Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Murray Richardson"'
Autor:
Inés Peraza, John Chételat, Murray Richardson, Thomas S Jung, Malik Awan, Steve Baryluk, Ashu Dastoor, William Harrower, Piia M Kukka, Christine McClelland, Garth Mowat, Nicolas Pelletier, Christine Rodford, Andrei Ryjkov
Publikováno v:
PLoS ONE, Vol 18, Iss 5, p e0285826 (2023)
Limited information exists on mercury concentrations and environmental drivers of mercury bioaccumulation in high latitude terrestrial carnivores. Spatial patterns of mercury concentrations in wolverine (Gulo gulo, n = 419) were assessed across a 1,6
Externí odkaz:
https://doaj.org/article/4e4b97c82ac54e5eb1504eef6814c227
Publikováno v:
Remote Sensing, Vol 7, Iss 10, Pp 13528-13563 (2015)
The Random Forest algorithm was used to classify 86 Wide Fine Quadrature Polarized RADARSAT-2 scenes, five Landsat 5 scenes, and a Digital Elevation Model covering an area approximately 81,000 km2 in size, and representing the entirety of Dease Strai
Externí odkaz:
https://doaj.org/article/83e212e4b8d94fec952edfdf5d45b789
Autor:
Koreen Millard, Murray Richardson
Publikováno v:
Remote Sensing, Vol 7, Iss 7, Pp 8489-8515 (2015)
Random Forest (RF) is a widely used algorithm for classification of remotely sensed data. Through a case study in peatland classification using LiDAR derivatives, we present an analysis of the effects of input data characteristics on RF classificatio
Externí odkaz:
https://doaj.org/article/44e8b4a6f79c4ed790a9230769631141
Publikováno v:
Remote Sensing, Vol 10, Iss 6, p 903 (2018)
The purpose of this research was to use empirical models to monitor temporal dynamics of soil moisture in a peatland using remotely sensed imagery, and to determine the predictive accuracy of the approach on dates outside the time series through stat
Externí odkaz:
https://doaj.org/article/6b365aebf89f43d481fa89d99ad4058b
Publikováno v:
Remote Sensing, Vol 9, Iss 7, p 696 (2017)
Abstract: High spatial resolution hyperspectral data often used in precision farming applications are not available from current satellite sensors, and difficult or expensive to acquire from standard aircraft. Alternatively, in precision farming, unm
Externí odkaz:
https://doaj.org/article/c01f92bcce184c14a704597265750704
Publikováno v:
Remote Sensing, Vol 9, Iss 6, p 573 (2017)
For this research, the Random Forest (RF) classifier was used to evaluate the potential of simulated RADARSAT Constellation Mission (RCM) data for mapping landcover within peatlands. Alfred Bog, a large peatland complex in Southern Ontario, was used
Externí odkaz:
https://doaj.org/article/f6b65aa6c45e4612ac85d2f00fc35a05
Publikováno v:
Exploration Geophysics. 51:193-202
The AusAEM airborne electromagnetic (AEM) survey is one of the main components of the Exploring for the Future program. This Australian Government initiative is aimed at enhancing the geosc...
Autor:
Colin P. R. McCarter, Chris McConnell, Carl P. J. Mitchell, George B. Arhonditsis, Murray Richardson, Huaxia Yao, Tim Field, Daniel Lane
Publikováno v:
Hydrological Processes. 34:598-614
The estimation of hydrologic transit times in a catchment provides insights into the integrated effects of water storage, mixing dynamics, and runoff generation processes. There has been limited effort to estimate transit times in southern boreal Pre
Publikováno v:
Remote Sensing of Environment. 283:113305
Publikováno v:
ASEG Extended Abstracts. 2018:1-8
In 1974, the Australian Bureau of Mineral Resources, Geology and Geophysics completed a 15-year systematic reconnaissance gravity survey of Australia with stations spaced at 11 km. The 1976 Gravity Map of Australia was a seminal product; half a centu