Satellite-unmanned aerial vehicle fusion-based method and system for spatial mapping of new bamboo yield of moso bamboo

By using multi-scale remote sensing data fused from satellite and drones, combined with ground plot surveys and remote sensing data to identify potential new bamboo areas and to retrieve old bamboo density using drones, the high cost and low efficiency of moso bamboo production monitoring have been solved, and high-precision spatial mapping of new bamboo production has been achieved.

CN122336042APending Publication Date: 2026-07-03CHUZHOU UNIV
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Patent Information

Application Number
CN202610517380.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-20
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

Existing technologies cannot integrate large-scale phenological identification by satellites with fine-structure inversion by UAVs, and cannot directly invert the yield of new bamboo shoots. This results in traditional bamboo yield monitoring being labor-intensive, costly, and unable to create large-area spatial maps.

Method used

By fusing multi-scale remote sensing data, ground plot surveys and satellite remote sensing data were used to identify potential areas for Hsinchu bamboo. The density of old bamboo was retrieved by combining UAV data, and a multinomial regression model and a decision tree classification model were constructed to generate a spatial distribution map of Hsinchu bamboo production.

Benefits of technology

It has achieved large-area, low-cost, and high-precision spatial mapping of new bamboo production, providing technical support for precise bamboo forest management and bamboo supply chain assessment.

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Abstract

This invention proposes a spatial mapping method and system for new bamboo shoot yield based on satellite-UAV fusion. Addressing the problem of remote sensing observation gaps caused by the underground growth of bamboo shoots, this invention constructs a multi-scale fusion framework of "satellite identification of potential high-yield areas + UAV inversion of old bamboo density." First, a new bamboo yield prediction model is constructed using old bamboo density and topographic factors as variables through ground-based sample plot surveys. Second, indices such as NDVI and LSWI are calculated using time-series imagery, and a decision tree model is used to identify potential new bamboo germination areas in high-yield bamboo forests. Then, based on high-resolution UAV imagery, a region growing algorithm is used to extract individual old bamboo plants and invert the spatial distribution of old bamboo density. Finally, using the potential areas as masks, old bamboo density and topographic factors are substituted to generate a spatial distribution map of new bamboo yield. This invention enables large-area, low-cost, and high-precision mapping of new bamboo shoot yield, providing core technical support for precise bamboo forest management.
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