Empirical modeling of remotely sensed data at regional to continental scales

Author

Robertson, Richard D. ; Kumar, Praveen ; Bajesy, P. ; Tcheng, David K.

Author_Institution

Dept. of Civil & Environ. Eng., Illinois Univ., Urbana-Champaign, IL

fYear

0

fDate

0-0 0

Abstract

A continental scale dataset was assembled to examine the drivers of greenness indices. Easily parallelized algorithms for ordinary least squares linear regression and a binary regression tree were implemented and used for the analysis. The most important drivers were found to be long and shortwave radiation, precipitation, elevation, and soil pH. This analysis shows that it is possible to perform empirical modeling with large datasets and thus the archives of remotely sensed data can and should be analyzed to shed light on models of large scale natural processes

Keywords

least squares approximations; regression analysis; terrain mapping; trees (mathematics); binary regression tree; continental scale dataset; elevation; greenness indices; least squares linear regression; precipitation; remotely sensed data; shortwave radiation; soil pH; Algorithm design and analysis; Assembly; Data engineering; High performance computing; Least squares approximation; Least squares methods; Linear regression; Performance analysis; Predictive models; Regression tree analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Space Mission Challenges for Information Technology, 2006. SMC-IT 2006. Second IEEE International Conference on

Conference_Location

Pasadena, CA

Print_ISBN

0-7695-2644-6

Type

conf

DOI

10.1109/SMC-IT.2006.1

Filename

1659547