Tree Detection Algorithm Research Articles

Approaches for optimal automated individual tree crown detection in regenerating coniferous forests

Automated tree detection provides a means to acquire information on tree abundance and spatial distribution, both of which are critical for evaluating the status of regenerating forests. It is also often a precursor to automated tree delineation, which typically utilizes image data surrounding a detected crown point. However, obtaining consistently accurate detection results has proven difficult owing to errors associated with image scale. In this paper, four approaches that reduce this scale dependence are evaluated, including (1) determination of optimum global image smoothing to apply predetection, (2) determination of optimum local image smoothing to apply predetection, (3) determination of the optimal local window size for use in the detection algorithm, and (4) post-detection merging of initially defined crown segments. Each approach was applied to three datasets acquired by different sensors and with different regenerating forest conditions. A common local maximum tree detection algorithm was implemented for approaches 1–3, and a watershed segmentation algorithm was applied in approach 4. Detection accuracy was evaluated using standardized methods. The highest accuracies for each dataset were obtained with approaches based on local scale representations where the regenerating structure favored such approaches. However, more consistent accuracies across all datasets were obtained with the optimum global scale approach. Post-detection merging of adjacent crown segments produced the poorest results. Error sources and the advantages and disadvantages of each approach are discussed in terms of developing more operational methods for automated tree detection in regenerating forests.

Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration

Ensuring successful forest regeneration requires an effective monitoring program to collect information regarding the status of young crop trees and nearby competing vegetation. Current field-based assessment methodology provides the needed information, but is costly, and therefore assessment frequency is low. This often allows undesirable forest structures to develop that do not coincide with management objectives. Remote sensing techniques provide a potentially low-cost alternative to field-based assessment, but require the development of methods to easily and accurately extract the required information. Automated tree detection and delineation algorithms may be an effective means to accomplish this task. In this study, a tree detection–delineation algorithm designed specifically for high-resolution digital imagery of 6-year-old trees is presented and rigorously evaluated. The algorithm is based on the analysis of local transects extending outward from a potential tree apex. The crown boundary is estimated using the point of maximum rate of change in the transect data and a rule base is applied to ensure that the point is contextually suitable. This transect approach is implemented in both the tree-detection and crown-delineation phases. The tree-detection algorithm refines the results of an initial local maximum filter by providing an outline for each detected tree and retaining only one local maximum value within this outline. The crown-delineation algorithm is similar to the detection algorithm, but applies a different rule set in creating a more detailed crown outline. Results show that the algorithm's tree-detection accuracy was better than that using commonly applied fixed-window local maximum filters; it achieved a best result of 91%. For the crown-delineation algorithm, measured diameters from delineated crowns were within 17.9% of field measurements of diameter at the crown base on an individual tree basis and within 3% when averaged for the study. Tests of image pixel spacings from 5 to 30 cm showed that tree-detection accuracy was stable except at the lowest (30-cm) resolution where errors were unacceptable. Delineated crown-diameter accuracy was more sensitive to image resolution, decreasing consistently and nonlinearly with increasing pixel spacing. These results highlight the need for very high resolution imagery in automated object-based analysis of forest regeneration.