| Project/Area Number |
24658024
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Single-year Grants |
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| Research Field |
Horticulture/Landscape architecture
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| Research Institution | The University of Tokyo |
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Principal Investigator |
OKUBO Satoru 東京大学, 農学生命科学研究科, 助教 (30334329)
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| Co-Investigator(Kenkyū-buntansha) |
HARASHINA Koji 岩手大学, 農学部, 准教授 (40396411)
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| Project Period (FY) |
2012-04-01 – 2014-03-31
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2013: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2012: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
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| Keywords | 高解像度衛星画像 / オブジェクト分類 / 分類木 / テクスチャ情報 / 分類樹木 |
|---|
| Research Abstract |
Monitoring land use/cover changes in humid tropical agricultural landscapes is crucial to establish sustainable rural developments. However, the characteristic spatio-temporal complexity of mosaic landscapes makes it difficult to obtain accurate land use/cover maps using single-dated and moderate-resolution remotely sensed images. In this study, we attempted to classify land use/cover by utilizing texture measures to improve object-oriented classification based on a single-dated QuickBird image. GLCM texture measures, especially entropy, improved classification accuracy in delineating paddy rice fields irrespective of the stage of rice growth. The classification rule set derived from the CART modelling was intuitively understandable: a whole image was divided into green or non-green texturally homogeneous or heterogeneous land use/cover classes, which seems to describe the fundamental nature of the characteristics of various land use/covers of agricultural landscapes.
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