. However, VIs are subjected to the influence of environmental condition and the capability of characterizing vegetation status in certain circumstance, which can introduce errors and uncertainties into AGB estimation (Ali et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based grassland aboveground biomass estimation and its response to climate variation in Southwest China

Liu

Zhang

et al. 2023

Front. Ecol. Evol.

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The demand for accurate estimation of aboveground biomass (AGB) at high spatial resolution is increasing in grassland-related research and management, especially for those regions with complex topography and fragmented landscapes, where grass and shrub are interspersed. In this study, based on 519 field AGB observations, integrating Synthetic Aperture Radar (SAR; Sentinel-1) and high-resolution (Sentinel-2) remote sensing images, environmental and topographical data, we estimated the AGB of mountain grassland in Southwest China (Yunnan Province and Guizhou Province) by using remote sensing algorithms ranging from traditional regression to cutting edge machine learning (ML) and deep learning (DL) models. Four models (i.e., multiple stepwise regression (MSR), random forest (RF), support vector machine (SVM) and convolutional neural network (CNN)) were developed and compared for AGB simulation purposes. The results indicated that the RF model performed the best among the four models (testing dataset: decision co-efficient (R2) was 0.80 for shrubland and 0.75 for grassland, respectively). Among all input variables in the RF model, the vegetation indices played the most important role in grassland AGB estimation, with 6 vegetation indices (EVI, EVI2, NDVI, NIRv, MSR and DVI) in the top 10 of input variables. For shrubland, however, topographical factors (elevation, 12.7% IncMSE (increase in mean squared error)) and SAR data (VH band, 11.3% IncMSE) were the variables which contributed the most in the AGB estimation model. By comparing the input variables to the RF model, we found that integrating SAR data has the potential to improve grassland AGB estimation, especially for shrubland (26.7% improvement in the estimation of shrubland AGB). Regional grassland AGB estimation showed a lower mean AGB in Yunnan Province (443.6 g/m2) than that in Guizhou Province (687.6 g/m2) in 2021. Moreover, the correlation between five consecutive years (2018–2022) of AGB data and climatic factors calculated by partial correlation analysis showed that regional AGB was positively related with mean annual precipitation in more than 70% of the grassland and 60% of the shrubland area, respectively. Also, we found a positive relationship with mean annual temperature in 62.8% of the grassland and 55.6% of the shrubland area, respectively. This study demonstrated that integrating SAR into grassland AGB estimation led to a remote sensing estimation model that greatly improved the accuracy of modeled mountain grassland AGB in southwest China, where the grassland consists of a complex mix of grass and shrubs.

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Increased Ecosystem Carbon Storage between 2001 and 2019 in the Northeastern Margin of the Qinghai-Tibet Plateau

Cited by 17 publications

References 49 publications

Spatio-temporal evolution and driving factors of carbon storage in the Western Sichuan Plateau

Spatio-temporal evolution and driving factors of carbon storage in the Western Sichuan Plateau

Spatio-temporal variation and future multi-scenario simulation of carbon storage in Bailong River Basin using GeoSOS-FLUS and InVEST models

Machine learning-based grassland aboveground biomass estimation and its response to climate variation in Southwest China

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