Watching Carbon from the Skies

3D vegetation structure of mangrove forests of Colombia measured with interferometric radar and lidar data. Courtesy of NASA.
3D vegetation structure of mangrove forests of Colombia measured with interferometric radar and lidar data. Courtesy of NASA.

C-16: Remote Sensing in Carbon Balance Evaluation and Monitoring

Carbon monitoring increasingly turns up as a major topic of concern because of the implications for climate change predictions. Scientists are very interested in developing accurate, reliable and repeatable ways to measure carbon. Remote sensing (RS) technologies provide unparalleled tools for doing just that.

This session brought together presenters from almost all the continents with the sole aim of exploring how traditional and novel RS techniques can be harnessed to quantify carbon stocks in different ecosystems. The techniques presented were as varied as the RS tools available.

Henry Neufeldt from the World Agroforestry Center in Nairobi, for example, talked about an index called Perpendicular Tree Index (PTI) that can be used with other algorithms to remove shadows (a major issue for RS) from imagery, enhancing the measurement of vegetation and other tree cover for accurate estimates of carbon from different landscapes.

Neufeldt also tied in socioeconomic data from western Kenya showing that secure land tenure encourages people to invest more into trees, while in adjudicated (insecure tenure) lands, there is a more pronounced loss of natural cover. From a carbon monitoring standpoint, these are important variables that can enhance data gathered from RS tools.

Yong Pang presented very interesting results from Asia showing how data from different RS platforms (such as SPOT, Landsat, MODIS, MERIS, and Spaceborne LIDAR) are critical to the development of carbon distribution maps for the greater Mekong region. The maps Pang presented showed country-specific differences, as well as ecosystem-specific variations, with respect the carbon stocks.

Similarly, Yuan Zeng from the Chinese Academy of Sciences used multispectral (broadband), hyperspectral (narrowband), and spaceborne LIDAR to generate detailed above ground (ABG) biomass maps for mainland China. Both carbon distribution and biomass maps are potentially valuable tools for crafting plans for management for these regions.

Svein Solberg from Norway showed how spaceborne RADAR imagery can be used to detect differences in tree canopy height, a critical indicator of carbon availability. A decrease in canopy height, for example, could indicate deforestation, logging, or other disturbance, which means more carbon is released, while an increase in height indicates no tree loss, thus more carbon retained. From this information, a researcher can build a carbon stock map, or a carbon stock change map, both of which can be used for comparative and time-series analyses in a variety of landscapes.

The beauty of using remote sensing in natural resources is that if data are available, researchers from other locations can modify methodologies so that techniques like those presented by the scientists in this session could be applicable to their own settings.

With more and more satellites being launched into space, resource scientists have every reason to be optimistic. They’ll have more eyes in space to watch the dynamics associated with carbon stocks on Earth.

Written by: Humphrey Kalibo

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