MEINMAHLA KYUN WILDLIFE SANCTUARY CONSERVATION PROGRAMME Land Cover Classification Meinmahla Kyun Wildlife Sanctuary Ayeyarwaddy Region, Myanmar: Pilot Project

Colin Harris, Katharina Lorenz, Narissa Bax, Patrick Oswald November 2016

TCP Report No. XX

Name of the project (if necessary)

With funding from:

MEINMAHLA KYUN WILDLIFE SANCTUARY CONSERVATION PROGRAMME

The Meinmahla Kyun Wildlife Sanctuary Conservation Programme is an program initiative of Fauna and Flora International (FFI) Myanmar Programme, implemented in collaboration with the Myanmar Forest Department and a number of local, national and international collaborators and stakeholders. FFI Myanmar operates the programme under a MoU with the Forest Department specifically for marine and terrestrial conservation activities in Meinmahla Kyun Wildlife Sanctuary region.

Funding This document has been produced with the financial assistance of the ASEAN Centre for Biodiversity (ACB).

Harris, C., Lorenz, K, Bax, N. & Oswald, P. 2016. Land Cover Suggested Classification of the Meinmahla Kyun Wildlife Sanctuary citation Ayeyarwaddy Region, Myanmar: Pilot Project. Unpublished report prepared by Environmental Research & Assessment, Cambridge, and Fauna & Flora International, Myanmar.

Environmental Research & Assessment specialises in Author environmental management and policy, protected areas, impact details assessment and environmental applications of Geographical Information Systems (GIS), Remote Sensing and Global Positioning Systems (GPS). Products are based on high-quality research and in-depth technical knowledge of environmental issues and analytical methods.

Copyright Reproduction of this report in full or in part is granted for the purposes of education, research or awareness, with the sole provision that the authors and authoring organisations be properly credited.

Cover images

Disclaimer The contents of this document are the sole responsibility of Fauna and Flora International and can under no circumstances be regarded as reflecting the position of the European Union or other donors.

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TABLE OF CONTENTS

TABLE OF CONTENTS ...... 3 ACRONYMS AND ABBREVIATIONS ...... 3 ACKNOWLEDGEMENTS ...... 4 EXECUTIVE SUMMARY ...... 4 1. INTRODUCTION ...... 5 2. METHODS ...... 6 2.1 Study area ...... 6 2.2 Imagery...... 6 2.3 Land Cover Classification System ...... 8 2.4 Land Cover Classification ...... 8 2.5 Land Cover Classification accuracy assessment ...... 13 2.6 Mangrove quality assessment...... 13 3. RESULTS & DISCUSSION ...... 14 3.1 Spatial patterns ...... 14 3.2 Percentage of land cover types ...... 18 3.3 Land cover classification reliability ...... 19 3.4 Limitations to the study ...... 23 4. CONCLUSION ...... 23 4.1 Further work ...... 24 4.2 Recommendations for the 5 year management plan ...... 24 5. REFERENCES ...... 28

ACRONYMS AND ABBREVIATIONS

MKWS Meinmahla Kyun Wildlife Sanctuary ML Maximum Likelihood NDVI Normalized Difference Vegetation Index SAM Spectral Angle Mapper

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Environmental Research & Assessment gratefully acknowledges contributions to this study from Narissa Bax (FFI), Patrick Oswald (FFI) and the FFI field team which gathered ground-truth information during the MKWS drone survey conducted in October 2016. The drone imagery proved invaluable and contributed highly to successful completion of this pilot study.

EXECUTIVE SUMMARY

Fauna and Flora international (FFI) seeks to develop understanding of deforestation and forest degradation in the Meinmahla Kyun Wildlife Sanctuary (MKWS) and in the coastal area of associated villages (the 22 FFI project villages) in the Ayeyarwaddy Delta region of Myanmar. Degradation is indicated by an increasing dominance of Phoenix palm Phoenix paladosa in the MKWS and a decline in distribution of the Critically Endangered mangrove species Sonneratia griffithii. Degradation is also indicated by decline in mangrove tree crown size, tree height, and in the density of mangrove vegetation cover. In addition, FFI seeks to identify evidence of paddy field development and land erosion in the coastal area occupied by the villages surrounding MKWS. This pilot study uses satellite Remote Sensing techniques to investigate the current patterns of spatial distribution of vegetation, paddy fields, areas of erosion and built-up areas lying within the study area. The findings from this study will contribute to management planning and help to make recommendations on next steps in the application of Remote Sensing to apply similar techniques to assessing landscape scale changes in quality of mangrove ecosystems in the region.

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ERA was tasked by FFI to conduct a pilot project mapping the present land cover at Meinmahla Kyun Wildlife Sanctuary (MKWS) and the surrounding coastal area including the 22 associated villages. The land cover classification aims to identify: 1. whether vegetation species (Phoenix palm, mangrove species) within the MKWS can be distinguished by distinct spectral signatures in freely available satellite imagery; 2. potential indicators of mangrove quality, such as vegetation density; 3. the extent and distribution of paddy fields and built-up areas; and 4. candidate sites of apparent erosion. On successful completion of the pilot study it is anticipated that a Scope of Work can be defined to apply similar techniques to historical imagery in order to investigate deforestation and forest degradation changes over time. The information from this analysis will then be used to make recommendations for the five year environmental management plan which is being developed by FFI for the MKWS. The pilot project was laid out as a desk-based study. However, an ecological and plant biodiversity assessment was performed by FFI in May 2016. Even though the original collected survey data was not at ERA’s disposal, ERA could draw on the outcome of this rapid assessment in form of a report and maps. Additionally, in October 2016 FFI conducted a drone survey in the MKWS. ERA was provided with aerial imagery from this survey for several areas in MKWS, which proved to be invaluable for conducting the land cover classification. Furthermore, GPS locations were provided with photos and information about the present vegetation. High-resolution imagery available in Google Earth was also used as an aid in the classification process. All this information was employed by ERA to undertake a more limited form of ‘supervised’ classification, here referred to as a ‘semi-supervised’ classification. In spite of the limitations resulting from a lack of ground-truth data specifically collected to verify image classes, the semi-supervised approach significantly improves upon the results that can be obtained from a fully unsupervised classification, and importantly enables the classes derived to be interpreted and correlated with real-world land cover features with a reasonable level of confidence.

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2.1 Study area

The Land Cover Classification study area covers the MKWS, including a 6 km buffer around the Wildlife Sanctuary boundary in order to include the 22 associated coastal villages (Figure 1).

2.2 Imagery

● ERA identified suitable imagery for the classification from the Sentinel-2 (10 m) satellite (see Table 1 for satellite properties). One recent nearly cloud-free Sentinel-2 satellite scene (acquired 13 March 2016) providing complete coverage of the MKWS study area was selected. The Sentinel-2 imagery was acquired around the same time that FFI fieldwork was conducted in May 2016 for the ecological and plant biodiversity assessment, so it is very likely that conditions on the ground at the time of the field work will correspond well with those captured in the imagery.

Table 1: Sentinel-2 satellite image properties

Sentinel-2 Earth coverage & 5 repeat cycle (days) Swath width (km) 290 Band Type1 Res (m) Multispectral band Band 1 Coastal aerosol 60 number, type and Band 2 Blue 10 spatial resolution Band 3 Green 10 Band 4 Red 10 Band 5 Vegetation Red Edge 1 20 Band 6 Vegetation Red Edge 2 20 Band 7 Vegetation Red Edge 3 20 Band 8 NIR 10 Band 8a Vegetation Red Edge 4 20 Band 9 Water vapour 60 Band 10 Cirrus 60 Band 11 SWIR 1 20 Band 12 SWIR 2 20 1. NIR – Near Infrared; SWIR – Shortwave Infrared

● The selected image was provided ortho-rectified using a global DEM and pixel radiometric values are provided in Top of Atmosphere (TOA) reflectances. ● An atmospheric correction was applied to derive Bottom of Atmosphere (BOA). ● Bands 2,3,4,5,6,7,8,8A,11 and 12 were used for the classification, and no band weighting was applied. ● Bands with a resolution of 20 m were resampled to 10 m.

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Figure 1: Meinmahla Kyun Wildlife Sanctuary - Study Area Page 7 of 28 MEINMAHLA KYUN WILDLIFE SANCTUARY CONSERVATION PROGRAMME 2.3 Land Cover Classification System

After reviewing the imagery and conducting initial analysis ERA developed a land cover classification system consisting of the following classes: ● Water ● Phoenix palm dominated ● Nypa palm dominated ● Mangrove trees ● Shrubby mangroves ● Grassland ● Bare ground / Road ● Agriculture Attempts were made to optimise analysis to separate out built-up areas (eg. villages) although this did not yield satisfactory results due to the spatial resolution of the imagery and the nature of the spectral response from the mix of buildings, paths, roads and vegetation present in built-up areas.

2.4 Land Cover Classification

1. Initially an unsupervised classification was run using ERDAS Imagine 2015. The classification was run to yield 35-40 classes based on spectral signatures to get a better understanding of spectral variability in the area of interest and possible problematic areas in a supervised classification. 2. Initially training areas were created based on Yong’s (2016) vegetation map. However, as the vegetation is only broadly indicated on the map, this did not result in a satisfactory classification result as vegetation classes spectrally overlapped and those training areas had to be discarded. 3. In October 2016 FFI carried out an aerial survey using a DJI Phantom 3 Professional drone with an integrated 12 MP camera with super-wide angle lens. Most of the images were taken from ~120 m above ground altitude (some at elevations of 75 – 200 m). The target shutter speed was 1/1000sec and maximal ISO200, although cloudy conditions necessitated shutter speeds as low as 1/450s and ISO800, degrading final image quality. 4. Revised training areas were created for the land cover classes (see Table 2) based on the supplied drone imagery and field observations and photographs taken during the FFI October 2016 field survey. 5. Two classifiers were run on the training areas – one using Maximum Likelihood (ML), the other using a Spectral Angle Mapper (SAM). This second classifier was applied as technique for cross-verification and ‘sensitivity testing’ of the results from the ML classifier. 6. A 3x3 median filter was applied to the land cover classifications to reduce the small amount of noise that appeared in initial results. The noise was apparent as small patches of mis-allocated pixels amongst large areas of correctly identified pixels.

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Table 2: Defined land cover classes in semi-supervised classification Land cover class Photo / drone imagery illustrating typical example Satellite imagery at corresponding site illustrating of landscape for class typical example landscape for class

Water

Phoenix palm dominated

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Land cover class Photo / drone imagery illustrating typical example Satellite imagery at corresponding site illustrating of landscape for class typical example landscape for class

Nypa palm dominated

Mangrove trees

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Land cover class Photo / drone imagery illustrating typical example Satellite imagery at corresponding site illustrating of landscape for class typical example landscape for class

Shrubby mangroves

Grassland

Photo originates from different location as shown in satellite image Page 11 of 28 MEINMAHLA KYUN WILDLIFE SANCTUARY CONSERVATION PROGRAMME

Land cover class Photo / drone imagery illustrating typical example Satellite imagery at corresponding site illustrating of landscape for class typical example landscape for class

Bare ground / Road Photo not available

Agriculture

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2.5 Land Cover Classification accuracy assessment

A land cover classification accuracy assessment was conducted for the ML classified image using 300 stratified randomly generated points which were verified against visual interpretation of the Sentinel-2, Google Earth high resolution imagery and the FFI drone imagery. Points for ‘Phoenix palm dominated’, ‘Mangrove trees’, and ‘Shrubby mangroves’ had to be limited to the areas covered by the FFI drone imagery as the resolution of Sentinel-2 and/or Google Earth imagery were not sufficient to aid the verification process. Extra random points were added to those classes that were under-sampled (‘Nypa palm dominated’, ‘Bare ground / Road’) due to their small spatial extent. Additional points were generated to bring their total number up to ten sample points, which resulted in a total of 320 points used for the accuracy assessment. Due to time and budget constraints no accuracy assessment was conducted for the SAM classified image and only a visual analysis was undertaken.

2.6 Mangrove quality assessment

General ground-truthing information was available on mangrove quality from the map prepared by Yong (2016). However, this map lacked sufficient detail to allow generalisation across the study region, and for this reason vegetation fraction was derived from the Normalized Difference Vegetation Index (NDVI). The method is based on a model developed by Gutman & Ignatov (1998) and was applied in a mangrove context by Giri et al. (2007). The method was applied by ERA in a previous land cover analysis along the Mawdin Coast, Myanmar (Harris, Lorenz & Zöckler 2016). NDVI was calculated for the imagery, then the percent canopy closure was calculated for the following vegetation classes: ‘Phoenix palm dominated’, ‘Nypa palm dominated’, ‘Mangrove trees’, ‘Shrubby mangroves’. The result should be interpreted as a qualitative assessment showing percent canopy closure and relative vegetation density from 0% to 100%, rather than actual mangrove quality per se.

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3. RESULTS & DISCUSSION

3.1 Spatial patterns

Land cover classification Figures 2 and 3 provide an overview of the land cover classification derived using the two classifiers. In general, the classifications show that MKWS is dominated by Phoenix palm (Phoenix paludosa). Other mangrove trees and shrubby mangroves are mostly found along the coastal margins, lining rivers meandering through the island and in areas further inland especially on the eastern side of the island. The region surrounding MKWS is dominated by agricultural land and mangrove vegetation has mostly disappeared. In this region mangrove vegetation can only be found in the immediate vicinity of rivers. Larger vegetated areas exist in the southwest of the study area, which the classification results suggest are also dominated by Phoenix palm. Comparing the land cover classification to the vegetation map prepared by Yong (2016) it is evident that the ‘high priority conservation areas’ identified by Yong (2016) containing pockets of old trees, river bank mangroves and regenerating mangroves correspond well with areas classified as ‘Mangrove trees’ using the ML method. The main difference that can be observed between the two classifiers is that ML identified larger areas of ‘Mangrove trees’ and smaller areas of ‘Shrubby mangroves’ inside the MKWS, whereas the SAM classified larger areas of ‘Shrubby mangroves’ and smaller areas of ‘Mangrove trees’. Outside the MKWS the ML method classified more strips of grassland between agricultural areas, whereas SAM classified those areas as agricultural. Narrow features such as roads or small rivers were resolved in greater detail and more accurately by the SAM classifier. Vegetation fraction Figure 4 provides an overview of the calculated vegetation fraction. In general, we observed good correspondence between the vegetation fraction produced by the model and the density of vegetation cover as evident in high-resolution imagery available in Google Earth and the drone imagery collected by FFI. Areas dominated by dense stands of Phoenix palm resulted in the highest vegetation fraction, whereas for ‘Mangrove trees’ and ‘Shrubby mangroves’ a lower vegetation fraction was observed. This lower vegetation fraction can be seen along many parts of the immediate shoreline and along river courses leading inland of the MKWS. Very apparent is also a long strip of lower vegetation fraction which runs along the eastern side of the MKWS, which corresponds with larger areas of ‘Mangrove trees’ and ‘Shrubby mangroves’. A more detailed investigation of the relationship between mangrove quality and vegetation fraction was not possible because there were no ground truth data available. A field study that specifically examined the relationship between the vegetation fraction as derived from remote sensing and mangrove quality could be very worthwhile should a more comprehensive field survey prove possible.

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Figure 2: Land cover classification using ML classifier

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Figure 3: Land cover classification using SAM classifier

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Figure 4: Vegetation Fraction

Page 17 of 28 MEINMAHLA KYUN WILDLIFE SANCTUARY CONSERVATION PROGRAMME 3.2 Percentage of land cover types

The relative amounts of each land cover type are shown in Table 3. The percentage of land cover types in each class has been calculated for both classifiers (ML and SAM), once for the entire study area and then for the area comprising only the MKWS.

Table 3: Land cover types by relative proportions within the study area and the Meinmahla Kyun Wildlife Sanctuary

Maximum Likelihood (%) Spectral Angle Mapper (%) Land cover Study Area MKWS Study Area MKWS Water 25.9 8.2 26.1 9.7 Phoenix palm dominated 16.8 64.2 19.9 68.1 Nypa palm dominated 0.03 0.0 0.1 0.0 Mangrove trees 13.2 22.8 4.2 6.7 Shrubby mangroves 3.2 4.5 9.3 14.7 Grassland 7.9 0.1 0.4 0.02 Bare ground / Road 0.4 0.01 0.2 0.0 Agriculture 32.6 0.2 39.9 0.7

Study Area as a whole Focusing first on the broad study area, the most dominant class for both classifiers is ‘Agriculture’ comprising ~1/3 of the area, with the SAM classifier giving an area 7% larger than the ML classifier. The second largest class is ‘Water’, with both methods classifying approximately the same percentage area. The SAM classifier identified a slightly larger ‘Phoenix palm dominated’ area than the ML classifier. The classifiers differ more substantially for the remaining land cover classes. The ML classifier identified a total area of ‘Mangrove trees’ more than three times greater than the SAM classifier. In contrast, the ML classifier identified only 1/3 of the area of ‘Shrubby mangroves’ than the SAM classifier. The difference is also substantial for the ‘Grassland’ class, with the SAM classifier identifying little grassland compared to the ML classifier. The remaining classes ‘Nypa palm dominated’ and ‘Bare ground / Road’ all cover a very small area, with both classifiers identifying roughly the same size areas. Within the MKWS The most dominant class within the MKWS for both classifiers is ‘Phoenix palm dominated’, with the SAM classifier identifying a larger area by ~4%. The ML method classified more than three times the area in the ‘Mangrove trees’ class than the SAM classifier. In contrast, for ‘Shrubby mangroves’ the ML method classified only 1/3 of the area as the SAM classifier. Water covers roughly the same area in both classifiers. The remaining classes ‘Nypa palm dominated’, ‘Grassland’ and ‘Bare

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ground / Road’ all cover a very small area, with both classifiers classifying roughly the same size area.

3.3 Land cover classification reliability

The classification gives an initial assessment of the broad land cover types in the MKWS area. Eight land cover classes were distinguished in the classification: ‘Water’, ‘Phoenix palm dominated’, ‘Nypa palm dominated’, ‘Mangrove trees’, ‘Shrubby mangroves’, ‘Grassland’, ‘Bare ground / Road’, and ‘Agriculture’. The accuracy assessment resulted in an overall classification accuracy of ~78% for the Maximum Likelihood classifier, which is comparable to accuracies achieved in other typical satellite remote sensing land cover classifications (e.g. Giri et al. 2007; Satyanarayana et al. 2011). This overall accuracy was better than we had expected in view of the absence of specific ground truth data, and we anticipate that this could be improved further should specific ground truth data collection be carried out in the future. More details are given on the ‘Producer’s’ and ‘User’s accuracy’ for each class in Table 4. Class: Water This class consists mainly of ocean, lakes and rivers. ‘User’s accuracy: 100%; ‘Producer’s accuracy’ 98%. The ‘User’s accuracy’ of 100% shows that all pixels assigned to the ‘Water’ class actually represented water when verified against the available independent sources. The ‘Producer’s accuracy’ was also the highest at ~98%, which means that this percentage of the actual water area was correctly classified. The 2% misclassified was ‘Mangrove trees’. Misclassification occurred over narrow rivers which can probably be attributed to a mixed spectral response between vegetation and water. Class: Phoenix palm dominated This class consists of vegetated areas dominated by Phoenix palm. ‘User’s accuracy: 78%; ‘Producer’s accuracy’ 85%. The ‘User’s accuracy’ for ‘Phoenix palm dominated’ resulted in ~78% of pixels classified as ‘Phoenix palm dominated’ being verified as Phoenix palm, meaning there is a ~22% chance that pixels shown in the class ‘Phoenix palm dominated’ are not Phoenix palm but belong to other classes. Most of the misclassified pixels were ‘Shrubby mangroves’, but minor misclassifications also occurred for ‘Mangrove trees’. The ‘Producer’s accuracy’ was higher at ~85%, which means that this percentage of the actual Phoenix palm present was correctly classified. Class: Nypa palm dominated This class consists of vegetated areas dominated by Nypa palm. ‘User’s accuracy: 80%; ‘Producer’s accuracy’ 89%. The ‘User’s accuracy’ for ‘Nypa palm dominated’ resulted in ~80% of pixels classified as ‘Nypa palm dominated’ being verified as Nypa palm, meaning there is a ~20% chance that pixels shown in the class ‘Nypa palm dominated’ are not Nypa palm but belong to other classes. All misclassified pixels were ‘Shrubby mangroves’. The ‘Producer’s accuracy’ was higher at ~89%, which means that this percentage of the actual Phoenix palm present was correctly classified. The achieved accuracies are possibly an exaggeration. The training areas for this class had to be taken from the vegetation map found in Yong (2016), as the areas with Nypa palms present in the drone imagery weren’t large enough to derive reliable training areas. Similarly, the points assessed Page 19 of 28 MEINMAHLA KYUN WILDLIFE SANCTUARY CONSERVATION PROGRAMME in the accuracy assessment also had to be based on the vegetation map found in Yong (2016), the results might therefore be slightly biased as no independent sources could be used in the accuracy assessment, with Google Earth imagery not having a high enough spatial resolution in order to distinguish Nypa palms with certainty. Class: Mangrove trees This class consists of vegetated areas containing mangrove trees other than Phoenix and Nypa palm trees. ‘User’s accuracy: 49%; ‘Producer’s accuracy’ 82%. The ‘User’s accuracy’ for ‘Mangrove trees’ resulted in ~49% of pixels classified as ‘Mangrove trees’ being verified as mangrove trees, meaning there is a ~51% chance that pixels shown in the class ‘Mangrove trees’ are not mangrove trees but belong to other classes. Most of the misclassified pixels were ‘Shrubby mangroves’, but minor misclassifications also occurred for ‘Water’, ‘Phoenix palm dominated’ and ‘Nypa palm dominated’. The ‘Producer’s accuracy’ was higher at ~82%, which means that this percentage of the actual mangrove trees present was correctly classified. The relatively poor User’s accuracy is likely to be linked to the fact that some of the mangrove tree areas are fairly small which leads to a lot of pixels having a mixed spectral response at a spatial resolution of 10 meters. Class: Shrubby mangroves This class consists of vegetated areas containing mangrove shrubs. ‘User’s accuracy: 69%; ‘Producer’s accuracy’ 25%. The ‘User’s accuracy’ for ‘Shrubby mangroves’ resulted in ~69% of pixels classified as ‘Shrubby mangroves’ being verified as shrubby mangroves, meaning there is a ~31% chance that pixels shown in the class ‘Shrubby mangroves’ are not shrubby mangroves but belong to other classes. Misclassification occurred for ‘Phoenix palm dominated’ and ‘Mangrove trees dominated’. The ‘Producer’s accuracy’ was one of the lowest at ~25%, which means this percentage of the actual shrubby mangroves present was correctly classified. Most misclassifications placed ‘Shrubby mangroves’ in the class ‘Mangrove trees’, which suggests that the class ‘Mangrove trees’ might be overrepresented and the class ‘Shrubby mangroves’ might be underrepresented. This might also be supported by the fact that the SAM method classified more ‘Shrubby mangroves’ than ‘Mangrove trees’, whereas the reverse was the case for the ML classifier. However, as no accuracy assessment was conducted for the SAM classifier this is just an assumption. Class: Grassland This class consists of grassland. ‘User’s accuracy: 25%; ‘Producer’s accuracy’ 75%. The ‘User’s accuracy’ for ‘Grassland’ resulted in ~25% of pixels classified as ‘Grassland’ being verified as grassland, meaning there is a ~75% chance that pixels shown in the class ‘Grassland’ are not grassland but belong to other classes. Most of the misclassified pixels were ‘Agriculture’, but minor misclassifications also occurred for ‘Shrubby mangroves’. The ‘Producer’s accuracy’ was higher at ~75%, which means that this percentage of the actual grassland present was correctly classified. The extremely low User’s accuracy can be explained with the fact that grassland and agricultural areas can have a very similar spectral response. Also the training areas for grassland were very limited and further ground-truthing areas would probably have improved the accuracy of this class.

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Class: Bare ground / Road This class consists mainly of bare ground and roads. ‘User’s accuracy: 20%; ‘Producer’s accuracy’ 40%. The ‘User’s accuracy’ for ‘Bare ground / Road’ resulted in ~20% of pixels classified as ‘Bare ground / Road’ being verified as Bare ground / Road, meaning there is a ~80% chance that pixels shown in the class ‘Bare ground / Road’ are not Bare ground / Road but belong to other classes. Most of the misclassified pixels were ‘Agriculture’. The ‘Producer’s accuracy’ was higher at ~40%, which means that this percentage of the actual Bare ground / Road present was correctly classified. The extremely low User’s and Producer’s accuracy can be explained with the fact that grassland and fallow agricultural areas can have a very similar spectral response. Also narrow roads were often misclassified as agricultural areas due to a mixed spectral response given the resolution of the imagery. Class: Agriculture This class consists of agricultural areas. ‘User’s accuracy: 92%; ‘Producer’s accuracy’ 79%. The ‘User’s accuracy’ for ‘Agriculture’ resulted in ~92% of pixels classified as ‘Agriculture’ being verified as agriculture, meaning there is a ~8% chance that pixels shown in the class ‘Agriculture’ are not agriculture but belong to other classes. The misclassifications were equally distributed across the three classes ‘Shrubby mangroves’, ‘Grassland’ and ‘Bare ground / Road’. The ‘Producer’s accuracy’ was lower at ~79%, which means that this percentage of the actual agriculture present was correctly classified. This is likely to be because many agricultural areas possess bare ground, with spectral signatures similar to those classes. Feature shape and pattern could be used to improve the classification, although this type of analysis is beyond the scope of the current study.

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Table 4: Accuracy Assessment Error Matrix for the ML classified image

Reference (Actual) Data

2 User’s Commission 1 2 3 4 5 6 7 8 Total 1 Accuracy (%) (%)

Water (1) 78 0 0 0 0 0 0 0 78 100 0

Phoenix palm dominated (2) 0 39 0 2 9 0 0 0 50 78 22 Nypa palm dominated (3) 0 0 8 0 2 0 0 0 10 80 20 Mangrove trees (4) 2 5 1 18 11 0 0 0 37 49 51

Shrubby mangroves (5) 0 2 0 2 9 0 0 0 13 69 31 Classified Data Grassland (6) 0 0 0 0 2 6 0 16 24 25 75 Bare ground / Road (7) 0 0 0 0 0 0 2 8 10 20 80 Agriculture (8) 0 0 0 0 3 2 3 90 98 92 8

Total 80 46 9 22 36 8 5 114 320

Producer’s Accuracy3 (%) 98 85 89 82 25 75 40 79 Overall accuracy: 78% Omission4 (%) 3 15 11 18 75 25 60 21

1User’s Accuracy: Accuracy from an observer’s perspective, informs the user about the reliability of the map as a predictive tool – what percentage of the classified image corresponds correctly to the same actual land cover type on the ground. 2Commission: Informs what percentage of a land cover class was erroneously assigned, or committed, to an incorrect land cover class on the map. 3Producer’s Accuracy: Accuracy from the model’s perspective, informs the analyst who prepared the classification how well a class was classified – what percentage of the actual land cover in that class was correctly classified. 4 Omission: Informs what percentage of a land cover class on the ground has been omitted from that class on the map.

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3.4 Limitations to the study

The semi-supervised classification offers a substantial improvement over a fully unsupervised classification, although has several limitations as a result of the limited ground truth data that have been applied in the analysis: ● A fully and specifically designed ground-truthing programme was not undertaken for the land cover classification. ● Detailed vegetation observations from Yong (2016) could not be made available to aid in the creation of training areas. This present study could therefore only use the output maps prepared based on those observations, which proved to lack sufficient detail to create land cover classification training areas. ● The training areas for vegetation classes within the MKWS are based on information collected during an FFI field campaign acquiring drone imagery over the area in October 2016. Ground- truthing for areas outside the MKWS were not available, and therefore had to be created based on the Sentinel-2 imagery and available Google Earth imagery. ● No training areas for mangrove vegetation were created outside of the MKWS as it was not possible to identify particular vegetation types using the resolution of imagery on Google Earth. ● Intact mangrove areas containing mature trees of e.g. Sonneratia are fairly small in size, which makes it difficult to be detectable in imagery with a spatial resolution of 10m. ● Attempts were made to classify urban areas, although the spectral response was always mixed with other land cover types due to the resolution of the imagery, resulting in many mis- classifications. The attempt to classify urban areas was therefore abandoned. ● Spectral responses based on NDVI capture the density of growth or canopy rather than the maturity of the trees, and hence may not accurately reflect the level of degradation. ● Spectral responses of different land cover types can be similar; for example agriculture areas may exhibit a similar spectral response to bare ground or grassland depending on the growing stage, and it may not be possible to separate them on the basis of spectral signature alone. ● Candidate sites for erosion could not be identified, in part because the spectral response of bare ground and agricultural areas are similar, and in part because only one point in time has been evaluated. ● The accuracy assessment for vegetated areas was limited to the areas covered by the drone survey. Vegetated areas outside the MKWS were therefore not included in the accuracy assessment, and for this reason the reliability of the classification in those areas remains less well known. ● The accuracy assessment was only undertaken for the ML classifier due to time and budget constraints in this pilot study. It would be beneficial to undertake an accuracy assessment for the SAM classifier, in order to investigate the behaviour of different classifiers and understand better the sensitivity of results to the application of different methods.

4. CONCLUSION

1. Despite the limited availability of ground-truthing data, the pilot study showed that it is possible to distinguish between Phoenix palm (Phoenix paludosa) and other mangrove trees within the MKWS. 2. It was confirmed that Phoenix paludosa is the dominant vegetation type within the MKWS and that other mangrove vegetation is generally only present along the shoreline and river banks. 3. Derived vegetation fraction from NDVI resembled the vegetation density well. 4. The study identified agricultural areas well. Page 23 of 28 MEINMAHLA KYUN WILDLIFE SANCTUARY CONSERVATION PROGRAMME

5. It was not feasible to identify built-up areas due to their small extent and mixed spectral response, given the spatial resolution of Sentinel-2 imagery. 6. The identification of potential erosion sites did not prove feasible given the spectral similarity between agricultural areas and bare ground and the fact that only one point in time was analysed. 7. The pilot study showed that high-resolution imagery acquired by drone is suitable to derive ground-truth locations for satellite image classification in the absence of ground-truth locations acquired on the ground. However, accurate identification of species present in the drone imagery remains a key step for high-quality satellite image classification. Future utilisation of drones for ground-truth acquisition should involve vegetation experts in the task of species identification in the drone images, and we expect this would result in improvements to the classification results.

4.1 Further work

● A more detailed ground-truthing programme specifically targeted at creating land cover classifications should be conducted to include also areas outside the MKWS if a more robust land cover classification is desired. ● The present study clearly identified a distinction between ‘Phoenix palm dominated’ and other mangrove vegetation. However, the study would benefit from a more detailed ground-truthing especially for ‘Mangrove trees’ and ‘Shrubby mangroves’ as mis-classification occurred especially between those two vegetation classes. Expert input from vegetation specialists interpreting the drone imagery is likely to be very helpful in the regard. ● Similar classifications could be derived using Landsat data, which is the data that most likely would be used to create land cover classification for historic dates, e.g. pre-Nargis, post-Nargis. However, given that the spatial resolution of Landsat is 30 m this will impose some additional limitations. Moreover, because the different vegetation classes cannot be visually distinguished in the Landsat imagery and ground-truthing is highly unlikely to be available for historical imagery, a spectral signature catalogue would need to be built based on current imagery, which could then be applied to the historical imagery to obtain a classification that was comparable. ● To quantify the rate at which mangrove loss or degradation is occurring, land cover classifications could be repeated for imagery at an appropriate time interval to identify changes in the landscape.

4.2 Recommendations for the 5 year management plan

Recommendation 1 The dominance of Phoenix palm in the degraded mangrove forest areas is quite remarkable, and to our knowledge is not recorded in such high abundance in any other mangrove reserve ecosystem (Macintosh, pers. comm. and literature searches producing nil results). Phoenix is usually restricted to back-mangrove zones and normally does not grow to the height seen in MKWS (Macintosh, pers. comm.). This dominance threatens the integrity of MKWS ecosystem, which is in danger of being lost. It is recommended that FFI assess the potential for a P. paludosa eradication plan, with the aim of replanting mangroves to restore ecosystem integrity in the MKWS. Recommendation 2 It is recommended that a study on the autecology of P. paludosa is undertaken to determine how this species is growing, reproducing and colonising in order to best understand the level of degradation, and potential for restoring mangrove forests to MKWS. Page 24 of 28 MEINMAHLA KYUN WILDLIFE SANCTUARY CONSERVATION PROGRAMME

Recommendation 3 Yong (2016) suggested that "There is no need to control the current excessive growth of Phoenix paludosa (Thinbaung), which is also harvested by the locals as secondary sources for building materials (e.g. poles)". However, to our knowledge Yong’s surveys were conducted mostly from the water, and the full extent of the P. paludosa monoculture may not have been observed. Our data show that this 'excessive growth' of P. paludosa is at a very wide scale and imposes a severe limitation to mangrove biodiversity. Our finding that the most intact mangrove habitat is largely restricted to the northeast provides some basis for identifying conservation zones in MKWS. It is recommended that FFI assess the feasibility of developing with locals a plan for utilisation of P. paludosa in the reserve to help address the dominance of this species. Such a plan would need to recognise the current level of illegal activity in the reserve, and the challenges and complexity of implementing regulations to allow access for some resources (P. paludosa) and not others (fish, crab, other mangrove). Recommendation 4 Yong (2016) recommended that " Elsewhere, Kanazo (Heritiera fomes), Intsia bijuga, Rhizophora (2 species), Bruguiera (4 species), Ceriops (2 species) and Xylocarpus (2 species) must be re-planted in large numbers to compensate for the large numbers harvested previously for fuelwood and charcoal production". However, because of the current lack of available cleared area in MKWS to undertake planting, it is recommended that an assessment of the feasibility of Phoenix eradication and mangrove regeneration is undertaken and a replanting strategy for the MKWS is implemented. Recommendation 5 Based on the limited mangrove other than P. paludosa both inside and outside the MKWS, the associated risk of erosion to communities, and the current reliance of locals on MKWS for fuelwood (Yong, 2016; Macintosh, 2016), it is recommended to combine riverbank protection and a wood lot system on a significant scale. This will help shift resource pressure away from MKWS, whilst still maintaining access to resources for the 22 project villages surrounding the sanctuary. This recommendation combines the follow suggestions proposed in other 2016 reports to FFI: Yong (2016) outlines the following erosion control measure: “The erosion control measures along the river banks (both east and west banks) could be carried out over 2- 4 years using Rhizophora apiculata (Byuchidauk-pho) and Rhizophora mucronata (Byuchidauk-ma) as the “frontline” species. This is followed by a second zone of Bruguiera gymnorhiza (Byu-oak-saung), Avicennia officinalis (Thame), and Ceriops decandra (Madama). The 3rd zone should contain Bruguiera sexangula, Heritiera fomes (Kanazo), Lumnitzera racemosa, and Xylocarpus granatum (Kyana). It is vital to re-build the entire mangrove species diversity at MKWS and the two adjacent banks (both east and west banks) on the basis that different species provide different form of leaf biological materials for different organisms (Ohn 1992). During decomposition, mangrove leaf litter becomes enriched in protein and serves as a food source for a wide variety of filter, particulate and deposit feeders such as molluscs, crabs, and polychaete worms. These primary consumers, which include representatives of most phyla, in turn form the food of a secondary consumer population. The secondary consumer level is usually dominated by small forage fish species and by the juveniles of the larger predatory species that form the third consumer level. In addition, there are important fishery species, such as prawns, which occupy both primary and secondary consumer levels. They feed directly on particular organic detritus and also feed to some extend upon primary consumers. An additional source of nutrition for estuarine organisms is provided by dissolved organic compounds (e.g. amino acids), is also largely of mangrove origin". "Behind these mangrove plants, a buffer zone of salt tolerant terrestrial trees like Eucalyptus camaldulensis, Acacia auriculiformis, Acacia mangium, Melaleuca cajaputi and Casuarina spp should be planted. Research is needed to find suitable native and non-native species to enrich the Page 25 of 28 MEINMAHLA KYUN WILDLIFE SANCTUARY CONSERVATION PROGRAMME

current exotic plant species brought in by various NGOs and international aid agencies to increase the species available for silvicultural community needs within the two banks opposite MKWS and also for the entire Ayeyarwady (or Irrawaddy) delta area". The addition of a wood lot system is proposed by Macintosh (2016): “There is a particular need to establish and manage wood lots in each village to offset the exploitation of mangrove trees in MKWS for fuelwood and poles. Woodlots can be developed in conjunction with the planting of trees (both mangrove and non-mangrove species) for riverbank protection against floods and soil erosion. This represents a major, but vital, undertaking that can be approached through the mechanism of Community Forests. Under a recently revised Forest Department directive, Community Forests have several objectives, including: a) to fulfill the basic needs of the community concerned with timber, pole wood, fire wood and charcoal; and b) to develop forest cover area and sustainability.” Recommendation 6 Our data corresponded well with the 'high priority conservation areas' recommended by Yong (2016), based on the ML method and our data support the immediate action plan proposed by Yong (2016) to preserve Sonneratia griffitii habitat in the north east of MKWS at sites A, B, C, D and E (Figure 5): “NO TAKE” Highest Priority Mangrove Conservation locality (recommended 1-2 km radius). Good regeneration of either Critically Endangered species or Old Growth Forest will take place if there is adequate protection. Site A – Sonneratia griffithii (IUCN Red List Critically Endangered species) Site B – Some Sonneratia griffithii Site C – NEW Sonneratia species or hybrid Site D – Some Sonneratia griffithii Site E – Large population of Sonneratia griffithii

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Figure 5: High Priority Conservation Area and Land cover classification using ML classifier

Page 27 of 28 MEINMAHLA KYUN WILDLIFE SANCTUARY CONSERVATION PROGRAMME 5. REFERENCES

Giri, C., Pengra, B., Zhu, Z., Singh, A. & Tieszen, L.L. 2007. Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuarine Coastal and Shelf Science 73: 91–100. doi:10.1016/j.ecss.2006.12.019 Gutman, G. & Ignatov, A. 1998. The derivation of the green vegetation fraction from NOAA / AVHRR data for use in numerical weather prediction models. International Journal of Remote Sensing 19(8): 1533–43. Harris, C., Lorenz, K. & Zöckler, C. 2016. Land cover classification, Mawdin Coast, Ayeyarwaddy Region, Myanmar. Unpublished report prepared by ERA & ArcCona for Fauna & Flora International. Macintosh, D.J. 2016. Assessment of fisheries management needs and sustainable livelihood opportunities in the villages surrounding Meinmahla Kyun Wildlife Sanctuary. Satyanarayana, B., Mohamad, K.A., Idris, I.F., Husain, M. & Dahdouh-Guebas, F. 2011. Assessment of mangrove vegetation based on remote sensing and ground-truth measurements at Tumpat , Kelantan Delta , East Coast of Peninsular Malaysia. International Journal of Remote Sensing 32(6): 1635–50. doi:10.1080/01431160903586781. Yong. J.W.H. eds (2016). An ecological and plant biodiversity assessment of Meinmahls Kyun Wildlife Sanctuary (MKWS) in relation to human livelihood and biodiversity conservation and restoration.

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