Large-scale forest observation trapezoid grid method

A large-scale, grid-based technology, applied in the field of forest observation, can solve the problem of low precision and achieve the effect of solving the difficulty of organization and implementation

Inactive Publication Date: 2014-02-05
BEIJING FORESTRY UNIVERSITY
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Problems solved by technology

[0004] In order to more accurately observe the coverage of forest vegetation in a large scale around the world, solve the problem of low accuracy caused by omissions in sampling plots in traditional sample plots, and make observations more accurate, the present invention uses a trapezoidal grid The method is applied to the observation of forest resources, and its characteristics are as follows: firstly, according to a certain scale, that is, 1:10,000, with a longitude difference of 3′45″ and a latitude difference of 2′30″, the whole world is divided into N equilateral trapezoids Grid, that is, the length and width of the trapezoidal grid are divided according to (2n-1) and (2n-1), then each grid can be regarded as equal longitude and latitude, and the climate, soil and climate contained in each grid , hydrology, vegetation and other characteristics can be regarded as equal and continuous distribution

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  • Large-scale forest observation trapezoid grid method

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Embodiment Construction

[0011] Applying the trapezoidal grid method to forest observations differs from existing methods in that:

[0012] (1) According to the scale of 1:10000, that is, the longitude difference is 3′45″ and the latitude difference is 2′30″, the world is divided into N trapezoidal grids with equal longitude and latitude, and the area of ​​each trapezoidal grid is 3*3 , 5*5, 7*7...(2n-1)(2n-1):

[0013] (2) Select any trapezoidal grid ABCD whose center is O. Then, extending from point O to the adjacent grid, there are grids in 8 directions adjacent to it, that is, these 8 grids have mutual influence with the trapezoidal ABCD grid. use model The relationship between a certain factor of the trapezoidal ABCD and the adjacent grid can be calculated, and the k value that no longer exists as the distance increases;

[0014] (3) With the trapezoidal ABCD as the center block segment expanding outward, the number of grids adjacent to it is (2n-1)(2n-1)-1. In this way, the above formula ca...

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Abstract

The invention discloses a large-scale forest observation trapezoid grid method. The globe is divided into (2n-1)*(2n-1) continuously distributed trapezoid grids having the same length and width by means of a 1:10000 measuring scale. Meanwhile, the interaction effect between each grid and the adjacent grid is calculated, influential factors are found out, the negligible farthest distance of the impact effect is calculated, cross validation is carried out, therefore, forest growth conditions of the grids are observed, and the method is a new method for observing a forest in a large-scale range in a continuous and seamless mode.

Description

1. Technical field [0001] The invention relates to a method for observing forests in practice, mainly applying trapezoidal grids to forest observations. 2. Technical background [0002] In the traditional large-scale forest observation method, the first, second and third types of forest resources are investigated by using artificially obtained sample plots combined with remote sensing technology. That is to say, taking provinces, prefectures, and counties as a whole, combined with aerospace remote sensing data, adopting the method of systematic layout and mechanical sampling, and regularly reviewing the sample plots. The purpose is to monitor and grasp the current situation of forest resources and the dynamics of growth and decline. However, this traditional sampling method causes certain errors to a large extent, and the implementation is difficult and expensive, and it is difficult to meet the increasingly sophisticated survey content and the increasing requirements. [...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01D21/00G06F19/00
Inventor 冯仲科毛海颖刘芳
Owner BEIJING FORESTRY UNIVERSITY
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