Full text: Papers accepted on the basis of peer-reviewed abstracts (Part B)

In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
411 
can be explained by our step wise processing which included 
the advantages of texture, ratio and complementary information 
from different sensors. In addition to the remote sensing data 
processing, the comprehensive and study area-specific nature of 
the field biomass data, and demonstrated accuracy of the 
allometric model (i.e. r^ of 0.93) devised for this study from the 
destructive sampling of 75 trees was instrumental in obtaining 
this high accuracy. This research used numerous processing 
steps and data combinations, but in other field conditions a 
similar approach can be adopted to identify the most suitable 
steps for that particular situation. 
ACKNOWLEDGMENTS 
The authors would like to acknowledge the Hong Kong 
Agriculture, Fisheries and Conservation Department (AFCD) 
for help with tree harvesting in country parks, as well as the 
Japan Aerospace Exploration Agency (JAXA) for the ALOS 
images under ALOS agreement no. 376. This project was also 
sponsored by GRF grant no. PolyU5281/09E. 
REFERENCES 
Boyd, D. S. Foody, G. M. Curran, P. J. Lucas R. M., Honzak, 
M. 1996. An assessment of radiance in Landsat TM middle and 
thermal infrared wavebands for the detection of tropical forest 
regeneration, International Journal of Remote Sensing, 17, 
pp.249-261. 
Boyd D. S., Danson, F. M. 2005. Satellite remote sensing of 
forest resources: Three decades of research development, 
Progress in Physical Geography, 29, pp.1-26. 
Brown, S. A. Gillespie J. R., Lugo, A. E. 1989. Biomass 
estimation methods for tropical forests with applications to 
forest inventory data, Forest Science, 35, pp.881-902. 
Brown., S. 1997. Estimating biomass and biomass change of 
tropical forests: A primer. FAO, USA. 
Champion, I. Dubois-Femandez, P. Guyon D., Cottrel, M. 
2008. Radar image texture as a function of forest stand age, 
International Journal of Remote Sensing, 29, pp.1795-1800. 
Dong, J. Kauffnann, R. K. Myneni, R. B. Tucker, C. J. Kauppi, 
P. E. Liski, J. Buermann, W. Alexeyev V., Hughes, M. K. 2003. 
Remote sensing estimates of boreal and temperate forest woody 
biomass: Carbon pools, sources, and sinks, Remote Sensing of 
Environment, 84, pp.393-410. 
Foody, G. M. Cutler, M. E. McMorrow, J. Pelz, D. Tangki, H. 
Boyd D. S., Douglas, I. 2001. Mapping the biomass of Bornean 
tropical rain forest from remotely sensed data, Global Ecology 
and Biogeography, 10, pp.379-387. 
Foody, G. M., Boyd D. S., Cutler, M E J, 2003. Predictive 
relations of tropical forest biomass from Landsat TM and 
transferability between regions, Remote Sensing of 
Environment, 85, pp.463-474. 
Fuchs, H. Magdon, P. Kleinn C., Flessa, H. 2009. Estimating 
aboveground carbon in a catchment of the Siberian forest 
tundra: Combining satellite imagery and field inventory, 
Remote Sensing of Environment, 113, pp.518-531. 
Haralick, R. M. Shanmugam K., Dinstein, I. 1973. Textural 
features for image classification, IEEE Transactions on 
Systems, Man and Cybernetics, smc 3, 610-621. 
Hyde, P. Dubayah, R. Walker, W. Blair, J. B. Hofton M., 
Hunsaker, C. 2006. Mapping forest structure for wildlife habitat 
analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, 
Quickbird) synergy, Remote Sensing of Environment, 102, 
pp.63-73. 
Kuplich, T. M. Curran P. J. and Atkinson, P. M. 2005. Relating 
SAR image texture to the biomass of regenerating tropical 
forests, International Journal of Remote Sensing, 26, pp.4829- 
4854. 
Lu, D. 2005. Aboveground biomass estimation using Landsat 
TM data in the Brazilian Amazon, International Journal of 
Remote Sensing, 26, pp.2509-2525. 
Lu, D. 2006. The potential and challenge of remote sensing- 
based biomass estimation, Int. J. Remote Sens., 27, 1297-1328, 
Muukkonen P., Heiskanen, J. 2005. Estimating biomass for 
boreal forests using ASTER satellite data combined with 
standwise forest inventory data, Remote Sensing of 
Environment, 99, pp.434-447. 
Overman, J. P. M., Witte H. J. L., Saldarriaga, J. G., 1994. 
Evaluation of regression models for above-ground biomass 
determination in Amazon rainforest, Journal of Tropical 
Ecology, 10, pp.207-218. 
Rahman, M. M. Csaplovics E. and Koch, B. 2005. An efficient 
regression strategy for extracting forest biomass information 
from satellite sensor data, International Journal of Remote 
Sensing, 26, pp. 1511-1519. 
Santos, J. R. Freitas, C. C. Araujo, L. S. Dutra, L. V. Mura, J. 
C. Gama, F. F. Soler L. S., 2003. Airborne P-band SAR applied 
to the aboveground biomass studies in the Brazilian tropical 
rainforest, Remote Sensing of Environment, 87, pp.482-493. 
Unser, M., 1986. 1986. Sum and difference histograms for 
texture classification. IEEE Transactions on Pattern Analaysis 
and Machine Intelligence, PAMI-8, pp.l 18-125. 
Wulder, M. A., Franklin S. E., Lavigne, M. B. 1996. High 
spatial resolution optical image texture for improved estimation 
of forest stand leaf area index, Canadian Journal of Remote 
Sensing, 22, pp.441-449. 
Zhao, K. Popescu S., Nelson, R. 2009. Lidar remote sensing of 
forest biomass: A scale-invariant estimation approach using 
airborne lasers, Remote Sensing of Environment, 113, pp.182- 
196. 
Zheng, D. Rademacher, J. Chen, J. Crow, T. Bresee, M. Le 
Moine J., Ryu, S. -, 2004. Estimating aboveground biomass 
using Landsat 7 ETM+ data across a managed landscape in 
northern Wisconsin, USA, Remote Sensing of Environment, 93, 
pp.402-411,
	        
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