Evaluation of the effects of artificial vegetation distribution identification and reconstruction based on UAV
Artificial vegetation reconstruction is the foundational work for land reclamation and ecological restoration in mining areas,and analyzing the effects of artificial vegetation reconstruction is an important link of ecological restoration quality evaluation.Taking the land reclamation demonstration area of Inner Mongolia Mengtai Buliangou Coal Mine as the main study area,Using UAV with multispectral cameras for data acquisition and three common machine learning algorithms for application,artificial vegetation data was extracted based on the differences in spectral and texture characteristics of land and land cover.On this basis,selecting the vegetation biomass index to analyze the growth status of artificial vegetation on the ridges of the study area,its artificial vegetation reconstruction effect was evaluated.The results showed that hyperparameter tuning for machine learning classification methods could effectively improve the classification accuracy of the model,the random forest classification model had the highest classification accuracy after hyperparameter tuning,with an overall accuracy of 82.11％and a Kappa coefficient of 0.77;the inversion results obtained by the random forest model had the highest accuracy,with an R2 of 0.93 and a root mean square error of 13.97;the reconstruction effect of herbaceous vegetation on the ridges of the demonstration area was poor,with nearly 66.6％of the area having a biomass grade in the range of 0-50 g/m2;the reconstruction effect of shrubs was better,with nearly 61.4％of the area having a biomass grade in the range of 100-150 g/m2.
UAV remote sensingartificial vegetation reconstructionland reclamation in mining areahyperparameter tuning
万方数据
维普
