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10 results on '"Zhang, Zongke"'

2. Spatiotemporal Assessment of Water Pollution for Beira Lake, Sri Lanka

Author

Prasad, Sangeeth, primary, Wei, Yuansong, additional, Chaminda, Tushara, additional, Ritigala, Tharindu, additional, Yu, Lijun, additional, Jinadasa, K. B. S. N., additional, Wasana, H. M. S., additional, Indika, Suresh, additional, Yapabandara, Isuru, additional, Hu, Dazhou, additional, Makehelwala, Madhubhashini, additional, Weragoda, Sujithra K., additional, Zhu, Jianfeng, additional, and Zhang, Zongke, additional

Published
2024
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3. Drought Characteristics and Drought-Induced Effects on Vegetation in Sri Lanka.

Author

Somasundaram, Deepakrishna, Zhu, Jianfeng, Zhang, Yuan, Nie, Yueping, Zhang, Zongke, and Yu, Lijun

Subjects
NORMALIZED difference vegetation index, CLIMATIC zones, AGRICULTURE, STATISTICAL correlation
Abstract

Understanding the spatiotemporal characteristics of drought and its impacts on vegetation is a timely prerequisite to ensuring agricultural, environmental, and socioeconomic sustainability in Sri Lanka. We investigated the drought characteristics (duration, severity, frequency, and intensity) from 1990 to 2020 by using the Standardized Precipitation Evapotranspiration Index (SPEI) at various timescales and the cumulative and lagged effects on vegetation between 2000 and 2020 across the climatic zones of Sri Lanka (Dry, Wet, and Intermediate). SPEI indexes at 1-, 3-, 6-, 12-, and 24-month scales were used to analyze the drought characteristics. Frequent droughts (~13%) were common in all zones, with a concentration in the Dry zone during the last decade. Drought occurrences mostly ranged from moderate to severe in all zones, with extreme events more common in the Dry zone. This research used SPEI and the Standardized Normalized Difference Vegetation Index (SNDVI) at 0 to 24-month scales to analyze the cumulative and lagged effects of drought on vegetation. Cumulated drought effects and vegetation had maximum correlation coefficient values concentrated in the −0.41–0.98 range in Sri Lanka. Cumulated drought effects affected 40% of Dry and 16% of Intermediate zone vegetation within 1–4 months. The maximum correlation between the lagged drought effect and vegetation SNDVI showed coefficient values from −0.31–0.94 across all zones, and the high correlation areas were primarily distributed in Dry and Intermediate zones. Over 60% of the Dry and Intermediate zones had a lagged drought impact within 0 to 1 month, while 52% of the Wet zone experienced it over 11 months. The resulting dominant shorter timescale responses indicate a higher sensitivity of vegetation to drought in Sri Lanka. The findings of this study provide important insights into possible spatiotemporal changes of droughts and their possible impact on vegetation across climate zones. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Learning vector quantization neural network for surface water extraction from Landsat OLI images

Author

Zhang Zongke, Bing Zhang, Fangfang Zhang, Deepakrishna Somasundaram, Huping Ye, and Shenglei Wang

Subjects
Learning vector quantization, Fuzzy clustering, 010504 meteorology & atmospheric sciences, Computer science, business.industry, 0211 other engineering and technologies, Water extraction, Pattern recognition, 02 engineering and technology, 01 natural sciences, Normalized Difference Vegetation Index, Support vector machine, General Earth and Planetary Sciences, Artificial intelligence, Cluster analysis, Quantization (image processing), business, Surface water, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences
Abstract

There is a growing concern over surface water dynamics due to an increased understanding of water availability and management with current climate trends. Remote sensing has now become an effective means of water extraction due to the availability of an enormous amount of data with diverse spatial, spectral, and temporal resolutions. However, water extraction from optical remote sensing data is associated with several major difficulties, such as the applicability of the extraction method over large areas and complex environments; shadow contamination from clouds, buildings, and mountains; and disclosure of shadowed water and exclusion of floating and submerged plants. To address these difficulties, a learning vector quantization (LVQ) neural network-based method was proposed and implemented to extract water using Landsat 8 imageries. This method is capable of separating water from clouds, build-up areas, shadows, and shadowed water by the ideal input of bands 1 to 7 and normalized difference vegetation index. This model learns water across Sri Lanka. Eight OLI scenes were tested, and the performance was compared with five widely used machine learning algorithms: support vector machine, K-nearest neighbor, discriminant analysis, combination of modified normalized difference water index and modified fuzzy clustering method, and K-means clustering methods. This method performed the best, achieving overall accuracies and the kappa coefficients between 97.8% and 99.7% and between 0.96 and 0.99, respectively. Results have demonstrated robustness, consistency, and preciseness in various dark surfaces, noisiest water environments, and highly water scarce scenes. LVQ revealed a good generalizing ability to detect all types of water with less amount of training samples. This method can be easily adaptable for other sensors and global water to support water resource studies.

Published
2020
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