干旱区生态环境知识资源中心(Arid): 基于HJ-CCD数据和决策树法的干旱半干旱灌区土地利用分类

Arid

	基于HJ-CCD数据和决策树法的干旱半干旱灌区土地利用分类
其他题名	Land use classification in arid and semi-arid irrigated area based on HJ-CCD data and decision tree method
	于文婧 1; 刘晓娜 2; 孙丹峰 1; 姜宛贝 1; 曲葳 1
来源期刊	农业工程学报
ISSN	1002-6819
出版年	2016
卷号	32 期号:2 页码:212-219
中文摘要	为了实现干旱半干旱灌区地表信息低成本、高效率的动态监测,利用HJ-CCD数据的多时相和多光谱信息,探讨了平罗县土地利用遥感分类方法。首先建立研究区内典型地物的NDVI时间序列曲线,提取反映该区物候模式的时序特征参数;然后对土壤信息丰富的3月份多光谱影像进行主成分变换,选取第1主成分(PC1)作为光谱特征参数,最后基于分类回归树(classification and regression tree,CART)算法进行决策树监督分类。总体分类精度达到92.26%,Kappa系数为0.91,比最大似然法分类结果精度提高了2.58%。研究表明:构建的NDVI时间序列曲线对研究区内的地类具有较强的代表性,提取的时间维和光谱维的分类参数对各地类均有很好地区分性,CART决策树算法分类结果清晰准确且精度较高。该方法为HJ小卫星在干旱半干旱区等区域的深入应用提供科学依据和实证基础。
英文摘要	HJ satellites with the characteristics of high temporal resolution, high spatial resolution and large coverage, can provide the regional land use/cover classification with high accuracy. Pingluo county is in the arid and semi-arid area of northwest China, the climate and human irrigation activities caused complicated land use/cover type and serious soil salinization in the study area. In order to achieve the dynamic monitoring of land surface information with low cost and high precision, a regional land use supervised classification based on the classification and regression tree (CART) algorithm was developed and discussed in Pingluo county using the multi-temporal and multi-spectral information of HJ satellite CCD data. Firstly, high quality HJ-1 CCD data (the interval was about 20 d) were selected, and preprocessed including geometric correction, radiometric calibration and atmospheric correction. The normalized difference vegetation index (NDVI) were calculated and overlapped together. Secondly, the land use types including double crops irrigated land, one crop irrigated land, paddy, sand, saline-alkali soil, forest land, construction land and water were adopted for the two-level classification system, and the training samples were selected to obtain the typical NDVI time-series curve of each land type. Then, the characteristic parameters (including maximum, minimum, range, the difference between the value of the July 29 and the May 10 phases, the difference between the value of the October 10 and the July 29 phases, the mean value of the October 4 to the November 8 phases) which could reflect the phonological pattern in the area were extracted through the analysis of the NDVI time-series curves. Thirdly, the principle component transform of a multi-spectral image in March with ample soil information was performed for improving the separation between the construction land and saline-alkali land when the first principal component (PC1) was chosen for a parameter band for classification. Finally, a CART decision tree classification was implemented by combining the multi-temporal and multi-spectral parameter bands in the area. The decision tree had a total of 102 leaf nodes and could be expressed as "If...Then..." forms. The results showed that the overall precision of this classification method was 92.26%. The Kappa coefficient was 0.91. The accuracy of the paddy field was the highest which reached 98.23%. The accuracies of sand, one crop irrigated land and water were all greater than 90%. Double crops irrigated land, forest land, saline-alkali land, construction land were all greater than 80%. The participation of PC1 had made great contributions in improving the classification accuracy, especially for construction land and saline-alkali land, their accuracy increased 26.34% and 12.14%, respectively. The overall accuracy of CART decision tree classification was increased 2.58% than maximum likelihood classification.
中文关键词	土地利用 ; 决策树 ; 分类 ; 归一化植被指数(NDVI) ; 时间序列 ; 干旱半干旱灌区
英文关键词	HJ-CCD land use decision trees classification HJ-CCD NDVI time series arid and semi-arid irrigated area
语种	中文
国家	中国
收录类别	CSCD
WOS类目	REMOTE SENSING
WOS研究方向	Remote Sensing
CSCD记录号	CSCD:5616413
来源机构	中国农业大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/234588
作者单位	1.中国农业大学资源与环境管理学院, 北京 100193, 中国; 2.北京市农林科学院农业综合发展研究所, 北京 100097, 中国
推荐引用方式 GB/T 7714	于文婧,刘晓娜,孙丹峰,等. 基于HJ-CCD数据和决策树法的干旱半干旱灌区土地利用分类[J]. 中国农业大学,2016,32(2):212-219.
APA	于文婧,刘晓娜,孙丹峰,姜宛贝,&曲葳.(2016).基于HJ-CCD数据和决策树法的干旱半干旱灌区土地利用分类.农业工程学报,32(2),212-219.
MLA	于文婧,et al."基于HJ-CCD数据和决策树法的干旱半干旱灌区土地利用分类".农业工程学报 32.2(2016):212-219.