Research hotspots and evolution trends of rural resilience in the perspective of biodiversity—Based on citeSpace’s visual analysis of bibliometrics

This work aims to propose a more accurate assessment method for forest health in natural larch pine forests of the Arxan by integrating remote sensing technology with tree crown feature analysis. Currently, forest health assessment of natural Larch pine forests relies mainly on ground surveys, and there is a gap in the application of remote sensing technology in this field. This work introduces deep learning technology and proposes a spectral-Gabor space discrimination and classification model to analyze multi-spectral remote sensing image features. Additionally, quantitative indicators, such as tree crown features, are incorporated into the forest health assessment system. The health status of natural Larch pine forests is evaluated using forest resource survey data. The results show that the health levels of natural Larch pine forests in different areas vary and are closely related to factors such as canopy density, community structure, age group, and slope. Both quantitative and qualitative indicators are used in the analysis. The introduction of this innovative method enhances the accuracy and efficiency of forest health assessment, providing significant support for forest protection and management. In addition, the classification accuracy of the health assessment model suggested that the maximum statistical values of average classification accuracy, average classification effectiveness, overall classification accuracy, and Kappa were 74.19%, 61.91%, 63.18%, and 57.63%, respectively. This demonstrates that the model can accurately identify the health status of natural larch forests. This work can effectively assess the health status of the natural larch forest in the Arxan and provide relevant suggestions based on the assessment results to offer a reference for the sustainable development of the forest system.

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The Spatial Association of Rural Human Settlement System Resilience with Land Use in Hunan Province, China, 2000–2020

Tang,

Huang,

Jiang

et al. 2023

Land

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In China, the rural human settlement system (RHSS) reflects the relationship between rural people and land but is affected by land use and land cover change (LUCC). Maintaining a harmonious development between RHSS resilience (RHSSR) and LUCC is an important rural development issue. However, the spatial association between LUCC and RHSSR remains unclear, constraining effective land use and rural policymaking. The association between RHSSR and LUCC was assessed from a spatial perspective. Using county-level spatial panel data for Hunan Province, China (2000–2020), an evaluation indicator system was established to measure RHSS and analyze the spatiotemporal evolution of RHSSR using a geospatial analysis and geodetector model. The average RHSSR level increased from 0.158 to 1.406. The RHSSR generally presented a three-level stepped spatial distribution feature of high in the east and low in the west, with a belt-like distribution. Additionally, the RHSSR was consistent with land use intensity (LUI), but there was significant spatial heterogeneity in the spatial relationship between LUI and RHSSR. The evolution of RHSSR occurred through industrial development, medical service improvement, increased income, and environmental protection. This has important implications for future rural development strategies, the sustainable development of rural land, and the integration of RHSS into regional planning.

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Research hotspots and evolution trends of rural resilience in the perspective of biodiversity—Based on citeSpace’s visual analysis of bibliometrics

Cited by 4 publications

References 59 publications

Analysis of the Impact of Major Emergencies on Scientific Research

Analysis of the Impact of Major Emergencies on Scientific Research

Health assessment of natural larch forest in arxan guided by forestry remote sensing integrated with canopy feature analysis

The Spatial Association of Rural Human Settlement System Resilience with Land Use in Hunan Province, China, 2000–2020

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