Time-series maps of aboveground carbon stocks in the forests of central Sumatra.

Background: Efforts to reduce emissions from deforestation and forest degradation in tropical Asia require accurate high-resolution mapping of forest carbon stocks and predictions of their likely future variation. Here we combine radar and LiDAR with field measurements to create a high-resolution aboveground forest carbon stock (AFCS) map and use spatial modeling to present probable future AFCS changes for the Riau province of central Sumatra.

Results: Our map provides spatially explicit estimates of the AFCS with an accuracy of ±23.

High-resolution prediction of leaf onset date in Japan in the 21st century under the IPCC A1B scenario.

Reports indicate that leaf onset (leaf flush) of deciduous trees in cool-temperate ecosystems is occurring earlier in the spring in response to global warming. In this study, we created two types of phenology models, one driven only by warmth (spring warming [SW] model) and another driven by both warmth and winter chilling (parallel chill [PC] model), to predict such phenomena in the Japanese Islands at high spatial resolution (500 m). We calibrated these models using leaf onset dates derived from satellite data (Terra/MODIS) and in situ temperature data derived from a dense network of ground stations Automated Meteorological Data Acquisition System.

Accurate measurement of optical properties of narrow leaves and conifer needles with a typical integrating sphere and spectroradiometer.

Accurate information on the optical properties (reflectance and transmittance spectra) of single leaves is important for an ecophysiological understanding of light use by leaves, radiative transfer models and remote sensing of terrestrial ecosystems. In general, leaf optical properties are measured with an integrating sphere and a spectroradiometer. However, this method is usually difficult to use with grass leaves and conifer needles because they are too narrow to cover the sample port of a typical integrating sphere.