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Object-oriented Classification of Coconut Palms Based on Gaofen-2 Imagery
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Hongxia LUO1, 2, Shengpei DAI1, 2, Maofen LI1, 2, Hailiang LI1, 2, Yingying HU1, 2, Qian ZHENG1, 2, Xuan YU1, 2
Chinese Journal of Tropical Crops | 2024, 45(5) : 1021 - 1030
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Chinese Journal of Tropical Crops | 2024, 45(5): 1021-1030
Agricultural Ecology & Environmental Protection
Object-oriented Classification of Coconut Palms Based on Gaofen-2 Imagery
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Hongxia LUO1, 2, Shengpei DAI1, 2, Maofen LI1, 2, Hailiang LI1, 2, Yingying HU1, 2, Qian ZHENG1, 2, Xuan YU1, 2
Affiliations
  • 1.Institute of Scientific and Technical Information, Chinese Academy of Tropical Agricultural Sciences / Key Laboratory of Applied Research on Tropical Crop Information Technology of Hainan, Haikou, Hainan 571101, China
  • 2.Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100100, China
Published: 2024-05-25 doi: 10.3969/j.issn.1000-2561.2024.05.017
Outline
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Coconut is an important economic crop in tropics. The area of coconut palm in Hainan Island accounts for 90% of the total production area in China. Up-to-date maps of coconut palm are essential for the industrial planning of tropical agriculture. In this study, an object-oriented image analysis method was used for mapping coconut palms based on high spatial resolution imagery of Gaofen-2. Dongjiao town in Wenchang was selected as the study area. An optimal segmentation scale of Gaofen-2 imagery was obtained using the fractal evolution approach. Then, a combined layer of four original bands, five vegetation indices and 32 gray-level co-occurrence (GLCM) texture indices were used as input features. Four different sets of features (including only original bands, original bands and textures indices, original bands and vegetation indices, and all features) were applied in object-based classification. The overall accuracy (OA) and user’s accuracy (UA) of pixel-based method achieved 87.05% and 85.21%, respectively. Compared with the pixel-based method, the object-based approaches significantly increased overall accuracy (OA) between 5.51%-8.72%. The OA and user’s accuracy (UA) reached 95.77% and 97.15% respectively, which was the optimal classification result, when the combination of original bands and textural features were used in coconut palms classifying. In addition, the combination of original bands and vegetation indexes achieved a satisfactory accuracy (OA=94.88% and UA=94.42%). While the combination of original bands, vegetation indexes and textural indexes performed a lower accuracy (OA=94.67% and UA=94.17%) in comparison with that of above two combination. Generally, our researches indicate that Gaofen-2 imagery has a good potential for coconut palms classification in complex tropical regions, and the textural indexes are very useful for identifying coconut palms at object-based level. The results would provide a reference framework for tropical agriculture decision management.

coconut palm  /  object-oriented classification  /  segmentation scale  /  Gaofen-2 imagery
Hongxia LUO, Shengpei DAI, Maofen LI, Hailiang LI, Yingying HU, Qian ZHENG, Xuan YU. Object-oriented Classification of Coconut Palms Based on Gaofen-2 Imagery[J]. Chinese Journal of Tropical Crops, 2024 , 45 (5) : 1021 -1030 . DOI: 10.3969/j.issn.1000-2561.2024.05.017
Year 2024 volume 45 Issue 5
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Article Info
doi: 10.3969/j.issn.1000-2561.2024.05.017
  • Receive Date:2022-09-27
  • Online Date:2026-06-23
  • Published:2024-05-25
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History
  • Received:2022-09-27
  • Revised:2022-12-15
Funding
Affiliations
    1.Institute of Scientific and Technical Information, Chinese Academy of Tropical Agricultural Sciences / Key Laboratory of Applied Research on Tropical Crop Information Technology of Hainan, Haikou, Hainan 571101, China
    2.Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100100, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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