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Your search keyword '"Dai, Xiaoai"' showing total 122 results
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122 results on '"Dai, Xiaoai"'

11. Investigating the Effects of Mining on Ecosystem Services in Panzhihua City: A Multi-Scenario Analysis.

Author

Peng, Xuanmiao, Dai, Xiaoai, Shi, Ryan, Zheng, Yujian, Liu, Xinyue, Xiao, Yuhe, Li, Weile, Zhang, Yang, Wang, Jue, and Huang, Huan

Subjects
ECOSYSTEM services, NICKEL mining, MUNICIPAL services, MINES & mineral resources, SOIL erosion, LANDSCAPE assessment, CONTENT mining
Abstract

Ecosystem services are fundamental for the sustainable management of urban environments, particularly in mining cities confronting unique socio-environmental complexities. This study explores the intricate interactions among ecosystem services in a representative mining city, focusing on the impact of mining activities. A novel approach is employed to introduce a comprehensive framework for scenario-based analysis of ecosystem services. Land use and ecosystem service values for 2050 were predicted under the following three scenarios: natural development, ecological protection, and farmland protection. Through the evaluation of four key ecosystem services, namely water yield, habitat quality, carbon storage, and soil conservation, ecosystem service bundles were identified, and the trade-offs and synergies among these bundles were explored. Moreover, ecosystem service bundles in the mining areas were analyzed compared to the region at large, underscoring how the mining of various mineral types distinctly influenced ecosystem services. The results showed a persistent decline in total ecosystem service values of the whole region during 2000–2020 due to the diminishing forest cover and the enlargement of farmland and impervious surfaces. Mining areas exhibited significant impacts, with the soil erosion bundle predominating. However, the soil erosion bundle significantly reduced in the granite, copper, and nickel mining areas. By 2050, total ecosystem service values are projected to slowly rise, except under the farmland protection scenario. The entire region is expected to be mostly occupied by the ecological vulnerability bundle. But the ecosystem vulnerability bundle of mining areas is projected to decrease, especially under the ecological protection scenario, highlighting the importance of conservation efforts. These changes will enhance the synergies between soil conservation and other ecosystem services. [ABSTRACT FROM AUTHOR]

Published
2024
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18. A gridded dataset of a leaf-age-dependent leaf area index seasonality product over tropical and subtropical evergreen broadleaved forests

Author

Yang, Xueqin, primary, Chen, Xiuzhi, additional, Ren, Jiashun, additional, Yuan, Wenping, additional, Liu, Liyang, additional, Liu, Juxiu, additional, Chen, Dexiang, additional, Xiao, Yihua, additional, Song, Qinghai, additional, Du, Yanjun, additional, Wu, Shengbiao, additional, Fan, Lei, additional, Dai, Xiaoai, additional, Wang, Yunpeng, additional, and Su, Yongxian, additional

Published
2023
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19. A gridded dataset of a leaf-age-dependent leaf area index seasonality product over tropical and subtropical evergreen broadleaved forests

Author

Yang, Xueqin, Chen, Xiuzhi, Ren, Jiashun, Yuan, Wenping, Liu, Liyang, Liu, Juxiu, Chen, Dexiang, Xiao, Yihua, Song, Qinghai, Du, Yanjun, Wu, Shengbiao, Fan, Lei, Dai, Xiaoai, Wang, Yunpeng, and Su, Yongxian

Abstract

The quantification of large-scale leaf-age-dependent leaf area index has been lacking in tropical and subtropical evergreen broadleaved forests (TEFs), despite the recognized importance of leaf age in influencing leaf photosynthetic capacity in this biome. Here, we simplified the canopy leaves of TEFs into three age cohorts (i.e., young, mature, and old, with different photosynthesis capacities; i.e., Vc,max) and proposed a novel neighbor-based approach to develop the first gridded dataset of a monthly leaf-age-dependent leaf area index (LAI) product (referred to as Lad-LAI) at 0.25∘ spatial resolution over the continental scale during 2001–2018 from satellite observations of sun-induced chlorophyll fluorescence (SIF) that was reconstructed from MODIS and TROPOMI (the TROPOspheric Monitoring Instrument). The new Lad-LAI products show good performance in capturing the seasonality of three LAI cohorts, i.e., young (LAIyoung; the Pearson correlation coefficient of R=0.36), mature (LAImature; R=0.77), and old (LAIold; R=0.59) leaves at eight camera-based observation sites (four in South America, three in subtropical Asia, and one in the Democratic Republic of the Congo (DRC)) and can also represent their interannual dynamics, validated only at the Barro Colorado site, with R being equal to 0.54, 0.64, and 0.49 for LAIyoung, LAImature, and LAIold, respectively. Additionally, the abrupt drops in LAIold are mostly consistent with the seasonal litterfall peaks at 53 in situ measurements across the whole tropical region (R=0.82). The LAI seasonality of young and mature leaves also agrees well with the seasonal dynamics of the enhanced vegetation index (EVI; R=0.61), which is a proxy for photosynthetically effective leaves. Spatially, the gridded Lad-LAI data capture a dry-season green-up of canopy leaves across the wet Amazonian areas, where mean annual precipitation exceeds 2000 mm yr−1, consistent with previous satellite-based analyses. The spatial patterns clustered from the three LAI cohorts also coincide with those clustered from climatic variables over the whole TEF region. Herein, we provide the average seasonality of three LAI cohorts as the main dataset and their time series as a supplementary dataset. These Lad-LAI products are available at https://doi.org/10.6084/m9.figshare.21700955.v4 (Yang et al., 2022).

Published
2023

22. Supplementary material to "A grid dataset of leaf age-dependent LAI seasonality product (Lad-LAI) over tropical and subtropical evergreen broadleaved forests"

Author

Yang, Xueqin, primary, Chen, Xiuzhi, additional, Ren, Jiashun, additional, Yuan, Wenping, additional, Liu, Liyang, additional, Liu, Juxiu, additional, Chen, Dexiang, additional, Xiao, Yihua, additional, Wu, Shengbiao, additional, Fan, Lei, additional, Dai, Xiaoai, additional, and Su, Yongxian, additional

Published
2023
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23. Ecological vulnerability assessment of a China's representative mining city based on hyperspectral remote sensing

Author

Dai, Xiaoai, primary, Feng, Haipeng, additional, Xiao, Lixiao, additional, Zhou, Jiayun, additional, Wang, Zekun, additional, Zhang, Junjun, additional, Fu, Tianzhang, additional, Shan, Yunfeng, additional, Yang, Xianhua, additional, Ye, Yakang, additional, Xu, Li, additional, Jiang, Xiaoli, additional, Fang, Shibo, additional, and Yao, Yuanzhi, additional

Published
2022
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25. Vegetation Subtype Classification of Evergreen Broad-Leaved Forests in Mountainous Areas Using a Hierarchy-Based Classifier.

Author

Zhang, Shiqi, Peng, Peihao, Bai, Maoyang, Wang, Xiao, Zhang, Lifu, Hu, Jiao, Wang, Meilian, Wang, Xueman, Wang, Juan, Zhang, Donghui, Sun, Xuejian, and Dai, Xiaoai

Subjects
VEGETATION classification, LIVESTOCK development, CARBON cycle, ECOLOGICAL engineering, FOREST biodiversity
Abstract

Evergreen broad-leaved forests with rich biodiversity play a key role in stabilizing global vegetation productivity and maintaining land carbon sinks. However, quantitative and accurate classification results for humid, evergreen, broad-leaved forests (HEBF) and semi-humid evergreen broad-leaved forests (SEBF) with different vegetation productivity and significant differences in species composition are lacking. Remote sensing technology brings the possibility of vegetation subtype classification. Taking the mountainous evergreen broad-leaved forests distributed in Sichuan Province as an example, this study proposed a hierarchy-based classifier combined with environmental variables to quantitatively classify the two vegetation subtypes with different ecological characteristics but similar image features. Additionally, we applied Sun–Canopy–Sensor and C parameter(SCS + C) topographic correction to preprocess the images, effectively correcting the radiometric distortion and enhancing the accuracy of vegetation classification. Finally, achieving an overall accuracy (OA) of 87.91% and a Kappa coefficient of 0.76, which is higher than that of directly using the classifier to classify the two vegetation subtypes. The study revealed the widespread distribution of evergreen broad-leaved forests in Sichuan, with a clear boundary between the distribution areas of HEBF and SEBF. The HEBF in the east is located in the basin and the low marginal mountains; the SEBF is located in the southwest dry valley. The methods employed in this study offer an effective approach to vegetation classification in mountainous areas. The findings can provide guidance for ecological engineering construction, ecological protection, and agricultural and livestock development. [ABSTRACT FROM AUTHOR]

Published
2023
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26. Effects of Mining on Urban Environmental Change: A Case Study of Panzhihua

Author

Dai, Xiaoai, primary, Li, Wenyu, additional, Liu, Zhilong, additional, Tong, Chenbo, additional, Li, Cheng, additional, Zeng, Jianwen, additional, Ye, Yakang, additional, Li, Weile, additional, Shan, Yunfeng, additional, Zhou, Jiayun, additional, Zhang, Junjun, additional, Xu, Li, additional, Jiang, Xiaoli, additional, Ruan, Huihua, additional, Zhang, Jinbiao, additional, and Huang, Wei, additional

Published
2022
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28. Evaluating Trade-Off and Synergies of Ecosystem Services Values of a Representative Resources-Based Urban Ecosystem: A Coupled Modeling Framework Applied to Panzhihua City, China

Author

Zeng, Jianwen, primary, Xu, Jipeng, additional, Li, Wenyu, additional, Dai, Xiaoai, additional, Zhou, Jiayun, additional, Shan, Yunfeng, additional, Zhang, Junjun, additional, Li, Weile, additional, Lu, Heng, additional, Ye, Yakang, additional, Xu, Li, additional, Liang, Shuneng, additional, and Wang, Youlin, additional

Published
2022
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31. Characterization of Long-Time Series Variation of Glacial Lakes in Southwestern Tibet: A Case Study in the Nyalam County

Author

Qu, Ge, primary, Dai, Xiaoai, additional, Cheng, Junying, additional, Li, Weile, additional, Wang, Meilian, additional, Liu, Wenxin, additional, Yang, Zhichong, additional, Shan, Yunfeng, additional, Ren, Jiashun, additional, Lu, Heng, additional, Wang, Youlin, additional, Zeng, Binyang, additional, and Atasoy, Murat, additional

Published
2022
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34. Research on Uncertainty of Landslide Susceptibility Prediction—Bibliometrics and Knowledge Graph Analysis

Author

Yang, Zhengli, primary, Liu, Chao, additional, Nie, Ruihua, additional, Zhang, Wanchang, additional, Zhang, Leili, additional, Zhang, Zhijie, additional, Li, Weile, additional, Liu, Gang, additional, Dai, Xiaoai, additional, Zhang, Donghui, additional, Zhang, Min, additional, Miao, Shuangxi, additional, Fu, Xiao, additional, Ren, Zhiming, additional, and Lu, Heng, additional

Published
2022
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35. LAI-Based Phenological Changes and Climate Sensitivity Analysis in the Three-River Headwaters Region

Author

Dai, Xiaoai, primary, Fan, Wenjie, additional, Shan, Yunfeng, additional, Gao, Yu, additional, Liu, Chao, additional, Nie, Ruihua, additional, Zhang, Donghui, additional, Li, Weile, additional, Zhang, Lifu, additional, Sun, Xuejian, additional, Liu, Tiegang, additional, Yang, Zhengli, additional, Fu, Xiao, additional, Ma, Lei, additional, Liang, Shuneng, additional, Wang, Youlin, additional, and Lu, Heng, additional

Published
2022
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37. A Comparative Analysis of Certainty Factor-Based Machine Learning Methods for Collapse and Landslide Susceptibility Mapping in Wenchuan County, China

Author

Yuan, Xinyue, primary, Liu, Chao, additional, Nie, Ruihua, additional, Yang, Zhengli, additional, Li, Weile, additional, Dai, Xiaoai, additional, Cheng, Junying, additional, Zhang, Junmin, additional, Ma, Lei, additional, Fu, Xiao, additional, Tang, Min, additional, Xu, Yina, additional, and Lu, Heng, additional

Published
2022
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39. Optimization Strategy of a Stacked Autoencoder and Deep Belief Network in a Hyperspectral Remote-Sensing Image Classification Model.

Author

Dai, Xiaoai, Cheng, Junying, Guo, Shouheng, Wang, Chengchen, Qu, Ge, Liu, Wenxin, Li, Weile, Lu, Heng, Wang, Youlin, Zeng, Binyang, Peng, Yunjie, and Liang, Shuneng

Subjects
*DEEP learning, *IMAGE recognition (Computer vision), *REMOTE-sensing images, *BOLTZMANN machine, *REMOTE sensing, *FEATURE extraction, *ELECTRONIC data processing
Abstract

Improvements in hyperspectral image technology, diversification methods, and cost reductions have increased the convenience of hyperspectral data acquisitions. However, because of their multiband and multiredundant characteristics, hyperspectral data processing is still complex. Two feature extraction algorithms, the autoencoder (AE) and restricted Boltzmann machine (RBM), were used to optimize the classification model parameters. The optimal classification model was obtained by comparing a stacked autoencoder (SAE) and a deep belief network (DBN). Finally, the SAE was further optimized by adding sparse representation constraints and GPU parallel computation to improve classification accuracy and speed. The research results show that the SAE enhanced by deep learning is superior to the traditional feature extraction algorithm. The optimal classification model based on deep learning, namely, the stacked sparse autoencoder, achieved 93.41% and 94.92% classification accuracy using two experimental datasets. The use of parallel computing increased the model's training speed by more than seven times, solving the model's lengthy training time limitation. [ABSTRACT FROM AUTHOR]

Published
2023
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43. Visualization analysis of rainfall-induced landslides hazards based on remote sensing and geographic information system-an overview.

Author

Yang, Zhengli, Lu, Heng, Zhang, Zhijie, Liu, Chao, Nie, Ruihua, Zhang, Wanchang, Fan, Gang, Chen, Chen, Ma, Lei, Dai, Xiaoai, Zhang, Min, and Zhang, Donghui

Subjects
LANDSLIDES, LANDSLIDE hazard analysis, NATURAL disaster warning systems, REMOTE sensing, LITERATURE reviews, INTERNATIONAL cooperation, DATA visualization, GEOGRAPHIC information systems
Abstract

In recent years, RS and GIS technologies have played an increasingly important role in various aspects of rainfall induced landslide research. In order to systematically understand their application situation, this paper extensively used various visualization analysis technologies for in-depth analysis of 1,161 documents collected by the WOS data platform in the past 27 years by combining quantitative and qualitative methods. Then, this article focuses on sub domain analysis from four aspects: landslide detection and monitoring, prediction models, sensitivity mapping, and risk assessment. The study found that the number of literature in this field has steadily increased and is expected to continue to rise. This literature review has attracted widespread attention from the academic community, but it is challenging to meet research needs. Frequent and effective cooperation between countries, institutions, and authors is very beneficial for promoting progress in this field. The future development direction is a new intelligent hybrid model that integrates multiple research methods. This study can provide researchers in this field with the core research force, hot topic evolution, and future development trends of future rainfall-induced landslides and contribute to landslide prevention and control decision-making and achieving the United Nations'sustainable development goals. [ABSTRACT FROM AUTHOR]

Published
2023
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