Analysis of forest surface litter loading estimation based on image features
[Objective]The loading of forest surface litter affects the occurrence of forest fires and a series of fire behavior characteristics exhibited by forest fires.Accurately obtaining the loading of surface litter is crucial.The Euler number of image feature can characterize the number of objects in the image,analyze the relationship between Euler number and loading,and establish a load prediction model based on image Euler number,which is of great significance for load research.[Method]The litter in typical forest stands of Cryptomeria fortunei and Phyllostachys heterocycla in Guizhou province was taken as the research object.Through forest stand and loading investigation,taking litter images and image feature processing,the relationship between Euler number and surface litter loading was analyzed.A load prediction model based on image Euler number was established,and the accuracy of the model was tested.[Result]1)After selecting different thresholds for image binarization,not all extracted Euler numbers were correlated with the litter loading.A threshold of 0.1 showed a highly significant correlation between the Euler numbers of binarized images and the two types of litter loading;2)As the Euler number of the image increased,the surface litter loading of forests of C.fortunei and P.heterocycla showed an overall downward trend;3)Linear regression was chosen to establish a litter loading prediction model based on image feature Euler number.The absolute errors of the prediction models for the litter load in C.fortunei and P.heterocycla forests were 1.60 t·hm-2 and 1.72 t·hm-2,respectively,with mean relative errors of 20.03%and 20.71%.The predicted effect of surface litter loading in C.fortunei forest was better than that in P.heterocycla forest.[Conclusion]Through this study,the feasibility of predicting forest surface litter loading based on image features has been preliminarily verified,providing new ideas for accurately obtaining load research and of great significance for scientific forest fire management.
NETL
NSTL
万方数据
