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Vegetation index prediction method, system and device based on extreme gradient boosting algorithm

A technology of vegetation index and prediction method, applied in the field of geographic information, can solve the problems of not extracting long-term vegetation index and short time period, and achieve the effect of improving vegetation index data

Active Publication Date: 2020-02-28
GUANGZHOU INST OF GEOGRAPHY GUANGDONG ACAD OF SCI
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AI Technical Summary

Benefits of technology

This patented method allows for better understanding about how plants grow by measuring their growth rate based on factors like temperature or light intensity during different stages of development. By comparing these measurements from multiple samples over several years, researchers have been able to make more accurate predictions regarding future crop yields that may be affected due to environmental changes such as droughts or water stress conditions.

Problems solved by technology

The technical problem addressed by this patented technology relates to efficiently analyzing large amounts of geographic area that includes multiple types of plants such as trees or grasses on different terrains over longer periods of time without sacrificing accuracy due to limited resources available during analysis.

Method used

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  • Vegetation index prediction method, system and device based on extreme gradient boosting algorithm
  • Vegetation index prediction method, system and device based on extreme gradient boosting algorithm
  • Vegetation index prediction method, system and device based on extreme gradient boosting algorithm

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Embodiment

[0051] see figure 1 , the present invention provides a vegetation index prediction method based on the extreme gradient lifting algorithm, comprising the following steps:

[0052] Step S1: Obtain vegetation index data, select the vegetation index data within a preset time period as a training data set, and select high-quality pixel values ​​from the training data set according to preset rules as the first input data.

[0053] In this embodiment, the vegetation index data is a total of 35 years and 828 phases of AVHRRGIMMS3g.v1 (AVHRR: Advanced Very High Resolution Radiometer.GIMMS: Global Inventory Modeling and Mapping Studies) vegetation index (NDVI, Normalized Difference Vegetation Index) data, selecting one of the vegetation index data for a period of time as the training data set in the vegetation index data refers to randomly extracting from the above vegetation index data, selecting 30 years of data as the training data set, and the remaining 5 The annual data is used a...

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Abstract

The invention relates to a vegetation index prediction method, a system and a device based on an extreme gradient lifting algorithm. The method comprises the following steps: constructing an extreme gradient lifting model by taking a vegetation index as a dependent variable and taking a global land data assimilation system basin surface model data set and elevation data as independent variables, performing iterative learning on sample data by utilizing the extreme gradient lifting model, predicting the vegetation index in a target time period, and obtaining a vegetation index prediction result. Compared with the prior art, the problem of lack of vegetation indexes in the prior art is solved, a user can use the method to realize vegetation index prediction in any time period, and vegetationindex data is perfected.

Description

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Claims

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Application Information

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Owner GUANGZHOU INST OF GEOGRAPHY GUANGDONG ACAD OF SCI
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