X-B8, 2012
Characteristics
Area: 20,000 km?
Altitude: 200-2,300m
JICA PAREDD
project site
Area: 4,400 km?
Altitude:400-2,000 m
10x10km test area
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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
adopted (IPCC(2003, 2006)). In this Project too, based on similar
approach, it was decided to prepare land use/cover maps upon
combining remote sensing technology with field surveys.
In Lao PDR, so far the FIPD has implemented National Forest
Inventory Surveys once every five years. Definition of forest had
changed recent year to (1) Tree height: 5m or higher, (2) Crown
density: 20% or more, and (3) Area: 0.5ha or greater. In this Project,
bearing in mind the land use categories (six main headings) required
for gauging forest carbon stock based on land use models in the IPCC
guidelines, etc., 11definitions and classification (level II forest
classification categories) suited to estimating of carbon stock due to
deforestation and forest degradation were established as shown in
Table 2, while taking consistency with existing forest survey maps
prepared by the FIPD in Lao PDR into account.
Land Use/Cover 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 use/cover category
3.13 Satellite Image Analyses for Land Use/Cover Mapping:
The necessary technical system was constructed out of the following
components: geometric correction and georeferencing, pre-processing,
multispectral analyses based on the ISODATA method, classification
and labelling of land use/cover categories, integration into land
use/cover maps, correction of classification results considering change
over time patterns and topographic information, supplementation based
on field survey and visual interpretation of images and so on.
LPB province was covered by two or more satellite imagery scenes.
Thus, land use/cover maps classified from individual scenes were
mosaiced to form a land use/cover map that covers the whole province.
A post-classification analysis was also performed to minimize
misclassifications due to seasonal changes or radiometric noises.
Finally, results from image interpretation and corrected land use/ cover
maps were merged into a final classification. Masked areas
representing cloud/cloud shadows from one time period were replaced
by land use/cover from another time period. Table 3 shows a list of
land use/cover maps in this study.
1993 | 1996 | 2000 | 2004 | 2007 | 2010
LPB o o
Pakxen o O O o
Khamk o o Oo
Phongx | o: LANDSAT/TM,SPOT, ©:AVNIR2 ©
Table 3 Land use/cover maps prepared in the Study
3.1.4 Forest Cover Changes and Reference Scenarios: Figure 2
show historical trends of forest cover changes of LPB province and
Pakxeng district. Forest cover changes in LPB indicate that “Current
Forest” areas decreased, while “Unstocked Forest” increased. À similar
historical trend is observed for Pakxeng district (LPB). However,
Current Forest and Unstocked Forest changes were greater from 2004
to 2007 in Pakxeng. Reference scenarios (REL) can be set up in order
to estimate the potential REDD+ credits.
(LPB province) (Pakxeng district)
Figure 5 Historical trends of forest cover changes and REL
3.15 Accuracy Analysis of Land Use/Cover Maps: Accuracy
analyses of the land use/cover maps were carried out three ways by
field survey, image interpretation higher resolution images
(pan-sharpened ALOS/AVNIR2 image), and interpretation and
measurement of tree heights using ALOS/PRISM stereo images in a
digital photogrammetric stereo plotter.
Overall accuracy of the land use/cover map of 2007 is 86 % at 4804
grid points for LPB province and 88 percent at 975 grid points for
Khamkeut district in BLK province. In terms of each individual
classification category, accuracy is generally between 80-90 percent.
As the third method of accuracy checking, ALOS/PRISM data was
used to assess the land use/cover maps. A total of 150 check points
(100 points for LPB province and 50 points for Khamkeut district
(BLK) were checked and the accuracy were 90% and 86%,
respectively, which is within the range of accuracies that can be
achieved with mid-resolution imagery (GOFC-GOLD, 2009). Since
the satellite images from 1993 and 2000 were also classified using this
method, it is thought that the land use/cover maps for LPB povince and
Khamkeut ditrict (BLK) are accurate too for the study.
3.2 Forest Biomass Classing
3.2.1 Objective and Method of Forest Biomass Classing: Forest
biomass classes (High, Medium, and Low) were devised for evaluating
in detail the distribution and changes of forest carbon stocks. Forest
biomass classing based on visual interpretation of ALOS/AVNIR2
images was carried out on 2 km and 1802 grid points that were
deemed to be Current Forest in the said images. In classing biomass,
the interpretation criteria indicated focusing on tree crown size, texture
and colour, etc. in the ALOS/AVNIR2 were configured based on the
ground truth data obtained from the field survey.
Then, biomass classing based on satellite image analysis was
conducted according to objects. This is done to conform with the
results of biomass class visual interpretation classified according to
objects (zones). For classifying objects, eCognition Developer was use.
Targeting the 2007SPOT and LANDSAT images of Current Forest
areas used to make the land use/cover maps of LPB province and
Khamkeut district (BLK) , segmentation (SP=10) by eCognition
