Remote sensing extraction of fruit tree planting area based on Sentinel-2 multi-temporal images

doi:10.3969/j.issn.1004-1524.2022.12.20

Acta Agriculturae Zhejiangensis ›› 2022, Vol. 34 ›› Issue (12): 2767-2777.DOI: 10.3969/j.issn.1004-1524.2022.12.20

• Biosystems Engineering • Previous Articles Next Articles

Remote sensing extraction of fruit tree planting area based on Sentinel-2 multi-temporal images

ZHOU Xinxing¹(), ZHAO Lin¹^,^*(), ZHANG Wenjie¹, TAN Changwei², LI Gangbo¹, SHI Mengyun¹, ZHANG Ting¹, YANG Feng¹

1. Xuzhou Institute of Agricultural Sciences in Xuhuai Area of Jiangsu, Xuzhou 221121, Jiangsu, China
2. Agricultural College of Yangzhou University, Yangzhou 225009, Jiangsu, China

Received:2021-10-18 Online:2022-12-25 Published:2022-12-26
Contact: ZHAO Lin

Abstract

Abstract:

In order to extract the spatial distribution information of fruit trees, the Dasha River Basin was selected as the study area, and the Sentinel-2 multispectral remote sensing images of different months were used as the data source. The best monitoring period was obtained by analyzing the spectral information of different months. On this basis, five vegetation indices of normalized differential vegetation index (NDVI), ratio vegetation index (RVI), enhance vegetation index (EVI), structure intensive pigment index (SIPI) and normalized difference water index (NDWI) in different periods were selected to construct a decision tree extraction model combined with machine learning technology. The results showed that the images in March, April, July, and August were suitable for the extraction of fruit tree planting area. The vegetation indices in different months with high contribution were selected through the attribute of Feature_importances_, as the input feature. Based on the combination of the hyperparameter learning curve and grid search technology, parameters of Max_depth and Min_samples_leaf were determined as 5 and 10, respectively, as the model effect was the best under these parameters. After the adjustment of parameters, the decision tree model was drawn, and the accuracies of the model on the training set and the test set were 0.919 4 and 0.875 1, respectively. The extraction results showed that fruit tree planting area was mainly in the banks of the Dasha River, and the planting plots of fruit trees in the east and northwest were relatively fragmented. The total planting area was 6 838 hm². On the basis of the verification sample, the accuracy of the extraction results was calculated by the confusion matrix. The Kappa coefficient was 0.87, and the user accuracy and mapping accuracy of fruit tree planting area extraction were 92.91% and 90.77%, respectively. Thus, the proposed method could be applied to remote sensing extraction of fruit trees in a large area, and could provide effective technical means for the monitor of fruit tree planting areas with medium and high resolution remote sensing images.

Key words: fruit tree planting area, remote sensing, decision tree, machin learning

CLC Number:

S127

ZHOU Xinxing, ZHAO Lin, ZHANG Wenjie, TAN Changwei, LI Gangbo, SHI Mengyun, ZHANG Ting, YANG Feng. Remote sensing extraction of fruit tree planting area based on Sentinel-2 multi-temporal images[J]. Acta Agriculturae Zhejiangensis, 2022, 34(12): 2767-2777.

Figures/Tables 8

Fig.1 Location of study area

Fig.2 Technical route of present research

Fig.3 Comparison of NDVI values of different vegetations in study area in June

Fig.4 Comparison of reflectance of fruit trees and woodland from January to December

Fig.5 Importance score of different characteristics

Fig.6 Hyperparametric learning curve with different features as variable A, B, C and D show the learning curves drawn with the selected features, the unselected features, the vegetation indices in March and July, and the vegetation indices in April and August as variables, respectively.

Fig.7 Decision tree model

Fig.8 Remote sensing extraction result of fruit tree planting area

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Remote sensing extraction of fruit tree planting area based on Sentinel-2 multi-temporal images

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