Abstract: [Objective] The effects of different segmentation scales and characteristics of objects on object-oriented forest gap based on aerial orthophoto were studied,and the suitability of forest gap classification based on aerial orthophoto was also evaluated in this research.[Method] In this study,based on true color aerial orthophoto data and object-oriented classification method,northeastern typical natural secondary forest-Mao'ershan Experimental Forest Farm Donglin industry zone was selected as the study area for classification of forest gap.In the process of object-oriented classification,for three components of aerial orthophoto (Blue,Green,Red),10 scales (10-100,step size 10) was divided to carry out segmentation,and topological combination accuracy (R_A(or) and R_A(os)) and position accuracy (D_sr) were used to evaluate the classification results.For each segmentation result of different scales,spectral features and combination features of spectrum and texture derived from aerial orthophoto were used,and support vector machine (SVM) with linear kernel classifier of object-oriented was used to classify the study area into forest gap,non-forest gap and canopy,totally 20 classification result were obtained.Later the accuracy of each classification result was evaluated by the 4 different evaluation indexes,namely producer accuracy calculated by Confusion matrix,user accuracy,classification accuracy and Kappa coefficient.[Result] Characteristics of objects (size and shape) were affected by scale parameter.Objects in small area were created by small scale segmentation,and objects in big area were created on large scale segmentation,however,segmentation of large scale could not efficiently distinguish obvious forest gaps from canopies,it indicates an obvious insufficient segmentation phenomenon.The topological combination accuracy (R_A(or) and R_A(os)) and position accuracy (D_sr) illustrated that the result of segmentation objects on 20 scale (R_A(or) and R_A(os) were higher and close,D_sr was lower) was most similar to the reference objects comparing to the result on other segmentation scales.The segmentation schemes of spectral features and combination features of spectrum and texture showed a similar variable trend:classification accuracies were lower on small scales,and then increased with scale increasing,after reached the maximum on a certain scale,they decreased to stable state.The classification accuracy using combination features of spectrum and texture was lower than that using spectral features only,especially on small scales.On the segmentation scale of 20,the overall classification accuracy using combination features of spectrum and texture was 19% lower than that using only spectral features,and accordingly 30% lower on the segmentation scale of 30.The optimal scale parameter of segmentation based on aerial orthophoto was 20.[Conclusion] The highest recognition accuracy was just 74%(Kappa=0.61) when using aerial orthophoto to recognize forest gaps,the producer and user accuracy of forest gap were all above 60%,and which of non-forest gap were around 90%.The classification accuracy based on aerial orthophoto would continually decrease when adding texture features.

Key words: forest gap, image segmentation, object features, aerial orthophoto, SVM, object-based