Application of Remote Sensing Technique for Mangrove Mapping at the Welu Estuary, Thailand
Abstract
Integrating approach based on satellite remote sensing technique and environmental data are applied for mangrove mapping and conservation. Mangrove area in the Welu estuary, Khlung district, Chanthaburi province, Thailand, is selected as study site. Soil pH and DO are major environmental factors related with the mangrove area. The results of supervised classification and post-classification integrated with soil pH and DO to classify L. racemosa, R. apiculata and X. granatum showed that overall accuracy was decreased from 91.09% to 62.35%. RGB (NDVI-Green-Blue) multispectral images, derived from linear contrast stretch of red and near infrared, after supervised classification were efficiently applied for mangroves mapping in other mangrove areas. Three mangrove zones (preservation, conservation and development zones) were classified and suggested for mangrove conservation. Keywords : mangrove conservation, environmental factors, remote sensing, ThailandReferences
Aksornkoae, S. (1975). Structure, Regeneration, and Productivity of Mangroves in Thailand. Doctoral dissertation, Department of Botany and Plant Pathology, Michigan State University.
Aksornkoae, S. (1993). Ecology and Management of Mangroves. Bangkok: International Union of Conservation of Nature and Natural Resources (IUCN).
Banko, G. (1998). A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data and of Methods Including Remote Sensing Data in Forest Inventory. Austria: International Institute for Applied Systems Analysis.
English, S., Wilkinson, C., & Basker, V. (1994). Survey manual for tropical marine resources. Townsville: Australian Institute of Marine Science.
Ferreira, T.O., Otero, X.L., Vidal-Torrado, P., & Macías, F. (2007). Redox Processes in Mangrove Soil under Rhizophora mangle in Relation to Different Environmental Conditions. Soil Science Society of America Journal, 71(2), 484-491.
Food and Agriculture Organization of The United Nations (FAO). (2007). The World’s Mangroves 1980-2005. Italy: Food and Agriculture Organization of The United Nations.
Food and Agriculture Organization of The United Nations (FAO). (2015). The Global Forest Resources Assessment 2015 Desk Reference. Italy: Food and Agriculture Organization of The United Nations.
Jensen, J.R. (1996). Introductory Digital Image Processing: a Remote Sensing Perspective. (2nd ed.). United States of America: Pearson Prentice Hall.
Kanniah, K.D. (2011). Worldview-2 Remote Sensing Data for Tropical Mangrove Species Classification. United States of America: DigitalGlobe® Incorporated.
Kovacs, J.M., Wang, J., & Flores-Verdugo, F. (2005). Mapping Mangrove Leaf Area Index at the Species Level Using IKONOS and LAI-2000 Sensors for the Agua Brava Lagoon, Mexican Pacific. Estuarine, Coastal and Shelf Science, 62, 377-384.
Krauss, K.W., Lovelock, C.E., McKee, K.L., Lopez-Hoffman, L., Ewe, S.M.L., & Sousa, W.P. (2008). Environmental Drivers in Mangrove Establishment and Early Development: a Review. Aquatic Botany, 89(2), 1-23.
Landis. J., & Koch, G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159-174.
Lugo, A.E., & Snedaker, S.C. (1974). The Ecology of Mangroves. Annual Review of Ecology and Systematics, 5, 39-65.
Mansilp, P. (2011). Evaluation of Procedure on Objective Plans, Division of Mangrove Administrative 1 Year 2011. Chonburi: Division of Mangrove Administrative 1.
Office of Environmental Policy and Planning (OEPP). (2002). An Inventory of Wetlands of International and National Importance in Thailand. Bangkok: Integrated Promotion Technology.
R Development Core Team. (2010). R: a Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.
Rossiter, D.G. (2004). Technical note: Statistical methods for accuracy assessment of classified thematic maps. Enschede (NL): International Institute for Geo-information Science & Earth Observation (ITC).
Satyanarayana, B., Raman, A.V., Mohd-Lokman, H., Dehairs, F., Sharma, V.S., & Dahdouh-Guebas, F. (2009). Multivariate Methods Distinguishing Mangrove Community Structure of Coringa in the Godavari Delta, East Coast of India. Aquatic Ecosystem Health and Management, 12, 401-408.
Stehman, S.V. (1996). Estimating the kappa coefficient and its variance under stratified random sampling. Photogrammetric Engineering and Remote Sensing (PE&RS), 62(4), 401-407.
Suk-ueng, N., Buranapratheprat, A., Gunbua, V., & Leadprathom, N. (2013). Mangrove Composition and Structure at the Welu Estuary, Khlung District, Chanthaburi Province, Thailand. IOSR Journal of Environmental Science, Toxicology and Food Technology (IOSR-JESTFT), 7(5), 17-24.
Suk-ueng, N. (2014). Integrating Geoinformatics and Environmental Data for Mangrove Conservation in Welu Estuary, Khlung District, Chanthaburi Province. Doctoral dissertation, Faculty of Science, Burapha University.
The International Institute for Aerospace Survey and Earth Sciences (ITC). (2001). ILWIS 3.0 Academic User’s Guide. Enschede: International Institute for Aerospace Survey and Earth Sciences (ITC).
Tomlinson, P.B. (1986). The Botany of Mangroves. United Kingdom: Cambridge University Press.
Tuck, C.H., Spalding, M., Baba, S., Kainuma, M., Sarre, A., & Johnson, S. (2012). Mapping mangrove. ITTO Tropical Forest Update, 21(2), 1-24.
Vaiphasa, C., Skidmore, A.K., & de Boer, W.F. (2006). A post-classifier for mangrove mapping using ecological data. ISPRS Journal of Photogrammetry & Remote Sensing, 61, 1-10.
Aksornkoae, S. (1993). Ecology and Management of Mangroves. Bangkok: International Union of Conservation of Nature and Natural Resources (IUCN).
Banko, G. (1998). A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data and of Methods Including Remote Sensing Data in Forest Inventory. Austria: International Institute for Applied Systems Analysis.
English, S., Wilkinson, C., & Basker, V. (1994). Survey manual for tropical marine resources. Townsville: Australian Institute of Marine Science.
Ferreira, T.O., Otero, X.L., Vidal-Torrado, P., & Macías, F. (2007). Redox Processes in Mangrove Soil under Rhizophora mangle in Relation to Different Environmental Conditions. Soil Science Society of America Journal, 71(2), 484-491.
Food and Agriculture Organization of The United Nations (FAO). (2007). The World’s Mangroves 1980-2005. Italy: Food and Agriculture Organization of The United Nations.
Food and Agriculture Organization of The United Nations (FAO). (2015). The Global Forest Resources Assessment 2015 Desk Reference. Italy: Food and Agriculture Organization of The United Nations.
Jensen, J.R. (1996). Introductory Digital Image Processing: a Remote Sensing Perspective. (2nd ed.). United States of America: Pearson Prentice Hall.
Kanniah, K.D. (2011). Worldview-2 Remote Sensing Data for Tropical Mangrove Species Classification. United States of America: DigitalGlobe® Incorporated.
Kovacs, J.M., Wang, J., & Flores-Verdugo, F. (2005). Mapping Mangrove Leaf Area Index at the Species Level Using IKONOS and LAI-2000 Sensors for the Agua Brava Lagoon, Mexican Pacific. Estuarine, Coastal and Shelf Science, 62, 377-384.
Krauss, K.W., Lovelock, C.E., McKee, K.L., Lopez-Hoffman, L., Ewe, S.M.L., & Sousa, W.P. (2008). Environmental Drivers in Mangrove Establishment and Early Development: a Review. Aquatic Botany, 89(2), 1-23.
Landis. J., & Koch, G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159-174.
Lugo, A.E., & Snedaker, S.C. (1974). The Ecology of Mangroves. Annual Review of Ecology and Systematics, 5, 39-65.
Mansilp, P. (2011). Evaluation of Procedure on Objective Plans, Division of Mangrove Administrative 1 Year 2011. Chonburi: Division of Mangrove Administrative 1.
Office of Environmental Policy and Planning (OEPP). (2002). An Inventory of Wetlands of International and National Importance in Thailand. Bangkok: Integrated Promotion Technology.
R Development Core Team. (2010). R: a Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.
Rossiter, D.G. (2004). Technical note: Statistical methods for accuracy assessment of classified thematic maps. Enschede (NL): International Institute for Geo-information Science & Earth Observation (ITC).
Satyanarayana, B., Raman, A.V., Mohd-Lokman, H., Dehairs, F., Sharma, V.S., & Dahdouh-Guebas, F. (2009). Multivariate Methods Distinguishing Mangrove Community Structure of Coringa in the Godavari Delta, East Coast of India. Aquatic Ecosystem Health and Management, 12, 401-408.
Stehman, S.V. (1996). Estimating the kappa coefficient and its variance under stratified random sampling. Photogrammetric Engineering and Remote Sensing (PE&RS), 62(4), 401-407.
Suk-ueng, N., Buranapratheprat, A., Gunbua, V., & Leadprathom, N. (2013). Mangrove Composition and Structure at the Welu Estuary, Khlung District, Chanthaburi Province, Thailand. IOSR Journal of Environmental Science, Toxicology and Food Technology (IOSR-JESTFT), 7(5), 17-24.
Suk-ueng, N. (2014). Integrating Geoinformatics and Environmental Data for Mangrove Conservation in Welu Estuary, Khlung District, Chanthaburi Province. Doctoral dissertation, Faculty of Science, Burapha University.
The International Institute for Aerospace Survey and Earth Sciences (ITC). (2001). ILWIS 3.0 Academic User’s Guide. Enschede: International Institute for Aerospace Survey and Earth Sciences (ITC).
Tomlinson, P.B. (1986). The Botany of Mangroves. United Kingdom: Cambridge University Press.
Tuck, C.H., Spalding, M., Baba, S., Kainuma, M., Sarre, A., & Johnson, S. (2012). Mapping mangrove. ITTO Tropical Forest Update, 21(2), 1-24.
Vaiphasa, C., Skidmore, A.K., & de Boer, W.F. (2006). A post-classifier for mangrove mapping using ecological data. ISPRS Journal of Photogrammetry & Remote Sensing, 61, 1-10.
Downloads
Published
2017-04-05
Issue
Section
Research Article
