Spatial snow water equivalent estimation for mountainous areas using wireless-sensor networks and remote-sensing products

Autor: Zheng, Zeshi, Molotch, Noah P., Oroza, Carlos A., Conklin, Martha H., Bales, Roger C.

Publikováno v: In Remote Sensing of Environment 15 September 2018 215:44-56

Multi-spatial-scale observational studies of the Sierra Nevada snowpack using wireless-sensor networks and multi-platform remote-sensing data

Autor: Zheng, Zeshi

Publikováno v: Zheng, Zeshi. (2018). Multi-spatial-scale observational studies of the Sierra Nevada snowpack using wireless-sensor networks and multi-platform remote-sensing data. UC Berkeley: Civil Engineering. Retrieved from: http://www.escholarship.org/uc/item/1hd045rj

The Sierra Nevada winter snowpack is the major water resource for the state of California. To better quantify the input of the water system, we deployed wireless-sensor networks across several basins in the Sierra Nevada. Together with operational an

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______325::9a2e992310c98851b29b810e8027d233
http://www.escholarship.org/uc/item/1hd045rj

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Optimizing embedded sensor network design for catchment-scale snow-depth estimation using LiDAR and machine learning

Autor: Oroza, Carlos A., Zheng, Zeshi, Glaser, Steven D., Tuia, Devis, Bales, Roger C.

Publikováno v: Water Resources Research, 52(10), 8174-8189
Water Resources Research 52 (2016) 10

We evaluate the accuracy of a machine-learning algorithm that uses LiDAR data to optimize ground-based sensor placements for catchment-scale snow measurements. Sampling locations that best represent catchment physiographic variables are identified wi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::911725ed4a0f661fe0afdeee42b0624a
https://www.zora.uzh.ch/id/eprint/128174/