A data-driven estimate of litterfall and forest carbon turnover and the drivers of their inter-annual variabilities in forest ecosystems across China

Zhao, Xilin; Tang, Xiaolu; Du, Jie; Pei, Xiangjun; Chen, Guo; Xu, Tingting

doi:10.1016/j.scitotenv.2022.153341

Cited by 5 publications

(5 citation statements)

References 83 publications

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“…In the CiteSpace software, "climate change" was the keyword phrase with the highest frequency of occurrence, appearing 1259 times in total. Climate change is a fundamental research foundation for terrestrial ecosystem carbon storage [29]. Under the In the CiteSpace software, "climate change" was the keyword phrase with the highest frequency of occurrence, appearing 1259 times in total.…”

Section: Research Hotspots and Trendsmentioning

confidence: 99%

“…Under the In the CiteSpace software, "climate change" was the keyword phrase with the highest frequency of occurrence, appearing 1259 times in total. Climate change is a fundamental research foundation for terrestrial ecosystem carbon storage [29]. Under the trend of global warming, increasing carbon storage has become one of the key strategies for mitigating climate change.…”

Section: Research Hotspots and Trendsmentioning

confidence: 99%

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A Bibliometric Analysis of the Research Progress and Trends during 2002–2022 on the Carbon Stocks in Terrestrial Ecosystems

Sawirdin,

Tian,

Shi

et al. 2023

Forests

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Improving the carbon storage in terrestrial ecosystems can effectively reduce atmospheric CO2, which is one of the important ways of mitigating global climate change. The knowledge on terrestrial carbon stock research is relatively mature in the international community; however, the research pulse, hotspots, and trends in terrestrial ecosystems carbon stock research are not clear. To better understand the research focus and developmental directions of terrestrial ecosystems’ carbon storage, we conducted a bibliometric analysis of 6305 research articles between 2002 and 2022, which were gathered from the Web of Science Core Database. The temporal distribution, country/region distribution, co-citation network, keyword evolution and clustering, journals, and authors of the literature were analyzed, and the knowledge domain was mapped using the CiteSpace visualization software. This study established the following three observations: (1) The number of publications on carbon stock research in terrestrial ecosystems continues to expand, and the trend in the number of publications proves that carbon sinks in terrestrial systems remain an important research topic internationally. (2) Important issues concerning terrestrial ecosystem carbon stock research have evolved from monitoring the carbon stock changes in terrestrial ecosystems to the mechanism of carbon stock formation for the realization of monitoring and management under global climate change. Furthermore, the research methodology has evolved from small-scale, fixed-point instrumental observations to large-scale remote sensing and model simulations, with diversifications in research content and methodology. (3) In the future, academic research on carbon stocks in terrestrial ecosystems will focus more on international and interdisciplinary cooperations, increasing the intensity in surveying and monitoring carbon stocks in terrestrial ecosystems, and realizing the accurate monitoring, assessment, management, and enhancement of carbon stocks from the integrated carbon stock computation system for “sky, air, and land.” The results of this study comprehensively demonstrate the current status and development of the research on carbon stocks in terrestrial ecosystems worldwide, and provide a reference for future research on the sustainable management of terrestrial ecosystems within the context of global warming.

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Section: Research Hotspots and Trendsmentioning

confidence: 99%

Section: Research Hotspots and Trendsmentioning

confidence: 99%

A Bibliometric Analysis of the Research Progress and Trends during 2002–2022 on the Carbon Stocks in Terrestrial Ecosystems

Sawirdin,

Tian,

Shi

et al. 2023

Forests

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“…Terrestrial ecosystems play a key role in the global carbon cycle [1,2] and are important for mitigating climate change and achieving "carbon neutrality" [3,4]. The Chinese government has announced that it will achieve "carbon neutral" by 2060 [5].…”

Section: Introductionmentioning

confidence: 99%

Carbon Storages and Densities of Different Ecosystems in Changzhou City, China: Subtropical Forests, Urban Green Spaces, and Wetlands

Deng,

Liu,

et al. 2024

Forests

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Climate change mitigation and carbon neutrality are current hot topics. Forests, urban green spaces, and wetland ecosystems are recognized as important carbon sinks. The Yangtze River Delta region in Eastern China, which plays a pivotal role in China’s economic and social development, is rich in such carbon-sink resources. There is, however, a lack of regional carbon data. The investigation of carbon storage and carbon densities of forest, urban green space, and wetland ecosystems is, therefore, of great importance. In this study, the forest resource management map (including wetland) and green space system planning map of Changzhou city, combined with a field investigation and laboratory experimental analysis, were used to estimate the carbon storages and carbon densities of the forest, urban green space, and wetland ecosystems in Changzhou city. The average carbon density and carbon storage in Changzhou were 83.34 ± 4.91 Mg C ha−1 and 11.30 ± 0.67 Tg C, respectively, of which soil accounted for 74%, plants accounted for 25%, and litter accounted for less than 1%. The forest ecosystem contributed the most to the carbon pool (72%), with the green space ecosystem and the wetland ecosystem each accounting for 14% of the carbon pools. Clearly, the forest, green space, and wetland ecosystems in Changzhou city have a large carbon storage capacity. This study is of significance as it provides data on the carbon sink functions of forest, green space, and wetland ecosystems at the provincial and national regional scales.

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“…Using individual studies, global and regional database of litterfall and publications), etc. and (b) Regional-Chinese forests (Zhao et al, 2022, Jia et al, 2020, European forests (Neumann et al, 2018) etc. Use of global data sets for regional variability and climate-based models & quanti cation of total litterfall ux have been critical in quantifying litterfall ux for different forest types (Mathews 1997) as well as to develop relation with climatic factors (Meentemeyer et al, 1982).…”

Section: Introductionmentioning

confidence: 99%

“…Recent methodological enhancements use improved geostatistical and machine learning methods on a wider set of geospatial covariates (Shen et al, 2019, Geng et al, 2022, Zhao et al, 2022 and use of remote sensing data in estimating litterfall (Wang et al, 2016, Hu et al, 2019, Shen et al, 2019. Recently there is spurt in studies reporting eld measurements and their statistical models (Wen and He, 2016; Shen et al, 2017, You et al, 2017, Jia et al, 2018Liu et al, 2019, Zhang et al, 2018, which however do not address the spatial variation at regional scale.…”

Section: Introductionmentioning

confidence: 99%

Spatial Assessment of forest litterfall in Central Himalayas (India): Comparison of geospatial, remote sensing and data-driven estimates

Kaushik

2023

Preprint

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A comparison of multiple approaches for annual litterfall estimation and spatial assessment of forests was carried out for the state of Uttarakhand (Geog. Area = 54533 sq. km, Forest Area- 24652.32 sq. km) in Central Himalayas, India. Non-spatial approach used meta-analysis of published litterfall studies in Uttarakhand (29 studies with 115 measurements over sites/years) classified by forest types and area under forest types estimated by remote sensing by Forest Survey of India. The measured mean litterfall ranged from a high of 7.88 t/ha/yr for the sub-tropical broad- leaved forests to a low of 3.70 t/ha/yr in plantations. Spatial models of litterfall used a data-driven approach with 100 measurements and a random forest (RF) model that used bioclimate, elevation and forest type as covariates at a spatial grid of 1km resolution. This estimate was compared with published global (Li et al., 2019) and European (Neumann et al., 2018) spatial models. The total litterfall with five different forest-type area and estimated mean litterfall varied between 12.34 to 14.69 Mt/yr and with spatial allocation to forest type map estimated 14.02 Mt/yr litterfall. Data-driven spatial model using Random Forest approach estimated 13.305 Mt/yr of total litterfall. Use of spatial litterfall models developed for other study areas resulted in estimates that ranged from 9.11–15.81 Mt/yr. The study provides important insights towards developing a spatial gridded annual litterfall dataset for India and its use for studying the dynamics of forest carbon cycle.

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A data-driven estimate of litterfall and forest carbon turnover and the drivers of their inter-annual variabilities in forest ecosystems across China

Cited by 5 publications

References 83 publications

A Bibliometric Analysis of the Research Progress and Trends during 2002–2022 on the Carbon Stocks in Terrestrial Ecosystems

A Bibliometric Analysis of the Research Progress and Trends during 2002–2022 on the Carbon Stocks in Terrestrial Ecosystems

Carbon Storages and Densities of Different Ecosystems in Changzhou City, China: Subtropical Forests, Urban Green Spaces, and Wetlands

Spatial Assessment of forest litterfall in Central Himalayas (India): Comparison of geospatial, remote sensing and data-driven estimates

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