Automatic corn mapping method based on active growth stage NMDI (normalized multi-band drought index) increase and decrease ratio indexes

Abstract

The invention relates to an automatic corn mapping method based on active growth stage NMDI (normalized multi-band drought index) increase and decrease ratio indexes. The mapping method includes the steps: building a vegetation index time sequence dataset and NMDI time sequence dataset of a research area pixel by pixel; calculating EVI (enhanced vegetation index) maximum values of crops in each growth period pixel by pixel, and acquiring growth peak value time of the crops; determining a front interval of the active growth stage of the crops and a back interval of the active growth stage of the crops according to the growth peak value time of the crops; respectively building NMDI increment indexes of the front interval of the active growth stage of the crops and NMDI increment indexes of the back interval of the active growth stage of the crops; respectively building NMDI decrement indexes of the front interval of the active growth stage of the crops and NMDI decrement indexes of the back interval of the active growth stage of the crops; building NMDI increase and decrease ratio indexes based on the NMDI increment indexes and the NMDI decrement indexes; performing mapping on corns in the research area according to the NMDI increase and decrease ratio indexes. The mapping method has the advantages of high automation degree and classification accuracy, simplicity, easiness in use, good robustness and the like.

Description

technical field [0001] The invention relates to an automatic mapping method for corn based on the NMDI increase-decrease ratio index in the peak growth period. Background technique [0002] Accurately and quickly grasping the planting area and its spatial distribution of crops is crucial to ensuring food security in a populous country. The traditional agricultural sampling survey method is difficult to break through the limitations of high cost, time-consuming and inability to fully cover. Remote sensing images have the characteristics of strong timeliness and large coverage, and have played an important role in the monitoring of crop area. Due to the certain similarity of the spectra of different crops, it brings serious challenges to the classification method based on a single or a few remote sensing images. Therefore, remote sensing monitoring of crop area based on time-series remote sensing images has become the core development direction. [0003] At present, the met...

AI Technical Summary

Problems solved by technology

The most fundamental problem faced by such methods is that due to changes in crop phenology, management measures, and climatic conditions in different regions, there may be very large differences in the annual vegetation index time-series curves of the same crop. It is difficult to establish the time-series curve of standard vegetation index for different crops

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