Land use classification of farming areas based on time series Sentinel-2A/B data and random forest algorithm

doi:10.6046/gtzyyg.2020.04.29

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Abstract

Land cover information in farming areas is the basis of land resource management and planning, which plays an important role in the rational development of land resources, adjustment of land use structure, and dynamic monitoring of land. Due to the complex land types and high heterogeneity in farming areas, the accuracy of land cover information extraction has been facing challenges. Therefore, this study used Sentinel-2A/B remote sensing data as the data source. Firstly, a normalized difference vegetation index (NDVI) time series data set and tasseled cap wetness (TCW) time series data set were constructed; Secondly, the J-M (Jeffries-Matusita) distance was used to analyze the separability of the surface features and select the best time series data combination of NDVI and TCW; Finally, combined with random forest (RF), support vector machine (SVM), maximum likelihood classification (MLC) and single phase remote sensing data, the classification of typical features in farming areas was studied, and the accuracy of classification results was evaluated and compared. The research results show that the classification accuracy of the time series data combined with the random forest classification algorithm is relatively high. The overall classification accuracy reaches 88.87%, and the Kappa coefficient reaches 0.855 7, which improves the classification accuracy by 10.05 percentage points and 0.209 3 respectively compared with that of the single remote sensing data. This fully demonstrates that the combination of time series data and random forest classification algorithm can effectively improve the classification accuracy of typical features in farming areas.

Keywords time series random forest land use classification farming area Sentinel-2A/B

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Issue Date: 23 December 2020

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	Dejun WANG
	Qigang JIANG
	Yuanhua LI
	Haitao GUAN
	Pengfei ZHAO
	Jing XI

Cite this article:

Dejun WANG,Qigang JIANG,Yuanhua LI, et al. Land use classification of farming areas based on time series Sentinel-2A/B data and random forest algorithm[J]. Remote Sensing for Land & Resources, 2020, 32(4): 236-243.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.04.29 OR https://www.gtzyyg.com/EN/Y2020/V32/I4/236

Fig.1 Image of Sentinel-2A B4(R), B3(G), B2(B) bands in the study area

Tab.1 Sentinel-2A/B images data list

Fig.2 Technology flowchart for the land use classification

Fig.3 NDVI time series curve of typical features

Fig.4 TCW time series curve of typical features

Tab.2 J-M distance of six typical features under different time series combination of Sentinel-2A/B data

Fig.5 J-M distance variation curve of typical features with different time phase data

Fig.6-1 Comparison of classification results

Fig.6-2 Comparison of classification results

Tab.3 Comparison of classification accuracy index

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