Non-linear regression models to identify functional forms of deforestation

Sojiro Tanaka, Ryuei Nishii

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

1 Citation (Scopus)

Abstract

Identification of limited number of factors shall provide comprehensive general understanding of deforestation at broad scale, as well as the projection for the future. Only two factors - human population and relief energy (difference of minimum altitude from the maximum in a sampled area) - were verified if they give sufficient elucidation of deforestation by a regression model, whose functional forms identified by linear combinations of dummy variables firstly explored with use of high-precision Japanese data. Likelihood with spatial dependency was derived and applied then to East-Asian data, with which our models systematically showed eminently good relative appropriateness to the real data.

Original languageEnglish
Title of host publication2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
Volume4
Edition1
DOIs
Publication statusPublished - 2008
Event2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings - Boston, MA, United States
Duration: Jul 6 2008Jul 11 2008

Other

Other2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
CountryUnited States
CityBoston, MA
Period7/6/087/11/08

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All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)
  • Computer Science Applications

Cite this

Tanaka, S., & Nishii, R. (2008). Non-linear regression models to identify functional forms of deforestation. In 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings (1 ed., Vol. 4). [4779653] https://doi.org/10.1109/IGARSS.2008.4779653