Reconstructed vegetation remote sensing discrimination method based on long-time sequence vegetation indexes

A technology of vegetation index and discrimination method, applied in data processing applications, character and pattern recognition, instruments, etc., can solve the problems that the reliability of discrimination results cannot be guaranteed, and the differences in vegetation spectral characteristics are not obvious.

Pending Publication Date: 2020-10-27
DATONG COAL MINE GRP +1
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Problems solved by technology

[0004] The current remote sensing discrimination method for reconstructed vegetation relies heavily on the difference in spectral characteristi

Method used

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  • Reconstructed vegetation remote sensing discrimination method based on long-time sequence vegetation indexes
  • Reconstructed vegetation remote sensing discrimination method based on long-time sequence vegetation indexes
  • Reconstructed vegetation remote sensing discrimination method based on long-time sequence vegetation indexes

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Embodiment 1

[0100] S1. Determine the vegetation range.

[0101] In this example, first use GoogleEarth software to locate the southern suburb of Datong City, Shanxi Province, and select two arbor-covered areas in the southern suburb of Datong City, Shanxi Province by visual interpretation with reference to the latest satellite remote sensing images: forest land A and forest land B, and then Use the tool that comes with the software to draw the rough boundaries of the two vegetation, and save it as a shp file in UTM coordinate system.

[0102] S2. Obtain time-series vegetation index data.

[0103] In this instance, through the USGS ( https: / / glovis.usgs.gov / ) obtained the normalized difference vegetation index (NDVI) data of all Landsat series remote sensing image files covering the southern suburbs of Datong City, Shanxi Province, and obtained 8-day interval NDVI data sets including Landsat4 / 5 TM, Landsat7 ETM and Landsat8OLC images, a total of 829 Period data, time series from M

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Abstract

The invention discloses a reconstructed vegetation remote sensing discrimination method based on long-time sequence vegetation indexes, and is suitable for the technical field of mine ecological environment monitoring. Through long-time sequence vegetation indexes of vegetation are used and after noise identification and prediction value calculation, a vegetation growth curve is constructed basedon Fourier series and a least square method, and the annual vegetation index mean value of the regional vegetation in the time sequence year is further obtained; and the method further includes calculating the change trend of the vegetation index in every five years by utilizing linear fitting, taking the difference degree between the past change trend and the future five years at a certain time point as a judgment index, and when the difference degree exceeds a set threshold value, determining that the vegetation is reconstructed and the year with the maximum difference degree is the vegetation reconstruction starting time. According to the invention, the complete growth process of the vegetation is effectively reflected, so that the one-sidedness of single-time-point data is reduced, theaccuracy of time series data is improved through noise identification and predicted value calculation, and the influence of abnormal data of a single year on the overall growth trend of the vegetation is reduced by taking the vegetation growth trend difference degree as a judgment index.

Description

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Claims

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Application Information

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Owner DATONG COAL MINE GRP
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