Future Atmospheric Rivers and Impacts on Precipitation: Overview of the ARTMIP Tier 2 High‐Resolution Global Warming Experiment

Autor: Christine A. Shields, Ashley E. Payne, Eric Jay Shearer, Michael F. Wehner, Travis Allen O’Brien, Jonathan J. Rutz, L. Ruby Leung, F. Martin Ralph, Allison B. Marquardt Collow, Paul A. Ullrich, Qizhen Dong, Alexander Gershunov, Helen Griffith, Bin Guan, Juan Manuel Lora, Mengqian Lu, Elizabeth McClenny, Kyle M. Nardi, Mengxin Pan, Yun Qian, Alexandre M. Ramos, Tamara Shulgina, Maximiliano Viale, Chandan Sarangi, Ricardo Tomé, Colin Zarzycki

Publikováno v: Geophysical Research Letters, Vol 50, Iss 6, Pp n/a-n/a (2023)

Abstract Atmospheric rivers (ARs) are long, narrow synoptic scale weather features important for Earth’s hydrological cycle typically transporting water vapor poleward, delivering precipitation important for local climates. Understanding ARs in a w

Externí odkaz: https://doaj.org/article/6ed5e58d8cde42ef86c3493a2903d9a9

River Detection in Remotely Sensed Imagery Using Gabor Filtering and Path Opening

Autor: Kang Yang, Manchun Li, Yongxue Liu, Liang Cheng, Qiuhao Huang, Yangming Chen

Publikováno v: Remote Sensing, Vol 7, Iss 7, Pp 8779-8802 (2015)

Detecting rivers from remotely sensed imagery is an initial yet important step in space-based river studies. This paper proposes an automatic approach to enhance and detect complete river networks. The main contribution of this work is the characteri

Externí odkaz: https://doaj.org/article/22da0b3018234fb49c88b0997066bbc7

River Planform Extraction From High-Resolution SAR Images Via Generalised Gamma Distribution Superpixel Classification

Autor: Alin Achim, B. A. Adams, Odysseas Pappas, Nantheera Anantrasirichai

Publikováno v: Pappas, O, Anantrasirichai, N, Achim, A M & Adams, B A 2020, ' River Planform Extraction From High-Resolution SAR Images Via Generalised Gamma Distribution Superpixel Classification ', IEEE Transactions on Geoscience and Remote Sensing, pp. 1-14 . https://doi.org/10.1109/TGRS.2020.3011209

The extraction of river planforms from remotely sensed satellite images is a task of crucial importance to many applications such as land planning, water resource monitoring or flood prediction. In this paper we present a novel framework for the extr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d0c0ade2ea323d4432df0362992007fd
https://hdl.handle.net/1983/4bed1854-b45f-453d-ac58-7961039b8162

SAR River Image Segmentation Based on Reciprocal Gray Entropy and Improved Chan-Vese Model

Autor: WU Shihua, WU Yiquan, ZHOU Jianjiang, MENG Tianliang

Publikováno v: Acta Geodaetica et Cartographica Sinica, Vol 44, Iss 11, Pp 1255-1262 (2015)

To further improve the accuracy and speed of river segmentation on synthetic aperture radar(SAR) images, a segmentation method is proposed, which is based on improved Chan-Vese(CV) model combining with reciprocal gray entropy multi-threshold selectio

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doajarticles::b9b9b0644eb8b0822d66a4fd64334eea
http://html.rhhz.net/CHXB/html/2015-11-1255.htm

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