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Abstract:The benefits of tree canopy in urban and suburban landscapes are increasingly well known: stormwater runoff control, air-pollution mitigation, temperature regulation, carbon storage, wildlife habitat, neighborhood cohesion, and other social indicators of quality of life. However, many urban areas lack high-resolution tree canopy maps that document baseline conditions or inform tree-planting programs, limiting effective study and management. This paper describes a GEOBIA approach to tree-canopy mapping that relies on existing public investments in LiDAR, multispectral imagery, and thematic GIS layers, thus eliminating or reducing data acquisition costs. This versatile approach accommodates datasets of varying content and quality, first using LiDAR derivatives to identify aboveground features and then a combination of LiDAR and imagery to differentiate trees from buildings and other anthropogenic structures. Initial tree canopy objects are then refined through contextual analysis, morphological smoothing, and small-gap filling. Case studies from locations in the United States and Canada show how a GEOBIA approach incorporating data fusion and enterprise processing can be used for producing high-accuracy, high-resolution maps for large geographic extents. These maps are designed specifically for practical application by planning and regulatory end users who expect not only high accuracy but also high realism and visual coherence.

Large portions of the Earth's landscape are now captured by high-resolution remotely-sensed datasets and turned into corresponding thematic data. Despite these advancements the number of comprehensive, high-resolution land-cover maps is surprisingly low. The value of high-resolution land-cover data in landscapes that are increasingly fragmented and heterogeneous is great, but so are the challenges associated with turning these disparate datasets into information. We argue that effective geographic object-based image analysis (GEOBIA) system design, while rarely discussed in the literature, is perhaps the most important factor in determinin g the success of projects whose focus is on broad-area mapping. Human resources, data, hardware, and software must be tightly integrated to make the system efficient and effective. At the same time, the object-based approaches used by such systems for land-cover mapping must try to replicate the human cognitive process as much as possible, using stable, context-based approaches to feature extraction that leverage the strengths of the various input datasets while minimizing their weaknesses. Drawing on our experience deriving 12 terabytes of high-resolution land cover for more than 232,000 km 2 in the United States, we describe the design considerations for GEOBIA systems that are capable of processing huge volumes of data. In addition, we provide examples of the techniques and approaches deployed within these systems that overcome the challenges associated with mapping land cover from massive, disparate datasets.