An Optimization Method for Time-Series ndvi Data Sequence

Abstract

The invention provides an optimization method for a time series NDVI data sequence. The method comprises the following steps of obtaining an initial first time series NDVI data sequence; under a limitation condition, optimizing the first time series NDVI data sequence by adopting a BISE method to obtain a second time series NDVI data sequence; and optimizing the second time series NDVI data sequence by adopting a wavelet transform method to obtain a target time series NDVI data sequence. By optimizing the initial time series NDVI data sequence by adopting the method, noise data in the initialtime series NDVI data sequence can be effectively removed, and the authenticity of time series NDVI data is kept, so that the time series NDVI data can reflect real change information of a monitored target more accurately.

Description

technical field [0001] The invention relates to the technical field of geographic information and remote sensing, in particular to a time-series NDVI data sequence optimization method. Background technique [0002] NDVI (Normalized Difference Vegetation Index) normalized difference vegetation index, also known as biomass index change. NDVI is the best indicator of vegetation growth status and vegetation coverage. NDVI can accurately reflect the vitality state of surface vegetation and the characteristics of vegetation seasonal changes. Based on this feature, NDVI has been widely used in the monitoring of various changes, such as urban expansion monitoring, forest disturbance monitoring, vegetation growth monitoring, climate change monitoring, mining disturbance monitoring, etc. The original time-series NDVI data is calculated by performing radiometric calibration and atmospheric correction on the remote sensing data obtained by satellite remote sensors. The process of rem...

AI Technical Summary

Problems solved by technology

When the above time-series NDVI data is used for change detection, the noise data will affect the authenticity of the detection results to a certain extent, causing the detection results to fail to reflect the real changes of the detected object, making people make wrong judgments based on the detection results

[0003] Common time-series NDVI data series optimization methods, such as using the best slope coefficient interception method BISE to optimize the time-series NDVI data series, although the noise data in the time-series NDVI data series can be removed to a large extent, the optimized time-series NDVI data series is still It has high discreteness, especially when BISE performs poorly in continuous noise; the Savitzky-Golay filter method is used to optimize the time series NDVI data sequence, which can reduce the optimized time series while removing the noise data in the time series NDVI data series The data in the NDVI data sequence is discrete, but there is a certain error in the phase and amplitude between the optimized time series NDVI data sequence and the real data sequence, and the error cannot be eliminated at present

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