Identification of Ecological Sources Using Ecosystem Service Value and Vegetation Productivity Indicators: A Case Study of the Three-River Headwaters Region, Qinghai–Tibetan Plateau, China

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(7), P. 1258 - 1258

Published: April 2, 2024

As a crucial component of the ecological security pattern, source (ES) plays vital role in providing ecosystem service value (ESV) and conserving biodiversity. Previous studies have mostly considered ES only from either landscape change pattern or function perspectives, ignored their integration spatio-temporal evolutionary modeling. In this study, we proposed multi-perspective framework for characteristics by ESV incorporating aesthetics, carbon sink characteristics, quality, kernel NDVI (kNDVI). By integrating revised normalized difference vegetation index as foundation, employed spatial priority model to identify ES. This improvement aims yield more practical specific result. Applying Three-River Headwaters Region (TRHR), significant sources has been observed 2000 2020. performance provided reference conservation TRHR. The results indicate that identification reliable accuracy efficiency compared with existing NRs method could reveal precise distributions ES, enhancing integrity technical modeling support developing cross-scale planning management strategies nature reserve boundaries. our research serve building networks other ecologically fragile areas.

Language: Английский

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Research progress and prospects for constructing ecological security pattern based on ecological network

Ecological Indicators, Journal Year: 2024, Volume and Issue: 168, P. 112800 - 112800

Published: Nov. 1, 2024

Language: Английский

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The Influence of Visual Landscapes on Road Traffic Safety: An Assessment Using Remote Sensing and Deep Learning

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(18), P. 4437 - 4437

Published: Sept. 9, 2023

Rapid global economic development, population growth, and increased motorization have resulted in significant issues urban traffic safety. This study explores the intrinsic connections between road environments driving safety by integrating multiple visual landscape elements. High-resolution remote sensing street-view images were used as primary data sources to obtain features of an expressway. Deep learning semantic segmentation was employed calculate features, a trend surface fitting model driver fatigue established based on experimental from 30 drivers who completed tasks random order. There spatial variations expressway city center periphery. Heart rate values fluctuated within range 0.2% with every 10% change speed complexity. Specifically, complexity changed 5.28 8.30, heart 91 96. suggests that higher degree richness effectively mitigates increases exerts positive impact provides reference for quantitative assessment research combines using sources. It may guide implementation measures during planning construction.

Language: Английский

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Historical and comparative overview of sponge campus construction and future challenges

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 907, P. 167477 - 167477

Published: Oct. 4, 2023

Language: Английский

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Construction of a blue-green ecological network in the Luoyuan Bay Area in Southeast China via the identification of important habitats

Ocean & Coastal Management, Journal Year: 2025, Volume and Issue: 261, P. 107541 - 107541

Published: Jan. 16, 2025

Language: Английский

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Enhancing Institutional Sustainability Through Process Optimization: A Hybrid Approach Using FMEA and Machine Learning

Sustainability, Journal Year: 2025, Volume and Issue: 17(4), P. 1357 - 1357

Published: Feb. 7, 2025

Administrative processes in higher education institutions often encounter inefficiencies, duplication of efforts, and a lack clarity, which undermine institutional sustainability user satisfaction. This study introduces hybrid optimization framework that integrates Failure Mode Effects Analysis (FMEA) with machine learning (ML) to enhance the reliability efficiency renowned university Ecuador. Due variability data, tailored model was developed for each ten critical analyzed. Two models were employed process: one focused on predicting high RPN values (current state) another evaluating proposed improvements leading low (optimized state). Significant reductions observed metrics such as Root Mean Square Error (RMSE) Absolute (MAE). For instance, RMSE decreased from maximum 9.07 4.24 model, while MAE improved 2.86 3.25 across processes. Key included addressing failure modes errors requirements, unclear steps, incomplete documentation. These findings underscore effectiveness combining FMEA ML optimize processes, align practices Sustainable Development Goals (SDGs), establish replicable promoting resilience, transparency, administrative management.

Language: Английский

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Radar-Based Precipitation Nowcasting Based on Improved U-Net Model

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(10), P. 1681 - 1681

Published: May 9, 2024

Rainfall nowcasting is the basis of extreme rainfall monitoring, flood prevention, and water resource scheduling. Based on structural features U-Net model, we proposed Double Recurrent Residual Attention Gates (DR2A-UNet) deep-learning model to carry out radar echo extrapolation. The was trained with mean square error (MSE) balanced (BMSE) as loss functions, respectively. dynamic Z-R relationship applied for quantitative estimation. reference U-Net++, ConvLSTM were used control experiments results showed that by BMSE had better For 1 h lead time, nowcasted each could reflect actual process. DR2A-UNet performed significantly than other models intense rainfall, a higher extrapolation accuracy intensity variability processes. At 2 nowcast reduced, but better.

Language: Английский

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A novel quantity assessment of landscape ecological risk using human-nature driving mechanism for sustainable society

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 947, P. 173892 - 173892

Published: June 13, 2024

Language: Английский

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Spatiotemporal changes of ecosystem health and the impact of its driving factors on the Loess Plateau in China

Ecological Indicators, Journal Year: 2024, Volume and Issue: 170, P. 113020 - 113020

Published: Dec. 27, 2024

Language: Английский

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A Novel Integrated Spatiotemporal-Variable Model of Landscape Changes in Traditional Villages in the Jinshaan Gorge, Yellow River Basin

Land, Journal Year: 2023, Volume and Issue: 12(9), P. 1666 - 1666

Published: Aug. 25, 2023

Spatiotemporal studies of landscape pattern evolution in traditional villages are beneficial for addressing complex urbanization and global climate change. Using the Jiaxian Linxian Jinshaan Gorge Yellow River Basin, this study employed a three-dimensional (3D) analysis involving three spatial scales (macro, meso, micro), temporal (past, present, future), variables (humanity, society, nature) based on methods spatiotemporal data (SDA), geographic information system, remote sensing, index (LPI) by Fragstats. On macro scale, significant turning point ecological conservation awareness was indicated LPI SDA. Urban rural construction land continuously increased because urbanization. Plowland, grassland, woodland were main influencing factors settlements, with 0.42% cumulative transformation rate. meso interactions mutual promotion mountain aquatic environments, facilities, agricultural production, cultural heritage have shaped socioeconomic dimensions evolution. micro urbanization, some humanistic spaces lost their original functions. A novel spatiotemporal-variable quantitative model explored characteristics human–land coupling, which can be used sustainable development river basins worldwide.

Language: Английский

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