The design of a Space-borne multispectral canopy LiDAR to estimate global carbon stock and gross primary productivity

Understanding the dynamics of the global carbon cycle is one of the most challenging issues for the scientific community. The ability to measure the magnitude of terrestrial carbon sinks as well as monitoring the short and long term changes is vital for environmental decision making. Forests form a significant part of the terrestrial biosystem and understanding the global carbon cycle, Above Ground Biomass (AGB) and Gross Primary Productivity (GPP) are critical parameters. Current estimates of AGB and GPP are not adequate to support models of the global carbon cycle and more accurate estimates would improve predictions of the future and estimates of the likely behaviour of these sinks. Various vegetation indices have been proposed for the characterisation of forests including canopy height, canopy area, Normalised Difference Vegetation Index (NDVI) and Photochemical Reflectance Index (PRI). Both NDVI and PRI are obtained from a measure of reflectivity at specific wavelengths and have been estimated from passive measurements. The use of multi-spectral LiDAR to measure NDVI and PRI and their vertical distribution within the forest represents a significant improvement over current techniques. This paper describes an approach to the design of an advanced Multi-Spectral Canopy LiDAR, using four wavelengths for measuring the vertical profile of the canopy simultaneously. It is proposed that the instrument be placed on a satellite orbiting the Earth on a sun synchronous polar orbit to provide samples on a rectangular grid at an approximate separation of 1km with a suitable revisit frequency. The systems engineering concept design will be presented

Field trial of an automated ground-based infrared cloud classification system

Automated classification of cloud types using a ground-based infrared imager can provide invaluable high resolution and localised information for Air Traffic Controllers. Observations can be made consistently, continuously in real time and accurately during both day and night operation. Details of a field trial of an automated, ground-based infrared cloud classification system are presented. The system was designed at Campbell Scientific ltd in collaboration with Loughborough University, UK. The main objective of the trial was to assess the performance of an automated infrared camera system with a lightning detector in classifying several types of clouds, specifically Cumulonimbus and Towering Cumulus, during continuous day and night operation. Results from the classification system were compared with those obtained from Meteorological Aerodrome Reports (METAR) and with data generated by the UK Meteorological Office from their radar and sferics automated cloud reports system. In comparisons with METAR data, a Probability of Detection of up to 82% was achieved, together with a minimum Probability of False Detection of 18%