Methods and Approaches to the Estimation of Forest Characteristics Using the Optical Satellite Data of Very High Spatial Resolution
The article presents a review of about sixty scientific publications regarding the main methods and algorithms for automated processing of the very high spatial resolution (VHR) satellite images obtained in the optical range of spectrum to determinate the forests characteristics. Using the examples of specific studies, we have demonstrated the remote assessment of the main characteristics of wood vegetation (biometric and morphostructural parameters, species diversity, disturbances of forest canopy, state of forests and its dynamics, phytomass and carbon stock); we have identified the most promising methodological approaches and algorithms taking into account the estimates of target parameters’ accuracy. Features of object-oriented approach (OBIA) concerning the processing of the VHR images were discussed, since such an approach is able to overcome the limitations of traditional methods of pixel-by-pixel image classification. OBIA is based on the spatial relationships (proximity) or objects properties and includes two main steps: image segmentation and objects classification. The main indicator that ensures the efficiency of classification in OBIA is an accuracy of image segmentation by canopy elements. Therefore, article’s special attention is paid to the difficulties and challenges of improving the methods of image segmentation to detect the individual tree crowns (OCD) and to separate the illuminated and shaded crown areas of the crown and inter-crown parts of tree canopy. Review presents the options for applying various algorithms and methods of classification and regression modeling: maximum likelihood method (MLC), Bayesian classifier (NB), support vector machine (SVM), decision trees (DT), ensemble of decision trees (Random Forest or RF), k-nearest neighbors method (KNN), Gaussian mixture model (GMM), artificial neural network (ANN), etc. The choice of an optimal algorithm depends on many factors: interpretation goals, characteristics of satellite and field data, quality of training samples, complexity of tree canopy structure in the forests, additional information about the forest area, etc. Key words: forest vegetation characteristics, tree stand parameters, forest canopy image, ultra-high spatial resolution (VHR) satellite data, optical survey range, object-oriented methods, classification algorithms, regression modeling.