Abstract
This paper establishes a statistical framework of forest coverage models for spatio-temporal data. The forest coverage ratio of grid-cell data is modeled by taking human population density and relief energy as explanatory variables. The likelihood of the forest ratios is decomposed by the product of two likelihoods. The first likelihood discussed by Nishii and Tanaka (2010) is due to trinomial logistic distributions on three categories: the ratios take zero, one, or values between zero and one. We consider a precise modeling to the second likelihood for partly-deforested ratios by considering a) spline functions to the additive mean structure, b) wide spatial dependency of normal error terms, and c) an extended logistic type transform to the forest ratio. For spatio-temporal data, we implement auto-regressive terms based on the ratios observed in past. The proposed model was applied to real grid-cell data and resulted significant improvement compared to our previous model.
| Original language | English |
|---|---|
| Title of host publication | Earth Resources and Environmental Remote Sensing/GIS Applications III |
| Volume | 8538 |
| DOIs | |
| Publication status | Published - Dec 1 2012 |
| Event | Earth Resources and Environmental Remote Sensing/GIS Applications III - Edinburgh, United Kingdom Duration: Sep 24 2012 → Sep 26 2012 |
Other
| Other | Earth Resources and Environmental Remote Sensing/GIS Applications III |
|---|---|
| Country | United Kingdom |
| City | Edinburgh |
| Period | 9/24/12 → 9/26/12 |
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All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering
Cite this
Statistical frameworking of deforestation models based on human population density and relief energy. / Nishii, Ryuei; Miyata, Daiki; Tanaka, Shojiro.
Earth Resources and Environmental Remote Sensing/GIS Applications III. Vol. 8538 2012. 85380K.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Statistical frameworking of deforestation models based on human population density and relief energy
AU - Nishii, Ryuei
AU - Miyata, Daiki
AU - Tanaka, Shojiro
PY - 2012/12/1
Y1 - 2012/12/1
N2 - This paper establishes a statistical framework of forest coverage models for spatio-temporal data. The forest coverage ratio of grid-cell data is modeled by taking human population density and relief energy as explanatory variables. The likelihood of the forest ratios is decomposed by the product of two likelihoods. The first likelihood discussed by Nishii and Tanaka (2010) is due to trinomial logistic distributions on three categories: the ratios take zero, one, or values between zero and one. We consider a precise modeling to the second likelihood for partly-deforested ratios by considering a) spline functions to the additive mean structure, b) wide spatial dependency of normal error terms, and c) an extended logistic type transform to the forest ratio. For spatio-temporal data, we implement auto-regressive terms based on the ratios observed in past. The proposed model was applied to real grid-cell data and resulted significant improvement compared to our previous model.
AB - This paper establishes a statistical framework of forest coverage models for spatio-temporal data. The forest coverage ratio of grid-cell data is modeled by taking human population density and relief energy as explanatory variables. The likelihood of the forest ratios is decomposed by the product of two likelihoods. The first likelihood discussed by Nishii and Tanaka (2010) is due to trinomial logistic distributions on three categories: the ratios take zero, one, or values between zero and one. We consider a precise modeling to the second likelihood for partly-deforested ratios by considering a) spline functions to the additive mean structure, b) wide spatial dependency of normal error terms, and c) an extended logistic type transform to the forest ratio. For spatio-temporal data, we implement auto-regressive terms based on the ratios observed in past. The proposed model was applied to real grid-cell data and resulted significant improvement compared to our previous model.
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UR - http://www.scopus.com/inward/citedby.url?scp=84875660582&partnerID=8YFLogxK
U2 - 10.1117/12.974583
DO - 10.1117/12.974583
M3 - Conference contribution
AN - SCOPUS:84875660582
SN - 9780819492784
VL - 8538
BT - Earth Resources and Environmental Remote Sensing/GIS Applications III
ER -