The development of plastic greenhouse index based on Logistic regression analysis

doi:10.6046/gtzyyg.2019.03.06

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Abstract

In order to accurately extract a large range of plastic greenhouse distribution information, the authors took Changzhou City, which is located in the Taihu Lake basin, as the study area, used Landsat8 imagery, employed plastic greenhouses spectral analysis and spectral separability analysis, selected seven multi-spectral data and one thermal infrared datum of Landsat8 image and three remote sensing indexes(NDVI, NDBaI and MNDWI)and, based on Logistic regression analysis, constructed a new plastic greenhouse index (NewPGI). Accuracy verification results show that, in the sample area, the high-resolution image of the plastic greenhouse reference map shows that NewPGI’s overall classification accuracy is 94.9%, and Kappa coefficient is 0.74. Throughout Changzhou, the verification sample points were selected based on the Google Earth image. The overall accuracy of NewPGI is 91.28%, and the Kappa coefficient is 0.78. Compared with the existing plastic greenhouse index, NewPGI can better extract plastic greenhouses under complex surface coverage.

Keywords plastic greenhouse remote sensing index Logistic regression analysis

S127TP79

Corresponding Authors: Runping SHEN E-mail: rpShen@nuist.edu.cn

Issue Date: 30 August 2019

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	Jun CHEN
	Runping SHEN
	Bolun LI
	Chaopu TI
	Xiaoyuan YAN
	Minyue ZHOU
	Shaowu WANG

Cite this article:

Jun CHEN,Runping SHEN,Bolun LI, et al. The development of plastic greenhouse index based on Logistic regression analysis[J]. Remote Sensing for Land & Resources, 2019, 31(3): 43-50.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.03.06 OR https://www.gtzyyg.com/EN/Y2019/V31/I3/43

Fig.1 Location and image of study area

Fig.2 Satellite data in the sample area

Fig.3 Reference of classification in the sample area

Fig.4 Validation data of study area

Tab.1 Spectral data based on Landsat8 image and various remote sensing indexes

Fig.5 Spectral curves of different land cover types (mean values)

Tab.2 Separation degree of plastic greenhouses from typical land cover types

Fig.6 Image of Sigmoid function

Tab.3 Parameter variables in NewPGI

Tab.4 Omnibus test of Logistic regression model coefficients and test of R-squared

Fig.7 Plastic greenhouse information extraction in the sample area

Tab.5 PGs/No PGs confusion matrix of the sample area

Fig.8 Plastic greenhouse information extraction in Changzhou City

Tab.6 PGs/No PGs confusion matrix of the study area

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