Environmental Science
Vol. 34, No. 6, pp. 461-471, December, 2018 https://doi.org/10.7747/JFES.2018.34.6.461
Evaluation of Suitable REDD+ Sites Based on Multiple-Criteria Decision Analysis (MCDA):
A Case Study of Myanmar
Jeongmook Park, Woodam Sim and Jungsoo Lee*
Department of Forest Management, Division of Forest Sciences, College of Forest and Environmental Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
Abstract
In this study, the deforestation and forest degradation areas have been obtained in Myanmar using a land cover lamp (LCM) and a tree cover map (TCM) to get the CO2 potential reduction and the strength of occurrence was evaluated by using the geostatistical technique. By applying a multiple criteria decision-making method to the regions having high strength of occurrence for the CO2 potential reduction for the deforestation and forest degradation areas, the priority was selected for candidate lands for REDD+ project. The areas of deforestation and forest degradation were 609,690ha and 43,515ha each from 2010 to 2015. By township, Mong Kung had the highest among the area of deforestation with 3,069ha while Thlangtlang had the highest in the area of forest degradation with 9,213 ha. The number of CO2
potential reduction hotspot areas among the deforestation areas was 15, taking up the CO2 potential reduction of 192,000 ton in average, which is 6 times higher than that of all target areas. Especially, the township of Hsipaw inside the Shan region had a CO2 potential reduction of about 772,000 tons, the largest reduction potential among the hotpot areas. There were many CO2 potential reduction hot spot areas among the forest degradation area in the eastern part of the target region and has the CO2 potential reduction of 1,164,000 tons, which was 27 times higher than that of the total area. AHP importance analysis showed that the topographic characteristic was 0.41 (0.40 for height from surface, 0.29 for the slope and 0.31 for the distance from water area) while the geographical characteristic was 0.59 (0.56 for the distance from road, 0.56 for the distance from settlement area and 0.19 for the distance from Capital).
Yawunghwe, Kalaw, and Hsi Hseng were selected as the preferred locations for the REDD+ candidate region for the deforestation area while Einme, Tiddim, and Falam were selected as the preferred locations for the forest degradation area.
Key Words: LULUCF, REDD+, spatial pattern analysis, AHP
Received: December 10, 2018. Revised: December 17, 2018. Accepted: December 17, 2018.
Corresponding author: Jungsoo Lee
Department of Forest Management, Division of Forest Sciences, College of Forest and Environmental Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
Tel: 82-33-250-8334, Fax: 82-33-259-5617, E-mail: [email protected]
Introduction
Due to the temperature rise around the world, a lot of natural disasters have occurred around the world, such as droughts, floods, urban heat islands, reduction in the gla- cier and other natural disasters, thus making climate change
the new top agenda. With the international community’s in- terests focusing on climate change, the Kyoto Protocol was adopted at the third COP (Conference of the Parties) in 1997. Forests are a unique source of absorbing carbon and storing it on Earth. As the forest absorbs or stores the car- bon dioxide, which takes up a large part of the greenhouse
gases, it plays a very important role in coping with the cli- mate changes (Korea Forest Service 2014). However, Article 3.3 of the Kyoto Protocol and Article 13 of the Determination in the Marrakesh Agreement made at the 7th meeting of COP in 2001 limited the application of LULUCF (Land Use, Land Use Change and Forestry) to afforestation and reforestation only as they are the target of CDM (Clean Development Mechanism) (Bae et al. 2013).
At the 11th COP, the REDD (Reducing from Deforestation and Forest Degradation in Developing Countries) was suggested by both Papua New Guinea and Costa Rica and that was developed by a lot of developing countries into the REDD+ which specifies not only the prevention of deforestation and forest degradation but also the preservation and increase of the forest carbon and sus- tainable forest management (Seok and Yoon 2010). At the 13th COP in 2007, the REDD+ was decided as the agen- da for post-2012 climate change agreement. As all member countries agree that in the Paris Agreement, the method- ology would be completed by 2015 and it would start in 2020, a lot of developing countries show a lot of expect- ations from the REDD+.
REDD+ has gotten the spotlight from around the world and each country is preparing for the REDD+
project. Germany has strengthened the payment of carbon emission right for the preservation of the forest and the re- duction in carbon through the REDD+ Early Movers program. It also provides countries that have prepared to inhibit climate change with financial support. In prepara- tion for the post-2020 new climate change system, Japan has developed JCM which is a GHG reduction mechanism and submitted it to UNFCCC and now continues to devel- op the JCM-REDD+ guideline (Park et al. 2016). Korea has conducted the REDD+ project along with Indonesia in 2012 and then with Cambodia, Myanmar and Laos. In addition, the Korea Forest Service has conducted a basic course on REDD++ to train 10 REDD+ experts to help them understand it better and grow their practical ability.
But compared to other advanced countries, Korea lags be- hind in terms of related study and policy.
On the other hand, for the conduct of the REDD+
project, a proper candidate land should be first selected in the target country. As for the studies on the selection of can- didate lands, Strassburg et al. (2013) have forecasted the
land changes up to 2050 by preparing a total index related to the economic factors of land and applying the effect of a protective area. Then, based on the results, they conducted the study for selecting the area where the deforestation or forest degradation occurs. Lin et al. (2012) conducted a study on the selection of candidate land for the REDD+
project based on biodiversity, poverty rate and carbon emission. In 2013, they conducted a study on the selection of candidate lands for the REDD+ project in Tanzania with consideration for carbon diversity, biodiversity, local community, opportunity cost of land and deforestation.
(Lin et al. 2013).
As for the domestic research cases, Tanaka (2014) set up and constructed the distance from a river which damages a forest, distance from road, distance from deforestation area and slope index in order to select the forest area having high probability of being damaged in the future as the candidate land for REDD+ (Shahid and Joshi 2017). In addition, she set the biodiversity based on the carbon storage and the topological diversity as the criteria for setting the land for REDD+ and constructed and applied the analyzed data to get the proper REDD lands by using the method of consid- ering the data’s accurateness. Bae and Seol (2012) esti- mated the qualitative carbon reduction potential from 2000 to 2010 and based on the result, selected the cooperation target countries and then selected the member countries of AFoCO, which is an internal forest organization or which are cooperative with the Korea Forest Service among the countries having high greenhouse gas reduction potentials as the first cooperation target countries.
However, the study on the selection of the candidate land for the REDD+ project mostly gets the deforestation and forest degradation areas and calculates the carbon storage volume. But there have been few studies which are applied with spatial pattern analysis or various factors to select the candidate lands. So, this study would calculate the CO2 po- tential reduction for the deforestation area and forest degra- dation area and then analyze the strength of occurrence us- ing the geostatistical technique. It also applies the multiple criteria decision-making process to select the candidate lands for the REDD+ project.
Table 1. Materials used in research
Category Data Resolution Ect
Remote sensing data Land cover map (LCM) 300 m European Space Agency 1992-2015 Year (Every year ) Tree cover map (TCM) 30 m USGS (2000, 2005, 2010, 2015 Year)
GIS data DEM 30 m USGS
Administrative boundary - GADM
Protected area - -
Road & River - Open Street Map
Fig. 1. Study area.
Materials and Methods
Study area
The Study area is located in Myanmar with the area of 67,659,000ha, where the area of forest takes up about 45%
as of 2010 (FAO 2015). Myanmar has 14 administrative regions made of 7 regions (tyne) and 7 states (pyi-ne). Each region or state has different tribes and in the case of region the Burmese tribe makes up the largest portion in most re- gion or state. In the case of state, most of the states are com- posed of minorities. The region and state are classified into 63 zones (Kayaing) and 286 township (Tawbship) (Fig. 1).
Study materials
The land cover map (LCM) and the tree cover map (TCM) were used as the spatial data to obtain the defor- estation and forest degradation while the DEM, road net- work, river network and administrative boundary were used for the construction of evaluation factors for the candi- date land (Table 1).
LMC is the processed data for the classification for the land cover and it supplies the annual time series data from 2000 to 2015 and the properties information of LCM were classified into 37 depending on the use of land. TCM was developed at ROS (U.S. Geological Survey Earth Resources Observation and Science Center) and Earth Explorer pro- vides 5-year time series data from 2000 to 2015 with the ap- proval of USGS (U.S. Geological Survey) while the TCM’s properties information is classified from 0 to 100 depending on the rate of trees which are 5m or taller.
Especially, the tree whose height is 5m or taller complies with the minimum forest area standard of FAO and Myanmar.
Method
The deforestation and forest degradation areas were ob- tained by using the LCM and the TCM which are time-series data for 5 years starting from 2000 to 2015 to choose the candidate lands for the REDD+ project in Myanmar. As for the deforestation and forest degradation areas, their CO2 potential reduction were obtained by using the CO2-eq coefficient of the forest emission (absorption) volume per ha of FREL of Myanmar (Forest Reference Emission Level). As for the CO2 potential reduction of the deforestation and forest degradation, the strength of occur- rence was analyzed using the geostatistic technique. After
Fig. 2. Social hazardous area and adjacent area.
selecting the evaluation factors affecting the deforestation and forest degradation, the priority of the candidate land for REDD+ project was selected by using the multiple criteria decision-making method. The selection of the candidate lands for REDD+ projects was targeted on all areas of Myanmar except the Social hazardous area which were de- cided according to the “list of the region from which evacu- ation is recommended” prepared by Ministry of Foreign Affairs. The candidate lands for the REDD+ project were selected for 178 townships after 40 townships targeted for evacuation recommendation and 48 nearby townships were deducted from 286 townships. That number takes up 45%
of the total area of Myanmar or 77,642,000 ha (Fig. 2).
Selecting the deforestation and forest degradation areas according to time series
The REDD project suggests that for the observation and trend analysis of the change in the forest, the change of the forest shall be observed for at least 10 years. Therefore, the deforestation and forest degradation areas were selected by using the 5-year time series data for 15 years among the LCM and the TCM data (American Carbon Registry
2010).
Deforestation means the long term or permanent loss of a forest and the criteria for deforestation area is an area where the forest is artificially changed to a non-forest area for at least 3 years (Verified Carbon Standard(VCS) 2017).
The deforestation area would be selected as follows:
LCM’s properties information is reclassified into the for- estland, cropland, grassland, wetland, settlement and other land and then the area which has changed from forestland to an cropland, grassland, wetland, settlement and other land was selected. The area which is converted to an crop- land and settlement would be the area where the local peo- ple currently reside. Although this area is hard to be used for carbon reduction such as planting or plant recovery, it is possible to do the project such as the preservation of bio- diversity, education on the reinforcement of the capability and prevention of degradation which belong to the positive aspects of the REDD+ project. So, it was included as the candidate land for REDD+ project (Park et al. 2017).
The forest degradation area is defined as the forest whose tree crown density or carbon volume is continually reduced due to grazing, fuel or tree cutting but which is kept as a forest. The area whose share of tree which is at least 5m tall of TCM among the LCM forest was reduced from 80% or more to below 80% was selected as the forest degradation area. (Bhagwat et al. 2017).
Obtaining CO2 potential reduction
CO2 potential reduction volume was estimated by apply- ing Myanmar’s FREL, which is obtained by multiplying the deforestation and forest degradation areas by CO2-eq coefficient of forest emission (absorption) volume per ha of FREL. The target of FREL is to provide the stakeholders with the information on the emission prospect based on a clear, transparent and consistent criteria for the evaluation of the management of the sustainable forest. It was pre- pared for the calculation of the potential carbon absorption volume in the forest and land use section.
Analysis of the spatial pattern of the CO2 potential reduction of the deforestation and forest degradation areas As for CO2 potential reduction of the areas of the defor- estation and forest degradation, the spatial pattern and the strength of occurrence were obtained through the analysis
Table 2. REDD+ candidate land factor
Category Factor Comments and assumptions
Topographic characteristic Elevation Higher elevations are more difficult to exploit and tend to be more remote from human populations
Slope Steep slopes are more difficult to access and exploit Distance from rivers Rivers provide access. Threat diminishes with distance Geographical characteristic Distance from roads Roads are a major feature providing access
Threat diminishes with distance Distance from settlements Settlements are associated with access Distance from Capital Capital are associated with access Tanaka. 2014; Fuller DO et al. 2010.
of hot spots. The hotspot in the hotspot analysis is the point which has a high value and which is surrounded by the points having high values. On the other hand, the cold spot is the point which has a low value and which is surrounded by the points having low values (Lee et al. 2011). The anal- ysis of hotspot can be made by Getis-Ord Gi*statistics. In Getis-Ord Gi*statistics, xj is the property value of spatial data j while wij is the spatial value of weight between the spatial data i and j. If the spatial data I and j are close, the spatial weight gets the value of 1. If not, it gets the value of 0. n is the number of data in the total space (Jo et al. 2017).
Selection of priority among the candidate lands for the REDD+ project using the multiple criteria decision- making technique
In this study, for the selection of the candidate lands for the REDD+ project, the evaluation factors which affect the REDD+ would be selected and then, the importance of the evaluation factors is obtained through the use of the AHP technique before selecting the priority for the candi- date lands for the REDD+ project. The evaluation factors for the candidate lands of the REDD+ project are classi- fied into the topographical and geographic characteristics for selection of 6 detailed evaluation factors (Table 2).
The importance of the evaluation factor was obtained by using the AHP technique, which is one of the methods of multiple criteria decision-making method. As AHP is the approach which is similar to human’s thinking system and can analyze the problem and structure it, it is recognized for its usefulness (Sin 2003).
The questionnaires were distributed to the experts for se-
lecting the candidate lands for the REDD+ project by us- ing the selected evaluation factors. The questionnaire was formed in a way that the relative importance can be ob- tained through pairwise comparison of each factor. The comparison of the evaluation factors is designed to evaluate how relatively important A is compared to B and an evalua- tion score ranging from1 to 7 was assigned (Kim et al.
2007). Based on the result of the questionnaire research, a pairwise comparison matrix was prepared to get the relative importance. In addition, the CR (Consistency Ratio) was used to measure the consistency of the result of the ques- tionnaire research.
The priority of the candidate lands for the REDD+
project was determined by using the value obtained from the standardization of 6 evaluation factors into the value ranging from 0 to 1 over the hot spot area for the CO2 po- tential reduction of the deforestation and forest degradation areas as well as the AHP technique (Fig. 3).
Results and Discussion
Areas of deforestation and forest degradation in Myanmar
The deforestation area in Myanmar according to time series is about 396,591 ha (1.1% of the total area) from 2000 to 2005 and about 169,650 ha (0.5% of the total areas) during 2005-2010 and 44,449ha (0.1% of total area) dur- ing 2010-2015, thus showing a gradual decrease. As for the share of land which was changed from forest to other lands, the rate of change to grass land was about 56% during 2000-2005, about 70% during 2005-2010 and about 80%
Fig. 3. Multi-criteria decision making (DCMA) algorithm.
during 2010-2015. As for the area of deforestation by town- ship, Pinlebu had about 49,362ha during 2000 to 2005, Hsipaw was about 21,516 ha during 2005 to 2010 and Mon Kung has about 3,069ha during 2010 to 2015. As for the rate of deforestation compared to the area of township for each township, Kyunhla had about 12.0% during 2005 to 2010, Mong Yai had 4.0% during 2010 to 2015 and Ho-Pong had about 1.4% during 2010 to 2015. The area and rate of deforestation during 2000 to 2015 have con- tinued to go down. The deforestation rate during 2000 to 2010 was high with Katha Banmauk Hsipaw or other town- ship located in the north. But in 2015, the deforestation rate in all areas was reduced.
The forest degradation area in Myanmar according to time series is about 61,675 ha (0.18% of the total area) dur- ing 2000 to 2005 and about 52,375 ha (0.15% of the total areas) during 2005 to 2010 and 43,515ha (0.15% of total area) during 2010 to 2015, thus showing the decrease of about 14% in 2015 compared to that in 2000. But. the for- est degradation rate was similar with the decrease of about 0.03%. As for the area of forest degradation by township, An had about 13,243ha during 2000 to 2005, Tozang had about 16,360 ha during 2005 to 2010 and Thlangtlang had about 9,213ha during 2010 to 2015. As for the forest deg- radation rate compared to the area of township for each township, as similar to the distribution of the deforestation, An had about 1.8% during 2005 to 2010, Tozang had about 3.7% during 2010 to 2015 and Thlangtlang had about 2.0% during 2010 to 2015 (Fig. 4).
Hotspot area of CO2 potential reduction volume for deforestation and forest degradation area in Myanmar
During 2000 to 2005, there were 17 townships having the CO2 potential reduction hotspots for deforestation area.
There were 13 townships having the hotspot’s significant probability of 99%, 3 townships having that of 95%, and 1 township having that of 90%. During 2005 to 2010, there were 3 townships having CO2 potential reduction hotspots for deforestation area. There were 2 townships having the hotspot’s significant probability of 99%, 1 township having that of 95%, and no township having that of 90%. During 2010 to 2015, there were 15 townships having the CO2 po- tential reduction hotspots for deforestation area. There were 7 townships having the hotspot’s significant probability of 99%, 6 townships having that of 95%, and 2 townships hav- ing that of 90%. As for the hotspot area of CO2 potential re- duction in the deforestation according to the time series from 2000 to 2015, Hsipaw township showed the highest reduction potential in all time series and there were several hotspots in the northern part of the target areas.
Accordingly, it is recommended that the REDD+ project for the deforestation area is to be implemented in Chin Sagaing Shan region which has the high CO2 potential reduction.
During 2000 to 2005, there were 13 townships having the CO2 potential reduction hotspots for forest degradation area. There were 11 townships having the hotspot’s sig- nificant probability of 99%, 1 township having that of 95%, and 1 township having that of 90%. During 2005 to 2010,
Fig. 4. Distribution of degraded forest and deforestation area by time series.
there were 4 townships having the CO2 potential reduction hotspots for forest degradation area. There were 2 town- ships having the hotspot’s significant probability of 99%, 1 township having that of 95%, and 1 township having that of 90%. During 2010 to 2015, there were 6 townships having the CO2 potential reduction hotspots for forest degradation area. There were 5 townships having the hotspot’s sig- nificant probability of 99%, 1 township having that of 95%, and no townships having that of 90%. As for the hotspot area of CO2 potential reduction in the forest degradation area according to the time series from 2000 to 2015, Tonzang and Hsipaw townships showed the high reduction potential and there were several hotspots in the eastern part of the target areas. Accordingly, it is recommended that the REDD+ project for the forest degradation area is to be im- plemented in Chin ․ Magway ․ Rakhine ․ Sagaing region which has the high CO2 potential reduction (Fig. 5).
Determination of priority among the candidate lands for the REDD+ project
Calculation of AHP importance degree
27 questionnaires were distributed for the evaluation of candidate land for the REDD+ project by using the AHP technique. The experts participating in the research in- cluded 15 graduate students, 6 employees of companies, 3 people from the National Forestry Cooperation Federation, 2 people from the Korea Forestry Promotion Institute and 1 from the Korea Forest Service and the response rate was 100%. In this study, as 26 responses among 27 responses were judged to have the CR value of 0.1 or below, 26 re- sponses were used to get the relative importance rate by us- ing the geometric mean. The weights of the main factors af- fecting the candidate lands for REDD+ project are as fol- lows: 0.59 for geographical characteristics, and 0.41 for topographical characteristics; Among the geographical
Fig. 5. Hot spot area disrtibution of CO2 potential reduction.
Table 3. Calculation Factor weights
Category Category
weights Factor Factor
weights Topographic
characteristic
0.41 Elevation 0.40
Slope 0.29
Distance from rivers 0.31 Geographical
characteristic
0.59 Distance from roads 0.56 Distance from settlements 0.25 Distance from Capital 0.19
characteristics, 0.56 for the distance to the road, 0.25 for distance to settlement, 0.19 for the distance to the Capital;
as for the topographical characteristics, 0.40 for height, 0.31 for water area, and 0.29 for slope (Table 3).
Evaluation of candidate land for REDD+ project The evaluation map for the deforestation area was ana- lyzed by reflecting the weights over the value for each factor
for the hotspot areas. The number of hotspot areas by town- ship was 17 during 2000 to 2005, 3 during 2005 to 2010 and 15 during 2010 to 2015. The townships selected as hot- spots for all 3 time-series were Mong Yai and Hsipaw.
Especially, Hsipaw was evaluated to get 90, 100 and 77 points depending on the time series and has the highest pri- ority among the candidate lands. Finally, the top priority was put on Kanbalu ․ Kale ․ Katha during 2000 to 2005, Hsipaw ․ Indaw ․ Mong Yai during 2005 to 2010, and Yawunghwe ․ Kalaw ․ Hsi Hseng during 2010 to 2015.
Most of the areas selected as the candidate land for the REDD+ project for the deforestation for each period were close to the road and settlement so that they got good evalu- ation scores. Especially, the distance to the road and the dis- tance to settlement were likely to be the key factors because the geographic characteristics make more effect on the im- portance degree than the topographical characteristics.
As for the evaluation map for the forest degradation area,
Fig. 6. REDD+ candidate land priority.
the weight was reflected on the value of each factor for the hotspot area. The number of hotspot areas by township for each period was 13 during 2000 to 2005, 4 during 2005 to 2010 and 6 during 2010 to 2015. The townships selected as hotspots for all 3 time-series were Tiddim, and Tonzang.
Especially, Tiddim was evaluated to get 45, 93 and 56 points depending on the time series and has the highest pri- ority among the candidate lands. Finally, the top priority was put on Tamu ․ Sidoktaya ․ Kalewa during 2000 to 2005, Haka ․ Tiddim ․ Tonzang during 2005 to 2010, and Kale ․ Tiddim ․ Falam during 2010 to 2015. Most of the areas as the candidate land for the REDD+ project for the forest degradation area for each period were selected due to their closeness to the road and settlement in the same way as for the deforestation area so that they got good evaluation scores (Fig. 6).
Conclusion
In this study, CO2 potential reduction for deforestation and forest degradation was calculated and then by using the hotspot method, the spatial pattern of the CO2 potential re- duction was analyzed. Then, by applying the multiple cri- teria decision-making method, the candidate land for the REDD+ project was evaluated.
The selection of forestation and forest degradation by us- ing the LCM and the TCM showed that the area of defor- estation in Myanmar had gradually decreased from 2000 to 2015 and especially, the reduced deforestation area was 43,449ha during 2010 to 2015, which was 1/9 of the area reduced during 2000 to 2005. As for the forest degradation area, it had decreased from 2000 to 2015 but the average forest degradation rate remained at about 0.17%. For the selected deforestation and forest degradation areas, their CO2 potential reduction volumes were calculated by using
the FREL coefficient of Myanmar. The analysis of hotspot according to the CO2 potential reduction in deforestation and forest degradation areas showed that there were many hotspot areas for the CO2 potential reduction in the defor- estation area in the northern part of Myanmar for all 3 time-series and they particularly was concentrated at Chin ․ Sagaing ․ Shan region. There were many hotspot areas for the CO2 potential reduction among the forest degradation area in the eastern part of Myanmar and particularly were concentrated at Chin ․ Magway ․ Rakhine․Sagaing region, which is considered to be the preferred place for the REDD+ project.
AHP technique was applied to the hotspot areas of CO2
potential reduction for deforestation and forest degradation areas to reflect the weight of the main factors affecting the REDD+. It was found that as for the deforestation area, Hsipaw township showed the highest score for all 3 time-series while, for forest degradation, Tiddim township showed the high weight for all 3 time-series. This result may be caused by the fact that the geographical character- istics is more important than the topographical characteristics.
It is recommended that Shan region which is located in Hsipaw township becomes the candidate land for the REDD+ project for the deforestation area in Myanmar and Chin area where Tiddim is located becomes the candi- date land for the REDD+ project for the forest degrada- tion area, as they both have high CO2 potential reduction for deforestation and forest degradation areas and they are close to the roads and settlements.
By applying the time-series satellite image information to the research results, the deforestation and forest degrada- tion area in Myanmar can be acquired. By estimating the CO2 potential reduction which can be the ground for the candidate land for the REDD+ project and conducting the analysis of hotspot, the spatial distribution pattern can be understood for each township. In addition, the candidate land for the REDD+ project can be evaluated by applying multiple criteria decision-making method with consid- eration for the topographical and geographical character- istics of the CO2 potential reduction hotspot area for defor- estation and forest degradation. It is hoped that these re- search results are used as the basic data for the REDD+
project in Myanmar.
Acknowledgments
This study was carried out with the support of ‘R&D Program for Forest Science Technology (Project No.
“2018112C10-1820-BB01”)’ provided by Korea Forest Service (Korea Forestry Promotion Institute).
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