Spatial correlations between landscape patterns and net primary productivity: A case study of the Shule River Basin, China

Zhou, Yanyan; Yue, Dongxia; Guo, Jianjun; Chen, Guanguang; Wang, Dong

doi:10.1016/j.ecolind.2021.108067

Cited by 36 publications

(17 citation statements)

References 83 publications

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“…The mean annual precipitation is 47-63 mm ; the potential evapotranspiration exceeds 2800 mm (Yue, Zhou, Guo, Chao, Liang, & Zheng, 2022). Shule River originates from Qilian Mountain, enters the Hexi Corridor plain from the south, flows through Changma and Huahai irrigation areas, and finally flows across an alluvial plain that forms part of the Gobi Desert (Zhou et al, 2021). Mountain rainfall and snowmelt penetrate deeply into the weathering shell, dissolve rock weathering products, and finally form mineralized water.…”

Section: Location Of the Study Areamentioning

confidence: 99%

Soil salinity estimation in Shule River Basin using support vector regression model

2023

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Soil salinization is a progressive degradation process that spreads globally and leads to a decline in soil fertility. Assessing the scale of salinization is crucial for sustainable agricultural development and saline‐land rehabilitation. In this study, we proposed a support vector regression spatial (SVR‐S) model that utilizes spatial dependence information to predict soil salinity over the irrigation area of the Shule River Basin in northwestern China. To investigate the performance of the SVR‐S model, 50 soil samples were collected in the field. Semivariograms of soil salinity (measured as total salt content and ion concentrations) were constructed to measure spatial dependence. The SVR‐S model was compared with the original SVR model and the geographically weighted regression (GWR) model regarding the salinity prediction ability. The soil salinity in the experimental area demonstrated a strong spatial dependence pattern. The SVR‐S model delivered a better performance than the SVR‐O and GWR. SVR‐S showed a correlation coefficient R of 0.87 and a root mean square error (RMSE) of 1.83%, while the performance of SVR‐O (R = 0.75, RMSE = 3.32%) and GWR (R = 0.73, RMSE = 3.47%) was comparatively poor. Topographic indices integrating spatial information contributed the most to the estimation of salinity in the study area. This study provides a new approach to integrating spatial information for accurate soil salinity mapping.

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Section: Location Of the Study Areamentioning

confidence: 99%

Soil salinity estimation in Shule River Basin using support vector regression model

2023

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“…For example, Asplund analyzed the impact of landscape pattern changes on the regional carbon sequestration capacity of the mangrove-seagrass coastal bays in northwestern Madagascar [17]. Furthermore, scholars analyzed the relationship between NPP and landscape patterns using landscape pattern indices in the Shule River Basin [18].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Green Spaces in Plain Urban Areas to Enhance Carbon Sequestration

Pan

Lin

Wang

et al. 2023

Land

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Global climate change and rapid urbanization have placed enormous pressure on the urban ecological environment worldwide. Urban green spaces, which are an important component of urban ecosystems, can maintain ecological and environmental sustainability and benefits, including biodiversity conservation and carbon sequestration. However, land use changes across urban landscapes, especially in plain urban areas with high development pressure, have significantly impacted the carbon sequestration efficiency of urban green spaces. Nevertheless, research examining the impact of land use change and development pressure on urban green spaces and carbon sequestration is relatively scarce. Understanding the carbon sequestration efficiency of urban green spaces and its determining factors will help predict future carbon capture trends within urban ecosystems and formulate more targeted sustainable urban planning and management strategies to improve urban carbon sink efficiency and achieve the goal of carbon neutrality. Therefore, to understand the factors affecting the carbon sequestration efficiency of urban green spaces, this paper used an integrated framework that combined the Carnegie–Ames–Stanford approach (CASA) model, landscape pattern index, multiple linear regression, and Markov–FLUS model. The study explored the impact of urban land use and land cover changes on carbon sequestration within the plain urban areas of Beijing at street scale. The results showed that, at street scale, there was a significant positive and negative correlation between the landscape pattern index and net primary productivity (NPP). In addition, the green spaces located in areas with more complex landscape structures had better carbon sequestration benefits. In addition, multiscenario carbon sequestration efficiency prediction suggested that the sustainable development (SD) scenario could achieve a positive increment of overall NPP. In contrast, the business-as-usual development (BD), the fast development (FD), and the low development (LD) scenarios showed a downward trend in NPP. This paper also proposed strategies for optimizing and enhancing green spaces within urban plain areas. Based on the strategies, the results guide decision making for sustainable urban green space planning that maintains the ecological, economic, and social integrity of urban landscapes during urbanization.

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“…NPP refers to the dry organic matter accumulated by plants per unit time and area through photosynthesis, which reflects the growth status of vegetation and the overall health status of the ecosystem (Girardin et al, 2010;Zhou et al, 2021). It plays an important role in promoting material cycling and energy flow in ecosystems and is commonly used to monitor changes in ecosystem function caused by land degradation or improvement (Cramer et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

“…The spatial-scale regulation mechanism of NPP determines that its supply is not only related to the elements of land use, but also affected by landscape structure and spatial pattern. For example, increases in the patches and diversity of natural landscapes can promote the supply of NPP (Zhou et al, 2021). A landscape unit is a regional complex with high spatial heterogeneity, composed of a mosaic of interacting ecosystems (Bojie et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Effects of built area on net primary productivity provided by different landscape characters and the promotion potential

Yang

Chi

et al. 2022

Front. Environ. Sci.

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Changes in natural and artificial landscapes due to rapid urbanization in recent decades have greatly altered the supply of net primary productivity (NPP) and its regulation mechanisms at the landscape scale, thus affecting the health of the whole ecosystem. Understanding the capacity and potential of NPP supply at the landscape scale based on landscape integrity is critical for regional ecosystem health and management. In this study, the NPP supply capacity of different types of landscape and the influence of urban built-up areas on it were assessed in Hubei Province, China. The optimal capacity criteria of NPP supply in different types of landscape under current conditions were identified, and the promotion potential of NPP was evaluated based on these criteria. The results show that the landscape with natural elements as the main components has a high NPP supply capacity, but it has been greatly influenced by urban development, and the closer the distance to the city is, the lower the NPP supply capacity will be. The plain landscape with construction land and farmland as the main components has weak NPP supply capacity and low sensitivity to urban development. The trend inflection points for the influence of urban development on the NPP supply capacity of different types of landscape can provide more realistic quantitative targets and spatial distribution of NPP improvement potential for decision making. The findings may help in the management of ecosystem health at the landscape scale.

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Spatial correlations between landscape patterns and net primary productivity: A case study of the Shule River Basin, China

Cited by 36 publications

References 83 publications

Soil salinity estimation in Shule River Basin using support vector regression model

Soil salinity estimation in Shule River Basin using support vector regression model

Optimization of Green Spaces in Plain Urban Areas to Enhance Carbon Sequestration

Effects of built area on net primary productivity provided by different landscape characters and the promotion potential

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