Driving forces of NPP change in debris flow prone area: A case study of a typical region in SW China

Zhao, Ping; Wang, Daojie; He, Songtang; Lan, Huijuan; Chen, Wenle; Qi, Yuchao

doi:10.1016/j.ecolind.2020.106811

Cited by 17 publications

(15 citation statements)

References 42 publications

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“…Comparing the characteristics of the distribution areas of subset I and IV, it is obvious that the optimization of ecosystem services in the distribution area of subset IV can be achieved by intervening in the indicators of NPP, forestland area, and ET. Studies have confirmed that NPP has a significant positive correlation with NDVI and a significant negative correlation with population density (Liu et al, 2015; Zhao et al, 2020). Therefore, the increase of NPP can be achieved by increasing regional vegetation coverage and reducing regional population density.…”

Section: Resultsmentioning

confidence: 86%

“…The ecological restoration areas can achieve ecological restoration by optimizing the spatial configuration of impact factors. NDVI and a significant negative correlation with population density (Liu et al, 2015;Zhao et al, 2020). Therefore, the increase of NPP can be achieved by increasing regional vegetation coverage and reducing regional population density.…”

Section: Optimization Resultsmentioning

confidence: 99%

“…Combined with the situation of study area and previous research results (He et al, 2017; Zhao et al, 2020), 17 natural and socioeconomic factors were selected as driving factors [Figure C1 (Data S1)]. Table C1 (Data S1) describes the driving factors, including terrain, climate, soil, market influence and accessibility, and human activities.…”

Section: Methodsmentioning

confidence: 99%

“…Ecological restoration and conservation are key areas of sustainable development that are not only conducive to the survival and development of contemporary people but also of great significance to the well‐being of future generations (Liu et al, 2020). Ecosystem services are various contributions of ecosystems to the maintenance of human survival and development (Zhao et al, 2020), which can quantitatively assess the ecological importance of land in ecologically fragile areas and thus make scientific ecological restoration and conservation decisions. In previous studies, the quantitative assessment of ecosystem services was used to determine ecological conservation priority areas, ecological sources, or ecological fragility (Dong et al, 2020; Jiang et al, 2021; Peng, Zhao, et al, 2019), which is of great reference value for formulating ecological conservation and restoration plans.…”

Section: Introductionmentioning

confidence: 99%

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Integrating a mixed‐cell cellular automata model and Bayesian belief network for ecosystem services optimization to guide ecological restoration and conservation

Zhou

2022

Land Degrad Dev

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An ecosystem is a complex system with a large number of dynamic variables, which poses challenges to the optimization of ecosystem services. However, traditional ecosystem services optimization methods do not take into account the complexity and uncertainty of variables. To address this complexity and uncertainty, we propose an innovative approach using a mixed‐cell cellular automata (MCCA) model and a Bayesian belief network (BBN) model for ecosystem service optimization. This approach was applied to the southern region of Sichuan Province, China, using an existing dataset to simulate land use patterns and predict ecosystem services in 2035 under different development scenarios. To achieve ecological restoration and conservation, we also determined the key factor combinations and key ecological regions at various ecosystem service levels. Results showed that ecological protection scenario design has important significance as a reference for maintaining and ameliorating regional ecosystem services and functions. We also identified that the highest level of ecosystem services was mainly located in the areas with the highest net primary productivity (NPP), the highest slope, the highest forestland area, and low ET. According to these findings, some suggestions for ecological restoration and conservation in key regions were put forward. This approach fully considers the uncertainty of factors; therefore, it can be used as an effective tool for designing ecosystem management strategies.

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Section: Resultsmentioning

confidence: 86%

Section: Optimization Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrating a mixed‐cell cellular automata model and Bayesian belief network for ecosystem services optimization to guide ecological restoration and conservation

Zhou

2022

Land Degrad Dev

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“…Remote sensing technology plays an important role in the fields of crop monitoring, geological investigation, and precision agriculture [1][2][3]. Carbon balance has always been a topic of concern worldwide, and forest resources largely contribute to the global carbon balance, so it is necessary to accurately monitor the dynamic changes of forest resources [4]. However, the use of remote sensing images to identify different features with high accuracy, and to classify and count various kinds of feature information, is a popular and difficult research point in remote sensing information extraction.…”

Section: Introductionmentioning

confidence: 99%

Improved U-Net Remote Sensing Classification Algorithm Based on Multi-Feature Fusion Perception

Yan

Fan

et al. 2022

Remote Sensing

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The selection and representation of remote sensing image classification features play crucial roles in image classification accuracy. To effectively improve the classification accuracy of features, an improved U-Net network framework based on multi-feature fusion perception is proposed in this paper. This framework adds the channel attention module (CAM-UNet) to the original U-Net framework and cascades the shallow features with the deep semantic features, replaces the classification layer in the original U-Net network with a support vector machine, and finally uses the majority voting game theory algorithm to fuse the multifeature classification results and obtain the final classification results. This study used the forest distribution in Xingbin District, Laibin City, Guangxi Zhuang Autonomous Region as the research object, which is based on Landsat 8 multispectral remote sensing images, and, by combining spectral features, spatial features, and advanced semantic features, overcame the influence of the reduction in spatial resolution that occurs with the deepening of the network on the classification results. The experimental results showed that the improved algorithm can improve classification accuracy. Before the improvement, the overall segmentation accuracy and segmentation accuracy of the forestland increased from 90.50% to 92.82% and from 95.66% to 97.16%, respectively. The forest cover results obtained by the algorithm proposed in this paper can be used as input data for regional ecological models, which is conducive to the development of accurate and real-time vegetation growth change models.

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Spatiotemporal variation in vegetation net primary productivity and its relationship with meteorological factors in the Tarim River Basin of China from 2001 to 2020 based on the Google Earth Engine

et al. 2022

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Driving forces of NPP change in debris flow prone area: A case study of a typical region in SW China

Cited by 17 publications

References 42 publications

Integrating a mixed‐cell cellular automata model and Bayesian belief network for ecosystem services optimization to guide ecological restoration and conservation

Integrating a mixed‐cell cellular automata model and Bayesian belief network for ecosystem services optimization to guide ecological restoration and conservation

Improved U-Net Remote Sensing Classification Algorithm Based on Multi-Feature Fusion Perception

Spatiotemporal variation in vegetation net primary productivity and its relationship with meteorological factors in the Tarim River Basin of China from 2001 to 2020 based on the Google Earth Engine

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