Development and application of multiscale object-based approaches for remote sensing landcover analysis and resource management

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This planet, and the landscapes it supports are complex systems composed of a large number of heterogeneous components that interact in non-linear ways, are hierarchically structured, spatially correlated and scale dependent. Remote sensing technologies represent the primary source for large area landcover data. Therefore, to fully understand, monitor, model, and manage our interaction within landscapes, we require approaches capable of integrating remote sensing imagery and appropriate multiscale-theory and methods to define landscape components (i.e., scene objects) at their specific scales of expression, and link/evaluate them with related objects at corresponding hierarchical scales. However, there are no objects in remote sensing images, only pixels, the concept of 'landscape' varies between users, there is no 'science of scale', nor are there fixed scaling laws or rules for translating data between scales. In a concerted effort to overcome these challenges the goal of my scientific program is to develop and apply innovative multiscale object-based approaches using remote sensing, geographic information systems, high-performance computing and geo-visualization. This will result in enhanced theory and understanding of landscape structure and dynamics at multiple scales, and innovative new tools and methods to better assess and manage complex environments. Specific projects focus on forest landscapes and the development of multiscale object-based tools for (i) improved forest biomass/carbon estimates, (ii) stratified forest sampling schemes to extend expensive LiDAR (Light Detection and Ranging) estimates over larger areas, and (iii) computer assisted forest inventory updating from individual trees, to stands and regions. This research program will take advantage of high-profile remote sensing data sets, high-performance computing, and collaborative research with experts in national and international projects (i.e., EOSD, and NFI). Considerable training of highly qualified personnel will be achieved (1 PDF, 4 PhD, 8 MSc students and 5 Field/Research Assistants).