Rapid forest land health measurement method based on unmanned aerial vehicle laser radar and multispectrum

Abstract

The invention belongs to the technical field of forest land health measurement, and particularly relates to a forest land health rapid measurement method based on an unmanned aerial vehicle laser radar and multispectrum, which comprises the following steps: S1, collecting laser radar data based on the unmanned aerial vehicle laser radar, and collecting multispectral remote sensing data based on a multispectral remote sensing device; s2, generating a canopy height model and multispectral remote sensing data based on the laser radar data; and S3, extracting a vegetation region through the normalized vegetation index. The unmanned aerial vehicle laser radar and multispectral remote sensing data are combined, technical support is provided for scientific evaluation and ecological management of urban vegetation health conditions, and a scientific basis is provided for optimization and sustainable development of urban green infrastructures. The method not only provides technical support for large-scale and refined urban green land health monitoring, but also lays a solid theoretical foundation for formulating a scientific and reasonable urban greening optimization strategy and an ecological restoration scheme.

Description

Technical Field

[0001] This invention relates to the field of forest health measurement technology, specifically a rapid measurement method for forest health based on UAV lidar and multispectral methods. Background Technology

[0002] With the acceleration of urbanization, the urban ecological environment is facing increasingly severe challenges. As an important component of urban green infrastructure, urban vegetation plays a crucial role in improving air quality, regulating climate, mitigating the urban heat island effect, and enhancing biodiversity.

[0003] Street trees, as a vital element of urban greening, not only beautify urban spaces and improve the quality of life for citizens, but also have a profound impact on the health of the urban ecosystem. However, factors such as environmental pollution, soil compaction, and traffic congestion brought about by urbanization have negatively affected the growth and health of trees. Therefore, how to quickly and accurately assess the health status of urban vegetation has become an important issue in current urban ecological management.

[0004] Traditional vegetation health assessment methods mainly rely on ground surveys. Although these methods can provide detailed health data, they usually require a lot of manpower and resources and have limited spatial coverage, making it difficult to achieve comprehensive monitoring of large-scale urban green spaces. Summary of the Invention

[0005] The purpose of this invention is to provide a rapid measurement method for forest health based on UAV lidar and multispectral forestry, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a rapid measurement method for forest health based on UAV lidar and multispectral imaging, comprising:

[0007] S1: Acquire lidar data based on UAV lidar and acquire multispectral remote sensing data based on multispectral remote sensing device;

[0008] S2: Canopy height model and multispectral remote sensing data generated based on lidar data; the canopy height model is the CHM.

[0009] S3: Vegetation areas are extracted using the Normalized Difference Vegetation Index (NDVI). Under the NDVI mask, the canopy volume of the vegetation area is calculated using CHM data. By weighted summation of the vegetation volumes of each pixel, the overall three-dimensional green distribution of the Huamu Street area is obtained.

[0010] Preferably, a digital surface model and a digital ground model are generated from lidar data. The digital surface model is called DSM and the digital ground model is called DTM. The CHM is calculated based on the digital surface model and the digital ground model to reflect the height information of vegetation.

[0011] CHM = DSM-DTM.

[0012] Preferably, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Red Edge Index (NDRE) are calculated to assess the vegetation health of the flower and tree street. NDVI quantifies the growth status of vegetation by measuring the difference in reflectance between the red and near-infrared bands.

[0013] The preferred formula for calculating the Normalized Difference Vegetation Index (NDVI) is:

[0014]

[0015] NIR represents the reflectance of the near-infrared band, RED represents the reflectance of the red band, and the NDVI value usually ranges from -1 to +1. A high value indicates good vegetation cover and growth, while a low value indicates poor or no vegetation.

[0016] Preferably, the Normalized Differential Red Edge Index (NDRE) reflects the health status of vegetation by analyzing the reflectance characteristics of the red edge zone. The formula for calculating NDRE is as follows:

[0017]

[0018] In the formula, REDedge is the reflectivity of the red edge band, and NIR is the reflectivity of the near-infrared band.

[0019] Preferably, a vegetation health index model is constructed based on UAV lidar data and multispectral remote sensing data. A linear regression model is constructed using vegetation health scores obtained from field surveys as the dependent variable, combined with three-dimensional green volume and vegetation indices.

[0020] H=a0+a1×V+a2×NDVI+a3×NDRE+∈

[0021] In the formula, H is the vegetation health score, V is the three-dimensional green volume of vegetation, NDVI is the normalized vegetation index, NDRE is the normalized differential red edge index, a0 is the constant term, a1, a2, a3 are regression coefficients, and ∈ is the error term.

[0022] Based on different variable types and combinations, the model parameters are fitted using the least squares method to determine the regression coefficients, and the coefficient of determination R is calculated. 2 The root mean square error (RMSE) was used to evaluate the model's fit, and the optimal model was selected for inverse analysis of the vegetation health status of the entire study area.

[0023]

[0024] Compared with the prior art, the beneficial effects of the present invention are:

[0025] The widespread application of drone-based LiDAR and multispectral remote sensing technologies is particularly noteworthy. LiDAR technology, through laser scanning of the earth's surface, can accurately acquire three-dimensional structural information of vegetation, making it especially suitable for high-precision monitoring of urban green spaces. Multispectral remote sensing, on the other hand, uses multi-band reflectance information to reflect the growth status and health of vegetation. Therefore, combining these two remote sensing technologies helps overcome the limitations of traditional methods and provides a new solution for the rapid assessment of urban vegetation health.

[0026] By combining UAV lidar with multispectral remote sensing data, we can provide technical support for the scientific assessment and ecological management of urban vegetation health, and provide a scientific basis for the optimization and sustainable development of urban green infrastructure.

[0027] This not only provides technical support for large-scale, refined urban green space health monitoring, but also lays a solid theoretical foundation for formulating scientific and reasonable urban greening optimization strategies and ecological restoration plans. Attached Figure Description

[0028] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0031] Example:

[0032] Please see Figure 1 This invention provides a technical solution: a rapid measurement method for forest health based on UAV lidar and multispectral imaging, comprising:

[0033] S1: Acquire lidar data based on UAV lidar and acquire multispectral remote sensing data based on multispectral remote sensing device;

[0034] S2: Canopy height model and multispectral remote sensing data generated based on lidar data; the canopy height model is the CHM.

[0035] S3: Vegetation areas are extracted using the Normalized Difference Vegetation Index (NDVI). Under the NDVI mask, the canopy volume of the vegetation area is calculated using CHM data. By weighted summation of the vegetation volumes of each pixel, the overall three-dimensional green distribution of the Huamu Street area is obtained.

[0036] This plan takes the overall three-dimensional green volume distribution of the Huamu Street area as an example. In actual use, this plan meets the practical needs of different areas, such as forest green volume distribution, mountain green volume distribution, and farmland green volume distribution.

[0037] The following section combines the Normalized Differential Red Edge Index (NDRE) and the Normalized Difference Red Edge Index (NDVI) to evaluate the health of the Huamu Street area. Due to the complex structure of the Huamu Street area, conventional single-method evaluations are inaccurate. The NDRE reflects the health status of vegetation by analyzing the red edge reflectance characteristics. A high NDVI value indicates good vegetation cover and growth, while a low value indicates poor or no vegetation.

[0038] Digital surface model (DSM) and digital ground model (DTM) are generated from lidar data. The CHM is calculated based on the digital surface model and digital ground model to reflect the height information of vegetation.

[0039] CHM = DSM-DTM.

[0040] The Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Red Edge Index (NDRE) were calculated to assess the vegetation health of Huamu Street. NDVI quantifies vegetation growth by measuring the difference in reflectance between red and near-infrared wavelengths. The formula for calculating the Normalized Difference Vegetation Index (NDVI) is as follows:

[0041]

[0042] NIR represents the reflectance of the near-infrared band, RED represents the reflectance of the red band, and the NDVI value usually ranges from -1 to +1. A high value indicates good vegetation cover and growth, while a low value indicates poor or no vegetation.

[0043] The Normalized Differential Red Edge Index (NDRE) reflects the health status of vegetation by analyzing the reflectance characteristics of the red edge zone. The formula for calculating NDRE is:

[0044]

[0045] In the formula, REDedge is the reflectivity of the red edge band, and NIR is the reflectivity of the near-infrared band.

[0046] A vegetation health index model was constructed based on UAV lidar data and multispectral remote sensing data. A linear regression model was built using vegetation health scores obtained from field surveys as the dependent variable, combined with three-dimensional green volume and vegetation indices.

[0047] H=a0+a1×V+a2×NDVI+a3×NDRE+∈

[0048] In the formula, H is the vegetation health score, V is the three-dimensional green volume of vegetation, NDVI is the normalized vegetation index, NDRE is the normalized differential red edge index, a0 is the constant term, a1, a2, a3 are regression coefficients, and ∈ is the error term.

[0049] Based on different variable types and combinations, the model parameters are fitted using the least squares method to determine the regression coefficients, and the coefficient of determination R is calculated. 2 The root mean square error (RMSE) was used to evaluate the model's fit, and the optimal model was selected for inverse analysis of the vegetation health status of the entire study area.

[0050]

[0051]

[0052] Coefficient of determination R 2 Both the root mean square error (RMSE) and the parameters are existing technical formulas and will not be elaborated here.

[0053] n: Sample size, i.e., the number of data points used for model fitting and evaluation.

[0054] y i : The actual observed value of the i-th sample.

[0055] The model's prediction for the i-th sample.

[0056] The average of all actual observations in the sample reflects the central tendency of the observations.

[0057] R 2 The closer a value is to 1, the better the model fits the data, meaning the higher the proportion of the total variance explained by the model.

[0058] RMSE reflects the average error between model predictions and actual observations. The smaller the value, the higher the model prediction accuracy and the better the fit.

[0059] The combination of lidar and multispectral remote sensing technologies enables efficient and accurate assessment of forest health. This technology can be widely applied to forest health monitoring, spatial distribution analysis of individual trees and forest populations, and carbon storage and biomass assessment. The three-dimensional structural data acquired by lidar and vegetation indices (such as NDVI and NDRE) extracted by multispectral remote sensing allow for dynamic monitoring and regular assessment of forest health, with significant value particularly in forest degradation, post-disaster recovery, and carbon sequestration management. Furthermore, this technology provides real-time and accurate data support for forest management, making it especially suitable for rapid assessment over large areas. Its advantage lies in its ability to efficiently acquire large-scale forest data non-contactly, significantly improving the scientific rigor and accuracy of forest resource management, and showing great potential for application in post-disaster assessment, recovery monitoring, and dynamic analysis of health changes.

[0060] The results showed that the canopy height model (CHM) could effectively characterize the three-dimensional structural features of vegetation, while the normalized difference vegetation index (NDVI) and the normalized difference red edge index (NDRE) could sensitively reflect the spectral response and physiological state of vegetation. When these three types of characteristic variables were considered together, the linear regression model significantly improved the prediction accuracy of vegetation health (R0). 2 =0.972, RMSE=0.155), demonstrating high robustness and reliability. This indicates that the combination of structural and spectral information can significantly improve the accuracy of vegetation health assessment, providing a scientific basis for health status inversion in complex urban environments. Three-dimensional structural parameters, by quantifying the vertical stratification characteristics of the canopy, can reveal vegetation biomass accumulation and light energy utilization efficiency. The canopy height model (CHM) not only reflects the vegetation growth stage but also indicates morphological adaptive changes caused by environmental stress through the spatial variation coefficient; these physical structural changes often precede the spectral signals of leaf physiological responses. Spectral indices (such as NDVI / NDRE) mainly capture biochemical parameters such as chlorophyll content, but they exhibit saturation when canopy closure exceeds a certain level. Three-dimensional structural information, by describing canopy geometry, can effectively overcome spectral saturation limitations and improve sensitivity to the health status of mature vegetation.

[0061] The inversion results indicate that the overall vegetation health of Huamu Street is relatively good, but some areas, especially along roads and areas under greater environmental pressure, have significantly lower vegetation health indices. Possible reasons include multiple adverse factors such as pollution from traffic activities, soil compaction, and restricted root growth. These findings suggest that future urban green space management should adopt differentiated management measures for these key areas, such as improving the soil environment, increasing aeration and nutrient content, and optimizing plant community structure, to enhance the adaptability and recovery capacity of the vegetation.

[0062] In conclusion, this scheme not only provides technical support for large-scale, refined urban green space health monitoring, but also lays a solid theoretical foundation for formulating scientific and reasonable urban greening optimization strategies and ecological restoration plans.

[0063] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or basic characteristics. Therefore, the embodiments should be considered exemplary and non-limiting in all respects. The scope of the invention is defined by the appended claims rather than the foregoing description. Therefore, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention, and no reference numerals in the claims should be construed as limiting the scope of the claims.

[0064] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A rapid measurement method for forest health based on UAV lidar and multispectral forest, characterized in that, include: S1: Acquire lidar data based on UAV lidar and acquire multispectral remote sensing data based on multispectral remote sensing device; S2: Canopy height model and multispectral remote sensing data generated based on lidar data; the canopy height model is the CHM. S3: Vegetation areas are extracted using the Normalized Difference Vegetation Index (NDVI). Under the NDVI mask, the canopy volume of the vegetation area is calculated using CHM data. By weighted summation of the vegetation volumes of each pixel, the overall three-dimensional green distribution of the Huamu Street area is obtained.

2. The rapid measurement method for forest health based on UAV lidar and multispectral forest as described in claim 1, characterized in that: Digital surface model (DSM) and digital ground model (DTM) are generated from lidar data. The CHM is calculated based on the digital surface model and digital ground model to reflect the height information of vegetation. CHM = DSM-DTM.

3. The rapid measurement method for forest health based on UAV lidar and multispectral forest as described in claim 2, characterized in that: The Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Red Edge Index (NDRE) were calculated to assess the vegetation health of Huamu Street. NDVI quantifies the growth status of vegetation by measuring the difference in reflectance between red and near-infrared wavelengths.

4. The rapid measurement method for forest health based on UAV lidar and multispectral forest as described in claim 3, characterized in that: The formula for calculating the Normalized Difference Vegetation Index (NDVI) is: NIR represents the reflectance of the near-infrared band, RED represents the reflectance of the red band, and the NDVI value usually ranges from -1 to +1. A high value indicates good vegetation cover and growth, while a low value indicates poor or no vegetation.

5. The rapid measurement method for forest health based on UAV lidar and multispectral forest as described in claim 4, characterized in that: The Normalized Differential Red Edge Index (NDRE) reflects the health status of vegetation by analyzing the reflectance characteristics of the red edge zone. The formula for calculating NDRE is: In the formula, REDedge is the reflectivity of the red edge band, and NIR is the reflectivity of the near-infrared band.

6. The rapid measurement method for forest health based on UAV lidar and multispectral forest as described in claim 5, characterized in that: Based on UAV lidar data and multispectral remote sensing data, a vegetation health index model was constructed. The vegetation health score obtained from field surveys was used as the dependent variable, and a linear regression model was constructed by combining three-dimensional green volume and vegetation index. H=a0+a1×V+a2×NDVI+a3×NDRE+∈ In the formula, H is the vegetation health score, V is the three-dimensional green volume of vegetation, NDVI is the normalized vegetation index, NDTE is the normalized differential red edge index, a0 is the constant term, a1, a2, a3 are regression coefficients, and ∈ is the error term. Based on different variable types and combinations, the model parameters are fitted using the least squares method to determine the regression coefficients, and the coefficient of determination R is calculated. 2 The root mean square error (RMSE) was used to evaluate the model's fit, and the optimal model was selected for inversion analysis of the vegetation health status of the entire study area.

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