Verification of deforestation in East Asia by spatial logit models due to population and relief energy

Shojiro Tanaka, Ryuei Nishii

Research output: Contribution to journalConference article

10 Citations (Scopus)

Abstract

Deforestation is a result of complex causality chains in most cases. But identification of limited number of factors shall provide comprehensive general understanding of the vital phenomenon at a broad scale, as well as projection for the future. Only two factors - human population size (N) and relief energy (R: difference of minimum altitude from the maximum in a sampled area) - were found to give sufficient elucidation of deforestation by nonlinear logit regression models, whose functional forms were suggested by step functions fitted to one-kilometer square high precision grid-cell data in Japan (n=6825). Likelihood with spatial dependency was derived, and several deforestation models were selected for the application to East Asia by calculating relative appropriateness to data. For the measure of appropriateness, Akaike's Information Criterion (AIC) was used. Logit model is employed so as to avoid anomaly in asymptotic lower and upper bounds. Therefore the forest areal rate, 0 < F < 1. To formulate East-Asian dataset, landcover dataset estimated from NOAA observations available at UNEP, Tsukuba for F, gridded population of the world of CIESIN, US for N, and GTOPO30 of USGS for R, were used. The resolutions were matched by taking their common multiple of 20 minutes square. It was suggested that data of full forest coverage, F = 1.0, which were not dealt in calculations due to logit transformation this time, should give important role in stabilizing parameter estimations.

Original languageEnglish
Article number59760W
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5976
DOIs
Publication statusPublished - Dec 1 2005
EventRemote Sensing for Agriculture, Ecosystems, and Hydrology VII - Bruges, Belgium
Duration: Sep 20 2005Sep 22 2005

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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