Assessing spatio-temporal characteristics and their driving factors of ecological vulnerability in the northwestern region of Liaoning Province (China)

Xie, Wenlan; Zhao, Xuesheng; Fan, Deqin; Zhang, Jinyu; Wang, Jinghui

doi:10.1016/j.ecolind.2023.111541

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…Most of the coniferous forests in northwestern Liaoning are distributed in the southwestern region, while they are relatively scarce in the northeastern region. Factors such as topography, population, and hydrothermal conditions can affect the distribution of coniferous forests in this region [46,47]. As an indispensable segment of the Three-North Shelter Forest Program [48], northwestern Liaoning contains a considerable portion of artificially planted coniferous forests, which are heavily influenced by human activities.…”

Section: Implications Of Mapping Coniferous Forests In Northwestern L...mentioning

confidence: 99%

Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China

Liu,

Zhang,

Guo

et al. 2024

Forests

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Information about the distribution of coniferous forests holds significance for enhancing forestry efficiency and making informed policy decisions. Accurately identifying and mapping coniferous forests can expedite the achievement of Sustainable Development Goal (SDG) 15, aimed at managing forests sustainably, combating desertification, halting and reversing land degradation, and halting biodiversity loss. However, traditional methods employed to identify and map coniferous forests are costly and labor-intensive, particularly in dealing with large-scale regions. Consequently, a methodological framework is proposed to identify coniferous forests in northwestern Liaoning, China, in which there are semi-arid and barren environment areas. This framework leverages a multi-classifier fusion algorithm that combines deep learning (U2-Net and Resnet-50) and shallow learning (support vector machines and random forests) methods deployed in the Google Earth Engine. Freely available remote sensing images are integrated from multiple sources, including Gaofen-1 and Sentinel-1, to enhance the accuracy and reliability of the results. The overall accuracy of the coniferous forest identification results reached 97.6%, highlighting the effectiveness of the proposed methodology. Further calculations were conducted to determine the area of coniferous forests in each administrative region of northwestern Liaoning. It was found that the total area of coniferous forests in the study area is about 6013.67 km2, accounting for 9.59% of northwestern Liaoning. The proposed framework has the potential to offer timely and accurate information on coniferous forests and holds promise for informed decision making and the sustainable development of ecological environment.

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Section: Implications Of Mapping Coniferous Forests In Northwestern L...mentioning

confidence: 99%

Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China

Liu,

Zhang,

Guo

et al. 2024

Forests

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Variable climatic conditions dominate decreased wetland vulnerability on the Qinghai‒Tibet Plateau: Insights from the ecosystem pattern-process-function framework

Zhao,

Fu,

Lü

et al. 2024

Journal of Cleaner Production

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Research on regional economic development and natural disaster risk assessment under the goal of carbon peak and carbon neutrality: A case study in Chengdu-Chongqing economic circle

Zhang,

Luo,

Zeng

et al. 2024

Land Use Policy

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Assessing spatio-temporal characteristics and their driving factors of ecological vulnerability in the northwestern region of Liaoning Province (China)

Cited by 3 publications

References 35 publications

Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China

Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China

Variable climatic conditions dominate decreased wetland vulnerability on the Qinghai‒Tibet Plateau: Insights from the ecosystem pattern-process-function framework

Research on regional economic development and natural disaster risk assessment under the goal of carbon peak and carbon neutrality: A case study in Chengdu-Chongqing economic circle

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