Research on high resolution remote sensing recognition method of elm sparse forest in Otindag sandy land

doi:10.6046/gtzyyg.2018.04.12

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Abstract

The elm sparse forest is an important component in the Otindag sandy land ecosystem, which is of great significance for windbreak and sand fixation. In order to obtain the spatial distribution information of elm trees quickly and accurately, this paper proposes a method of automatic sand elm identification based on remote sensing technology. With the data of domestic high spatial resolution satellite GF-2, the research was implemented on Zhenglan Banner, Xilin Gol League, Inner Mongolia. Combined with the characteristics of elm sparse distribution in the sand, normalizd difference vegetation index (NDVI) threshold was firstly used to quickly extract the coarse distribution of elm. Then, a method based on geographic object based image analysis (GEOBIA) was used to extract the distribution of elm accurately. To compensate for the uncertainty of GEOBIA method in feature selection and rule set construction, this study used SEaTH algorithm to optimize features and automatically calculate the feature threshold. The results show that the proposed methods reached the overall accuracy of 88.17% and Kappa coefficient of 0.76 in identifying the sparse elm. Among them, elm mapping accuracy could reach 99.14%. Therefore, it is effective to identify elms by using GF-2 and the method proposed in this study. This method can provide technical support for the further research and production practices of elm sparse forest in the Otindag sandy land.

Keywords Otindag sandy land elm sparse GF-2 GEOBIA SEaTH

TP79

Corresponding Authors: Zhihai GAO E-mail: zhgao@caf.ac.cn

Issue Date: 07 December 2018

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	Chuanping XUE
	Zhihai GAO
	Bin SUN
	Changlong LI
	Yan WANG
	Yuanyuan ZHANG

Cite this article:

Chuanping XUE,Zhihai GAO,Bin SUN, et al. Research on high resolution remote sensing recognition method of elm sparse forest in Otindag sandy land[J]. Remote Sensing for Land & Resources, 2018, 30(4): 74-81.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.04.12 OR https://www.gtzyyg.com/EN/Y2018/V30/I4/74

Fig.1 Location of the study site

Tab.1 GF-2 satellite sensor parameters

Fig.2 Process of elm coarse extraction

对象特征	最小值	最大值	均值	公式^①
NDVI均值	0.13	0.73	0.52	$b = 1 n × ∑ i = 1 n b i$
NIR标准差	488	1 123	713	$σ = 1 n × ∑ i = 1 n (c i - c) 2$
面积	40	2 236	250	$a = n × p$
椭圆度	0.43	0.96	0.86	—
圆度	0.04	1.2	0.26	$r = s - l$

Tab.2 Eigenvalue statistics of reference data

Fig.3 Results of local elm coarse extraction

Tab.3 Standard deviation and correlation coefficient of GF-2 Bands

Tab.4 OIF value of GF-2

Fig.4 Segmentation results in local region

地物类别	区分类别	区分规则^①	J-M距离	T	$m 2$
榆树	灌木	Ratio G<0.183	1.61	0.173	0.192
	草地	Standard deviation NIR>123	1.79	123	70
	草本湿地	GLCM Entropy (90)<5.97	1.61	5.54	6.40
	人工杨树林	Ratio R>0.119 6	1.83	0.119 6	0.095 4
	沙地	HSI Transformation Intensity(R=r,G=g,B=b)<0.027	1.93	0.027	0.047

Tab.5 Extraction rules of elm

Fig.5 Results of local elm recognition

Tab.6 Accuracy evaluation of elm recognition

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