| Project/Area Number |
16K12658
|
|---|
| Research Category |
Grant-in-Aid for Challenging Exploratory Research
|
| Allocation Type | Multi-year Fund |
|---|
| Research Field |
Environmental policy and social systems
|
|---|
| Research Institution | Kanazawa University |
|---|
Principal Investigator |
|
|---|
| Project Period (FY) |
2016-04-01 – 2018-03-31
|
| Project Status |
Completed (Fiscal Year 2017)
|
| Budget Amount *help |
¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Fiscal Year 2017: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
|
|---|
| Keywords | 統計 / 環境 / パラメーター推算 / 乾燥地林 / モデル / 乾燥地 / 疎林 / spatial statistics / 地球温暖化対策 / 植林 / 炭酸ガス固定 / 個木位置情報 / 衛星データ |
|---|
| Outline of Final Research Achievements |
We have been tried to develop methods for characterizing the sparse trees in arid region to facilitate the afforestation for carbon sequestration.
During the analysis of sparse trees, we came up with a new method of statistical parameter estimation. In the analysis of sparse trees, we concern the generalized extreme value distribution (GEV). For some range of parameters, the maximum likelihood (ML) method does not work for estimation of GEV parameters. Among the alternatives of ML, the method of maximum spacings (MPS) are convenient because it can be applied wider range of parameters and relatively small error of estimation.
Our method is a variant of MPS and it is in general less bias than MPS, and theoretical more proxy to ML.
|
