Object-Oriented Canopy Gap Extraction from UAV Images Based on Edge Enhancement.

Efficient and accurate identification of canopy gaps is the basis of forest ecosystem research, which is of great significance to further forest monitoring and management. Among the existing studies that incorporate remote sensing to map canopy gaps, the object-oriented classification has proved successful due to its merits in overcoming the problem that the same object may have different spectra while different objects may have the same spectra. However, mountainous land cover is unusually fragmented, and the terrain is undulating. One major limitation of the traditional methods is that they cannot finely extract the complex edges of canopy gaps in mountainous areas. To address this problem, we proposed an object-oriented classification method that integrates multi-source information. Firstly, we used the Roberts operator to obtain image edge information for segmentation. Secondly, a variety of features extracted from the image objects, including spectral information, texture, and the vegetation index, were used as input for three classifiers, namely, random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). To evaluate the performance of this method, we used confusion matrices to assess the classification accuracy of different geo-objects. Then, the classification results were screened and verified according to the area and height information. Finally, canopy gap maps of two mountainous forest areas in Yunnan Province, China, were generated. The results show that the proposed method can effectively improve the segmentation quality and classification accuracy. After adding edge information, the overall accuracy (OA) of the three classifiers in the two study areas improved to more than 90%, and the classification accuracy of canopy gaps reached a high level. The random forest classifier obtained the highest OA and Kappa coefficient, which could be used for extracting canopy gap information effectively. The research shows that the combination of the object-oriented method integrating multi-source information and the RF classifier provides an efficient and powerful method for extracting forest gaps from UAV images in mountainous areas. [ABSTRACT FROM AUTHOR]