Isolating the direct and indirect impacts of urbanization on vegetation carbon sequestration capacity in a large oasis city: evidence from Urumqi, China

Zhuang, Qingwei; Shao, Zhenfeng; Li, Deren; Huang, Xiao; Altan, Orhan; Wu, Shanshan; Li, Yuzhen

doi:10.1080/10095020.2022.2118624

Cited by 22 publications

(10 citation statements)

References 67 publications

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“…Future research should consider the opinions of multidisciplinary experts, establish a comprehensive indicator system, and provide better references for decision makers [69]. However, this paper focuses particularly on using the photosynthesis reaction equation to calculate the amount of CO 2 fixed, which represents the carbon fixation capacity of vegetation [34,70]. The NPP used here refers to the fraction of organic carbon fixed by vegetation, less its own respiratory consumption [71].…”

Section: Discussionmentioning

confidence: 99%

“…Referring to the "Yunnan Province Forest Resource Planning and Design Survey Operation Rules", the slope was divided into six levels and the aspect was divided into nine directions [45,46]. Using the reclassification tool in ArcGIS, the slope was divided into six categories (in degrees): gentle slope (0-5), mild slope (6)(7)(8)(9)(10)(11)(12)(13)(14)(15), moderate slope (16)(17)(18)(19)(20)(21)(22)(23)(24)(25), steep slope (26)(27)(28)(29)(30)(31)(32)(33)(34)(35), sharp slope (36)(37)(38)(39)(40)(41)(42)(43)(44)(45), and dangerous slope (≥46). The remaining six variables were converted from continuous to categorical using the natural break point classification method (Table 1).…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Anthropogenic factors mainly include changes in land-use types due to human activities. It has been shown that the conversion of land-use types caused by urban expansion has a negative impact on the carbon fixation capacity of vegetation [32][33][34]. Current studies suggest that the commonly held view that conservation farming is beneficial for carbon fixation might be an illusion created by sampling methods; the data reported so far are not convincing, so the impact of cultivated land on relevant carbon fixation remains uncertain [35].…”

Section: Introductionmentioning

confidence: 99%

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The Relationship between the Carbon Fixation Capacity of Vegetation and Cultivated Land Expansion and Its Driving Factors in an Oasis in the Arid Region of Xinjiang, China

Sun,

Jiang,

et al. 2024

Forests

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In the process of agricultural development in arid and semi-arid areas, the carbon fixation capacity of vegetation can be affected to different degrees, but research on its driving factors is lacking. Consequently, this paper focuses on the Weiku Oasis in Xinjiang as its research area, in which the carbon fixation capacity of vegetation is estimated with the chemical equation of a photochemical reaction, using methods such as linear system models and Geodetector to analyze the relationship between cropland expansion characteristics and the carbon fixation capacity of vegetation from 1990 to 2020. The influence of land-use changes on the space differentiation of carbon fixation was elucidated through a time series relationship, and the synergistic effects of nine influencing factors on the carbon fixation capacity during the process of vegetation changes were discussed. The results were as follows: (1) In the process of agricultural development, the proportions of cultivated land area and spatial agglomeration had significant negative correlations with carbon sequestration, and the significance was rising, but the effect of cultivated land area proportion was more significant. (2) Through temporal sequential cooperativity analysis, when other land-use types were converted into cultivated land, the carbon fixation capacity of vegetation suddenly and significantly decreased in the initial year of the transformation, but the effect of cultivated land reclamation on the carbon fixation capacity of vegetation did not have a significant time lag. Moreover, after a certain period of time, cultivated land can gradually recover part of its lost carbon fixation capacity. (3) Among the nine driving factors, potential evapotranspiration is the most prominent in explaining the carbon fixation capacity of vegetation. This single-factor pairwise interaction presents the relationship between bivariate enhancement and nonlinear enhancement. When terrain factors interact with other factors, the enhancement effect of the influence on the carbon fixation capacity of vegetation has an obvious promotion effect. However, the change in the carbon fixation capacity of vegetation is more significantly influenced by potential evapotranspiration and the interaction between the normalized difference vegetation index (NDVI) and other factors. This research is helpful to understanding the basic theories related to the change in the carbon fixation capacity of vegetation during the process of agricultural development in arid and semi-arid areas, as well as providing theoretical reference for ecological environment construction and sustainable development.

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Section: Discussionmentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Relationship between the Carbon Fixation Capacity of Vegetation and Cultivated Land Expansion and Its Driving Factors in an Oasis in the Arid Region of Xinjiang, China

Sun,

Jiang,

et al. 2024

Forests

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“…Third, the low spatial resolution of SIF used in this study is subject to scale effects (Liu et al, 2019;Shao et al, 2021), making it necessary to use high-resolution remote sensing data to derive vegetation phenology for the study. Finally, the impact of urban expansion (Zhuang et al, 2022a) and salinization (Zhuang et al, 2021(Zhuang et al, , 2022b, which affect the photosynthesis of vegetation and cause changes in phenology, may also be considered. Therefore, future work will investigate the mechanisms and future predictions of climatic conditions on phenology evolution, the effects of urbanization and land use change on phenology and the combined effects of lag and scale effects on photosynthetic and greenness phenology.…”

Section: Limitations and Outlookmentioning

confidence: 99%

Vegetation greenness and photosynthetic phenology in response to climatic determinants

Dang

Shao

Huang

et al. 2023

Front. For. Glob. Change

Self Cite

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Vegetation phenology is a key indicator of vegetation-climate interactions and carbon sink changes in ecosystems. Therefore, it is very important to understand the temporal and spatial variability of vegetation phenology and the driving climatic determinants [e.g., temperature (Ts) and soil moisture (SM)]. Vegetation greenness and photosynthetic phenology were derived using the double logistic (DL) method to enhance vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF) spring and autumn phenology, respectively. The growing season length (GSL) of greenness phenology (about 100 days) derived EVI was longer than GSL of photosynthetic phenology (about 80 days) derived SIF. Although their overall spatiotemporal pattern trends were consistent, photosynthetic phenology varied 1.4 to 3.1 times more than greenness phenology over time. In addition, SIF-based photosynthetic phenology and EVI-based greenness phenology showed consistent factors of drivers but differed to some extent in spatial patterns and the most relevant preseason dates. Spring photosynthetic phenology was mainly influenced by pre-season mean cumulative Ts (about 90 days). However, greenness phenology was controlled by both pre-seasons mean cumulative Ts [(about 55 days) and mean cumulative SM (about 40 days)]. Autumn photosynthetic phenology was controlled by both periods’ mean cumulative Ts [(about 20 days) and SM (about 20 days)], but autumn greenness phenology was mainly influenced by pre-season mean cumulative Ts (85 days). The comparison analysis of SIF and EVI phenology helps to understand the difference between photosynthetic phenology and greenness phenology at a regional scale.

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“…Random Forest (RF) 4 : RF is a decision tree-based algorithm that combines multiple decision trees to improve prediction accuracy. Each decision tree in the forest is trained on a random subset of the data and features.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Carbon Neutrality Strategy in Sports Event Management based on STIRPAT-GRU and Transfer Learning

Zhang

2023

Preprint

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With the increasing concern about carbon emissions and climate change, carbon neutrality has become a crucial issue in various domains, including sports event management. Artificial intelligence has shown great potential in addressing environmental challenges and improving sustainability. To develop AI-powered carbon neutrality strategies for sports event managementthe ,this paper proposed a method based on STIRPAT-GRU and transfer learning. The primary objective of this research is to analyze the impact of population, wealth, and technology on carbon emissions in sports event management and to predict future trends and patterns of carbon emissions. Firstly, the STIRPAT model is used to identify the key drivers of carbon emissions in sports events. Then, the GRU neural network is then used to develop a predictive model to forecast future trends and patterns of carbon emissions. Finally, transfer learning is used to improve the accuracy and robustness of the model by leveraging knowledge and experience gained from other domains.The experimental results show that the proposed approach outperforms other methods, demonstrating its effectiveness in predicting carbon emissions in sports events and developing a carbon neutrality strategy. The significance of this research lies in its potential to provide sports event managers with a data-driven approach to manage carbon emissions, leading to a more sustainable and environmentally friendly sports industry.

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Isolating the direct and indirect impacts of urbanization on vegetation carbon sequestration capacity in a large oasis city: evidence from Urumqi, China

Cited by 22 publications

References 67 publications

The Relationship between the Carbon Fixation Capacity of Vegetation and Cultivated Land Expansion and Its Driving Factors in an Oasis in the Arid Region of Xinjiang, China

The Relationship between the Carbon Fixation Capacity of Vegetation and Cultivated Land Expansion and Its Driving Factors in an Oasis in the Arid Region of Xinjiang, China

Vegetation greenness and photosynthetic phenology in response to climatic determinants

Artificial Intelligence Carbon Neutrality Strategy in Sports Event Management based on STIRPAT-GRU and Transfer Learning

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