The potential of low cost, high-resolution airborne digital camera imagery for use in early stage forest regeneration assessment was investigated. Airborne imagery with 2.5-cm pixel size was acquired near Sault Ste. Marie, Ontario, over a forest vegetation management research site to: i) evaluate capabilities for identification and stem counting of two-year old conifer crop species under leaf-off and leaf-on conditions using classification of spectral and textural image information, and ii) develop models relating vegetation cover parameters to image spectral and texture information. Results indicate strong potential for identification and counting of conifer trees when competing vegetation cover is low or in leaf-off condition. However, systematic decreases in class separability and conifer count accuracy were observed with increasing competition. In image modelling of competition Leaf Area Index and Cover, statistically significant relations were found using primarily spectral measures. Stratification by competition species improved model fits and included texture measures in some models. Key words: airborne remote sensing, forest vegetation management, regeneration, digital cameras, leaf area index, cover, tree classification
Rice crop monitoring using multirotor UAV and RGB digital camera at early stage of growth
