Detection of True Mangroves in Indonesia Using Satellite Remote Sensing

Autores

  • Atriyon Julzarika Universitas Gadjah Mada (UGM) & Indonesian National Institute of Aeronautics and Space (LAPAN)
  • Nanin Anggraini Indonesian National Institute of Aeronautics and Space (LAPAN)
  • Syifa Wismayati Adawiah Indonesian National Institute of Aeronautics and Space (LAPAN)

DOI:

https://doi.org/10.24221/jeap.4.3.2019.2488.157-167

Palavras-chave:

Landsat 8, true mangrove, Optimum Index Factor

Resumo

Mangrove existence is necessary to protect coastal. One method that can be used to keep mangrove existence were using satellite imagery monitoring. The number of bands in the imagery led to the selection for the RGB composite bands was difficult because a lot of combinations to try. One technique that can be done to get the best RGB combination of an object is to use Optimum Index Factor (OIF). OIF is a statistical technique for selecting three combinations of imagery bands to visualize the image display to the fullest. It is based on the value of total variance and the correlation coefficient between the bands. Landsat 8 has 7 bands with 30 m resolution, one panchromatic band with  15 m resolution, and two bands with 100 m resolution. The purpose of this study was to detect true mangrove using three bands from  OIF value of  Landsat 8. The results of the processing from 6 bands (2-7), obtained 20 bands combinations  with the highest value of OIF is 0,168, ie, bands 2-56 (Blue, NIR, SWIR-1). Based on the combination, the next step was unsupervised classification process for true mangrove identification (Rizhopora, Brugueira, Avicennia, Soneratia). The best classification using band combination 2-7 with true mangrove reached 4.041 ha.

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Biografia do Autor

Atriyon Julzarika, Universitas Gadjah Mada (UGM) & Indonesian National Institute of Aeronautics and Space (LAPAN)

Geodesy and Geomatics Engineering, Remote Sensing

Nanin Anggraini, Indonesian National Institute of Aeronautics and Space (LAPAN)

Environmental Remote Sensing

Syifa Wismayati Adawiah, Indonesian National Institute of Aeronautics and Space (LAPAN)

Coastal Water Quality

Referências

ADAM, E.; MUTANGA, O.; RUGEGE, D., 2010. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetl. Ecol. Manag. 18, 281– 296.

ANGGARITA, J. L.; TILLEY, A.; HAWKINS, J. P.; PEDRAZAD, C.; ROBERTS, C. M., 2018. Land use patterns and influences of protected areas on mangroves of the eastern tropical Pacific. Biological Conservation 227 82-91. scince direct.

ARUMUGAM, G.; RAJENDRAN, R.; GANESAN, A.; SETHU, R., 2018. Bioaccumulation and translocation of heavy metals in mangrove rhizosphere sediments to tissues of Avicenia marina–A field study from tropical mangrove forest. Environmental Nanotechnology, Monitoring, & Management. Science Direct.

ASRININGRUM, W., 2002. Studi Kemampuan Landsat ETM+ untuk Identifikasi Bentuk Lahan (Landform) di Daerah Jakarta (Study of Landsat ETM + Ability to Identify Land Forms in the Jakarta Area). Thesis. in Indonesian. Bogor Agriculture University. Indonesia.

ASRININGRUM, W., 2008. Model Analisis Terumbu Karang menggunakan data penginderaan jauh. Bunga rampai: Analisis Geomorfologi Terumbu Karang di Kabupaten Sikka (The Coral Reef Analysis Model uses remote sensing data. Book chapter: Analysis of Coral Reef Geomorphology in Sikka Regency). in Indonesian. LAPAN. Indonesia.

BLASCHKE, T.; HAY, G.J.; KELLY, M.; LANG, S.; HOFMANN, P.; ADDINK, E.; QUEIROZ FEITOSA, R.; VAN DER MEER, F.; VAN DER WERFF, H.; VAN COILLIE, F.; TIEDE, D., 2014. Geographic object-based image analysis – towards a new paradigm. ISPRS J. Photogramm. RemoteSens. 87, 180–191.

CHAVEZ, P.S.,JR.; BERLIN, G. L.; SOWERS, L. B., 1982. Statistical Method For Selectinglandsat MSS Ratios, Journal of Applied Photographic Engineering, 8(1):23

CLARK, B.; SUOMALAINEN, J.; PELLIKKA, P., 2011. An historical empirical line method for the retrieval of surface reflectance factor from multitemporal SPOT HRV, HRVIR and HRG multispectral satellite imagery. Int. J. Appl. Earth Obs. Geoinform. 13, 292–307.

DEBDIP, B., 2013. Optimum Index Factor (Oif) For Landsat Data: A Case Study On Barasat Town, West Bengal, India. International Journal of Remote Sensing & Geoscience (IJRSG). Volume 2. Issue 5. Sept. 2013. ISSN No: 2319-3484

DENG, D.; CHEN, H.; TAM, N.F.Y., 2015. Temporal and spatial contamination of polybrominated diphenyl ethers (PBDEs) in wastewater treatment plants in Hong Kong. Sci. Total Environ. 502, 133–142.

FARZANA, S.; CHEN, J.; PAN, Y.; WONG, Y. S.; TAM, N. F.Y., 2017. Antioxidative response of Kandelia obovata, a true mangrove species, to polybrominated diphenyl ethers (BDE-99 and BDE-209) during germination and early growth. Marine Pollution Bulletin 124 1063-1070. Science Direct.

GREEN, E.P.; CLARK, C.D.; EDWARDS, A.J., 2000. Image classification and habitat mapping. In: Remote Sensing Handbook for Tropical Coastal Management. UNESCO, Paris, pp. 141-154

HAUSER, L.T.; VU, G.N.; NGUYEN, B.A.; DADE, E.; NGUYEN, H.M.; NGUYEN, T.T.Q.; LE, T.Q.; VU, L.H.; TONG, A.T.H.; PHAM, H.V., 2017. Uncovering the spatio-temporal dynamics of land cover change and fragmentation of mangroves in the Ca Mau peninsula, Vietnam using multitemporal SPOT satellite imagery (2004–2013). Appl. Geogr. 86, 197–207

HOSSAIN, M. D.; INAFUKU, M.; IWASAKI, H.; TAIRA, N.; MOSTOFA, M. G.; OKU, H., 2017. Differential enzymatic defense mechanisms in leaves and roots of two true mangrove species under long- term salt stress. Aquatic Botany 142 32-40. Science Direct.

JOHNSON, B.; XIE, Z., 2011. Unsupervised image segmentation evaluation and refinement using a multi-scale approach. ISPRS J. Photogramm. Remote Sens. 66, 473–483.

JUN, L., 2008. Research on False Color Image and Enhancement Methods Based on Ratio Images. The International Archives of the Photogrammetry, Remote Sensing and Spasial Information Scienses. Vo. XXXVII. Part B7. Beijing. Hal 1151-1154.

KAMAL, M.; JOHANSEN, K., 2017. Explicit area-based accuracy assessment for mangrove tree crown delineation using Geographic object-Based image analysis (GEOBIA), Earth resources and environmental remote sensing/GIS applications VIII. Int. Soc. Opt. Photonics 104280I.

KATHIRESAN, K.; BINGHAM, B. 2001. Biology of mangroves and mangrove ecosystems. Adv. Mar. Biol. 40, 81-251.

KIRUI, K.B.; KAIRO, J.G.; BOSIRE, J.; VIERGEVER, K.M.; RUDRA, S.; HUXHAM, M.; BRIERS, R.A., 2013. Mapping of mangrove forest land cover change along the Kenya coastline using Landsat imagery. Ocean Coast. Manag. 83, 19–24.

KITAMURA, S.; ANWAR, A.; CHANIAGO, A.; BABA, S., 1997. Handbook of Mangrove in Indonesia. Edisi ke-3. Ministry of Foresty Indonesia, Publications. 199pp

KRAUSS, K.W.; MCKEE, K.L.; LOVELOCK, C.E.; CAHOON, D.R.; SAINTILAN, N.; REEF, R.; CHEN, L., 2014. How mangrove forests adjust to rising sea level. New Phytol. 202, 19–34.

KUENZER, C.; BLUEMEL, A.; GEBHARDT, S.; QUOC, T.V.; DECH, S., 2011. Remote sensing of mangrove ecosystems: a review. Remote Sens. 3, 878–928.

KUSUMANINGTYAS, M. A.; HUTAHAEAN, A. A.; FISCHER H. W.; MAYO, M. P.; RANSBY, D.; JENNERJAHN, T. C., 2019. Variability in the organic carbon stocks, sources, and accumulation rates of Indonesian mangrove ecosystems. Estuarine, Coastal, and Shelf Science

LU, D.; WENG, Q., 2007. A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 28, 823–870.

LUGO, A. E.; SNEDAKER, S. C., 1974. The ecology of mangroves. Ann. Rev. Ecol. Systemat. 5, 39-64.

LUONG, N.V.; TATEISHI, R.; HOAN, N.T., 2015. Analysis of an impact of succession in mangrove forest association using remote sensing and GIS technology. J. Geogr. Geol. 7, 106–116

MARINI, Y.; MANOPPO, A. K. S.; ANGGRAINI, N., 2015. Teknik Penentuan Citra Komposit Untuk Identifikasi Mangrove Menggunakan Landsat - 8 di Pulau Subi Kecil. Bunga Rampai: Mangrove, Citra Penginderaan Jauh dan Identifikasinya (Techniques for Determining Composite Imagery for Mangrove Identification Using Landsat-8 on Small Subi Island. Book chapter: Mangrove, Remote Sensing Image and Identification). Ed: Asriningrium, W. dan Parwati, E. Page: 1 - 19. IPB Press.

NASA, 2019. Landsat 8. NASA, accessed March 24, 2019 from https://landsat.gsfc.nasa.gov/landsat-data-c

NONTJI, A., 2005. Laut Nusantara (Indonesian Sea). book. in Indonesian. Djambatan. Indonesia.

PHAM, T.D.; YOSHINO, K., 2016. Impacts of mangrove management systems on mangrove changes in the Northern Coast of Vietnam. Tropics 24, 141–151.

QAID, A. M.; BASAVARAJAPPA, H. T., 2008. Application of Optimum Index Factor Technique to Landsat-7 Data for Geological Mapping of North East of Hajjah, Yemen. American-Eurasian Journal of Scientific Research 3 (1): 84-91. ISSN 1818-6785.

THU, P.M.; POPULUS, J., 2007. Status and changes of mangrove forest in Mekong Delta: case study in Tra Vinh, Vietnam. Estuar., Coast. Shelf Sci. 71, 98–109.

TOMLINSON, P. B., 1994. The Botany of mangrove. Cambridge university Press, New York. 436p

VO, Q.T.; OPPELT, N.; LEINENKUGEL, P.; KUENZER, C., 2013. Remote sensing in mapping mangrove ecosystems—an object-based approach. Remote Sens. 5, 183–201.

WANG, M.; CAO, W.; GUAN, Q.; WU, G.; WANG, F., 2018. Assessing changes of mangrove forest in a coastal region of southeast China using multi-temporal satellite images. Estuarine, Coastal, and Shelf Science 207 283-292. Science Direct.

WIBISONO, M. S., 2011. Pengantar Ilmu Kelautan (Introduction to Marine Sciences). book. in Indonesian. University of Indonesia.

ZHAN, Q., MOLENAAR, M., TEMPFLI, K., SHI, W., 2005. Quality assessment for geo‐spatial objects derived from remotely sensed data. Int. J. Remote Sens. 26, 2953–2974

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Publicado

2019-09-18

Como Citar

Julzarika, A., Anggraini, N., & Adawiah, S. W. (2019). Detection of True Mangroves in Indonesia Using Satellite Remote Sensing. Journal of Environmental Analysis and Progress, 4(3), 157–167. https://doi.org/10.24221/jeap.4.3.2019.2488.157-167