Comparing vegetation indices for mangrove forest mapping using remontely sensed data in kien thuy and do son district hai phong city

MINISTRY OF AGRICULTURE AND RURAL DEVELOPMENT VIETNAM FORESTRY UNIVERSITY STUDENT THESIS COMPARING VEGETATION INDICES FOR MANGROVE FOREST MAPPING USING REMOTELY SENSED DATA IN KIEN THUY AND DO SON DISTRICT, HAI PHONG CITY Major: Natural Resources Management (Advanced Curriculum) Code: D850101 Faculty: Forest Resources and Environmental Management Student: Ha Duc Thien Student ID:1053091718 Class: K55 Natural Resources Management Course: 2010 - 2014 Advanced Education Program Developed in collaboration with Colorado State University, USA Supervisor: Dr Nguyen Hai Hoa Hanoi, November 2015 ACKNOWLEDGMENTS “People not lack strength; they lack will” Victor Hugo I would like express my gratitude to my supervisor, Dr Nguyen Hai Hoa for his support, guidance and encouragement throughout the process of this study My sincere appreciation to the members of my teammate whose loyalty and creative endeavor made possible this paper ABSTRACT The use of vegetation indices of remote sensing data in vegetation mapping has been long recognized However, the accuracy of mapping through the use of vegetation indices model has limitations, and has so far not been investigated This study compared performance of the vegetation indices (Normalized Difference Vegetation Index- NDVI, Simple Vegetation Index-SVI, Soil-Adjusted Vegetation Index- SAVI) for mangrove mapping in Kien Thuy district and Do Son county, Hai Phong city Landsat Image was used as a primary data to derive mangrove vegetation class from three vegetation indices models A total of three mangrove habitat categories were detected consisting of kandelia Obovate and Sonneratia Caseolaris, Mixed - Kandelia Obovate and Sonneratia Caseolaris, Mixed Sonneratia Caseolaris The accuracy assessment of vegetation indices were ranged from 68.3% to 75.8% The results indicated that the SAVI was the best index for mangrove mapping compared to other indices with accuracy of 75.8% and able to determine three mangrove classes i KEY WORDS Vegetation indices, Landsat Image, mangrove mapping performance, accuracy assessment ACRONYMS GIS Geographic Information System GPS Global Positioning System SVI Simple Vegetation Index NDVI Normalized Difference Vegetation Index SAVI Soil-Adjusted Vegetation Index ii TABLE OF CONTENTS ABSTRACT i KEY WORDS ii ACRONYMS ii TABLE OF CONTENTS iii LIST OF TABLES vi LIST OF FIGURES .vii Chapter I Introduction Chapter II Literature Review 2.1 Overview of using vegetation index for mangrove mapping 2.2 Key vegetation indexes for coastal mangrove mapping 2.3 Significance of study site 14 Chapter III Study goals, Objectives and Methodology 15 3.1 Study goals and Objectives 15 3.1.1 Study goal 15 3.1.2 Study objectives 15 3.2 Study scope 15 3.3 Methodology 16 3.3.1 Investigation and determination of coastal mangrove species composition and its habitat 16 3.3.2 Calculation of different types of vegetation indices used to classify mangroves 17 iii 3.3.2.1 Image pre-processing 17 3.3.2.2 Image processing 18 3.3.2.3 Calculating vegetation indices for mangrove classification 19 3.3.2.4 Accuracy assessment 20 3.3.2.5 Post classification 22 3.3.2.6 Mangrove mapping 22 3.3.2.7 Assessing and comparing different kinds of vegetation indices for mangrove mapping 22 3.3 Quantifying spatial dynamics of coastal mangroves in study areas during period 2010 – 2014 22 Chapter IV STUDY SITE, NATURAL AND SOCIOECONOMIC FEATURES 23 4.1 Natural characteristics 23 4.1.1 Geographical location 23 4.1.2 Topography 24 4.1.3 Climate 24 4.1.4 Hydrology 24 4.1.5 Natural resources 25 4.2 Socioeconomic conditions 25 4.2.1 Population 25 4.2.2 Economy 26 4.2.3 Ecological and economic values of mangroves 26 iv Chapter V RESULTS AND DISCUSSION 29 5.1 Spatial distribution and structures of coastal mangroves in study sites 29 5.2 Comparison of different kinds of vegetation indces for mangrove classification 33 5.3 Dynamics of coastal mangroves during 2010- 2014 37 5.3.1 Thematic maps and dynamics of coastal mangroves 37 5.3.2 Key drivers of coastal mangrove changes from 2010 to 2014 41 Chapter VI GENERAL CONCLUSION, LIMITATION AND FURTHER STUDY 43 6.1 Conclusions 43 6.2 Limitations and further study 43 REFERENCES 45 v LIST OF TABLES Table.3.1 Landsat data used this study 18 Table 5.1: Synthesis of average mangrove structure characeteristics 32 Table.5.2.1 Values of vegetation Indices for mangrove classification 33 Table 5.2.2 Accuracy assessment of image classified using SVI in 2014 34 Table.5.2.3 Accuracy assessment of image classified using NDVI in 2014 35 Table.5.2.4 Accuracy assessment of image classified using SAVI in 2014 36 Table.5.2.5.Summarization of vegetation indices for mangrove classification 37 Table 5.3.1: The extent of coastal mangroves in the study areas (ha) 38 Table 5.3.2: Dynamic of mangroves during period 2010 -2013 39 Table 5.3.3: Dynamic of mangroves during period 2013 -2014 39 vi LIST OF FIGURES Fig 3.1: Clipped images of study sites in Hai Phong: (a) image in 2010, (b) image in 2013, (c) image in 2014 19 Fig.4.1 Study sites in Hai Phong where: (a) Viet Nam map, (b) Hai Phong city map (c) Selected sites as Kien Thuy District and Do Son County 23 Fig 5.1: Species distribution of coastal magroves in Bang La and Dai Hop, Hai Phong 29 Fig.5.2 Mangrove species in different vegetation indices: (a) SVI – Simple Vegetation Index; (b) NDVI- Normalized Difference Vegetation Index; (c) SAVI- Soil Adjusted Vegetation Index 33 Fig 5.3.1: Distribution of mangrove extents during the period 2010 - 2014 38 Fig 5.3.2: Spatial dynamics of coastal mangroves in study sites during two periods 40 Fig 5.3.3: Fluctuation of mangroves area in study area 41 vii Chapter I Introduction Mangrove forests appear in the inter-tidal zones along the coast in most tropical and semi-tropical regions (Tuan, Oanh et al., 2002).They are among the most important and productive of ecosystems and provide habitat for wildlife (Wolanski, Brinson et al., 2009) Mangroves play an important role in coastal zones and can reduce damage from the effects of tsunamis The most obvious evidence can be found from the Indian Ocean tsunami of Dec, 2004 (Danielsen, Sorensen et al., 2005) Moreover, mangrove ecosystems stabilize coastlines, clean water, protect land from erosion, and in many cases promote coastal accretion, and provide a natural barrier against storms, cyclones, tidal bores and other potentially damaging natural forces For centuries, mangroves have contributed significantly to the socioeconomic lives of coastal dwellers In addition, they are a source of timber for fire-wood and provide building materials, charcoal, tannin, food, honey, herbal medicines, and other forest products (Hong and San, 1993) Importantly, mangrove forests are amongst the most carbon-rich ecosystems in the tropics (Donato, Kauffman et al., 2011) and are recognized as performing a vital role in climate change mitigation thanks to “blue carbon“ storage (Pendleton L, 2012) Despite their enormous socio-economic value, mangrove ecosystems are under severe threats High population growth, and migration into coastal areas, has led to an increased demand for their products This situation is further exacerbated by insufficient governance, poor planning, and un-coordinated economic development in coastal zones Globally more than 3.6 million hectares of Mangroves have been lost since 1980, and Asia has suffered the greatest loss of 1.9 million hectares (FAO 2007) Accuracy Assessment of mangrove classification Table 5.2.2 Accuracy assessment of image classified using SVI in 2014 Field-based GPS in 2014 Image classified used SVI 2014 Field work Kandelia and Sonneratia Mixed Kandelia and Sonneratia Mixed Sonneratia Water Wetlands Agriculture Kandelia and Sonneratia 33 1 Mixed - Kandelia and Sonneratia 35 Mixed Sonneratia Water Wetlands Total Accuracy (%) 43 76.7 49 71.4 12 50.0 50.0 10 50.0 120 68.3 Agriculture Built-up Total Built-up 40 48 15 34 12 Table.5.2.3 Accuracy assessment of image classified using NDVI in 2014 Field-based GPS in 2014 Image classified used NDVI 2014 Field work Mixed Sonneratia 37 Mixed Kandelia and Sonneratia 40 Mixed Sonneratia Water Wetlands Kandelia and Sonneratia Mixed - Kandelia and Sonneratia Kandelia and Sonneratia Water Wetlands Total Accuracy (%) 44 84.1 52 76.9 44.4 33.3 44.4 120 72.5 Agriculture Built-up Total Agriculture Built-up 41 52 10 35 10 Table.5.2.4 Accuracy assessment of image classified using SAVI in 2014 Image classified used SAVI 2014 Field-based GPS in 2014 Field work SAVI Image Kandelia and Sonneratia Mixed Mixed Water Kandelia Sonneratia and Sonneratia Kandelia and Sonneratia 39 Mixed - Kandelia and Sonneratia 44 Mixed Sonneratia 3 Wetlands Agriculture Built-up Total Accuracy (%) 47 83.0 55 80.0 33.3 Water 60.0 Wetlands 1 50.0 120 75.8 Agriculture Built-up Total 49 54 36 Table.5.2.5.Summarization of vegetation indices for mangrove classification Classifier Total Accuracy Number of mangrove classes SVI 68.3 3/3 NDVI 72.5 3/3 SAVI 75.8 3/3 As a result in Table 5.2.5 shows SVI is the lowest accuracy (68.3%) and distinguishs three classes (Graetz, 1990), stated that the canopy texture is the main effect to SVI model, although it has ability to identify the soil and vegetation well, but not in shady area In contrast, the highest accuracy obtained for three vegetation indices was SAVI (75.8%), indicating SAVI is the most useful vegetation index to classify the mangroves (Table 5.2.5) In this study, the number of mangroves can be increased by adjusting the parameter of L from 0.5 into 0.16 as recommended by Rondeaux et al., (1996) The value of L = 0.16 rather than 0.5 is found to give satisfactory reduction of soil noise both at low and high vegetation cover SAVI also can be applied for general purpose vegetation classes because it is more constant sensitivity over the full range of vegetation cover The NDVI performance shows that there is 72.5% accuracy This accuracy is very closed to SAVI, presenting NDVI is a good indicator for discriminating species classes 5.3 Dynamics of coastal mangroves during 2010- 2014 5.3.1 Thematic maps and dynamics of coastal mangroves As findings found in 5.2 section, there is no much difference between SAVI and NDVI used for mangrove classification Therefore, this study uses NDVI (Normalized Difference Vegetation Index) to further examine changes of coastal mangroves from 2010 to 2014 Table 5.3 shows that the extent of coastal mangroves has generally increased since 37 2010 The extent of mangroves accounted for 381.8 in 2010, increased to 460.5 in 2013 and 472.8 in 2014 It means that the extent of mangroves increased 78.7 during period 2010 - 2013 and 12.5 between 2013 and 2014 On the contrast, non-mangrove areas decreased from 2010 to 2014 In particular, non-mangrove areas decreased by 12.2 from 2010 to 2013 and by 60.9 (182.7 decrease 121.8 ha) from 2013 to 2014 The area covered by water fluctuated over year from 2010 to 2013 In fact, the area decreased by 102.9 ha, but increased again by 48.6 from 2013 to 2014 Table 5.3.1: The extent of coastal mangroves in the study areas (ha) Year Mangroves Non-mangroves Water 2010 381.8 194.9 273.2 2013 460.5 182.7 170.3 2014 472.8 121.8 218.9 2013 2010 2014 Fig 5.3.1: Distribution of mangrove extents during the period 2010 - 2014 38 Dynamics of coastal mangroves during period 2010 – 2014 In general, coastal mangroves have relatively small fluctuations during the period 2010 to 2014 (Table 5.3.2 and Table 5.3.3), but its extent increased over last years The extent of mangroves increased by 20.6% from 2010 to 2013, greater than the period of 2013 - 2014 (2.7%) In contrast, the areas with non-mangroves in period 2010 - 2013 (6.3%) is less than period 2013 - 2014 (33.3%) It indicates that mangrove restoration and rehabititation programs are effective during this period (2010- 2014) these study sites Table 5.3.2: Dynamic of mangroves during period 2010 -2013 Years Changes 2010 2013 Classified objects Extent (ha) % Mangroves (ha) 381.8 460.5 78.7 20.6 Non-mangroves (ha) 194.9 182.7 -12.2 -6.3 Water (ha) 273.2 170.3 -102.9 -37.7 Table 5.3.3: Dynamic of mangroves during period 2013 -2014 Years Changes 2013 2014 Classified objects Extent (ha) % Mangroves (ha) 460.5 472.8 12.3 2.7 Non-mangroves (ha) 182.7 121.8 -60.9 -33.3 Water (ha) 170.3 218.9 -48.6 -28.5 39 Fig 5.3.2: Spatial dynamics of coastal mangroves in study sites during two periods Fig 5.3.1 and Fig 5.3.2 shows the extents of mangroves, non- mangroves and waters increased or decreased during the certain period as the following: Period 2010 - 2013: In this period, mangroves area has increased significantly There was 381.8 of mangroves in 2010 and 472.8 of mangroves in 2013 This is the period that the area of mangroves increased due to planting and protecting mangrove to disaster preparedness Mangrove plantation was supported by the central government through the 327 Program (now 661 Program) and the project of the Danish Red Cross, resulting in a significant increase of mangrove forest areas Period 2013 – 2014: In 2013, there were 219.87 of mangroves and forest area increased slightly to 408.96 in 2014 This is also the period that government of Hai 40 Phong city focuses on mangroves protecting activities and propaganda activities, so mangroves area and quality increases continuously 500 Area (ha) 400 300 200 100 2010 2013 Years Mangroves Non-mangroves 2014 Water Fig 5.3.3: Fluctuation of mangroves area in study area 5.3.2 Key drivers of coastal mangrove changes from 2010 to 2014 As shown from the study, the extent of mangroves has generally an increasing trend from 2010 – 2014 In particular, mangroves was 381.8 in 2010, 460.5 in 2013 and 472.8 in 2014 Total increasing areas are 91 from 2010 to 2014, especially increasing area is 78.7 allocated during the period of 2010 to 2013 The key drivers contribute to an increase of coastal managroves summarised as the following: Good management policies Policies were the guideline for the conservation and sustainable development of forest system in general and mangroves in particular Since the 1990s of the 20th century, countries in the region and the world was acutely aware of the importance of the promulgation of policies relating to coastal mangroves The policies system of 41 government, which was performed in the area of coastal provinces in Northern Vietnam, were born in 1990s In study site, coastal mangrove management policies has been applied more detail since 1998 Bang La and Dai Hop are two adjacent communes Research area located in both communes For this reason, that strip of coastal mangroves outside of more than 7km of sea dyke, which influenced by two management policy systems as well as two specialized teams of two communes In both Dai Hop and Bang La maintained the ranger department annually However, there are differences about changing the awareness of local people These differences result to the difference between mangroves quality in two communes Therefore, after 2005, local administrator and Red Cross of HaiPhong city continue to implement the last period of some projects In conclusion, related – policies and projects on coastal mangrove management in study site had been influenced by common features of eras of natural resources policies in Vietnam Policies have positive changes Starting point at the primitive period (1990 1997); Recovery & development period (1997 – 2005); and continuously sustainable development period (2005 - 2014) Improved local livelihoods A result of interviewing local households in Dai Hop and Bang La communes and local administrations shown that there were the significant changes in relation to local livelihood 42 Chapter VI GENERAL CONCLUSION, LIMITATION AND FURTHER STUDY 6.1 Conclusions Based on findings obtained after studying coastal mangroves in Kien Thuy and Do Son district, Hai Phong during the period of 2010 - 2014, this study draws some key conclusions as the following: Study sites are located in Kien Thuy and Do Son district, Hai Phong city with, nearly 8000 Coastal mangrove species are mainly Sonneratia caseolaris and Kandelia obovata Three vegetation indices are good and useful for mangrove classification However, as results show that SAVI (75.8%) and NDVI (72.5%) have higher accuracy than SVI (68.3%) These findings are consistent with other studies Therefore, it is suggested that NDVI and SAVI should use for coastal mangrove classification The extent of coastal mangroves generally increased from 2010 to 2014 Mangrove extents accounted for 381.8 in 2010, increased to 460.5 in 2013 and 472.8 in 2014 The key drivers significanly contribute to an increase of coastal managroves, namely good management policies on coastal management and improved local livelihoods 6.2 Limitations and further study Although it has achieved some significant results, study still remains shortcomings The scope of study is quite large, some coastal mangroves species could be losing To overcome the shortcomings and achieve better results, further studies should 43 with higher spatial resolution images such SPOT and QuickBird images be conducted to determined more clearly In addition, a number of sampling points should be increased for enhancing the accuracy of image classifications In addition, future study should use higher spatial resolution images for mangrove mapping 44 REFERENCES [1] Badhwar, G.D; Verhoef, W.; Bunnick,N.J.J., (1985) Comparative of suits and SAIL canopy reflectance models Remote Sensing of Environment ,17: 179-260 [2] Baret F, Guyot G and Major D (1989), TSAVI: A Vegetation Index Which Minimizes Soil Brightness Effects on LAI and APAR Estimation, 12th Canadian Symposium on Remote Sensing and IGARSS'90, Vancouver, Canada, Vol [3] Baret F Guyot G (1991), Potential and Limits of Vegetation Indices for LAI and AFAR Assessment Remote Sensing and the Environment, Vol 35, pp 161-173 [4] Bannaii A, Huete A R, Morin D and Zagolski (1996), EfFets de la Couleur et de la Brilliance du sol sur les Indices ds Vegetation, International Journal of Remote Sensing, Vol 17, No 10, pp 1885-1906 [5] Congalton , R.G , (1991) A review of assessing the accuracy of classification of remoted sensed data Remote Sensing Environment , 37(5): 35-46 [6] Elmore , A.J., Mustard , J.F., Minning , S.J 2000 Quantifying vegetation change in semiarid environment : Presicion and accuract of spectral mixture analysis and the normalized difference vegetation index Remote Sensing of Environment , 73: 83-102 [7 Tuan, L X., Y Munekage, Q T Q Dao, N H Tho and P T A Dao (2003) Management in Mangrove Areas Environmental Informatics Archives 1: 38-52 [8] Gao, J., Chen, H.F., Zhang, Y., & Zha, Y (2004) Knowledge based approaches to accurate mapping of mangroves from satellite data Photogrammetric Engineering and Remote Sensing, 70, 1241-48 [9] Gong, P., Pu, R., and Miller, J.R 1995 Conifers forest leaf area index estimation along the Oregon transect using Airborne Spectrographic Imager data Photogrammetric Engineering and Remote Sensing , 61: 1107-1117 45 [10] Graet, R.D 1990 Remote sensing of terrestrial ecosystem structure : an ecologist’s pragmatic view Pp 5-30 InR.J Hobbs and H.A Mooney (Eds.), Remote sensing of Biospere Functioning New York : Springer – Vrlag [11] Green , E.P.; Clark,C.D.; Mumby, P.J.; (1998) Remote sensing technique for mangrove mapping Int J of Remote Sensing , 19 (5):935-956 [12] Guyot, G., & Gu, x (1994) Effect of Radiometric Corrections on NDVI-Determined from SPOT-HRV and Landsat-TM Data Remote Sensing of Environment, 49,169180 doi: 10.1016/0034-4257(94)90012-4 [13] Huete A R (1988), A Soil Adjusted Vegetation Index (SAVI), Remote Sensing and the Environment, Vol 25, pp 53-70 [14] Jiang , Z., Huete, A.R., Chen, j., Chen, Y., 2006 Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction Remote Sensing of Environment , 101: 366-378 [15] Lillesand, T.M., and R.W Kiefer and J.W Chipman Remote Sensing and Image Interpretation 6th ed New York: Wiley, 2007 [16] Ramsey, E.W and Jensen, J.L 1996 Remote sensing of mangrove wetlands: Relating canopy spactra to site specific data Photogrammetric Engineering and Remote Sensing , 62(8):939-948 [17] Richerdson A J, and Wiegand C L (1977), Distinguishing Vegetation from Soil Background Information, Photogramm Eng Remote Sens., Vol 43, pp 1541-1552 [18] Rondeaux, G.; Steven , M.; Baret, F., (1996) Optimization of soil adjusted vegetation indices Remote Sensing of Environment , 51: 375-384 [19] Rose J W Jr., Mass R H, Schell J A, Deering D W and Harlan J C (1974),Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation, NASA/GSFC Type III Final Report, Greenbelt, MD, pp 371 46 [20] Rose J W Jr, Mass R H, Schell J A, and Deering D W (1973), Monitoring Vegetation Systems in The Great Plains with ERTS, Earth Resource Technology Satellite-1 Symposium, Goddard Space Flight Center, Washington DC, pp 309-317 [21] Qi J, Chehbouni A, Huete A R, Kerr Y H and Sorooshian S (1994), A Modified Soil Adjusted Vegetation Index Remote Sensing and the Environment, Vol 48, pp 119-126 [22] Suffian , I., (2001) Udentification of Vegetation Species in the East Coast Mangrove of Peninsular Malaysia Using Vegetation Index M.Sc Thesis , KoLej Universiti Sains dan Teknologi Malaysia, Terengganu , Malaysia 190p [23] Hai-Hoa, N., (2013) The relation of coastal mangrove changes and adjacent land-use: A review in Southeast Asia and Kien Giang, Vietnam Ocean and Coastal Management 90:1-10 [24] Tuan, L D., T T K Oanh, C V Thanh and N D and Quy (2002) Can Gio Mangrove Biosphere Reserve Ho Chi Minh city, Agricultural Publishing house [25] Wolanski, E., M M Brinson, D R Cahoon and G M E Perillo (2009) Coastal Wetlands: An Integrated Ecosystem Approach The Netherlands, Elsevier [26] Campbell, J.B., (1996) Introduction to Remote Sensing Taylor and Francis, London, 622p [27] Dat, P T and K Yoshino (2013) Comparing Mangrove Forest Management in Hai Phong City, Vietnam towards Sustainable Aquaculture Procedia Environmental Sciences 17(0): 109-118 [28] Donato, D C., J B Kauffman, D Murdiyarso, S Kurnianto, M Stidham and M Kanninen (2011) Mangroves among the most carbon-rich forests in the tropics Nature Geosci 4(5): 293-297 47 [29] FAO (2007) The World's mangroves 1980-2005 A thematic study prepared in the framework of the Global Forest Resources Assessment 2005 FAO Forestry Paper Rome, Food and Agriculture Organization of the United Nations 153: 89 [30] NEA (2003) Strategies for Wetland Management in Vietnam 48 ... SAVI) for mangrove mapping in Kien Thuy district and Do Son county, Hai Phong city Landsat Image was used as a primary data to derive mangrove vegetation class from three vegetation indices models... (Normalized Difference Vegetation Index) and SAVI (Soil-adjusted Vegetation Index), in order to map mangrove forest for the Kien Thuy and Do Son coasts, Hai Phong, Vietnam using multi-temporal image... Hop (Kien Thuy) and Bang La (Do Son) as indicated in Fig 4.1 Fig.4.1 Study sites in Hai Phong where: (a) Viet Nam map, (b) Hai Phong city map (c) Selected sites as Kien Thuy District and Do Son