Yong Piao
6 Papers
Yong Piao is an academic researcher. The author has contributed to research in topics: Environmental science & Computer science. The author has an hindex of 1, co-authored 5 publications.
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Papers
Forest fire susceptibility assessment using google earth engine in Gangwon-do, Republic of Korea
TL;DR: In this paper , a forest fire susceptibility map (FFSM) of Gangwon-do was constructed using Google Earth Engine (GEE) and three machine learning algorithms: Classification and Regression Trees (CART), Random Forest (RF), and Boosted Regression Tree (BRT).
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Using buffer analysis to determine urban park cooling intensity: Five estimation methods for Nanjing, China.
TL;DR: Wang et al. as discussed by the authors employed spatial and statistical analyses to further assess the autocorrelation of park cooling intensity and its relationship with park landscape features, and found that different methods had significant effects on the estimated PCI, were positively correlated, and had similar spatial heterogeneity.
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A comprehensive framework of cooling effect-accessibility-urban development to assessing and planning park cooling services
Ying Xiao,Yong Piao,Wei Wei,Chaopeng Pan,Dong-Kun Lee,Bing Zhao +5 more
TL;DR: This study proposes a comprehensive framework to assess and plan park cooling services in urban areas, highlighting unequal distribution of cooling services and identifying strategies to enhance equity and promote urban parks through increased park area and vegetation.
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Multi-hazard mapping of droughts and forest fires using a multi-layer hazards approach with machine learning algorithms
TL;DR: In this article , a multi-hazard probability map (MHPM) for two related hazards (forest fires and droughts) based on a multilayer hazards approach and machine learning algorithms for monitoring forest fire susceptibility areas was proposed.
17
Monitoring Land Use/Land Cover and Landscape Pattern Changes at a Local Scale: A Case Study of Pyongyang, North Korea
TL;DR: In this article , the authors used a random-forest algorithm and Landsat satellite dataset to classify land use/land cover (LULC) changes in North Korea and found an increasing trend in built-up and forest areas in Pyongyang, while cropland showed a decreasing trend and landscape fragmentation increased.