Nonlinear regression models to identify functional forms of deforestation in East Asia

Shojiro Tanaka, Ryuei Nishii

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Identification of the factors involved in deforestation could lead to a comprehensive understanding of deforestation on a broad scale, as well as prediction capability. In this paper, regression models with two explanatory variableshuman population and relief energy, i.e., the difference between the maximum and minimum altitudes in a sampled areawere verified as to whether they could elucidate aspects of deforestation. The functional forms of the nonlinear regression models were estimated by step functions analyzed with the use of high-precision Japanese data. Candidate smooth regression models were then derived from the obtained sigmoidal shapes by the step functions. Models with spatially dependent errors were also developed. Akaike's information criterion was used to evaluate the models on four data sets for the East Asia region. From the evaluation, we selected the best three models that systematically showed the best relative appropriateness to the real data.

Original languageEnglish
Article number4895355
Pages (from-to)2617-2626
Number of pages10
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume47
Issue number8
DOIs
Publication statusPublished - Aug 1 2009

Fingerprint

Deforestation
deforestation
Akaike information criterion
Asia
relief
prediction
energy

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

Cite this

Nonlinear regression models to identify functional forms of deforestation in East Asia. / Tanaka, Shojiro; Nishii, Ryuei.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 47, No. 8, 4895355, 01.08.2009, p. 2617-2626.

Research output: Contribution to journalArticle

@article{45c62ed959134f8684a45ededf87912d,
title = "Nonlinear regression models to identify functional forms of deforestation in East Asia",
abstract = "Identification of the factors involved in deforestation could lead to a comprehensive understanding of deforestation on a broad scale, as well as prediction capability. In this paper, regression models with two explanatory variableshuman population and relief energy, i.e., the difference between the maximum and minimum altitudes in a sampled areawere verified as to whether they could elucidate aspects of deforestation. The functional forms of the nonlinear regression models were estimated by step functions analyzed with the use of high-precision Japanese data. Candidate smooth regression models were then derived from the obtained sigmoidal shapes by the step functions. Models with spatially dependent errors were also developed. Akaike's information criterion was used to evaluate the models on four data sets for the East Asia region. From the evaluation, we selected the best three models that systematically showed the best relative appropriateness to the real data.",
author = "Shojiro Tanaka and Ryuei Nishii",
year = "2009",
month = "8",
day = "1",
doi = "10.1109/TGRS.2009.2015659",
language = "English",
volume = "47",
pages = "2617--2626",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
issn = "0196-2892",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "8",

}

TY - JOUR

T1 - Nonlinear regression models to identify functional forms of deforestation in East Asia

AU - Tanaka, Shojiro

AU - Nishii, Ryuei

PY - 2009/8/1

Y1 - 2009/8/1

N2 - Identification of the factors involved in deforestation could lead to a comprehensive understanding of deforestation on a broad scale, as well as prediction capability. In this paper, regression models with two explanatory variableshuman population and relief energy, i.e., the difference between the maximum and minimum altitudes in a sampled areawere verified as to whether they could elucidate aspects of deforestation. The functional forms of the nonlinear regression models were estimated by step functions analyzed with the use of high-precision Japanese data. Candidate smooth regression models were then derived from the obtained sigmoidal shapes by the step functions. Models with spatially dependent errors were also developed. Akaike's information criterion was used to evaluate the models on four data sets for the East Asia region. From the evaluation, we selected the best three models that systematically showed the best relative appropriateness to the real data.

AB - Identification of the factors involved in deforestation could lead to a comprehensive understanding of deforestation on a broad scale, as well as prediction capability. In this paper, regression models with two explanatory variableshuman population and relief energy, i.e., the difference between the maximum and minimum altitudes in a sampled areawere verified as to whether they could elucidate aspects of deforestation. The functional forms of the nonlinear regression models were estimated by step functions analyzed with the use of high-precision Japanese data. Candidate smooth regression models were then derived from the obtained sigmoidal shapes by the step functions. Models with spatially dependent errors were also developed. Akaike's information criterion was used to evaluate the models on four data sets for the East Asia region. From the evaluation, we selected the best three models that systematically showed the best relative appropriateness to the real data.

UR - http://www.scopus.com/inward/record.url?scp=67949111065&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=67949111065&partnerID=8YFLogxK

U2 - 10.1109/TGRS.2009.2015659

DO - 10.1109/TGRS.2009.2015659

M3 - Article

AN - SCOPUS:67949111065

VL - 47

SP - 2617

EP - 2626

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

SN - 0196-2892

IS - 8

M1 - 4895355

ER -