While research on the three-dimensional structural data of forests and vegetation is increasing, their ecological value and significance are being discussed. As LiDAR technology develops, three dimensional data such as spatial heterogeneity and identifying rugosity is being dealt within the boundary of ecology.
Previous studies to detect the influence to target species with a variable of the habitat was conducted on a two-dimensional surface, and connectivity studies typically included resistance values for species data, topography and vegetation structure, and habitat quality. Then, what is the significance of structural data in 3D space? In this study, three types of environmental variables used in terms of connectivity are organized and their significance and future development direction are discussed. 1) Geographical environment, topographical/climatic factors indicating vulnerability to climate change, 2) 2D structural variables including forest area or land cover, vegetation structure and habitat quality, and biodiversity, 3) 3D structural variables such as canopy height or density, layer structure, and buildings.
These variables change due to anthropogenic influence, and the pattern of influence appears differently depending on the movement distance and characteristics of each taxon and species. In addition, since resistance values can often be calculated as the opposite of suitability, in this study, previous studies were organized according to suitability and types of structural variables used to calculate resistance values.
We checked usefulness of 3D structural data in detecting changes in the spatial distribution pattern of species due to the artificial intervention in advance in the environmental impact assessment. The framework was sugessted with converging ICT monitoring technology, it can function as a decision-making support tool for the local area.