Research on remote sensing classification of fruit trees based on Sentinel-2 multi-temporal imageries

Abstract

Accurately obtaining the spatial distribution information of fruit tree planting is of great significance to the development of fruit tree growth monitoring, disease and pest control, and yield estimation. In this study, the Sentenel-2 multispectral remote sensing imageries of different months during the growth period of the fruit trees were used as the data source, and single month vegetation indices, accumulated monthly vegetation indices (∑VIs), and difference vegetation indices between adjacent months (∆VIs) were constructed as input variables. Four conventional vegetation indices of NDVI, PSRI, GNDVI, and RVI and four improved vegetation indices of NDVIre1, NDVIre2, NDVIre3, and NDVIre4 based on the red-edge band were selected to construct a decision tree classification model combined with machine learning technology. Through the analysis of vegetation indices under different treatments and different months, combined with the attribute of Feature_importances_, the vegetation indices of different periods with high contribution were selected as input features, and the Max_depth values of the decision tree model were determined by the hyperparameter learning curve. The results have shown that when the Max_depth value of the decision tree model of the vegetation indices under the three treatments was 6, 8, and 8, the model classification was the best. The accuracy of the three vegetation index processing models on the training set were 0.8936, 0.9153, and 0.8887, and the accuracy on the test set were 0.8355, 0.7611, and 0.7940, respectively. This method could be applied to remote sensing classification of fruit trees in a large area, and could provide effective technical means for monitoring fruit tree planting areas with medium and high resolution remote sensing imageries.

Rapid Identification of Main Vegetation Types in the Lingkong Mountain Nature Reserve Based on Multi-Temporal Modified Vegetation Indices

“…Time series remote sensing imageries have not only the spectral information of a single-phase imagery, but they also have a series of time information [ 8 ].The main reason for thus is the seasonal variance of the vegetation’s spectral reflectance, which changes according to the season and the growing stage for each vegetation type [ 9 ]. This is of great significance for the extraction of vegetation-type information.…”

Optimal feature extraction from multidimensional remote sensing data for orchard identification based on deep learning methods

“…Few studies in deep learning combine dimension reduction and classification for orchard classification 69 . For instance, Zhou et al., 70 conducted a study on remote sensing classification of fruit trees based on Sentinel-2 multi-temporal images, and the highest accuracy on the training set were 0.9153, and the highest accuracy on the test set were 0.8355. The classification results are not satisfactory without utilizing multi-source remote sensing images, spatial information, and deep learning.…”

Soil Salinity Estimation in Cotton Fields in Arid Regions Based on Multi-Granularity Spectral Segmentation (MGSS)

“…However, their methods have some defects. For example, the bands used in the construction of spectral indices are very close, resulting in the poor stability of the models [55]. Therefore, some scholars further improved soil salinity estimation accuracy by transforming raw spectra to extract more spectral features [56].…”