Prediction method of ginkgo tree canopy biomass based on orthophoto images of unmanned aerial vehicles

A prediction method and ginkgo tree technology, applied in image enhancement, image analysis, image data processing and other directions, can solve the problems of limited representation, data lag, and high cost, and achieve real-time data update, accurate calculation results, and high efficiency. Effect
CN109376579AInactive Publication Date: 2019-02-22ZHEJIANG FORESTRY UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
ZHEJIANG FORESTRY UNIVERSITY
Publication Date
2019-02-22
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a prediction method of ginkgo tree canopy biomass based on orthophoto images of unmanned aerial vehicles. the invention obtains the orthophoto image of the ginkgo tree by the unmanned aerial vehicle, Then the canopy area extraction, canopy width extraction, canopy height extraction, canopy area crown width tree height-DBH model, single tree canopy biomass empirical equationare performed, and single tree canopy biomass prediction is performed, the used tools include Pix4D, Arcgis; Constructed crown area & crown width & tree height- diameter at breast height (CA & CW & CHM-DBH) model has three variables, and the results of the model are more accurate. The method is convenient, efficient, low cost, high efficiency, data can be updated in real time; The invention has more accurate calculation results of the model; Features of low cost, high efficiency and real-time data updating.
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Description

technical field

[0001] The invention relates to the technical field, in particular to a method for predicting ginkgo tree canopy biomass based on UAV orthophoto images with low survey cost, high efficiency and real-time update of data. Background technique

[0002] Forest biomass is the energy basis and material source for the operation of forest ecosystems, and an important indicator for evaluating the structure and function of forest ecosystems. The influencing factors of canopy biomass include tree species, DBH, tree height, stand density, environment and so on. Among them, the DBH and tree height of the tree have a greater impact on the canopy biomass, but because there is a strong correlation between the DBH and the tree height, and the measurement of the tree height is not as convenient and accurate as the DBH, so scholars focus on the study of different Changes in distribution of canopy biomass under diameter at breast height. A lot of research has been done on the ...

Claims

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