Empirical modeling of remotely sensed data at regional to continental scales

Richard D. Robertson, Peter Bajcsy, Praveen Kumar, David K. Tcheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings - SMC-IT 2006
Subtitle of host publication2nd IEEE International Conference on Space Mission Challenges for Information Technology
Pages157-162
Number of pages6
DOIs
StatePublished - 2006
EventSMC-IT 2006: 2nd IEEE International Conference on Space Mission Challenges for Information Technology - Pasadena, CA, United States
Duration: Jul 17 2006Jul 20 2006

Publication series

NameProceedings - SMC-IT 2006: 2nd IEEE International Conference on Space Mission Challenges for Information Technology
Volume2006

Other

OtherSMC-IT 2006: 2nd IEEE International Conference on Space Mission Challenges for Information Technology
Country/TerritoryUnited States
CityPasadena, CA
Period7/17/067/20/06

ASJC Scopus subject areas

  • Computer Science(all)
  • Aerospace Engineering

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