Vegetation phenological period extraction method based on effective pixels of digital camera image

Abstract

The invention discloses a vegetation phenological period extraction method based on effective pixels of a digital camera image, and belongs to the technical field of vegetation parameter remote sensing inversion methods. For a phenological camera or a common digital camera, a green vegetation index of a pixel scale is calculated by using a day-by-day multi-temporal observation image; based on the change amplitude of a green vegetation index in a year, an amplitude threshold value is screened, ground feature identification is carried out on an image region of interest, vegetation leaf pixels and non-leaf pixels such as soil and branches are rapidly identified, and when a mean value of the vegetation green index of the day-by-day image region of interest is obtained, the non-leaf pixels are removed. The signal-to-noise ratio of the vegetation greenness index mean value is increased, and the key phenological period of the vegetation growth cycle is extracted by using the vegetation index mean value time series data after noise reduction. According to the method, the influence of surface landscape change on vegetation phenology extraction of the region of interest can be effectively reduced, and the method has higher applicability to deciduous vegetation with medium and low coverage.

Description

technical field [0001] The invention belongs to the technical field of vegetation parameter remote sensing inversion methods, and in particular relates to a method for extracting vegetation phenology based on effective pixels of digital camera images. Background technique [0002] Plant phenology refers to a natural phenomenon that occurs in a yearly cycle when plants are affected by environmental factors such as climate. It is a growth and development rhythm formed by plants adapting to a seasonally changing environment for a long time. Generally speaking, phenology refers to the time node of a certain event in the plant growth cycle or the turning point between different states, such as the germination of deciduous plants, the turning point of the leaves of evergreen plants entering the photosynthetic state from dormancy, etc. Mark D. Schwartz et al. reviewed the progress of phenology research in the book "Phenology: An Integrative Environmental Science" in 2013, pointing ...

AI Technical Summary

Problems solved by technology

[0006] However, for non-uniform underlying surfaces, especially in areas with sparse vegetation, there may be a large number of invalid pixels such as soil and vegetation branches in AOI

If these invalid pixels and vegetation canopy (leaf) effective pixels are used to calculate the AOI pixel value, it will inevitably reduce the signal-to-noise ratio of the image, introduce noise into the vegetation index time series data, and then affect the extraction accuracy of surface phenology

If artificial visual interpretation or image classification technology is introduced to identify effective AOI pixels, it may increase labor or computing costs; at the same time, it is susceptible to the impact of the time when the image to be interpreted is taken, because camera images in different seasons and phases may The resulting effective pixel interpretation results are inconsistent, leading to new uncertainties

Images