and the use of lidar thus is limited in many cases, especially in developing
countries.
Moreover, these two methods are limited to mapping and scaling up of spatial data
for continuous variables and cannot be used to mapping and aggregation of spatial
data for categorical variables. The potential improvement thus lies at developing
image-based indicator cosimulation. In addition, these methods require the normal
score transformation of spatial data to meet the assumption of the multivariate
Gaussian distribution. In practice, however, there is no effective way to ensure
that spatial data have multivariate Gaussian distribution. Therefore, developing a new
method that does not require multivariate Gaussian distribution is necessary.
6.6 SUMMARY
By combining plot data and TM images at 30 m spatial resolution, this study
compared two upscaling methods—point simple cokriging point cosimulation and
point simple cokriging block cosimulation—to map above-ground forest carbon at
990 m pixel size in Lin-An County in China. The results showed both methods not
only scaled up the spatial data but also modeled the propagation of input uncertainties
from a finer spatial resolution to a coarser one. The output uncertainties reflected the
spatial variability of the estimation accuracy due to the spatial configuration of the
input data locations and their values.
ACKNOWLEDGMENT
This research was partly funded by the National Natural Science Foundation of China
(No. 30972360).
REFERENCES
Atkinson, P. M., and Kelly R. E. 1997. Scaling up point snow depth data in the U. K. for
comparison with SSM/I imagery. International Journal of Remote Sensing 18:437–443.
Chinese Ministry of Forestry (CMF), Department of Forest Resource and Management. 1996.
Forest Resources of China 1949 to 1993. Liu-hai-hu-tong 7, Beijing: Chinese Forestry
Press.
Fang, W., and Wu, Y. 2006. A study on the rate of hardwood tissue. Journal of Fujian College
of Forestry 26(3):224–228.
Fang, Y. 1999. Variation of wood gravity for Pinus Massoniana plantations. Forestry Science
and Technology 6:37.
Gehlke, C. E., and Biehl, K. 1934. Certain effects of grouping upon the size of the correlation
coefficient in census tract material. Journal of the American Statistical Association
Supplement 29:169–170.
Goovaerts, P. 1997. Geostatistics for Natural Resources Evaluation. New York: Oxford
University Press.
REFERENCES
123
countries.
Moreover, these two methods are limited to mapping and scaling up of spatial data
for continuous variables and cannot be used to mapping and aggregation of spatial
data for categorical variables. The potential improvement thus lies at developing
image-based indicator cosimulation. In addition, these methods require the normal
score transformation of spatial data to meet the assumption of the multivariate
Gaussian distribution. In practice, however, there is no effective way to ensure
that spatial data have multivariate Gaussian distribution. Therefore, developing a new
method that does not require multivariate Gaussian distribution is necessary.
6.6 SUMMARY
By combining plot data and TM images at 30 m spatial resolution, this study
compared two upscaling methods—point simple cokriging point cosimulation and
point simple cokriging block cosimulation—to map above-ground forest carbon at
990 m pixel size in Lin-An County in China. The results showed both methods not
only scaled up the spatial data but also modeled the propagation of input uncertainties
from a finer spatial resolution to a coarser one. The output uncertainties reflected the
spatial variability of the estimation accuracy due to the spatial configuration of the
input data locations and their values.
ACKNOWLEDGMENT
This research was partly funded by the National Natural Science Foundation of China
(No. 30972360).
REFERENCES
Atkinson, P. M., and Kelly R. E. 1997. Scaling up point snow depth data in the U. K. for
comparison with SSM/I imagery. International Journal of Remote Sensing 18:437–443.
Chinese Ministry of Forestry (CMF), Department of Forest Resource and Management. 1996.
Forest Resources of China 1949 to 1993. Liu-hai-hu-tong 7, Beijing: Chinese Forestry
Press.
Fang, W., and Wu, Y. 2006. A study on the rate of hardwood tissue. Journal of Fujian College
of Forestry 26(3):224–228.
Fang, Y. 1999. Variation of wood gravity for Pinus Massoniana plantations. Forestry Science
and Technology 6:37.
Gehlke, C. E., and Biehl, K. 1934. Certain effects of grouping upon the size of the correlation
coefficient in census tract material. Journal of the American Statistical Association
Supplement 29:169–170.
Goovaerts, P. 1997. Geostatistics for Natural Resources Evaluation. New York: Oxford
University Press.
REFERENCES
123