The distance to structural complement (DiSCo) approach for expressing forest structure described by Aerial Photograph Interpretation data sets

At the landscape-level Aerial Photograph Interpretation (API) is one of the oldest and most common tools for mapping forest structure. The variety of attributes available for API classifications can produce 100s of different patch types as a basis for mapping landscape mosaics. However, these maps are often difficult to interpret or use for monitoring the impacts of management and natural disturbance. In this study, we demonstrate an approach for quantifying the landscape forest structure described by API data sets. For this purpose we utilised a forest dataset comprising 1197 field plots and API mapping of crown structural characteristics for 773,280 ha of State Forest in Victoria, Australia. Our approach involved: (i) stratification of the landscape into distinct forest communities; (ii) construction of stand-level structural complexity indices for each forest community; (iii) use of stand-level indices of structural complexity to classify API typing into distinct canopy structural classes; (iv) calculation of the distance from each point within a landscape grid to achieve a full complement of canopy structural classes within each forest community. We term our methodology the distance to structural complement (DiSCo) approach, because it identifies the minimum distance to achieve a full complement of structural units within the landscape. We demonstrate the use of these values in mapping landscape structure and their potential for monitoring and modelling the effects of disturbance at this scale, including impacts on heterogeneity, connectivity, individual faunal species and particular forest communities.