Study on the Relationship between Topological Characteristics of Vegetation Ecospatial Network and Carbon Sequestration Capacity in the Yellow River Basin, China

Achieving carbon neutrality is a necessary effort to rid humanity of a catastrophic climate and is a goal for China in the future. Ecological space plays an important role in the realization of carbon neutrality, but the relationship between the structure of vegetation ecological space and vegetation carbon sequestration capacity has been the focus of research. In this study, we extracted the base data from MODIS products and other remote sensing products, and then combined them with the MCR model to construct a vegetation ecospatial network in the Yellow River Basin in 2018. Afterward, we calculated the topological indicators of ecological nodes in the network and analyzed the relationship between the carbon sequestration capacity (net biome productivity) of ecological nodes and these topological indicators in combination with the Biome-BGC model. The results showed that there was a negative linear correlation between the betweenness centrality of forest nodes and their carbon sequestration capacity in the Yellow River Basin (p < 0.05, R2 = 0.59). On the other hand, there was a positive linear correlation between the clustering coefficient of grassland nodes and their carbon sequestration capacity (p < 0.01, R2 = 0.49). In addition, we briefly evaluated the vegetation ecospatial network in the Yellow River BASIN and suggested its optimization direction under the background of carbon neutrality in the future. Increasing the carbon sequestration capacity of vegetation through the construction of national ecological projects is one of the ways to achieve carbon neutrality, and this study provides a reference for the planning of future national ecological projects in the Yellow River Basin. Furthermore, this is also a case study of the application of remote sensing in vegetation carbon budgeting.

[1]  Lihua Yuan,et al.  Spatio-temporal analysis of vegetation variation in the Yellow River Basin , 2015 .

[2]  Xia Li,et al.  A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data , 2010, Landscape Ecology.

[3]  Rui Guo,et al.  Effects of climate warming on net primary productivity in China during 1961–2010 , 2017, Ecology and evolution.

[4]  M. Goodchild The Validity and Usefulness of Laws in Geographic Information Science and Geography , 2004 .

[5]  A. Binley,et al.  Spatial variations in soil-water carrying capacity of three typical revegetation species on the Loess Plateau, China , 2019, Agriculture, Ecosystems & Environment.

[6]  L. Zhen,et al.  Ecological and socioeconomic effects of ecological restoration in China's Three Rivers Source Region. , 2019, The Science of the total environment.

[7]  Qianxi Li,et al.  Forest dynamics and carbon storage under climate change in a subtropical mountainous region in central China , 2020, Ecosphere.

[8]  L. da F. Costa,et al.  Characterization of complex networks: A survey of measurements , 2005, cond-mat/0505185.

[9]  F. Yang,et al.  Constructing and optimizing ecological network at county and town Scale: The case of Anji County, China , 2021, Ecological Indicators.

[10]  Qibin Zhang,et al.  The optimization of urban ecological infrastructure network based on the changes of county landscape patterns: a typical case study of ecological fragile zone located at Deng Kou (Inner Mongolia) , 2017 .

[11]  Yan Zhao,et al.  Sustainable development in the Yellow River Basin: Issues and strategies , 2020 .

[12]  Bojie Fu,et al.  Landscape ecology: Coupling of pattern, process, and scale , 2011 .

[13]  Fang Jing,et al.  IMPLICATIONS AND ESTIMATIONS OF FOUR TERRESTRIAL PRODUCTIVITY PARAMETERS , 2001 .

[14]  Shenglu Zhou,et al.  Multiple landscape “source–sink” structures for the monitoring and management of non-point source organic carbon loss in a peri-urban watershed , 2016 .

[15]  L. Cui,et al.  Construction and optimization of green space ecological networks in urban fringe areas: A case study with the urban fringe area of Tongzhou district in Beijing , 2020 .

[16]  David Pont,et al.  Forest-Scale Phenotyping: Productivity Characterisation Through Machine Learning , 2020, Frontiers in Plant Science.

[17]  H. Gauch,et al.  Multivariate analysis of plant communities and environmental factors in Ngari, Tibet , 1986 .

[18]  Steven W. Running,et al.  Reconciling satellite with ground data to estimate forest productivity at national scales , 2012 .

[19]  B. Fu,et al.  Source-sink landscape theory and its ecological significance , 2008, Frontiers of Biology in China.

[20]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[21]  M. Kimberley,et al.  Predicting the spatial distribution of Cupressus lusitanica productivity in New Zealand , 2009 .

[22]  Fei Wang,et al.  Quantifying the impact of climate variability and human activities on streamflow in the middle reaches of the Yellow River basin, China , 2014 .

[23]  Haijun Wang,et al.  Constructing and optimizing urban ecological network in the context of rapid urbanization for improving landscape connectivity , 2021, Ecological Indicators.

[24]  Lei Deng,et al.  Effects of national ecological restoration projects on carbon sequestration in China from 2001 to 2010 , 2018, Proceedings of the National Academy of Sciences.

[25]  Qiang Yu,et al.  Optimization of ecological node layout and stability analysis of ecological network in desert oasis:a typical case study of ecological fragile zone located at Deng Kou County(Inner Mongolia) , 2018 .

[26]  Stefan Richter,et al.  Centrality Indices , 2004, Network Analysis.

[27]  Qiang Yu,et al.  Optimization of landscape spatial structure aiming at achieving carbon neutrality in desert and mining areas , 2021, Journal of Cleaner Production.

[28]  O. Sun,et al.  Testing parameter sensitivities and uncertainty analysis of Biome-BGC model in simulating carbon and water fluxes in broadleaved-Korean pine forests , 2018 .

[29]  G. S. Cumming,et al.  Improving network approaches to the study of complex social–ecological interdependencies , 2019, Nature Sustainability.

[30]  G. Bellocchi,et al.  Spatial probability modelling of forest productivity indicator in Italy , 2020, Ecological Indicators.

[31]  Min Chen,et al.  Soil seed bank and vegetation differences following channel diversion in the Yellow River Delta. , 2019, The Science of the total environment.

[32]  Eric Ceschia,et al.  The role of grazing management for the net biome productivity and greenhouse gas budget (CO2, N2O and CH4) of semi-natural grassland , 2007 .

[33]  S. Running,et al.  Numerical Terradynamic Simulation Group 7-2010 Simulations show decreasing carbon stocks and potential for carbon emissions in Rocky Mountain forests over the next century , 2018 .

[34]  W. Ju,et al.  Spatial distribution of carbon sources and sinks in Canada’s forests , 2003 .

[35]  Philippe Ciais,et al.  Reduced sediment transport in the Yellow River due to anthropogenic changes , 2016 .

[36]  Xu Han-qiu,et al.  A Study on Information Extraction of Water Body with the Modified Normalized Difference Water Index (MNDWI) , 2005, National Remote Sensing Bulletin.

[37]  Jinman Wang,et al.  Construction of landscape ecological network based on landscape ecological risk assessment in a large-scale opencast coal mine area , 2021 .

[38]  Pete Smith,et al.  Co‐benefits, trade‐offs, barriers and policies for greenhouse gas mitigation in the agriculture, forestry and other land use (AFOLU) sector , 2014, Global change biology.

[39]  Guirui Yu,et al.  Climate change, human impacts, and carbon sequestration in China , 2018, Proceedings of the National Academy of Sciences.

[40]  Hirofumi Hashimoto,et al.  Modeling the interannual variability and trends in gross and net primary productivity of tropical forests from 1982 to 1999 , 2005 .

[41]  Fei Wang,et al.  Capability of Remotely Sensed Drought Indices for Representing the Spatio-Temporal Variations of the Meteorological Droughts in the Yellow River Basin , 2018, Remote. Sens..

[42]  Zhili Liu,et al.  Simulation of a forest-grass ecological network in a typical desert oasis based on multiple scenes , 2019 .

[43]  W. Cohen,et al.  Evaluation of MODIS NPP and GPP products across multiple biomes. , 2006 .

[44]  Lunche Wang,et al.  Vegetation dynamics and the relations with climate change at multiple time scales in the Yangtze River and Yellow River Basin, China , 2020 .

[45]  Manuel Campos-Taberner,et al.  Remote Sensing and Bio-Geochemical Modeling of Forest Carbon Storage in Spain , 2020, Remote. Sens..

[46]  Shi-liang Liu,et al.  Ecological network construction of the heterogeneous agro-pastoral areas in the upper Yellow River basin , 2020 .

[47]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[48]  Gaohuan Liu,et al.  A River Basin over the Course of Time: Multi-Temporal Analyses of Land Surface Dynamics in the Yellow River Basin (China) Based on Medium Resolution Remote Sensing Data , 2016, Remote. Sens..

[49]  W. Tobler A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .