Permafrost in the Da and Xiao Xing'anling Mountains in northeastern China is warm, thin and sensitive to climatic warming. In the 1970s, the southern limit of permafrost (SLP) was empirically correlated to the −1 to 0°C isotherms of mean annual air temperature (MAAT) in the western part of the Da Xing'anling Mountains, to about 0°C in the northern part of the Songnen Plain, and to 0 to +1°C in the eastern part of the Xiao Xing'anling Mountains. Climate warming and deforestation have led to permafrost degradation as shown by deepening of the active layer, thinning permafrost, rising ground temperatures, expanding taliks and the disappearance of permafrost patches. The present position of the SLP was estimated using the −1.0 to +1.0°C MAAT isotherms for 1991–2000. Compared to the SLP in the 1970s, areas of sporadic discontinuous and isolated patchy permafrost have decreased by 90,000–100,000 km2, or 35–37% of their total areal extent (260,000–270,000 km2) in the 1970s. Recent field observations along the Hei'he to Bei'an Highway, the proposed Mo'he to Daqing Crude Oil Pipeline route and the Hai'lar to Daqing Highway confirm these changes. Continuing northward shifting of the SLP is likely to occur during the next 40–50 years under a warming of 1.0–1.5 °C, reducing the permafrost areal extent to an estimated 35% of that in the 1970s and 1980s. Copyright © 2007 John Wiley & Sons, Ltd.

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The use of lidar and radar instruments to measure forest structure attributes such as height and biomass at global scales is being considered for a future Earth Observation satellite mission, DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice). Large footprint lidar makes a direct measurement of the heights of scatterers in the illuminated footprint and can yield accurate information about the vertical profile of the canopy within lidar footprint samples. Synthetic Aperture Radar (SAR) is known to sense the canopy volume, especially at longer wavelengths and provides image data. Methods for biomass mapping by a combination of lidar sampling and radar mapping need to be developed. In this study, several issues in this respect were investigated using aircraft borne lidar and SAR data in Howland, Maine, USA. The stepwise regression selected the height indices rh50 and rh75 of the Laser Vegetation Imaging Sensor (LVIS) data for predicting field measured biomass with a R 2 of 0.71 and RMSE of 31.33 Mg/ha. The above-ground biomass map generated from this regression model was considered to represent the true biomass of the area and was used as a reference map since no better biomass map exists for the area. Random samples were taken from the biomass map and the correlation between the sampled biomass and co-located SAR signature was studied. The best models were used to extend the biomass from lidar samples into all forested areas in the study area, which mimics a procedure that could be used for the future DESDYnI mission. It was found that depending on the data types used (quad-pol or dual-pol) the SAR data can predict the lidar biomass samples with R 2 of 0.63-0.71, RMSE of 32.0-28.2 Mg/ha up to biomass levels of 200-250 Mg/ha. The mean biomass of the study area calculated from the biomass maps generated by lidar-SAR synergy was within 10% of the reference biomass map derived from LVIS data. The results from this study are preliminary, but do show the potential of the combined use of lidar samples and radar imagery for forest biomass mapping. Various issues regarding lidar/radar data synergies for biomass mapping are discussed in the paper.