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Your search keyword '"Gao, Xiangwei"' showing total 4 results
Start Over Author "Gao, Xiangwei" Journal remote sensing
4 results on '"Gao, Xiangwei"'

1. The Ecological Risks in Arid Zones from a Production–Living–Ecological Space Perspective: A Case Study of the Tuha Region in Xinjiang, China.

Author

Yuan, Weiting, Bai, Linyan, Gao, Xiangwei, Zhou, Kefa, Gao, Yue, Zhou, Xiaozhen, Qiu, Ziyun, Kou, Yanfei, Lv, Zhihong, Zhao, Dequan, and Zhang, Qing

Subjects
ARID regions, ECOLOGICAL zones, RESTORATION ecology, ENVIRONMENTAL security, REGIONAL development, ENVIRONMENTAL risk
Abstract

The ecological and environmental problems of arid zones have become an urgent global concern. Current research on ecological risk is based mainly on the dominant functions of land use, with a primary focus on land use landscape projections and less consideration of potential risks to ecosystems, system resilience and interactions between nature and future sustainable development. In this study, a potential–connectivity–resilience ecological risk assessment model based on the SDGs was constructed using multisource data to spatially quantify indicators at the grid scale in the Turpan and Hami regions of Xinjiang, China. This model was used as a basis for studying ecological risk in arid zones from a production–living–ecological space (PLES) perspective. The results revealed that, during the period 2000–2020, PLESs in the Turpan and Hami regions presented significant spatial similarity, with an increasing trend in overall risk. The production space in the Turpan and Hami regions showed a parabolic trend of increasing and then decreasing, whereas the living space and ecological space in the Hami region showed continuous linear upward trends. The state of ecological security in the Turpan and Hami regions is gradually deteriorating, and comprehensive ecological protection and restoration measures are urgently needed to rationally allocate the structure and layout of the production-–living-–ecological space. The study of ecological risk from a PLES perspective not only helps in fully understanding the development trend of the arid zone; it also provides new ideas and methods for evaluating regional ecological environmental safety and scientific references for formulating regional sustainable development, ecological risk prevention and control and the rational allocation of resources. [ABSTRACT FROM AUTHOR]

Published
2024
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3. Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images

Author

Yayi Zhang, Huimin Zhao, Xianyun Fei, Zhou Chen, Xiaoxue Wang, Yuanzhi Zhang, Wang Jian, Xia Lu, and Gao Xiangwei

Subjects
Feature data, 010504 meteorology & atmospheric sciences, Contextual image classification, Computer science, Science, 0211 other engineering and technologies, Feature selection, 02 engineering and technology, Land cover, 01 natural sciences, Wetland classification, RF algorithm, Random forest, Support vector machine, classification, General Earth and Planetary Sciences, coastal wetland, Algorithm, Image resolution, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences
Abstract

Wetlands are one of the world’s most important ecosystems, playing an important role in regulating climate and protecting the environment. However, human activities have changed the land cover of wetlands, leading to direct destruction of the environment. If wetlands are to be protected, their land cover must be classified and changes to it monitored using remote sensing technology. The random forest (RF) machine learning algorithm, which offers clear advantages (e.g., processing feature data without feature selection and preferable classification result) for high spatial image classification, has been used in many study areas. In this research, to verify the effectiveness of this algorithm for remote sensing image classification of coastal wetlands, two types of spatial resolution images of the Linhong Estuary wetland in Lianyungang—Worldview-2 and Landsat-8 images—were used for land cover classification using the RF method. To demonstrate the preferable classification accuracy of the RF algorithm, the support vector machine (SVM) and k-nearest neighbor (k-NN) methods were also used to classify the same area of land cover for comparison with the results of RF classification. The study results showed that (1) the overall accuracy of the RF method reached 91.86%, higher than the SVM and k-NN methods by 4.68% and 4.72%, respectively, for Worldview-2 images; (2) at the same time, the classification accuracies of RF, SVM, and k-NN were 86.61%, 79.96%, and 77.23%, respectively, for Landsat-8 images; (3) for some land cover types having only a small number of samples, the RF algorithm also achieved better classification results using Worldview-2 and Landsat-8 images, and (4) the addition texture features could improve the classification accuracy of the RF method when using Worldview-2 images. Research indicated that high-resolution remote sensing images are more suitable for small-scale land cover classification image and that the RF algorithm can provide better classification accuracy and is more suitable for coastal wetland classification than the SVM and k-NN algorithms are.

Published
2019

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