Individual tree crown detection from high spatial resolution imagery using a revised local maximum filtering

Accurate tree density and location are important information for optimizing the management and production of forest. Combination of remote sensing techniques and local maximum (LM) filtering algorithm provides a feasible approach to individual tree crown detection, but still faces high error under complicate canopy structure. In this study, a revised LM (RLM) algorithm is presented and evaluated for identifying individual trees from four high spatial-resolution images. Instead of a moving window technique, the RLM algorithm finds crown center seeds by searching local maximal in the transects along row and column directions of the image. Each of final crown centers is then searched using a variable window centered at the crown center seed. Strategies for splitting and merging crowns are implemented in the RLM algorithm to reduce false detection. Result showed that accuracy of the RLM algorithm was more sensitive to its minimum crown length parameter (CLmin). The RLM algorithm driven by the CLmin estimates achieved high overall accuracies between 85% and 91% and low commission (9–14%) and omission errors (8–15%) for the four images. Splitting and merging strategies implemented in the RLM algorithm effectively reduced commission and omission errors. These results indicate that the RLM algorithm is a feasible method with well-defined parameters for automatically detecting individual trees with satisfactory detection accuracy.