Vegetation mapping using hierarchical object-based image analysis applied to aerial imagery and lidar data

Aims: The primary objective of this study is to map the distribution and quantify the cover of vegetation alliances over the entirety of San Clemente Island (SCI). To this end, we develop and evaluate the mapping method of hierarchical object-based classification with a rule-based expert system. Location: San Clemente Island, California, USA. Methods: We developed and tested an approach based on hierarchical object-based classification with a rule-based expert system to effectively map vegetation communities on SCI following the Manual of California Vegetation classification system. In this mapping approach, the shrub species defining each vegetation community and non-shrub growth forms were first mapped using aerial imagery and lidar data, then used as input in an automated mapping rule set that incorporates the percent cover rules of a field-based mapping rule set. Results: The final vegetation map portrays the distribution of 19 vegetation communities across SCI, with the largest areas comprised of California Annual and Perennial Grassland (35%) and three types of coastal sage scrub and maritime succulent scrub, comprising a combined 53% of the area. Map accuracy was assessed to be 79% based on fuzzy methods and 61% with a traditional accuracy assessment. The accuracy of tree identification was assessed to be 81%, but species-level tree accuracy was 45%. Conclusions: Semi-automated approaches to vegetation community mapping can produce repeatable maps over large spatial extents that facilitate ecological management efforts. However, some low-statured shrub community types were difficult to differentiate due to patchy canopies of co-occurring species including abundant non-native grasses characteristic of complex disturbance histories. Species-level tree mapping accuracy was low due to the difficulty of identifying species within poorly illuminated canyons, resulting from sub-optimal image acquisition timing.