Draft...Draft 1 1 Quantifying forest loss and forest degradation in Myanmar’s “Home of 2 Teak” 3 Thu Ya Kyaw, René H. Germain, Stephen V. Stehman, and Lindi J. Quackenbush 4

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Quantifying forest loss and forest degradation in Myanmar's "Home of Teak"

Journal: Canadian Journal of Forest Research

Manuscript ID cjfr-2018-0508.R2

Manuscript Type: Article

Date Submitted by the Author: 31-Oct-2019

Complete List of Authors: Kyaw, Thu Ya; SUNY College of Environmental Science and Forestry, Department of Forest and Natural Resources ManagementGermain, Rene; SUNY College of Environmental Science and Forestry, Department of Forest and Natural Resources ManagementStehman, Stephen; SUNY College of Environmental Science and Forestry, Department of Forest and Natural Resources ManagementQuackenbush, Lindi; SUNY College of Environmental Science and Forestry, Department of Environmental Resources Engineering

Keyword: Bago Mountain Range, land cover, Landsat, training data, bamboo-dominated degraded forests

Is the invited manuscript for consideration in a Special

Issue? :Not applicable (regular submission)

https://mc06.manuscriptcentral.com/cjfr-pubs

Canadian Journal of Forest Research

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1 Quantifying forest loss and forest degradation in Myanmar’s “Home of

2 Teak”

3 Thu Ya Kyaw, René H. Germain, Stephen V. Stehman, and Lindi J. Quackenbush

4 Thu Ya Kyaw, R. Germain, and S. Stehman. Department of Forest and Natural Resources

5 Management, State University of New York College of Environmental Science and Forestry, USA

6 L. Quackenbush. Department of Environmental Resources Engineering, State University of New York

7 College of Environmental Science and Forestry, USA

8 Corresponding author. Thu Ya Kyaw (email: [email protected]).

9 Abstract: The Bago Mountain Range in Myanmar is known as the “Home of Teak” (Tectona

10 grandis Linn. f.) because of its bountiful, naturally growing teak-bearing forests. Accelerating

11 forest loss and degradation are threatening the sustainable production of teak in the region.

12 Changes in land cover between 2000 and 2017 in four reserve forests of the Bago Mountain

13 Range were mapped using supervised classification of Landsat imagery and training data

14 collected in the field. A stratified random sample was used to collect reference data to assess

15 accuracy of the maps and to estimate area. Based on the reference sample, forests declined from

16 an estimated 71,240 ha (1,524 SE) in 2000 to 40,891 ha (4,404 SE) in 2017, while degraded

17 forests increased from 88,797 ha (1,694 SE) to 97,013 ha (5,395 SE). The annualized gross forest

18 loss rate was 1.03% and annualized gross forest degradation rate was 0.97%, indicating forest

19 degradation paralleled forest loss. In many degraded areas, there is an opportunity to ameliorate

20 the situation through silviculture. The 2017 map identifies bamboo-dominated degraded forests

21 where enrichment planting or reforestation is recommended.

22 Key words: Bago Mountain Range, land cover, Landsat, training data, bamboo-dominated

23 degraded forests.

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24 Introduction

25 The forests of Myanmar are under increasing pressure due to high resource consumption

26 associated with population growth and timber demand from adjacent countries (Brunner et al.

27 1998; Laurance 2007; Mon et al. 2012b), resulting in a decline in both the quantity and quality of

28 forest resources (Htun et al. 2009). During the period of 1990–2000, the country’s foreign

29 earnings were highly dependent on the export of teak (Tectona grandis) and other marketable

30 hardwood species to support the country’s economy (Mon et al. 2012b; Win et al. 2012b).

31 Although the country has a long history of forest management (Win et al. 2009) and still

32 maintains a considerable amount of forested area in Southeast Asia, forest cover sharply

33 decreased from 58% in 1990 to 43% in 2015 (FAO 2015). As a consequence, Myanmar was

34 declared the third most deforested country in the world (FAO 2015). However, Brancalion et al.

35 (2019) listed Myanmar as one of the top 10 hotspot countries critical for forest conservation and

36 restoration, so Myanmar has great potential for rehabilitation of forest area.

37 In 2016, the Forest Department of Myanmar developed the Myanmar Reforestation and

38 Rehabilitation Program (MRRP) with a primary goal to implement forest rehabilitation activities

39 across the nation through 2026. Coupled with the MRRP, the government legislated a nation-

40 wide one-year logging ban (Shimizu et al. 2017). The logging ban was extended to a decade for

41 the timber rich Bago Mountain Range, a region representing 11.3% of all teak-bearing forests in

42 the country (Zin 2000), and recognized as the prestigious “Home of Teak” due to its high-density

43 teak forests (Chan et al. 2013, 2016; Maung and Yamamoto 2008; Mon et al. 2010, 2012a; Win

44 et al. 2009, 2012a, 2012b). Teak is among the world’s highest quality and most sought after

45 hardwood timber species (Mon et al. 2012a; Palanisamy and Subramanian 2001; Pandey and

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46 Brown 2000). Given the high global demand, teak was the most harvested species in the Bago

47 Mountain Range (Win et al. 2012b).

48 The Bago Mountain Range consists primarily of mixed deciduous forests, which is the

49 major forest type contributing to commercial timber production in Myanmar (Mon et al. 2010;

50 Shimizu et al. 2017). The region supports teak as a principal species, along with other

51 commercial hardwood species such as pyinkado (Xylia xylocarpa), padauk (Terocarpus

52 macrocarpus), taukkyan (Terminalia tomentosa) and Dipterocarpus species (Khai et al. 2016;

53 Mon et al. 2010; Shimizu et al. 2016; Thein et al. 2007). Bamboo species such as Bambusa

54 polymorpha and Cephalostachyum pergracile are also associated with teak forests; however,

55 when the forest stocking is characterized by high percentages of sympodial bamboo, it is an

56 indicator of a degraded forest condition (Larpkern et al. 2009; Khai et al. 2016). FAO (2001)

57 defined forest degradation as “changes within the forest which negatively affect the structure or

58 function of the stand or site, and thereby lower the capacity to supply products and/or services”.

59 The Millennium Ecosystem Assessment (2005) defines a degraded forest as a forest as long as it

60 is not completely converted into another land cover type, but once degraded, the ability of these

61 forests to provide ecosystem services is lost or devalued due to substantial changes in species

62 composition resulting from exploitation, invasive species, pollution, fires, or other influencing

63 factors.

64 The Myanmar Selection System (MSS) has been used to manage natural teak forests in

65 the Bago Mountain Range since 1856 (Gyi and Tint 1995; Mon et al. 2012b; Shimizu et al. 2017;

66 Win et al. 2009, 2012a). The MSS is a selective logging protocol consisting of felling the most

67 valuable and desirable timber species in natural forests while maintaining the residual stands

68 until the next cutting rotation (Bawa and Seidler 1998; Win et al. 2009). According to the MSS,

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69 marketable trees beyond the pre-determined diameter limit are selectively cut on a 30-year

70 felling cycle. The annual allowable cut (AAC) is estimated from periodic timber inventories (Gyi

71 and Tint 1995; Mon et al. 2012b; Shimizu et al. 2017; Win et al. 2009, 2012a, 2012b).

72 Within Myanmar, the MSS is regarded as a proven means of ensuring the sustained yield

73 of timber; however, recent practices in the Bago Mountain Range ignored the MSS protocol.

74 Heavy reliance on foreign income from timber exports served as a catalyst to shorten the felling

75 cycle and abandon the AAC (Brunner et al. 1998; Htun 2009; Mon et al. 2012b). Historic

76 logging records indicated that repeated timber harvesting at shorter intervals occurred in the

77 forests of the Bago Mountain Range, confirming over-exploitation, which violated the MSS rule

78 of a 30-year felling cycle and led to unsustainable harvesting practices that have resulted in

79 severely degraded, bamboo-dominated forests (Khai et al. 2016; Mon et al. 2012a, 2012b). The

80 end result is that the Bago Mountain Range has become a region in crisis, requiring expeditious

81 forest restoration and rehabilitation. Although this crisis requires immediate attention from forest

82 managers and decision makers, access to high quality forest cover data is required before

83 implementation of an action plan.

84 Time series satellite images over extensive areas can help resource managers interpret

85 and understand historical disturbances that affect forest ecosystems (Romijn et al. 2012, 2015;

86 Shimizu et al. 2016). Remote sensing is a widely recognized technology to mass produce timely

87 information for forest management (Win et al. 2012b), and it is most effective when results

88 represent realistic ground conditions from a practical standpoint. Consequently, in this study, a

89 combination of ground training data collection (i.e., recording the spatial coordinates and

90 corresponding land cover features using a global positioning system (GPS) receiver), geographic

91 information system (GIS), remote sensing data and software, and interviews with local people

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92 and forestry experts in the area were used to quantify and interpret land and forest cover changes

93 in the Bago Mountain Range.

94 There have been several studies documenting deforestation in the Bago Mountain Range,

95 but there is little information available to forest managers and planners on the spatial and

96 temporal patterns of forest degradation. Providing high quality land cover maps documenting not

97 only forest loss, but also forest degradation, will help managers prioritize reforestation and

98 rehabilitation efforts in the coming decades. Specifically, this study provides area estimates as

99 well as spatial information about forest loss and degradation between 2000 and 2017 to support

100 sustainable forest management in the Bago Mountain Range. Supporting that primary goal are

101 the following objectives:

102 (1) To quantify land cover changes between 2000 and 2017,

103 (2) To describe the annualized rates of forest loss and forest degradation between 2000 and

104 2017,

105 (3) To spatially identify areas with forest loss, gain, and degradation.

106 These objectives address the question of what change processes led to the current status

107 of land cover in the “Home of Teak” and quantify how much area is potentially available for

108 rehabilitation. The estimates from the reference sample provide the areas of land cover and

109 change in aggregate, and the maps provide spatially explicit details to facilitate forest

110 management.

111 Materials and methods

112 Study area

113 The study area consists of four reserved forests: Baing Dar, Kawliya, Shwe Laung Ko Du

114 Gwe, and South Zamayi, situated on the eastern portion of the Bago Mountain Range, Myanmar

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115 (17° 37' - 18° 08' N, 96° 00' - 96° 31' E; Fig. 1). Located in three townships of the Bago District,

116 namely, Kyauktaga, Bago and Daik-U, the study site has a total area of approximately 175,971

117 hectares (ha). The elevation ranges from 26–734 m above sea level. Due to its monsoon climate,

118 three distinct seasons are found: a rainy season from the end of May to November with rainfall

119 intensity peaking in July and August, a winter dry season from December to the end of January,

120 and a hot dry season from February to May (Win et al. 2009, 2012b; Zin 2000). The average

121 annual rainfall varies between 2,520 mm and 3,793 mm, with a temperature range of 13.7°–27.4°

122 C (Bago District Working Plan 2016).

123 Ground training data collection

124 The training data used to produce the supervised classification were collected in the field

125 during May and June of 2017. Spatial coordinates and associated land cover types were recorded

126 with a GPS receiver for each location visited (Fig. 2). Land cover types were categorized into

127 five classes: 1) forest, 2) degraded forest, 3) other wooded land, 4) other land and 5) water (Table

128 1). We recorded a total of 181 points: 36 for forest, 73 for degraded forest, 51 for other wooded

129 land and 21 for other land. The number of field points was influenced by road access due to

130 weather constraints during the collection time. Since water was easily recognizable in the

131 satellite imagery, we did not record water locations in the field. The training points were

132 established in the middle of homogeneous regions so that the associated land cover types could

133 be easily identified during satellite image interpretation. Photos were taken at each location to

134 improve the understanding of specific land cover types (Fig. 3).

135 GPS was the primary tool for indicating and recording the locations. In addition, Google

136 Maps version 4.31.1 (Google Inc.) installed on an iPhone was used to learn land cover types

137 while in the field. The location was recorded with the GPS receiver, but the advantage of using

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138 Google Maps was that the color tones in the base image associated with an underlying land cover

139 type could be determined in order to expand training areas. Experienced remote sensing and GIS

140 personnel from the Myanmar Forest Department were consulted prior to the fieldwork to

141 reconnoiter promising field locations for collecting training locations. Landsat and high-

142 resolution imagery in Google Earth Pro were examined to identify locations of different land

143 cover types to organize the fieldwork more efficiently. Moreover, the inputs of local foresters

144 provided preliminary information about the land cover conditions of the study area.

145 Data processing and supervised classification

146 Landsat images, available at the United States Geological Survey (USGS) website, were

147 employed in this study. The study area completely fell within one Landsat scene (path 132 and

148 row 48). Two images were used in the study, a Landsat 5 TM Collection 1 Level 1 image,

149 acquired on December 17, 2000, and a Landsat 8 OLI Collection 1 Level-1 image, acquired on

150 January 30, 2017. All data used WGS 1984, Universal Transverse Mercator (UTM) coordinates

151 in zone 47 N. UTM, which is the official coordinate system of the Forest Department of

152 Myanmar, was consistently applied throughout this study.

153 We considered year 2017 as the end date to provide current land cover information. Year

154 2000 was taken as the start date because one of the major causes of land use change in the study

155 area was dam construction, most of which started in the early 2000s. Additionally, highway

156 construction completed during the 2000–2017 period provided improved access to the study

157 area, facilitating settlement encroachment and illicit cutting.

158 For both 2000 and 2017 images, the five land cover types described in Table 1 were

159 mapped using supervised classification (Fig. 4) by implementing the maximum likelihood

160 classification algorithm with ERDAS Imagine 2016 (Hexagon Geospatial). As pre-processing

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161 steps, bands 1 to 7 from the 2017 Landsat 8 image were stacked and similarly, bands 1 to 5 and 7

162 from the 2000 Landsat 5 image were stacked. Then, the images were clipped to the extent of the

163 study area.

164 Based on the land cover information obtained from the ground training data points, we

165 selected 140 training locations for the 2017 land cover map: 27 for forest, 54 for degraded forest,

166 16 for other wooded land, 14 for other land and 29 for water to generate a classified land cover

167 map for 2017. Training locations were constrained to fall within polygons of homogeneous land

168 cover. More training locations were selected for bamboo-dominated degraded forests due to its

169 pervasiveness in the study area and its importance to the mapping objectives. The “other wooded

170 land” class occupied a small proportion of the study region and “other land” was readily

171 distinguishable spectrally from other classes. Therefore, fewer training locations were selected

172 for “other wooded land” and “other land”. Water was found to have variable spectral response,

173 so more training locations were designated for water.

174 To produce the year 2000 land cover map, visual interpretation of the imagery based on

175 spectrally similar areas and the classified 2017 land cover map were used to guide selection of

176 training locations because historical ancillary data that could help interpret the 2000 image were

177 not available. We selected 123 training locations: 32 for forest, 15 for degraded forest, 12 for

178 other wooded land, 32 for other land and 32 for water. The reason for decreasing the total

179 number of training locations for 2000 (versus 2017) was a less fragmented landscape. Producing

180 the 2000 land cover map was less challenging than producing the 2017 map because disturbances

181 such as infrastructure development and forest degradation were much less prevalent in 2000. The

182 initial supervised classification had a salt-and-pepper appearance due to the inherent variability

183 of the spectral characteristics (Bischof et al. 1992). Thus, smoothing of the initial classification

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184 was implemented using the neighborhood tool available in ERDAS Imagine with a 3×3 majority

185 filter applied as the focal kernel.

186 Accuracy assessment and area estimation

187 Stratified random sampling (Stehman and Foody 2009) was implemented to collect

188 reference data to evaluate the accuracy of the 2000 and 2017 land cover maps and the 2000–2017

189 change map. A stratified random sample was selected using the year 2000 land cover map to

190 define the strata. The same sample locations were used to address all three accuracy assessment

191 objectives: accuracy of the maps for each year (2000 and 2017) and accuracy of the change map.

192 Typically for accuracy assessment of change, it is necessary to define strata using map changes

193 to increase the sample size because change is often a rare phenomenon (Olofsson et al. 2014). In

194 our study, over 50% of the year 2000 forest stratum was mapped as changed by 2017 so there

195 was no concern that the sample size of the critical type of change, forest loss, would be

196 inadequate. The sample of 370 pixels was selected using the land cover types of the year 2000

197 map as the strata. Forest and degraded forest represented the highest proportion of the area, so

198 the largest sample size of 100 pixels was allocated to each of those strata. The other wooded

199 land stratum was allocated 70 pixels, and 50 pixels were allocated to each of the other land and

200 water strata. The “create random points” tool in ArcMap 10.4.1 (Esri; Redlands, CA) was used to

201 generate the sample points for each class.

202 The sample points were then exported into Google Earth Pro (accessed 20 June 2017) and

203 the reference class (i.e., “ground condition”) of each sample point was then determined visually.

204 The reference interpretation was conducted with the interpreter “blind” to knowledge of the map

205 labels of the sample pixels. The first author interpreted the reference class of all sample pixels by

206 examining the reference imagery and relying on field experience gained while collecting the

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207 ground training data used for the supervised classification. High resolution images, e.g., from

208 CNES/ Airbus and Digital Globe, were available in Google Earth for all sample points in 2017,

209 but only Landsat imagery was available in 2000. In addition to shape, size, color and textures

210 observed in the imagery, surrounding features (such as roads, dams and agriculture) and

211 topographic characteristics (such as ridges, valleys and watersheds) were carefully observed,

212 especially for edge pixels, to determine the reference land cover classes. To clearly see

213 topographic variation, terrain mode in Google Earth Pro was activated. Recent plantations with

214 some exposed soils, crown forms and canopy structures of both trees and bamboos could be

215 detected, especially for 2017 with access to high-resolution imagery. With respect to

216 differentiating between the five land cover classes, the specific criteria for interpreting the

217 Google Earth imagery are detailed in Table 2.

218 The error matrix, estimates of accuracy, estimates of area (based on the reference

219 classification), and estimated standard errors were produced from the reference sample data.

220 Two versions of change accuracy were assessed: 1) change in land cover (with the “Other land”

221 and “Water” categories combined to reduce the size of the change error matrix), and 2) change as

222 represented by a 4-class legend—forest gain, forest loss, stable forest, and non-forest—where the

223 forest class included forest and degraded forest. For the year 2000 estimates, the estimation

224 formulas provided by Olofsson et al. (2014) or Stehman and Foody (2009) can be applied

225 because the strata used in the sampling design correspond to the map classes. For the year 2017

226 estimates and the change estimates, the strata used in the sampling design are not the same as the

227 map classes, so the indicator variable method described in Stehman (2014) was used to produce

228 the estimates. Standard errors (SEs) can be used to construct confidence intervals for parameters

229 of interest by taking the estimate and adding and subtracting 1.645*SE or 1.96*SE to produce a

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230 90% or 95% confidence interval. The estimation formulas were implemented in the Statistical

231 Analysis System (SAS version 9.3, Cary, North Carolina, USA).

232 Estimates of area of net forest loss (i.e., area that changed from forest to non-forest) and

233 net forest gain (i.e., area that changed from non-forest to forest) were also produced from both

234 the map and reference sample data. For the map-based approach, the net change of a given land

235 cover type is simply the difference in the area mapped of that class in 2000 versus the area

236 mapped in 2017. For the estimates based on the reference data used in the accuracy assessment,

237 we defined the variable y such that y=1 if the sample pixel changed from non-forest in 2000 to

238 forest in 2017, y=-1 if the pixel changed from forest to non-forest, and y=0 otherwise. We then

239 applied stratified random sampling formulas to estimate the total number of pixels of net change

240 (i.e., the population total) and the standard error of this estimate, and then converted the

241 estimates to area of net change by multiplying the estimated total and standard error by the area

242 of a pixel (900 m2). In addition, annualized rates of gross forest loss and gross forest degradation

243 were estimated from the reference classification. Post classification change detection was used to

244 provide a spatial depiction of the gross changes in the land cover types. The general approach is

245 based on Stehman and Foody (2019), which is to use the sample and reference classification to

246 provide estimates of area and to employ a map to provide a spatially explicit representation of

247 where the area of the different cover and change types are found.

248 Results

249 Map accuracy

250 The overall accuracy of the 2000 classified land cover map was 85% (SE =2%) (Table 3).

251 Producer’s accuracies of the important forest and degraded forest classes were 91% (SE=3%)

252 and 82% (SE=3%), respectively. User’s accuracy of the forest class was 83% (SE=3%) and

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253 user’s accuracy of degraded forest was 89% (SE=3%). The overall accuracy of the 2017

254 classified land cover map was 82% (SE=3%) (Table 4). The producer’s accuracies of forest and

255 degraded forest were 72% (SE=6%) and 92% (SE=2%), respectively. For user’s accuracies, the

256 forest class attained 92% (SE=4%) and the degraded forest was 80% (SE=3%). Given the

257 application of the 2017 map to identify potential degraded forest sites for restoration, the user’s

258 accuracy for degraded forest indicates that 80% of the area mapped as degraded will in fact be

259 degraded, with 10% commission error to each of forest and other wooded land. The producer’s

260 accuracy of 92% for degraded forest indicates that the 2017 map omits few areas of actual

261 degraded forest.

262 The overall accuracy of the land cover change map for forest condition (i.e., gain, loss,

263 stable, and non-forest) was 87% (SE= 2%) (Table 5). Forest gain was difficult to map as user’s

264 and producer’s accuracies were 59% (SE=7%) and 57% (SE=13%). Better success was achieved

265 at mapping forest loss with user’s and producer’s accuracies of 77% (SE=8%) and 64% (SE=8%)

266 respectively. The large error matrix describing accuracy of change of the land-cover classes is

267 provided in Appendix Table A1.

268 Land cover changes

269 Based on the 2000 and 2017 reference sample data, forest area decreased from 40.5% to

270 23.2% and degraded forests increased from 50.5% to 55.1% (Table 6, Fig. 5). The conversion of

271 an estimated 27,338 ha (SE=4,087 ha) from forest to degraded forest represented the largest

272 gross change during the study period, followed by an estimated 15,204 ha (SE=3,254 ha)

273 changing from degraded forest to other wooded land (Table 7, Fig. 6). The ratio of forest to

274 degraded forest changed considerably between 2000 and 2017, even though the combined area of

275 both classes decreased only slightly from 160,037 ha in 2000 to 137,907 ha in 2017 (Table 6). In

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276 2000, the ratio of degraded forest to forest was 1.25, increasing to 2.37 in 2017, indicating a

277 substantial change in forest composition to bamboo dominated degraded forest cover. In

278 addition to conversion to degraded forest, the other primary conversions of forest were to other

279 wooded land and water. In terms of total forest cover (as represented by the combination of the

280 forest and degraded forest classes), an estimated 29,026 ha (SE=4,354 ha) were lost, and an

281 estimated 6,893 ha (SE=1,552 ha) of forest cover were gained over the 16-year period (Table 8,

282 Fig. 7). These losses and gains translate into an annualized gross forest loss rate of 1.03% and

283 annualized gross forest gain rate of 0.25%. An estimated 27,338 ha (SE=4,087 ha) was converted

284 from forest to degraded forest representing an annualized forest degradation rate of 0.97%.

285 Discussion

286 Land cover mapping

287 The overall accuracies of 85% for the 2000 map and 82% for the 2017 map obtained in

288 our study were comparable to or better than previous mapping efforts in this region of Myanmar.

289 Using a combination of supervised classification and normalized difference vegetation index

290 (NDVI) image-differencing methods, Win et al. (2009) attained overall accuracies of 79% and

291 74% for the 1989-2000 and 2000-2003 forest cover change maps of the Bago Mountain Range.

292 They categorized the land cover classes into forest, degraded forest, bare land and grassland, and

293 water. Applying the Forest Canopy Density (FCD) Mapper software to Landsat images, Mon et

294 al. (2010) achieved an overall accuracy of 81% for mapping the forest cover of the core Bago

295 Mountain Range. They classified land into non-forest (FCD < 10%) and forest (FCD ≥ 10%) and

296 thereafter, three categories of forest were further defined as open canopy forest (10% ≤ FCD <

297 40%), medium canopy forest (40% ≤ FCD < 70%), and closed canopy forest (FCD ≥ 70%). This

298 study’s higher accuracy was attributable to the incorporation of field inspections, previous work

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299 experience in the region, auxiliary land use data derived from the Forest Department and the use

300 of field foresters and image processing experts to provide more local knowledge.

301 Mon et al. (2012a) assessed forest cover in the Bago Mountain Range and found that the

302 maximum likelihood classifier (overall accuracy of 65%) was less accurate than using the FCD

303 mapper (overall accuracy of 68%). Our observations during field examinations suggested that

304 forest classification based on canopy cover percentage was unlikely to provide accurate results

305 due to the dominance of bamboo species in the study area, which are often confused with timber

306 forests if mapping is focused on canopy cover without identifying the source of that cover.

307 We established criteria (Table 1) to define the individual land cover classes and

308 implemented a protocol for ground training data collection specifically tailored to better

309 discriminate between timber trees and bamboo in advance of map production. For future studies,

310 vegetation indices such as NDVI, Green-Red Vegetation Index (GRVI), and other classification

311 algorithms are worth investigating. For example, Linear Mixture Modeling has been employed to

312 classify Amazonian vegetation (Lu et al. 2003), and spectral unmixing has been applied to

313 Landsat time series data to quantify tropical degradation (Bullock et al. 2019). Alternatively,

314 using high-resolution multispectral imagery may assist in differentiating between trees and

315 bamboo plants. As an example, Tang et al. (2016) used WorldView-2 (WV-2) imagery for

316 bamboo mapping in a mountainous region of China.

317 Forest Management implications

318 Our study found that forest area declined from 40.5% of the region in 2000 to 23.2% of

319 the region in 2017, while bamboo-dominated degraded forest area increased from 50.5% to

320 55.1% during the same time period. The increase in area of water was the result of four new

321 dams constructed during the study period as the new reservoirs decreased forest cover due to the

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322 submersion of forested areas. Improved access associated with dam construction also likely

323 facilitated an increase in illegal cutting. Another factor potentially influencing forest cover

324 change was a highway road construction project between the two commercially important cities

325 of Yangon and Mandalay. The accelerated forest cover changes may have also been driven by

326 the extraction of a substantial amount of commercial timber beyond the allowable limit prior to

327 the 2016 logging ban (Khai et al. 2016; Mon et al. 2012a, 2012b).

328 This study found that the annualized gross forest degradation rate (0.97%) was almost

329 equivalent to that of forest loss (1.03%). In another forest cover study of the Bago Mountain

330 Range for 1989–2006, Mon et al. (2010) reported an annualized forest degradation rate of 2.5%

331 and forest loss rate of 0.2%. At the national level, the focus has been on deforestation. Wang and

332 Myint (2016) reported that Myanmar’s annualized rate of deforestation between 2001 and 2010

333 was 0.81%, whereas FAO (2015) reported an annualized deforestation rate of 0.9% between

334 2000 and 2010 and 1.8% between 2010 and 2015. The annualized forest loss rate of 1.03% from

335 our study area closely parallels these recent estimates at the national scale.

336 Forest degradation studies are rare in the literature relative to the number of studies

337 assessing deforestation due to the subtleties and nuances associated with detection of

338 degradation. Furthermore, the consequences of forest degradation, particularly in the short-term,

339 may be less dramatic than those of deforestation, thereby not attracting as much attention from

340 forest managers and planners (Sasaki and Putz 2009). Given the high rate of forest degradation

341 in the Bago Mountain Range (and beyond), further research is needed on rehabilitation

342 strategies with regards to species composition, diversity, and successional forest structure after

343 exploitative logging (Win et al. 2009). Forest degradation is customarily due to irresponsible or

344 illicit logging practices which compromise the forest’s ability to provide a wide spectrum of

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345 ecosystem services, such as reducing emissions due to forest degradation (GFOI 2016), when

346 compared to undisturbed and well-managed forests. Fortunately, all is not lost to complete

347 conversion as there remains the possibility of rehabilitation. Thus, Sasaki and Putz (2009)

348 emphasized the need for distinguishing between forest and degraded forest because of the

349 implications for forest management.

350 A unique contribution of our study is the classification of a bamboo dominated forest as

351 an indicator of forest degradation in which most (or all) of the teak and other commercial

352 hardwood species have been harvested, resulting in an unsustainable “bamboo thicket” for years

353 to come. Through field surveys, Khai et al. (2016) also verified forest degradation in the South

354 Zamayi reserved forest (i.e., within our study area) and advocated for the rehabilitation of

355 degraded, bamboo-dominated forests. They observed that density of bamboo clumps (116 ha-1)

356 was much higher than that of trees (41 ha-1). As the density of bamboo increases, the potential

357 for natural tree regeneration is greatly diminished and eventually impossible as bamboo takes

358 control of the site. Intervention and restoration strategies by forest managers is highly influenced

359 by the level of bamboo density in a given management unit (Larpkern et al. 2011). When

360 bamboo stem densities are too high to rely on natural regeneration or even enrichment planting,

361 then the only option is complete removal of the bamboo followed by the establishment of teak

362 plantations.

363 Historically, teak plantations were established using the taungya method throughout the

364 Bago Mountain Range (Bryant 1997; Jordan et al. 1992; Mon et al. 2012b; Shimizu et al. 2017;

365 Suzuki et al. 2004; Takeda et al. 2005; Yukako 1998). The taungya method, which employs

366 inter-planting agricultural crops during the early stages of teak plantations, has been applied in

367 many countries as a means for restoring degraded forests (Tani 2000). Bamboo-dominated

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368 degraded forests are the most favored taungya sites because the Forest Department of Myanmar

369 prioritizes forests with less valuable timber trees for reforestation (Suzuki et al. 2009). Our 2017

370 land cover map provides the locations of such bamboo dominated degraded forests.

371 Consequently, the 2017 map supplies a critical data input for spatial analyses used to determine

372 suitable taungya teak reforestation sites in the study area. The Myanmar Forest Department has

373 been implementing a national reforestation plan since 2017 to restore degraded forests, so

374 prioritizing hotspots (e.g., degraded forests) for forest enrichment and reforestation across

375 Myanmar will be greatly enhanced with the aid of geospatial technology.

376 Conclusion

377 The dramatic loss of forest cover in Myanmar’s “Home of Teak” prompted this spatio-

378 temporal land cover change study. Land cover maps for 2000 and 2017 were developed to

379 document recent land cover changes. Through change detection analysis, forest loss and forest

380 degradation were quantified. Forest cover change observed through this study confirmed that

381 forest loss and degradation is threatening the sustainability of forested areas in the Bago

382 Mountain Range. Although the rate of forest loss continues to be alarming, the extent of forest

383 degradation is equally troubling, requiring the immediate attention from forest managers. The

384 exploitation of teak and other commercial hardwoods in the Bago Mountain Range, as well as

385 other regions of Myanmar, provides short-term economic gains at the expense of the forest

386 resource and associated ecosystem benefits. It does not have to be a zero-sum situation. With

387 sustainable forest management, Myanmar’s “Home of Teak” can continue to earn its namesake

388 long into the future by not only providing high quality teak for foreign markets, but also serving

389 critical non-commodity benefits such as wildlife habitat, water conservation and carbon

390 sequestration.

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391 Acknowledgements

392 We would like to thank the Forest Department of Myanmar, especially the Forest

393 Department personnel from the Bago region, for assisting with field work and data support. We

394 also thank the Associate Editor and two anonymous reviewers for their constructive suggestions.

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References

Ahmad, F., Goparaju, L., and Qayum, A. 2017. Agroforestry suitability analysis based upon

nutrient availability mapping: a GIS based suitability mapping. AIMS Agric. Food 2(2):

201-220.

Bawa, K.S. and Seidler, R. 1998. Natural forest management and conservation of biodiversity in

tropical forests. Conserv. Biol. 12(1): 46-55.

Bago District Working Plan 2016. Forest Department, Ministry of Natural Resources and

Environmental Conservation, Myanmar.

Bhagwat, T., Hess, A., Horning, N., Khaing, T., Thein, Z.M., Aung, K.M., Aung, K.H., Phyo, P.,

Tun, Y.L., and Oo, A.H. 2017. Losing a jewel—Rapid declines in Myanmar’s intact

forests from 2002-2014. PloS one 12(5): e0176364.

Bischof, H., Schneider, W., and Pinz, A.J. 1992. Multispectral classification of Landsat-images

using neural networks. IEEE Trans. Geosci. Remote Sens. 30(3): 482-490.

Brancalion, P.H.S., Niamir, A., Broadbent, E., Crouzeilles, R., Barros, F.S.M, et al. 2019. Global

restoration opportunities in tropical rainforest landscapes. Sci. Adv. 5(7): eaav3223.

Brunner, J., Talbott, K., and Elkin, C. 1998. Logging Burma's frontier forests: resources and the

regime. World Resources Institute Washington, DC. ISBN: 1569732663.

Bryant, R.L. 1997. The political ecology of forestry in Burma: 1824-1994. University of Hawaii

Press.

Bullock, E.L., Woodcock, C.E., and Olofsson, P. 2019. Monitoring tropical forest degradation

using spectral unmixing and Landsat time series analysis. Remote Sens. Environ. (in

press).

Page 19 of 40



Draft

20

Chan, N., Takeda, S., Suzuki, R., and Yamamoto, S. 2013. Establishment of allometric models

and estimation of biomass recovery of swidden cultivation fallows in mixed deciduous

forests of the Bago Mountains, Myanmar. For. Ecol. Manage. 304: 427-436.

Chan, N., Takeda, S., Suzuki, R., and Yamamoto, S. 2016. Assessment of biomass recovery and

soil carbon storage of fallow forests after swidden cultivation in the Bago Mountains,

Myanmar. New For. 47(4): 565-585.

Edwards, D.P., Larsen, T.H., Docherty, T.D., Ansell, F.A., Hsu, W.W., Derhé, M.A., Hamer,

K.C., and Wilcove, D.S. 2010. Degraded lands worth protecting: the biological

importance of Southeast Asia's repeatedly logged forests. Proc. Biol. Sci. 278(1702): 82-

90.

FAO. 2000. Global forest resources assessment 2000 Main report. Food and Agriculture

Organization of the United Nations, Rome, Italy.

FAO. 2001. Global ecological zoning for the global forest resources assessment 2000. Forestry

Working Paper 56. Food and Agriculture Organization of the United Nations, Rome,

Italy.

FAO. 2005. Global forest resources assessment 2005. Food and Agriculture Organization of the

United Nations, Rome, Italy.

FAO. 2015. Global forest resources assessment 2015 Desk reference. Food and Agriculture

Organization of the United Nations, Rome, Italy.

GFOI. 2016. Integration of remote-sensing and ground-based observations for estimation of

emissions and removals of greenhouse gases in forests: Methods and guidance from the

Global Forest Observations Initiative, edition 2.0. Food and Agriculture Organization of

the United Nations. pp. 1-224.

Page 20 of 40



Draft

21

Gong, P., Zhao, Y., Yu, L., and Liang, L. 2011. Development of an integrated software platform

for global mapping and analysis. Geomatics World 2: 34-37.

Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang, X., Fu, H., and Liu, S.

2013. Finer resolution observation and monitoring of global land cover: first mapping

results with Landsat TM and ETM+ data. Int. J. Remote Sens. 34(7): 2607-2654.

Gyi, K.K. and Tint, K. 1995. Status of management of natural teak forests. In FAO corporate

document repository: Paper presented to the second regional seminar on teak. 23 May-3

June 1995, Yangon, Myanmar. Available from http://www.fao.org/docrep/005/AC773E/

ac773e07.htm [accessed 14 January 2018].

Htun, K. 2009. Myanmar forestry outlook study. Asia-Pacific forestry sector outlook study II.

Working Paper Series, Working Paper No. APFSOS II/WP/2009/07, Food and

Agriculture Organization of the United Nations, Regional Office for Asia the Pacific,

Bangkok.

Htun, N.Z., Mizoue, N., Kajisa, T., and Yoshida, S. 2009. Deforestation and forest degradation

as measures of Popa Mountain Park (Myanmar) effectiveness. Environ. Conserv. 36(3):

218-224.

Jensen, J.R. and Lulla, K. 1987. Introductory digital image processing: A remote sensing

perspective. Geocarto Int. 2(1): 65.

Jordan, C.F., Gajaseni, J. and Watanabe, H. 1992. Taungya: forest plantations with agriculture in

Southeast Asia. No. 634.990959 T226. CAB International.

Khai, T.C., Mizoue, N., Kajisa, T., Ota, T., and Yoshida, S. 2016. Stand structure, composition

and illegal logging in selectively logged production forests of Myanmar: comparison of

Page 21 of 40



Draft

22

two compartments subject to different cutting frequency. Glob. Ecol. Conserv. 7: 132-

140. doi: 10.1016/j.gecco.2016.06.001.

Larpkern, P., Moe, S.R., and Totland, Ø. 2009. The effects of environmental variables and

human disturbance on woody species richness and diversity in a bamboo–deciduous

forest in northeastern Thailand. Ecol. Res. 24(1): 147-156.

Larpkern, P., Moe, S.R., and Totland, Ø. 2011. Bamboo dominance reduces tree regeneration in

a disturbed tropical forest. Oecol. 165(1): 161-168.

Laurance, W.F. 2007. Forest destruction in tropical Asia. Curr. Sci. 93: 1544-1550.

Lu, D. and Weng, Q. 2007. A survey of image classification methods and techniques for

improving classification performance. Int. J. Remote Sens. 28(5): 823-870.

Lu, D., Moran, E., and Batistella, M. 2003. Linear mixture model applied to Amazonian

vegetation classification. Remote Sens. Environ. 87(4): 456-469.

Maung, T.M. and Yamamoto, M. 2008. Exploring the socio-economic situation of plantation

villagers: a case study in Myanmar Bago Yoma. Small Scale For. 7(1): 29-48.

Millennium Ecosystem Assessment 2005. Ecosystems and human well-being: synthesis. Island

Press, Washington, DC.

Mon, M.S., Kajisa, T., Mizoue, N., and Yoshida, S. 2010. Monitoring deforestation and forest

degradation using FCD Mapper in Bago Mountain Areas, Myanmar. J. For. Plann. 15(2):

63-72.

Mon, M.S., Mizoue, N., Htun, N.Z., Kajisa, T., and Yoshida, S. 2012a. Estimating forest canopy

density of tropical mixed deciduous vegetation using Landsat data: A comparison of three

classification approaches. Int. J. Remote Sens. 33(4): 1042-1057. doi:10.1080/ 01431161.

2010. 549851.

Page 22 of 40



Draft

23

Mon, M.S., Mizoue, N., Htun, N.Z., Kajisa, T., and Yoshida, S. 2012b. Factors affecting

deforestation and forest degradation in selectively logged production forest: A case study

in Myanmar. For. Ecol. Manage. 267: 190-198.

Olofsson, P., Foody, G.M., Herold, M., Stehman, S.V., Woodcock, C.E., and Wulder, M.A.

2014. Good practices for estimating area and assessing accuracy of land change. Remote

Sens. Environ. 148: 42-57.

Palanisamy, K. and Subramanian, K. 2001. Vegetative propagation of mature teak trees (Tectona

grandis L.). Silvae Genet. 50(5-6): 188-191.

Pandey, D. and Brown, C. 2000. Teak: a global overview. Unasylva 51: 3-13.

Romijn, E., Herold, M., Kooistra, L., Murdiyarso, D., and Verchot, L. 2012. Assessing capacities

of non-Annex I countries for national forest monitoring in the context of REDD+.

Environ. Sci. Policy 19-20: 33-48. doi:10.1016/j.envsci.2012.01.005.

Romijn, E., Lantican, C.B., Herold, M., Lindquist, E., Ochieng, R., Wijaya, A., Murdiyarso, D.,

and Verchot, L. 2015. Assessing change in national forest monitoring capacities of 99

tropical countries. For. Ecol. Manage. 352: 109-123.

Sasaki, N. and Putz, F.E. 2009. Critical need for new definitions of “forest” and “forest

degradation” in global climate change agreements. Conserv. Lett. 2(5): 226-232.

Shalaby, A. and Tateishi, R. 2007. Remote sensing and GIS for mapping and monitoring land

cover and land-use changes in the Northwestern coastal zone of Egypt. Appl. Geogr.

27(1): 28-41.

Shimizu, K., Ponce-Hernandez, R., Ahmed, O.S., Ota, T., Win, Z.C., Mizoue, N., and Yoshida,

S. 2016. Using Landsat time series imagery to detect forest disturbance in selectively

logged tropical forests in Myanmar. Can. J. For. Res. 47(3): 289-296.

Page 23 of 40



Draft

24

Shimizu, K., Ahmed, O.S., Ponce-Hernandez, R., Ota, T., Win, Z.C., Mizoue, N., and Yoshida,

S. 2017. Attribution of Disturbance Agents to Forest Change Using a Landsat Time

Series in Tropical Seasonal Forests in the Bago Mountains, Myanmar. Forests 8(6): 218.

Stehman, S.V. 2014. Estimating area and map accuracy for stratified random sampling when the

strata are different from the map classes. Int. J. Remote Sens. 35(13): 4923-4939.

Stehman, S.V. and Foody, G.M. 2009. Accuracy assessment. In The SAGE handbook of remote

sensing. Edited by T.A. Warner, M.D. Nellis, and G.M. Foody. Sage Publications Ltd.,

London. pp. 297-309.

Stehman, S.V. and Foody, G.M. 2019. Key issues in rigorous accuracy assessment of land cover

products. Remote Sens. Environ. (in press).

Suzuki, R., Takeda, S., and Keh, S.K. 2004. The impact of forest fires on the long-term

sustainability of taungya teak reforestation in Bago Yoma, Myanmar. Tropics 14(1): 87-

102.

Suzuki, R., Takeda, S., and Thein, H.M. 2009. Effect of slash-and-burn on nutrient dynamics

during the intercropping period of taungya teak reforestation in the Bago mountains,

Myanmar. Trop. Agr. Develop. 53(3): 82-89.

Takeda, S., Suzuki, R., and Thein, H.M. 2005. Three-year monitoring of shifting cultivation

fields in a Karen area of the Bago Mountains, Myanmar. Nihon Shinrin Gakkai Taikai

Koen Yoshishu.

Tang, Y., Jing, L., Li, H., Liu, Q., Yan, Q., and Li, X. 2016. Bamboo classification using

worldview-2 imagery of giant panda habitat in a large shaded area in Wolong, Sichuan

province, China. Sensors 16(11): 1957.

Page 24 of 40



Draft

25

Tani, Y. 2000. Ecological Factors Affecting Taungya Farmers Behavior in Teak Plantation

Projects: A Case Study in Bago Range, Union of Myanmar. Tropics 10(2): 273-286.

Thein, H.M., Kanzaki, M., Fukushima, M., and Minn, Y. 2007. Structure and composition of a

teak-bearing forest under the Myanmar Selection System: Impacts of logging and

bamboo flowering. J. Southeast Asian Stud. 45(3): 303-316.

Wang, C. and Myint, S.W. 2016. Environmental concerns of deforestation in Myanmar 2001–

2010. Remote Sens. 8(9): 728.

Win, R.N., Reiji, S., and Shinya, T. 2009. Forest cover changes under selective logging in the

Kabaung Reserved Forest, Bago Mountains, Myanmar. Mt. Res. Dev. 29(4): 328-338.

Win, R.N., Suzuki, R., and Takeda, S. 2012a. Effects of selective logging on the regeneration of

two commercial tree species in the Kabaung Reserved Forest, Bago Mountains,

Myanmar. J. Trop. For. Sci. 24(3): 312-321.

Win, R.N., Suzuki, R., and Takeda, S. 2012b. Remote sensing analysis of forest damage by

selection logging in the Kabaung Reserved Forest, Bago Mountains, Myanmar. J. For.

Res. 17(2): 121-128.

Yu, L. and Gong, P. 2012. Google Earth as a virtual globe tool for Earth science applications at

the global scale: progress and perspectives. Int. J. Remote Sens. 33(12): 3966-3986.

Yukako, T. 1998. Forest people and forest policy: The effect of forest village policy on the

Karen of Pegu Yoma, Burma. J. Southeast Asian Stud. 35: 224-245.

Zin, A.T. 2000. Potentialities and constraints of teak bearing forest for sustainable forest

management under current management system in Oattwin Township forest, Bago Yoma

Region, Myanmar. M.Sc. thesis, Dresden Technical University, Tharandt, Germany.

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Table 1. Definition of land cover types.

Land Cover Class Definition

Forest Areas where trees dominate and associate with bamboo. Bamboo plants within this land cover class are larger in size than those of degraded forest.

Degraded Forest Areas with predominant composition of bamboo, combined with small trees and a few large trees. Degradation here does not mean diminishing site quality but simply implies there are few large trees. It is recognized as “forest” in this study.

Other Wooded Land This land cover class is not recognized as forest. It includes scrubland, bushes, and young plantations.

Other Land All areas that are not forest, degraded forest, other wooded land, or water. This class includes roads, agriculture, open/bare soil, settlements, and infrastructure.

Water Streams, irrigation channels, and reservoirs impounded by dams.

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Table 2. Criteria for differentiating land cover classes in the Google Earth imagery.

Land Cover Class Reference Imagery Interpretation Criteria

Forest Green and dark green areas Dominated by tree canopy, which can be detected in high-

resolution imagery Typically surrounded by continuous dense forest cover Mostly inaccessible Mountain tops, ridges, and watershed areas

Degraded Forest Light green, yellowish green and yellowish areas Dominated by bamboo canopy, which can be detected in high-

resolution imagery Relatively dense forest cover, especially near man-made

features such as plantations and agriculture Accessible Mixed with “Forest” land cover class (usually patchy)

Other Wooded Land Young plantations (e.g., patterns of rows of plants cultivated in straight lines)

Slash-and-burn areas for shifting cultivation Site preparation areas for plantation establishment Patches of low vegetation cover (such as shrubs)

Other Land Bare soils, settlements, roads and agriculture

Water Reservoirs impounded by dams, streams, and irrigation channels

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Table 3. Error matrix and accuracy estimates for the 2000 classified map (cell entries represent

percent of area). Overall accuracy is 85% (SE=2%).

ReferenceLand Cover Class (Map)

ForestDegraded

Forest

Other

Wooded

Land

Other

LandWater

Row

Total

Sample

Size

Total

User’s

Accuracy

(%) with

SE in ( )

Forest 36.9 7.6 0 0 0 44.5 100 83 (3)

Degraded Forest 3.3 41.6 1.9 0 0 46.8 100 89 (3)

Other Wooded Land 0.2 1.2 4.7 0.2 0 6.3 70 76 (4)

Other Land 0 0 0.1 0.6 0 0.7 50 88 (4)

Water 0.1 0.1 0 0 1.7 1.9 50 88 (4)

Column Total 40.5 50.5 6.7 0.8 1.7 100

Sample Size Total 95 124 61 46 44 370

Producer’s Accuracy (%)

with SE in ( )91 (3) 82 (3) 71 (10) 77 (13) 100 (0)

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Table 4. Error matrix and accuracy estimates for the 2017 classified map (cell entries represent

percent of area). Overall accuracy is 82% (SE=3%).

Reference

Land Cover Class (Map)

ForestDegraded

Forest

Other

Wooded

Land

Other

LandWater

Row

Total

Sample

Size

Total

User’s

Accuracy

(%) with

SE in ( )

Forest 16.7 1.6 0 0 0 18.3 47 92 (4)

Degraded Forest 6.1 50.6 6.4 0 0 63.1 180 80 (3)

Other Wooded Land 0.4 3.0 10.2 0 0 13.6 71 75 (8)

Other Land 0 0 0.1 1.0 0 1.1 43 95 (4)

Water 0 0 0 0.5 3.4 3.9 29 88 (11)

Column Total 23.2 55.2 16.7 1.5 3.4 100

Sample Size Total 59 157 83 43 28 370

Producer’s Accuracy (%)

with SE in ( )72 (6) 92 (2) 61 (8) 68 (21) 100 (0)

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Table 5. Error matrix and accuracy estimates of the land cover change map (cell entries

represent percent of area). Overall accuracy is 87% (SE=2%).

ReferenceLand Cover Changes

(Map)Forest

Gain

Forest

Loss

Stable

Forest

Non-

Forest

Row

Total

Sample

Size

Total

User’s

Accuracy

(%) with

SE in ( )

Forest Gain 2.3 0.1 1.1 0.4 3.8 57 59 (7)

Forest Loss 0 10.5 3.2 0 13.7 30 77 (8)

Stable Forest 1.4 5.5 70.2 0.5 77.5 170 90 (2)

Non-Forest 0.3 0.4 0 4.3 5.0 113 86 (4)

Column Total 3.9 16.5 74.5 5.1 100

Sample Size Total 41 44 175 110

Producer’s Accuracy

(%) with SE in ( )57 (13) 64 (8) 94 (2) 84 (8)

Table 6. Land cover area (ha) for 2000 and 2017 estimated from the stratified sample and

reference classification (see appendix Table A2 for areas based on the land cover maps).

2000 2017 Net ChangeLand Cover

Area (SE) % Area (SE) % Area (SE) %

Forest 71,240 (1,524) 40.5 40,891 (4,404) 23.2 -30,349 (4,416) -17.3

Degraded Forest 88,797 (1,694) 50.5 97,014 (5,395) 55.1 8,216 (5,527) +4.6

Other Wooded Land 11,655 (1,270) 6.6 29,376 (4,121) 16.7 17,721 (4,276) +10.1

Other Land 1,343 (186) 0.8 2,674 (874) 1.5 1,331 (894) +0.7

Water 2,932 (57) 1.7 6,013 (1,834) 3.4 3,081 (1,836) +1.7

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Table 7. Gross changes from 2000 to 2017 in area estimated from the reference sample data.

Standard errors are provided within the parentheses.

Year: 2017

Year: 2000 ForestDegraded

Forest

Other

Wooded

Land

Other

LandWater

Row

Total

Forest 33,659 (3,895)

27,338 (4,087)

8,677 (2,462)

0 1,566 (1,101)

71,240 (3,639)

Degraded Forest 6,537 (2,226)

63,477 (4,512)

15,204 (3,254)

1,112 (842)

2,467 (1,410)

88,797 (3,963)

Other Wooded Land 648(306)

5,589 (1,530)

4,591 (1,033)

48 (33)

780 (227)

11,656 (1,716)

Other Land 48 (33)

477 (277)

304 (73)

516 (83)

0 1,345 (226)

Water 0 134(93)

600 (183)

999 (218)

1,200 (228)

2,933 (155)

Column Total 40,892 (4,190)

97,015 (5,179)

29,376 (4,134)

2,675 (871)

6,013 (1,835)

175,971

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Table 8. Area estimates of forest loss and forest gain from reference sample. Standard errors are

provided within the parentheses.

Area (ha) Area (%)

Forest Gain 6,893 (1,552) 3.9 (0.9)

Forest Loss 29,026 (4,354) 16.5 (2.5)

Stable Forest 131,014 (4,545) 74.5 (2.6)

Non-Forest 9,038 (1,063) 5.1 (0.6)

Total 175,971 100

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Table A1. Error matrix and accuracy estimates of the change of the land-cover classes. In the table, 1=Forest, 2=Degraded Forest, 3=Other Wooded Land, and 4=combination of Other Land and Water. The first land cover category represents 2000 and the latter is 2017 (e.g., for Class 12, 1=Forest in 2000 and 2=Degraded Forest in 2017). Overall accuracy is 71% (SE=3%).

ReferenceLandCover (Map) 11 12 13 14 21 22 23 24 31 32 33 34 41 42 43 44

Row Total

User’s Accuracy

Sample Size

Total11 15.124 0.890 - - 1.335 - - - - - - - - - - - 17.349 87.2 3912 3.559 10.231 1.335 - 0.890 4.004 0.890 - - - - - - - - - 20.907 48.9 4713 0.445 0.890 3.559 - - - 0.445 - - - - - - - - - 5.338 66.7 1214 - - - 0.890 - - - - - - - - - - - - 0.890 100.0 221 - - - - - 0.468 - - - - - - - - - - 0.468 0.0 122 - 3.273 - - 1.403 28.985 3.273 - - 1.403 0.468 - - - - - 38.802 74.7 8323 - - - - - 1.870 3.740 - - - - - - - - - 5.610 66.7 1224 - - - - - - - 1.870 - - - - - - - - 1.870 100.0 431 - - - - - 0.177 - - 0.177 - - - - - - - 0.355 50.0 432 - 0.177 - - 0.089 0.532 0.089 - 0.177 1.508 0.266 - - 0.177 - - 3.015 50.0 3433 - - - - - - 0.177 - - 0.266 1.862 - - - - - 2.306 80.8 2634 - - - - - - - 0.089 - - - 0.443 - - - - 0.532 83.3 641 - - - - - - - - 0.013 - - - 0.013 0.013 - - 0.040 33.3 342 - 0.076 - - - 0.038 0.027 - - - 0.013 - 0.013 0.315 0.076 0.013 0.571 55.2 3043 - - 0.038 - - - - - - - - - - - 0.227 - 0.265 85.7 744 - - - - - - - 0.076 - - - 0.027 - 0.013 0.038 1.529 1.683 90.9 60

Column Total 19.128 15.536 4.931 0.890 3.715 36.073 8.640 2.034 0.368 3.176 2.609 0.470 0.027 0.519 0.341 1.543 100.000 370

Producer’s Accuracy 79.1 65.9 72.2 100.0 0.0 80.4 43.3 91.9 48.2 47.5 71.4 94.3 50.0 60.7 66.7 99.1

Sample Size

Total43 38 12 2 9 85 23 7 5 23 26 7 2 24 9 55 370

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Table A2. Map area of land cover for 2000 and 2017 and map net change.

2000 2017 Net ChangeLand Cover

Area (ha) % Area (ha) % Area (ha) %

Forest 78,276 44.5 34,077 19.4 -44,199 -25.1

Degraded Forest 82,264 46.8 110,739 62.9 +28,475 +16.1

Other Wooded Land 10,924 6.2 21,189 12.0 +10,265 +5.8

Other Land 1,171 0.7 3,486 2.0 +2,315 +1.3

Water 3,332 1.9 6,476 3.7 +3,144 +1.8

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Fig. 1. Location of the study area: (a) Baing Dar, (b) South Zamayi, (c) Shwe Laung Ko Du

Gwe, and (d) Kawliya reserved forests. The study area is part of the Bago Mountain Range in

Myanmar.

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Fig. 2. Locations of training data points collected with GPS displayed on the 2017 Landsat

image.

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Fig. 3. Land cover photos (a) Forest, (b) Degraded forest, (c) Other wooded land (scrubland),

and (d) Other land (road).

(a) (b)

(c) (d)

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Fig. 4. Supervised image classification flowchart.

Landsat imagery acquisition from USGS website

(cloud less than 10% & path 132/ row 48)

Pre-processing: Subsetting & Layer

stacking

Interpretation: Training samples

selection and supervised

classification using maximum likelihood

algorithm

Post-Processing: Recoding andmajority filter

(neighbourhood)

Fusion with GIS for accuracy assessment & quantitative forest

cover change detection

Fig. 5. Classified land cover maps for 2000 and 2017.

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Fig. 6. Land cover changes between 2000 and 2017. In the legend, the first land cover category

represents 2000 and the latter is 2017. If the first and second categories are the same, no change

occurred during the period. Other wooded land is abbreviated as OWL.

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Fig. 7. Forest loss and forest gain areas.

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Documents

Draft...Draft 1 1 Quantifying forest loss and forest degradation in Myanmar’s “Home of 2 Teak” 3 Thu Ya Kyaw, René H. Germain, Stephen V. Stehman, and Lindi J. Quackenbush 4