adopted IPCC2003, 2006. In this Project to approach, it was decided to prepare land u
combining remote sensing technology with field s In Lao PDR, so far the FIPD has implemen
Inventory Surveys once every five years. Def changed recent year to 1 Tree height: 5m o
density: 20 or more, and 3 Area: 0.5ha or g bearing in mind the land use categories six ma
for gauging forest carbon stock based on land us guidelines, etc., 11definitions and classifica
classification categories suited to estimating of deforestation and forest degradation were esta
Table 2, while taking consistency with existing prepared by the FIPD in Lao PDR into account.
Land UseCover Category 1. Current Forest
7. Plantation 2 2. Plantation 1
8. Grassland 3. Unstocked Forest
9. Others 4. Bamboo
10. Water 5. Ray
11. Cloud 6. Crop Land
Table 2 Land usecover category
3.1.3 Satellite Image Analyses for Land U
The necessary technical system was constructed components: geometric correction and georeferen
multispectral analyses based on the ISODATA m and labelling of land usecover categories, i
usecover maps, correction of classification resul over time patterns and topographic information, s
on field survey and visual interpretation of images LPB province was covered by two or more sate
Thus, land usecover maps classified from ind mosaiced to form a land usecover map that cover
A post-classification analysis was also perf misclassifications due to seasonal changes or
Finally, results from image interpretation and cor maps were merged into a final classificat
representing cloudcloud shadows from one time by land usecover from another time period. Ta
land usecover maps in this study. 1993
1996 2000
200 LPB
Pakxen Khamk
Phongx : LANDSATTM,SPOT,
◎: Table 3 Land usecover maps prepared
3.1.4 Forest Cover Changes and Reference S
show historical trends of forest cover changes o Pakxeng district. Forest cover changes in LPB i
t too, based on similar usecover maps upon
ld surveys.
ented National Forest efinition of forest had
or higher, 2 Crown greater. In this Project,
ain headings required use models in the IPCC
cation level II forest of carbon stock due to
stablished as shown in ing forest survey maps
UseCover Mapping:
ted out of the following rencing, pre-processing,
method, classification , integration into land
sults considering change , supplementation based
ges and so on. satellite imagery scenes.
individual scenes were vers the whole province.
erformed to minimize or radiometric noises.
orrected land use cover cation. Masked areas
e period were replaced Table 3 shows a list of
004 2007
2010
:AVNIR2 ◎
ed in the Study
e Scenarios: Figure 2
of LPB province and B indicate that “Current
Forest” areas decreased, while “Unsto historical trend is observed for Pak
Current Forest and Unstocked Forest to 2007 in Pakxeng. Reference scenar
to estimate the potential REDD+ credi
LPB province Pa Figure 5 Historical trends of forest cov
3.1.5 Accuracy Analysis of Land
analyses of the land usecover maps field survey, image interpretatio
pan-sharpened ALOSAVNIR2 im measurement of tree heights using A
digital photogrammetric stereo plotter. Overall accuracy of the land usecove
grid points for LPB province and 88 Khamkeut district in BLK provinc
classification category, accuracy is ge As the third method of accuracy che
used to assess the land usecover ma 100 points for LPB province and 5
BLK were checked and the ac respectively, which is within the ra
achieved with mid-resolution imager the satellite images from 1993 and 20
method, it is thought that the land use Khamkeut ditrict BLK are a
3.2 Forest Biomass Classing 3.2.1 Objective and Method of For
biomass classes High, Medium, and L in detail the distribution and changes
biomass classing based on visual in images was carried out on 2 km a
deemed to be Current Forest in the s the interpretation criteria indicated foc
and colour, etc. in the ALOSAVNIR ground truth data obtained from the fie
Then, biomass classing based on conducted according to objects. Thi
results of biomass class visual interp objects zones. For classifying objects
Targeting the 2007SPOT and LAND areas used to make the land usecov
Khamkeut district BLK , segmen
Ray Current Forest
Unstocked F.
stocked Forest” increased. A similar akxeng district LPB. However,
st changes were greater from 2004 narios REL can be set up in order
edits.
Pakxeng district over changes and REL
nd UseCover Maps: Accuracy
ps were carried out three ways by tion higher resolution images
image, and interpretation and ALOSPRISM stereo images in a
ter.
over map of 2007 is 86 at 4804 88 percent at 975 grid points for
nce. In terms of each individual generally between 80~90 percent.
checking, ALOSPRISM data was maps. A total of 150 check points
d 50 points for Khamkeut district accuracy were 90 and 86,
range of accuracies that can be ery GOFC-GOLD, 2009. Since
2000 were also classified using this secover maps for LPB povince and
accurate too for the study.
orest Biomass Classing: Forest
d Low were devised for evaluating ges of forest carbon stocks. Forest
interpretation of ALOSAVNIR2 and 1802 grid points that were
e said images. In classing biomass, focusing on tree crown size, texture
IR2 were configured based on the field survey.
on satellite image analysis was his is done to conform with the
terpretation classified according to cts, eCognition Developer was use.
NDSAT images of Current Forest cover maps of LPB province and
entation SP=10 by eCognition
Current F. Reference
Credits Ray
Unstocked F.
XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australia
421
Legend Legend
Legend Legend
High Medium
Low
Legend Legend
Legend Legend
High Medium
Low
Developer was carried out. Next, training data for each biomass class were prepared in reference to the results of visual interpretation, and
the biomass classes were classified by the maximum-likelihood method based on the training data. The results of LPB and BLK are
shown in Figure 2 and 3, respectively.
Figure 2. Biomass classing results 2007, LPB province
Year Area 1993 ha
2000ha 2007ha
Bio- mass
class H
76,414 58,196
49,541 M
136,412 142,817
132,875 L
76,820 65,963
71,074 Total
289,646 266,976
253,490 Figure 3. Biomass classing results 1993,2000, and 2007, Khamkeut
district BLK based on 491 visual interpretation data Overall accuracy of matching of biomass classification was
approximately 60 in both LPB province and Khamkeut district. Targeting Khamkeut district where the accuracy of each biomass class
was relatively high, biomass classing was implemented on Current Forest from two past periods 1993 and 2000 utilizing same method
that was used in 2007. The results of biomass classing in 2007 were referred to as training data for the maximum-likelihood method. Figure
3 shows the results of Current Forest biomass classing from three periods, and statistics of forest covers and biomass changes over time.
The high biomass area decreased including transition to the lower class due deforestation and forest degradation.
The results of the biomass classing were used to evaluate wall-to-wall above-ground forest carbon stocks as discussed in Section 4.
4. ESTIMATION OF FOREST CARBON STOCK 4.1 Tier Levels for Forest Carbon Stock Estimation
