Integration of varying spatial, spectral and temporal high-resolution optical images for individual tree crown isolation

Abstract

Automated methods for capturing geometric and spectral properties of individual tree crowns are becoming increasingly viable options for use in natural resource planning. Crown isolation techniques are needed that are capable of adapting to the changing availability and resolutions of remotely sensed data. Data integration, or the fusion of two distinct data entities, offers a methodological framework that can compensate for the shortcomings of individual datasets while enhancing their desirable features. This study sought to develop a method of data integration for high-resolution optical images of varying spatial and temporal resolutions to improve the automatic detection and delineation of individual tree crowns. A marker-controlled watershed segmentation (MCWS) algorithm was developed for a 30-cm-per-pixel-side airborne colour infrared (CIR) digital image of a leaf-on apple (Malus spp.) orchard. Three methods of obtaining the markers needed for the MCWS algorithm were tested: (1) manual marker selection, (2) template/correlation selection using the 30-cm CIR image, and (3) template/correlation selection using a 15-cm-per-pixel-side true (TRU) colour leaf-off aerial image. The effectiveness of integrating marker data of different temporal and spatial resolutions with the segmentation process of the CIR image scene was tested. A comparison of crown isolation results using markers derived within the segmented 30-cm CIR digital imagery with results from markers derived from the 15-cm TRU image scene indicated greater accuracies to detect and isolate tree crowns with data integration.