Thomas Loveland (Former Employee)
Science and Products
Filter Total Items: 138
Evaluation of AMOEBA: a spectral-spatial classification method
Muitispectral remotely sensed images have been treated as arbitrary multivariate spectral data for purposes of clustering and classifying. However, the spatial properties of image data can also be exploited. AMOEBA is a clustering and classification method that is based on a spatially derived model for image data. In an evaluation test, Landsat data were classified with both AMOEBA and a widely us
Authors
Susan K. Jenson, Thomas R. Loveland, J. Bryant
Remote sensing applied to irrigation engineering
No abstract available.
Authors
Gary E. Johnson, Robert F. Vining, Thomas Loveland
The role of remotely sensed and other spatial data for predictive modeling: the Umatilla, Oregon example
The U. S. Geological Survey's Earth Resources Observations Systems Data Center, in cooperation with the U.S. Army Corps of Engineers, Portland District, developed and tested techniques that used remotely sensed and other spatial data in predictive models to evaluate irrigation agriculture in the Umatilla River Basin of north-central Oregon. Landsat data and 1:24,000-scale aerial photographs were
Authors
Thomas R. Loveland, Gary E. Johnson
The Columbia River and tributaries irrigation withdrawals analysis project—Feasibility analysis and future plans
No abstract available.
Authors
Gary E. Johnson, Thomas Loveland
A selected bibliography of remote sensing applications to soil science
The bibliography contains approximately 200 references dealing with the application of remote sensing technology to the identification and analysis of soils. The scientific papers and reports listed describe procedures and methods used in data collection and include specific applications of those data to soil studies. Most citations discuss current work from 1970 to 1978 and all references are cat
Authors
Thomas R. Loveland, Daniel B. Carter, William C. Draeger
Land capability studies of the South Dakota Automated Geographic Information System (AGIS)
No abstract available.
Authors
J. Schlesinger, B. Ripple, Thomas R. Loveland
Science and Products
Filter Total Items: 138
Evaluation of AMOEBA: a spectral-spatial classification method
Muitispectral remotely sensed images have been treated as arbitrary multivariate spectral data for purposes of clustering and classifying. However, the spatial properties of image data can also be exploited. AMOEBA is a clustering and classification method that is based on a spatially derived model for image data. In an evaluation test, Landsat data were classified with both AMOEBA and a widely us
Authors
Susan K. Jenson, Thomas R. Loveland, J. Bryant
Remote sensing applied to irrigation engineering
No abstract available.
Authors
Gary E. Johnson, Robert F. Vining, Thomas Loveland
The role of remotely sensed and other spatial data for predictive modeling: the Umatilla, Oregon example
The U. S. Geological Survey's Earth Resources Observations Systems Data Center, in cooperation with the U.S. Army Corps of Engineers, Portland District, developed and tested techniques that used remotely sensed and other spatial data in predictive models to evaluate irrigation agriculture in the Umatilla River Basin of north-central Oregon. Landsat data and 1:24,000-scale aerial photographs were
Authors
Thomas R. Loveland, Gary E. Johnson
The Columbia River and tributaries irrigation withdrawals analysis project—Feasibility analysis and future plans
No abstract available.
Authors
Gary E. Johnson, Thomas Loveland
A selected bibliography of remote sensing applications to soil science
The bibliography contains approximately 200 references dealing with the application of remote sensing technology to the identification and analysis of soils. The scientific papers and reports listed describe procedures and methods used in data collection and include specific applications of those data to soil studies. Most citations discuss current work from 1970 to 1978 and all references are cat
Authors
Thomas R. Loveland, Daniel B. Carter, William C. Draeger
Land capability studies of the South Dakota Automated Geographic Information System (AGIS)
No abstract available.
Authors
J. Schlesinger, B. Ripple, Thomas R. Loveland