Although open forests represent approximately 30% of the world's forest resources, there is a clear lack of reliable inventory data to allow sustainable management of this valuable resource from semi‐arid areas. This paper demonstrates that the low ground cover of open forest offers a unique opportunity for deriving single tree attributes from high‐resolution satellite imagery, allowing reliable biomass estimation. More particularly, this study investigates the relationship between field‐measured stem volume and tree attributes, including tree crown area and tree shadow area, measured from pan‐sharpened Quickbird imagery with a 0.61 m resolution in a sparse Crimean juniper (Juniperus excelsa M.Bieb.) forest in south‐western Turkey. First tree shadows and crowns were identified and delineated as individual polygons. Both visual delineation and computer‐aided automatic classification methods were tested. After delineation, stem volume as a function of these image‐measured attributes was modelled using linear regression. The statistical analyses indicated that stem volume was correlated with both shadow area and crown area. The best model for stem volume using shadow area resulted in an adjusted R ² = 0.67, with a root mean square error (RMSE) of 12.5%. The model for stem volume using crown area resulted in an adjusted R ² = 0.51, with a RMSE of 15.2%. The results showed that pan‐sharpened Quickbird imagery is suitable for estimating stem volume and may be useful in reducing the time required for obtaining inventory data in open Crimean juniper forests and other similar open forests.

This study investigates the potential of object-based texture parameters extracted from 15m spatial resolution ASTER imagery for estimating tree size diversity in a Mediterranean forested landscape in Turkey. Tree size diversity based on tree basal area was determined using the Shannon index and Gini Coefficient at the sampling plot level. Image texture parameters were calculated based on the grey level co-occurrence matrix (GLCM) for various image segmentation levels. Analyses of relationships between tree size diversity and texture parameters found that relationships between the Gini Coefficient and the GLCM values were the most statistically significant, with the highest correlation (r=0.69) being with GLCM Homogeneity values. In contrast, Shannon Index values were weakly correlated with image derived texture parameters. The results suggest that 15m resolution Aster imagery has considerable potential in estimating tree size diversity based on the Gini Coefficient for heterogeneous Mediterranean forests.

Seabirds are those waterbirds that directly or indirectly depend on the marine environment over the waters, i.e., they foraged at sea either near shore or offshore and inhabit in coastal areas, islands, estuaries, wetlands, and ocean islands. They are mostly aerial waterbirds sailing above sea spending much of their time (weeks, months, and even years) in marine environments or floating on the water surface or diving in deep sea in search of food. Seabirds encompass of 65 genera, 222 marine, and 72 partially marine bird species. Seabirds have been used as good indicators (i.e., bioindicators) of marine ecosystems due to cause-effect association with different microclimate and habitats. They exploit broad scale of habitat, quickly respond to environmental changes, they can be detected easily (i.e., they showed their presence through vocalization), easy to identify, can be surveyed efficiently over large spatial scale, e.g., presence, abundance, and influenced by surrounding habitats as compared to other animals. Employing seabird as bioindicators is a cost-effective and informative tool (well defined matrix) to determine the effects of disturbances, contamination, i.e., effects of pollutants, organic substances, and oil-spills of the marine environment. Seabirds are top predators in the marine food chain and key component of the food web. Seabirds may indicate the status of habitat, reduction in food occurrence and abundance, rate of the predation, an effect of weather (climate change), and threats. The other reason could be that, seabirds often closely associate with inter-site more distinctly than other animals and may breed in the same site each year, easy to catch while incubating and during rearing chicks. Hence, it is crucially important to use seabirds as bioindicators within the context of ecological and spatial parameters to determine the effects of disturbances in the marine environment and for effective conservation and better management of seabirds in the future.