Dynamics of the water environment in a water quantity-quality-soil model of China’s Yellow River basin: imbalance and driving factors

Yang, Lihua; Xu, Changxin; Zhu, Hongbing; Fu, Tianbo

doi:10.1007/s10661-023-11001-6

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…The feature space method could fully consider the complex impacts of the interaction between different biotic and abiotic factors on the process of desertification. In addition, the feature space model could cover multi-dimensional information about the evolutionary process of desertification, such as soil, vegetation, and climate, and could evaluate the development trend and influencing factors of desertification more comprehensively [36]. Based on the feature space, this study introduced the KNDVI as the desertification characterization parameter to construct a desertification remote-sensing monitoring index and achieved accurate results [37].…”

Section: Transformation Of Desertification Degree In Gulang Countymentioning

confidence: 99%

A New Large-Scale Monitoring Index of Desertification Based on Kernel Normalized Difference Vegetation Index and Feature Space Model

Guo,

Zhang,

et al. 2024

Remote Sensing

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As a new vegetation monitoring index, the KNDVI has certain advantages in characterizing the evolutionary process of regional desertification. However, there are few reports on desertification monitoring based on KNDVI and feature space models. In this study, seven feature parameters, including the kernel normalized difference vegetation index (KNDVI) and Albedo, were introduced to construct different models for desertification remote-sensing monitoring. The optimal desertification remote-sensing monitoring index model was determined with the measured data; then, the spatiotemporal evolution pattern of desertification in Gulang County from 2013 to 2023 was analyzed and revealed. The main conclusions were as follows: (1) Compared with the NDVI and MSAVI, the KNDVI showed more advantages in the characterization of the desertification evolution process. (2) The point–line pattern KNDVI-Albedo remote-sensing index model had the highest monitoring accuracy, reaching 94.93%, while the point–line pattern NDVI-TGSI remote-sensing monitoring index had the lowest accuracy of 54.38%. (3) From 2013 to 2023, the overall desertification situation in Gulang County showed a trend of improvement with a pattern of “firstly aggravation and then alleviation.” Additionally, the gravity center of desertification in Gulang County first shifted to the southeast and then to the northeast, indicating that the northeast’s aggravating rate of desertification was higher than in the southwest during the period. (4) From 2013 to 2023, the area of stable desertification in Gulang County was the largest, followed by the slightly weakened zone, and the most significant transition area was that of extreme desertification to severe desertification. The research results provide important decision support for the precise monitoring and governance of regional desertification.

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Section: Transformation Of Desertification Degree In Gulang Countymentioning

confidence: 99%

A New Large-Scale Monitoring Index of Desertification Based on Kernel Normalized Difference Vegetation Index and Feature Space Model

Guo,

Zhang,

et al. 2024

Remote Sensing

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Evaluation on the effect of water environment treatment –A new exploration considering time based on the RCS

Chen,

et al. 2024

Appl Intell

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Exploring the nonlinear association between agri-environmental regulation and green growth: The mediating effect of agricultural production methods

Zhu,

Yang,

et al. 2024

Journal of Cleaner Production

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Dynamics of the water environment in a water quantity-quality-soil model of China’s Yellow River basin: imbalance and driving factors

Cited by 5 publications

References 39 publications

A New Large-Scale Monitoring Index of Desertification Based on Kernel Normalized Difference Vegetation Index and Feature Space Model

A New Large-Scale Monitoring Index of Desertification Based on Kernel Normalized Difference Vegetation Index and Feature Space Model

Evaluation on the effect of water environment treatment –A new exploration considering time based on the RCS

Exploring the nonlinear association between agri-environmental regulation and green growth: The mediating effect of agricultural production methods

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