Abstract
Tree barriers in transmission line corridors are an important safety hazard.Scientific prediction of tree height and monitoring tree height changes are essential to solve this hidden danger. In this paper, the advantages of airborne lidar and optical remote sensing data are combined to research the method of tree height inversion. Based on glas data of lidar,waveform parameters such as waveform length, waveform leading edge length and waveform trailing edge length were extracted from waveform data by gaussian decomposition method.Terrain feature parameters were extracted from aster gdem data.The tree crown information was extracted from the optical remote sensing image by means of the mean shift algorithm. Then extract the vegetation index with high correlation with tree height.Finally, the extracted waveform feature parameters, topographic feature parameters, and crown index and vegetation index with high correlation are used as model input variables. The tree height inversion model was established using four regression methods, including multiple linear regression (mlr), support vector machine (svm), random forest (rf), and bp neural network (bpnn). The accuracy evaluation was conducted, and it was concluded that the tree height inversion model based on random forest obtained the best accuracy effect.
Highlights
In the 21st century, with the continuous emergence of high spatial resolution remote sensing images, radar, and lidar data, The appliance of remote sensing technology in power line inspections has evolved from the initial information extraction and obstacle type identification to a more refined direction[1]
The input variables for constructing four machine learning models are the wavelength, the length of the leading edge of the wave, the length of the trailing edge of the wave, the terrain index, the standard deviation of the terrain, the measured crown amplitude obtained from the image crown amplitude inversion, and highly relevant vegetation indices Normalized difference vegetation index (NDVI), Enhanced vegetation index 2 (EVI2), Normalized difference greenness vegetation index (NDGI), ratio vegetation index (RVI)
By comparing the accuracy of the four models, it is found that the accuracy of the three machine learning algorithm models is significantly improved compared with the multiple linear regression model, and they show good stability
Summary
In the 21st century, with the continuous emergence of high spatial resolution remote sensing images, radar, and lidar data, The appliance of remote sensing technology in power line inspections has evolved from the initial information extraction and obstacle type identification to a more refined direction[1]. To obtain forest’s horizontal and vertical structure information, Forest parameter inversion methods combining Lidar and optical remote sensing data have been rapidly developed. It has became a research direction worthy of attention in recent years. The waveform leading edge length ,the waveform trailing edge length, the topographical feature parameters, the crown width, and the vegetation index with high correlation with the tree height as the model input variables,establish four tree height inversion models of multiple linear regression, support vector machine, random forest and BP neural network. evaluate the accuracy of the four models. The optimal tree height inversion model is optimized to estimate the tree height
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