Natural resource managers, policy makers and researchers demand knowledge of deforestation and forest degradation over increasingly large spatial and temporal extents for addressing many pressing issues such as climate change mitigation and adaptation, carbon dynamics, biodiversity, and food security. The scientific community is witnessing a significant increase in the availability of different global satellite derived biophysical data sets (e.g. biomass and surface photosynthesis). However, the use of such data is not supported by accurate in-situ biophysical measurements (e.g. canopy structure) for the monitoring of forest and land dynamics. Consequently, there is an urgent need for methods to measure in-situ canopy structure accurately and better integrate with improved and innovative remote sensing approaches.
Major advancements in laser technology and earth observation are offering a revolution in information. An innovative science program is required to fully realise the benefits of these discrete technological advancements. This proposal addresses this need by solving three core challenges. Firstly, methods are developed to retrieve forest canopy structure attributes and biomass using a novel type of ground-based upward-looking laser scanner. Secondly, a physical modelling approach is used which provides a more rigorous framework than prior methods, which largely used regression relationships, to study relationship between the retrieved canopy attributes and satellite data. Finally, these accurate satellite-derived biophysical data sets enable assessment deforestation and forest degradation. However, existing methods to detect changes in satellite data are not able account for seasonal climatic variations. A new approach is therefore proposed to account for seasonality while detecting changes in forest ecosystems. The research efforts are part of a coordinated research activity among groups in Europe, Australia, and USA.