Research on Wetland Type Classification Based on Improved Remote Sensing Index of Worldview-2 Data

LING Cheng-xing; JU Hong-bo; ZHANG Huai-qing; SUN Hua

Taking the core area in wetland nature reserve of Dongting Lake in Hunan province as the reserch plot, the high resolution image Worldview-2 data were analyzed and used to establish the improved remote sensing index. The information about the wetland was extracted with the strategy of data threshold hierarchical classification. The result showed that accuracy of wetland type classification reached 92.24%, with Kappa coefficient at 0.902. The accuracy of classification, especially for processing the data of marsh, meadow and bare land, was 8.18% higher compared to the original remote sensing index method. It is proved that the improved remote sensing index extration mothod is an effective way in wetland remote sensing data classification.

1. Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

Received Date: 2013-05-25

Abstract: Taking the core area in wetland nature reserve of Dongting Lake in Hunan province as the reserch plot, the high resolution image Worldview-2 data were analyzed and used to establish the improved remote sensing index. The information about the wetland was extracted with the strategy of data threshold hierarchical classification. The result showed that accuracy of wetland type classification reached 92.24%, with Kappa coefficient at 0.902. The accuracy of classification, especially for processing the data of marsh, meadow and bare land, was 8.18% higher compared to the original remote sensing index method. It is proved that the improved remote sensing index extration mothod is an effective way in wetland remote sensing data classification.

HTML

Reference (12)

[1]	刘华,鞠洪波,邹文涛,等.长江源典型区湿地对区域气候变化的响应[J].林业科学研究,2013, 26(4):406-413.
[2]	张怀清,赵峰,崔丽娟.红树林湿地恢复遥感动态监测技术研究[J].林业科学研究,2008, 21(增刊):32-36.
[3]	邹文涛,张怀清,鞠洪波,等.基于决策树的高寒湿地类型遥感分类方法研究[J].林业科学研究,2011, 24(4):464-469.
[4]	Jensen.J R, Rutchey K. Narumalani 1995 Inland wetland change detection in the Everglades Water Conservation Area 2A using a time series of normalized sensed data[J]. Photogrammetric Engineering and Remote Sensing,1995,61:199-209.
[5]	Finlayson C M, Rea N. Reasons for the loss and degradation of Australian wetlands[J]. Wetlands Ecology and Management, 1999,7(1-2): 1-11.
[6]	Carter J H. The Immune System as a Model for Pattern Recognition and Classification [J]. Journal of the American Medical Informatics Association,2000,7(3):28-41.
[7]	钟文君,兰樟仁.基于高空间分辨率遥感影像的湿地信息提取技术研究[J].云南地理环境研究,2007,19(5):134-139.
[8]	章仲楚,张秀英. 基于RS和GIS的西溪湿地景观格局变化研究[J] 浙江林业科技, 2007,27(4): 38-41.
[9]	Chris Wright,Alisa Gallant. Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data[J]. Remote Sensing of Environment,2007,107(4):582-605.
[10]	那晓东,张树清. 基于决策树方法的淡水沼泽湿地信息提取-以三江平原东北部为例[J]. 遥感技术与应用, 2008,23(4):365-372.
[11]	谢静,王宗明,毛德华,等.基于面向对象方法和多时相HJ-1影像的湿地遥感分类——以完达山以北三江平原为例[J].湿地科学,2012,10(4):429-438.
[12]	臧淑英,张策,张丽娟,等.遗传算法优化的支持向量机湿地遥感分类——以洪河国家级自然保护区为例[J].地理科学, 2012,32(4):434-441.

Research on Wetland Type Classification Based on Improved Remote Sensing Index of Worldview-2 Data

Abstract

References

Proportional views

通讯作者: 陈斌, bchen63@163.com

Proportional views