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Machine learning-based vegetation net primary production remote sensing estimation method

A net primary productivity and machine learning technology, applied in the field of machine learning, can solve problems such as the influence of knowledge background and the difficulty of data acquisition

Inactive Publication Date: 2017-02-01
三亚中科遥感研究所
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Problems solved by technology

[0009] The purpose of the present invention is to provide a method for NPP estimation based on multi-source remote sensing data to train NPP through machine learning in order to solve the shortcomings of the current main estimation model of NPP, which are difficult to obtain data and are seriously affected by the distribution of research sites and the knowledge background of researchers. model method

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  • Machine learning-based vegetation net primary production remote sensing estimation method
  • Machine learning-based vegetation net primary production remote sensing estimation method
  • Machine learning-based vegetation net primary production remote sensing estimation method

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[0038] The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. Apparently, the described embodiments are only some embodiments of the present application, not all examples.

[0039] The embodiment of this application selects and collects 28 global-scale space observation remote sensing products related to NPP estimation from 2005 to 2011, and BP's global energy consumption and carbon dioxide emission statistics for the corresponding years. Among them, all relevant remote sensing products are generated and downloaded on the NASA GES DISC website, and are resampled to a spatial resolution of 1°X1°. BP statistics use ArcGIS 10.1 software to generate map images with a resolution of 1°X1°. In using random forest to train the NPP estimation model, the data from 2005 to 2010 is selected as the training data, and the data from 2011 is selected as the test data.

[0040...

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Abstract

The invention discloses a method for building a vegetation net primary production (NPP) estimation model through machine learning based on a multi-source remote sensing product. According to the method, for analysis characteristics of massive data products, it is proposed that a global vegetation NPP estimation model is simulated and built by adopting the machine learning method, and the importance of related characteristic products in vegetation NPP estimation is calculated based on the model. The method mainly comprises four steps of (1) collecting NPP spatial observation products and spatial observation products of NPP related variables; (2) performing data normalization processing; (3) training the NPP estimation model; and (4) assessing the importance of each factor in the NPP estimation model. The method provides a new idea for performing vegetation NPP estimation by utilizing multi-spatial observation data.

Description

Technical field: [0001] The invention relates to the technical field of machine learning, and is a vegetation net primary productivity estimation model based on multi-source remote sensing products. Background technique: [0002] Vegetation net primary production (Net Primary Production, NPP) refers to the organic matter content accumulated in a certain period of time, and the organic matter is the remaining net organic matter content after the organic matter produced by photosynthesis is consumed by autotrophic respiration. direct or indirect food sources, and key factors regulating ecological processes. NPP is not only the main factor that characterizes the response of ecosystems to global changes, but also an important indicator used to assist government decision-making and measure the degree of economic development. Therefore, comprehensive and accurate observation of NPP has important scientific and social significance for mastering the quality of ecosystem and natural...

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Application Information

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IPC IPC(8): G06N99/00
CPCG06N20/00
Inventor 于博陈方
Owner 三亚中科遥感研究所
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