Directional Local Filtering for Stand Density Estimation in Closed Forest Canopies Using VHR Optical and LiDAR Data

In this letter, we present a novel object-based approach addressing individual tree crown (ITC) detection to assess stand density from remotely sensed imagery in closed forest canopies: directional local filtering (DLF). DLF is a variant of local maximum filtering (LMF). Within locally homogeneous areas, it uses a 1-D neighborhood and simultaneously searches for local directional maxima and minima. From the extracted local maxima and minima, a proxy for crown dimensions is inferred, which is in turn related to stand density. Developed on artificial imagery, the new object-based ITC method was tested on three different forest types in Belgium, which were all characterized by dense closed canopies: 1) a coniferous forest; 2) a mixed forest; and 3) a deciduous forest. Very high resolution aerial photographs, IKONOS imagery, and Light Detection and Ranging data, in conjunction with manually digitized and field survey data, were used to evaluate the new technique. The directional DLF approach yielded consistently stronger relations (in terms of R²) when compared with the conventional omnidirectional LMF technique. The qualitative evaluation clearly demonstrated that, next to stand density estimation, DLF also offered opportunities for full crown delineation.