Тип публикации: статья из журнала
Год издания: 2021
Идентификатор DOI: 10.3390/drones5030077
Ключевые слова: aerial photo and multispectral images, individual tree crowns delineation, pattern recognition, remote sensing, species classification, unmanned aerial vehicle, vital status assessment
Аннотация: Monitoring the structure parameters and damage to trees plays an important role in forest management. Remote-sensing data collected by an unmanned aerial vehicle (UAV) provides valuable resources to improve the efficiency of decision making. In this work, we propose an approach to enhance algorithms for species classification and aПоказать полностьюssessment of the vital status of forest stands by using automated individual tree crowns delineation (ITCD). The approach can be potentially used for inventory and identifying the health status of trees in regional-scale forest areas. The proposed ITCD algorithm goes through three stages: preprocessing (contrast enhancement), crown segmentation based on wavelet transformation and morphological operations, and boundaries detection. The performance of the ITCD algorithm was demonstrated for different test plots containing homogeneous and complex structured forest stands. For typical scenes, the crown contouring accuracy is about 95%. The pixel-by-pixel classification is based on the ensemble supervised classification method error correcting output codes with the Gaussian kernel support vector machine chosen as a binary learner. We demonstrated that pixel-by-pixel species classification of multi-spectral images can be performed with a total error of about 1%, which is significantly less than by processing RGB images. The advantage of the proposed approach lies in the combined processing of multispectral and RGB photo images. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Журнал: Drones
Выпуск журнала: Vol. 5, Is. 3
Номера страниц: 77
ISSN журнала: 2504446X
Издатель: MDPI AG