Growing Stock Estimation for Small-Areas from Large-Area Forest Inventory data and Satellite Data in Korea Jong Su Yim 1 , Sung Ho Kim 2 , Jin Hyun Jeong 2 and Man Yong Shin 1 1 Department of Forest Resources, College of Forest Sciences, Kookmin University, Seoul 136- 702, Korea; 82-2-910-4815; [email protected]; [email protected]2 Division of Forest Resource Information, Korea Forest Research Institute, Seoul 130-712, Korea; 82-2-961-2842; [email protected]; [email protected]Introduction The Korean National Forest Inventory (NFI) has been implemented to provide forest information at the national as well as the local levels. The NFI should address (i) estimates of the total growing stock for the entire country, (ii) different forest strata (e.g., forest cover types, dominant tree species, age classes, etc.) and (iii) different ownerships and administrative units (KFRI, 2008). Until the 4 th NFI system in Korea, which was carried out by a rotation system by administrative unit (province) over a 10-years period, forest statistics for small-area units were derived from a calibration method using administrative units due to a small sample size (KFS, 2004). In this case, the forest statistics may not represent a real forest because this method did not incorporate spatial heterogeneity of forests across an administrative unit. The Korean NFI system has changed to a systematic sampling, in which field plots are systematically distributed and have been collected about 20% of a total samples per year (KFRI, 2008). Therefore, suitable estimators for small-areas are needed to predict reliable forest statistics. Small-area estimation is a statistical method that allows to estimate various information at a subpopulation or domain which do not consider in sampling design process (Rao 2003). The objective of this study is to determine a suitable method for estimating forest growing stock at a small-area unit in Korea. To achieve this goal, two calibration methods were applied; (i) administrative and (ii) space-based geographic neighboring units. In addition, the k-nearest neighbor (k-NN) technique by combining forest inventory data and satellite data was also used (Tomppo et al. 2008). Materials and Methods A. Source data Field data from the 5 th Korean NFI were collected in 2006~2008 years. In this study, three small areas; Muju (Jeon-Buk province), Youngdong (Chung-Buk province) and Kimcheon (Kyung-Buk province) were selected. These counties belong to different administrative units, but they are geographically contiguous with each other (Figure 1). The field plots were post-stratified three forest cover types (coniferous, deciduous, and mixed forests) based on basal areas by tree species per field plot. Post-stratified field plots were used to estimate stratum weights and means for each stratum (Table 1).
Transcript
Growing Stock Estimation for Small-Areas from Large-Area Forest Inventory data and Satellite Data in Korea
Jong Su Yim1, Sung Ho Kim2, Jin Hyun Jeong2 and Man Yong Shin1
1 Department of Forest Resources, College of Forest Sciences, Kookmin University, Seoul 136-702, Korea; 82-2-910-4815; [email protected]; [email protected]
2 Division of Forest Resource Information, Korea Forest Research Institute, Seoul 130-712, Korea; 82-2-961-2842; [email protected]; [email protected] Introduction The Korean National Forest Inventory (NFI) has been implemented to provide forest information at the national as well as the local levels. The NFI should address (i) estimates of the total growing stock for the entire country, (ii) different forest strata (e.g., forest cover types, dominant tree species, age classes, etc.) and (iii) different ownerships and administrative units (KFRI, 2008). Until the 4th NFI system in Korea, which was carried out by a rotation system by administrative unit (province) over a 10-years period, forest statistics for small-area units were derived from a calibration method using administrative units due to a small sample size (KFS, 2004). In this case, the forest statistics may not represent a real forest because this method did not incorporate spatial heterogeneity of forests across an administrative unit. The Korean NFI system has changed to a systematic sampling, in which field plots are systematically distributed and have been collected about 20% of a total samples per year (KFRI, 2008). Therefore, suitable estimators for small-areas are needed to predict reliable forest statistics. Small-area estimation is a statistical method that allows to estimate various information at a subpopulation or domain which do not consider in sampling design process (Rao 2003). The objective of this study is to determine a suitable method for estimating forest growing stock at a small-area unit in Korea. To achieve this goal, two calibration methods were applied; (i) administrative and (ii) space-based geographic neighboring units. In addition, the k-nearest neighbor (k-NN) technique by combining forest inventory data and satellite data was also used (Tomppo et al. 2008). Materials and Methods A. Source data Field data from the 5th Korean NFI were collected in 2006~2008 years. In this study, three small areas; Muju (Jeon-Buk province), Youngdong (Chung-Buk province) and Kimcheon (Kyung-Buk province) were selected. These counties belong to different administrative units, but they are geographically contiguous with each other (Figure 1). The field plots were post-stratified three forest cover types (coniferous, deciduous, and mixed forests) based on basal areas by tree species per field plot. Post-stratified field plots were used to estimate stratum weights and means for each stratum (Table 1).
Figure 1 Table 1. Number of field plots for forest strata and
County NFI*-field data
C D Muju 21 30Youngdong 8 50Kimcheon 33 37NFI* = National Forest Inventory B. Calibration method The calibration using administrative units (ADU) ignorehigh estimation errors. While considers spatial characteristics of forests (Rao 2003). counties for each county which are border on the three counties, respectively; Youngdong (6 counties), Muju (5 counties), and Kimcheon (7 counties)samples per stratum for the two calibration methods are shown in Table
Youngdong county
Figure 2. The diagram showing blue color.
1. Location of the three counties in this study.
s for forest strata and two different calibration
field data CalibrationAdministrative unit
M C D M C30 28 181 223 162 50 23 126 298 218 37 22 697 737 599
National Forest Inventory; C = Coniferous cover type; D = Deciduous, M = Mixed
calibration using administrative units (ADU) ignores the spatial heterogeneityhigh estimation errors. While the calibration method using geographic neighboring units (GEO) considers spatial characteristics of forests (Rao 2003). For the GEO, there are
ounty which are border on the three counties, respectively; Youngdong (6 Muju (5 counties), and Kimcheon (7 counties) (Figure 2). The numbers of
two calibration methods are shown in Table 1.
Muju county Kimcheon city
The diagram showing neighboring counties of each study county that represented by
.
calibration methods. Calibration
Geographical unit C D M 152 213 156 108 209 144 189 234 204
; C = Coniferous cover type; D = Deciduous, M = Mixed
heterogeneity that may cause calibration method using geographic neighboring units (GEO)
here are several neighboring ounty which are border on the three counties, respectively; Youngdong (6
The numbers of available
Kimcheon city
counties of each study county that represented by
Table 2. Estimators of stratum mean and weight for the two calibration methods Administrative unit Geographical unit
phw , : stratum weight for each stratum h within an administrative unit p, hiv , : growing stock at plot i in stratum h
phn , : number of samples in stratum h within an administrative unit p,
pn : total samples within an administrative unit p, phI , : samples in stratum h within an administrative unit p, ,
ghw , : stratum weight per stratum h within a geographical unit g,
ghn , : number of samples in stratum h within a geographical unit g,
gn : total samples within a geographical unit g, ghI , : samples in stratum h within a geographical unit g.
In order to estimate stratum weight and stratum mean for the two calibration methods, the estimators for simple random sampling with post-stratification were applied (Table 2). C. k-Nearest Neighbor Using the k-NN technique, various thematic maps of forest attributes can be generated by combining forest inventory data and satellite data (Tomppo et al. 2008). In this study, thematic maps of growing stock and forest cover type derived from the k-NN technique by combining a Landsat TM-5 (acquired on April 28. 2005) and the NFI-field data within the imagery (n=2733) were produced. In the k-NN process, general options were applied as shown in Table 3. In order to produce a forest cover map, the post-stratified NFI-field plots within the imagery were used as training data. The number of available plots of non-forest was very small compared to those of other forests(Table 4). Table 3. Applied characteristics in the k-nearest neighbor method
Operational options Growing stock map (continuous) Forest cover type map
Distance-weighting for neighbors Inversely proportional to the distance - Value of k 5 1
Table 4. Distribution of field plots for each stratum for mapping forest cover type
Stratum Forest Non-forest Total Coniferous (C) Deciduous (D) Mixed (M)*
Number of points 929 949 855 220 2953
Mixed forest*: 24-74 % of the sum of basal areas by deciduous tree species
D. Test Statistics In order to compare the estimated stratum mean and weight for different methods, two test-statistics; T-test and Chi-square test were conducted, respectively (Table 5). Table 5. Test-statistics used in this study.
T- test Chi-square test
DSEDt =* å -
=ected
ectedobservedexp
)exp( 22c
where D is the mean difference between stratum means of a pairs of two methods, DSE is the
standard error of D , the expected classes are the areas per each stratum from digital forest maps, while the observed classes are derived from the three methods. Results and Discussion The stratum weights and stratum means of growing stock for different methods were shown in Table 6. Overall, the variation in the estimated stratum mean and weight using the k-NN method was small while it was relatively large using the NFI-field data(Table 6). Table 6. Stratum weights and means derived from different methods.
County Forest strata
NFI-field data Calibration k-NN map Administrative unit Geographical unit Mean
(m3/ha) weight Mean
(m3/ha) weight Mean
(m3/ha) weight Mean
(m3/ha) weight
Muju C 198.7 0.27 172.6 0.33 162.5 0.29 143.7 0.29 D 98.5 0.38 99.6 0.39 105.6 0.41 126.3 0.44 M 131.8 0.35 122.4 0.28 125.8 0.30 135.1 0.26
Youngdong C 142.2 0.10 151.4 0.20 150.5 0.23 129.1 0.31 D 101.0 0.62 102.1 0.46 106.4 0.45 115.5 0.38 M 107.6 0.28 122.3 0.34 114.7 0.31 121.6 0.30
Kimcheon C 159.0 0.36 133.8 0.34 146.2 0.30 132.2 0.32 D 108.6 0.40 112.8 0.36 106.8 0.37 115.8 0.38 M 133.6 0.24 125.4 0.29 117.7 0.33 124.6 0.30
Table 7. The result of the T-test for pairs of different methods. Classification* n D DSE t- value Prob.< t
A 9 -4.30 4.75 -0.90 0.3933 B 9 -5.06 4.90 -1.03 0.3344 C 9 -4.15 8.41 -0.49 0.6369 D 9 -0.15 5.77 -0.03 0.9777 E 9 -0.91 4.94 -0.18 0.8581 F 9 0.75 2.56 0.29 0.7786
* A :Calibration (ADU) - Field data, B:Calibration (GEO) - field data, C:k-NN map - field data D:Calibration (ADU) - k-NN map, E:Calibration (GEO) - k-NN map, F:Calibration (ADU) - Calibration (GEO)
When paired of the estimated stratum mean from NFI-field data with those for the other methods (Classification A, B, and C), the mean difference was relatively high (-5.06 to -4.15m3/ha) (Table 7). In contrast, the other pairs (Classification D, E, and F) were lower than 1m3/ha. However, the estimated stratum means of growing stock by the different methods didn’t show statistically significant (Table 7). Table 8 shows the result of the Chi-square test for digital forest maps derived from aerial photo interpretation with different methods. It shows that the stratum weights for the calibration with geographical unit and the k-NN were close to the true strata. However, the difference between NFI-field data and digital forest maps was the highest that means that the stratum weights from field data only within a county could not provide reliable stratum weights.
Table 8. Chi-square result for the different methods with digital forest maps.
County NFI Calibration
k-NN map Administrative unit Geographical unit Muju 677.5 517.3 32.6 344.6
Conclusion This study shows a calibration method based on space-based geographical units is suitable to provide more reliable forest information in the context of small-areas than one with administrative units. The k-NN map also provides similar results to the calibration method based on geographical units. The 5th NFI has been implemented over a 5-year period that means the NFI system collects about 20% of the total samples over the entire country per year. When the total samples are collected, it is possible to provide more reliable and accurate forest information at a local level. Acknowledgement This work was carried out with the support of ‘Forest Science & Technology Projects (Project No. S120707L1101104) provided by Korea Forest Service. Literature Cited Korea Forest Research Institute (KFRI). 2008. The field manual for the 5th National Forest Inventory. Korea Forest Service (KFS). 2004. The reorganization of National Forest Inventory System by a change of demands of society and international process for forest resource III. 284p. (in Korean) Rao, J.N. 2003. Small area estimation. John Wiley & Sons, Inc. Tomppo, E., Olsson, H. Stahl, G., Nilsson, M., Hngner, O. and Katila, M. 2008. Combining national forest inventory field plots and remote sensing data for forest database. Remote Sensing of Environment 112:1982-1999.
