Mapping mangrove forest distribution on Banten, Jakarta, and West Java Ecotone Zone from Sentinel-2-derived indices using cloud computing based Random Forest
Abstract
Mangrove ecosystem is a very potential area, generally located in ecoton areas (a combination of intertidal and supratidal areas), where there is interaction between waters (sea, brackish water, and rivers) with land areas. Indonesia, especially the Banten and West Java regions, have vast mangrove areas and are currently under threat of land conversion. Moreover, mapping the distribution of mangrove forests using the Google Earth Engine platform based on Cloud Computing is less published. Therefore, this research was conducted by introducing the distribution of mangrove forests which involved the Random Forest (RF) classification algorithm method, and looking for the best modification of the index. The combination test was carried out by involving the NDVI, EVI, ARVI, SLAVI, IRECI, RVI, DVI, SAVI, IBI, GNDVI, NDWI, MNDWI, and LSWI indexes. There is a distribution of mangroves in three provinces (West Java, Banten, and Jakarta) which are 933.54 ha (8.372%), 1,537.89 ha (18.231%), and 8,184.82 ha (73.397%). Of the 70 combination tests, the LSWI index (K13, Type-A) is the combination with the lowest accuracy rate of 58.45% (Overal Accuracy) and 39.59 (Kappa statistic), and the combination of K23 (SAVI-MNDWI-IBI) is a combination the best are 96.48% and 92.79. The results and recommendations in this study are expected to be used as a reference in determining policies for the protection of mangrove areas and a reference for further research
References
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Han H, Lee S, Kim HC, Kim M. 2021. Retrieval of summer sea ice concentration in the Pacific Arctic Ocean from AMSR2 observations and numerical weather data using random forest regression. Remote Sensing. 13(12): 1-20. doi: https://doi.org/10.3390/rs13122283.
Handayani. 2006. Bioakumulasi logam berat dalam mangrove Rhizophora mucronata dan Avicennia marina di Muara Angke Jakarta. Jurnal Teknik Lingkungan. 7(3): 266-270.
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Yanuartanti IW, Kusmana C, Ismail A. 2015. Feasibility study of mangrove rehabilitation using guludan technique in carbon trade perspective in protected mangrove area in Muara Angke, DKI Jakarta Province. Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan. 5(2): 180-186.
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[BPS of West Java Province] Badan Pusat Statistik of West Java Province. 2021. Jawa Barat Dalam Angka 2021 [Internet]. Jakarta (ID): BPS Indonesia. [downloaded 2021 Jun 23]. Available at: https://jabar.bps.go.id/.
[DKP Tangerang Regency] Dinas Perikanan dan Kelautan of Tangerang Regency. 2013. Profil Dinas Perikanan dan Kelautan Kabupaten Tangerang. Tangerang (ID): Regency Maritime Affairs and Fisheries Service.
Ambinari M, Darusman D, Alikodra HS, Santoso N. 2015. Community-bades mangrove management: The relationship between Perhutani and cultivators in Muara Gembong, Bekasi Regency, West Java Province. International Journal of Sciences: Basic and Applied Research. 23(2): 204-214.
Aronoff S. 1985. The minimum accuracy value as an index of classification accuracy. Photocrammetric Engneering and Remote Sensing. 51(1): 99-111.
Aprilianti HS, Asy’ari R, Ranti A, Aslam MF. 2021. Identification and classification of cloud computing-based vegetation index values on several lands used in Bogor Regency, Indonesia. IOP Conference Series: Earth and Environmental Science. 918(1): 1-18. doi: https://doi.org/10.1088/1755-1315/918/1/012011.
Asy’ari R, Putra MCA. 2021. MAKABUT (mangroves-bekantan-gambut): A solution for the development of integrated eco-tourism area in mangrove-proboscis monkey’s habitat in Batu Ampar, Kubu Raya Regency, West Kalimantan. IOP Conference Series: Earth and Environmental Science. 771(1): 1-14. doi: https://doi.org/10.1088/1755-1315/771/1/012033.
Baloloy AB, Blancoa AC, Anaa RRCS, Nadaokac K. 2020. Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping. ISPRS Journal of Photogrammetry and Remote Sensing. 166: 95-117. doi: https://doi.org/10.1016/j.isprsjprs.2020.06.001.
Barbier EB, Hacker SD, Kennedy C, Koch EW, Stier AC, Silliman BR. 2011. The value of estuarine and coastal ecosystem services. Ecological Monographs. 81(2): 169-193. doi: https://doi.org/10. 1890/10-1510.1.
Bazzi H, Baghdadi N, Hajj ME, Zribi M, Minh DHT, Ndikumana E, Courault D, Belhouchette H. 2019. Mapping paddy rice using Sentinel-1 SAR Time Series in Camargue, France. Remote Sensing. 11: 1-11. doi: https://doi.org/10.3390/rs11070887.
Benfield SL, Guzman HM, Mair JM. 2005. Temporal mangrove dynamics in relation to coastal development in Pacific Panama. Journal of Environment Management. 76(3): 263-276. doi: http://dx.doi.org/10.1016/j.jenvman.2005.02.004.
Berlanga-Robles CA, Ruiz-Luna A. 2020. Assessing seasonal and long-term mangrove canopy variations in Sinaloa, northwest Mexico, based on time series of enhanced vegetation index (EVI) data. Wetlands Ecology and Management. 28(2): 229-249. doi: https://doi.org/10.1007/s11273-020-09709-0(0123456789().
Blasco F, Gauquelin T, Rasolofoharinoro M, Denis J, Aizpuru M, Caldairou V. 1998. Recent advances in mangrove studies using remote sensing data. Marine and Freshwater Research. 49(4): 287-296. doi: https://doi.org/10.1071/MF97153.
Bouillon S, Borges AV, Castañeda-Moya E, Diele K, Dittmar T, Duke NC, Kristensen E, Lee SY, Marchand C, Middelburg JJ et al. 2008. Mangrove production and carbon sinks: A revision of global budget estimates. Global Biogeochem Cycles. 22(2): 1-12. doi: https://doi.org/10.1029/2007GB003052.
Cao J, Liu K, Liu L, Zhu Y, Li J, He Z. 2018. Identifying mangrove species using field close-range snapshot hyperspectral imaging and machine-learning techniques. Remote Sensing. 10(12): 20-47. doi: https://doi.org/10.3390/rs10122047.
Chow J. 2018. Determinants of household fuelwood collection from mangrove plantations in coastal Bangladesh. Forest Policy Economy. 96(1): 83-92. doi: https://doi.org/10.1016/j.forpol.2018.08.007.
Collins L, McCarthy G, Mellor A, Newell G, Smith L. 2020. Training data requirements for fire severity mapping using Landsat imagery and random forest. Remote Sensing of Environment. 245: 2-14. doi: https://doi.org/10.1016/j.rse.2020.111839.
Danielsen F, Sørensen MK, Olwig MF, Selvam V, Parish F, Burgess ND, Hiraishi T, Karunagaran VM, Rasmussen MS, Hansen LB et al. 2005. The Asian tsunami: a protective role for coastal vegetation. Science. 310(5748): 643. doi: http://dx.doi.org/10.1126/science.1118387.
Donato DC, Kauffman JB, Murdiyarso D, Kurnianto S, Stidham M. 2011. Mangroves among the most carbon-rich forests in the tropics. Nature Geoscice. 4(1): 1-5. doi: http://dx.doi.org/10.1038/ngeo1123.
Dsikowitzky L, Damar A, Ferse SCA, Irianto HE, Jennerjahn TC, Lukas MC, Nordhaus I, Pohlmann T, Schwarzbauer J, Sugama K et al. 2019. Java island, Indonesia. In: Sheppard C, editor. World Seas: An Environmental Evaluation. London (GB): Elsivier.
Duarte CM, Middelburg JJ, Caraco N. 2005. Major role of marine vegetation on the oceanic carbon cycle. Biogeosciences. 1(1): 1-8. doi: https://doi.org/10.5194/bg-2-1-2005.
Fitzgerald RW, Lees BG. 1994. Assessing the classification accuracy of multisource remote sensing data. Remote Sensing of Environmental. 47: 362-368. doi: https://doi.org/10.1016/0034-4257(94)90103-1.
Foody GM. 2002. Status of land cover classification accuracy assessment. Remote Sensing of Environment. 80: 185-201. doi: https://doi.org/10.1016/S0034-4257(01)00295-4.
Giri C, Ochieng E, Tieszen LL, Zhu Z, Singh A, Loveland T, Masek J, Duke N. 2011. Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography. 20(1): 154-159. doi: 10.11111/geb.2011.20.issue-1.
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. 2017. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment. 202: 18-27. doi: https://doi.org/10.1016/j.rse.2017.06.031.
Han H, Lee S, Kim HC, Kim M. 2021. Retrieval of summer sea ice concentration in the Pacific Arctic Ocean from AMSR2 observations and numerical weather data using random forest regression. Remote Sensing. 13(12): 1-20. doi: https://doi.org/10.3390/rs13122283.
Handayani. 2006. Bioakumulasi logam berat dalam mangrove Rhizophora mucronata dan Avicennia marina di Muara Angke Jakarta. Jurnal Teknik Lingkungan. 7(3): 266-270.
Hikmah WF. 2017. Komposisi jenis dan struktur hutan mangrove di Pantai Ciletuh, Sukabumi, Jawa Barat [skripsi]. Bogor (ID): Institut Pertanian Bogor.
Kusmana C. 2013. Distribution and current status of mangrove forest in Indonesia. In: Faridah-Hanum I, Latiff A, Hakeem KR, Ozturk M, editor. Mangrove Ecosytem of Asia. London (GB): Springer Nature.
Kustanti A. 2011. Manajemen Hutan Mangrove. Bogor (ID): PT Penerbit IPB Press.
Landis JR, Koch GG. 1977. The measurement of observer agreement for categorical data. Biometrics. 33(1): 159-174. doi: https://www.jstor.org/stable/2529310.
Lyon JG, Yuan D, Lunetta RS, Elvidge CD. 1998. A change detection experiment using vegetation indices photogrammetric engineering & remote sensing. Photogrammetric Engineering & Remote Sensing. 64(2): 143-150.
Mahardhika SM, Saputra SW, Ain C. 2018. Valuasi ekonomi sumberdaya ikan dan ekowisata mangrove di Muara Angke, Jakarta. Management of Aquatic Resources Journal (MAQUARES). 7(4): 458-464. doi: https://doi.org/10.14710/marj.v7i4.22670.
Muhd-Ekhzarizal ME, Mohd-Hasmadi I, Hamdan O, Mohamad-Roslan MK, Noor-Shaila S. 2018. Estimation of aboveground biomass in mangrove forests using vegetation indices from SPOT-5 Image. Journal of Tropical Forest Science. 30(2): 224-233. doi: https://www.jstor.org/stable/26409971.
Natharani C. 2007. Penurunan luasan ekosistem mangrove dan keterkaitannya dengan sumberdaya perikanan di Kabupaten Tangerang [skripsi]. Bogor (ID): Institut Pertanian Bogor.
Navarro JA, Algeet N, Fernández-Landa A, Esteban J, Rodríguez-Noriega P, Guillén-Climent ML. 2019. Integration of UAV, Sentinel-1, and Sentinel-2 data for mangrove plantation aboveground biomass monitoring in Senegal. Remote Sensing. 11(1): 1-23. doi: https://doi.org/10.3390/rs11010077.
Nguyen MD, Baez-Villanueva OM, Bui DD, Nguyen PT, Ribbe L. 2020. Harmonization of landsat and Sentinel 2 for crop monitoring in drought prone areas: case studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon). Remote Sensing. 12(2): 1-18. doi: https://doi.org/10.3390/rs12020281.
Perich G, Aasen H, Verrelst J, Argento F, Walter A, Liebisch F. 2021. Crop nitrogen retrieval methods for simulated Sentinel-2 data using in-field spectrometer data. Remote Sensing. 13(12): 1-24. doi: https://doi.org/10.3390/rs13122404.
Putra IS, Gumilang. 2019. The impact of reclamation to sedimentation the potency of mangrove growth in Jakarta Bay (Muara Angke). Jurnal Sumber Daya Air. 15(2): 81-94.
Rahmawati AD, Asy’ari R. 2022. Google earth engine: utilization of cloud computing-based mapping platform in detecting mangrove distribution with Sentinel-2 Images in Jakarta City. Prosiding Semnas Geomatik 2021. 6: 423-434. doi: https://cloud.big.go.id/index.php/s/ProsidingSNGVI2021#pdfviewer.
Rees WG. 1999. The Remote Sensing Data Book. Cambridge Cambridge university press.
Rodriguez-Galiano VF, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sanchez JP. 2012. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing. 67: 93-104. doi: https://doi.org/10.1016/j.isprsjprs.2011.11.002.
Rwanga SS, Ndambuki JM. 2017. Accuracy assessment of land use/land cover classification using remote sensing and GIS. International Journal of Geosciences. 8: 611-622. doi: https://doi.org/10.4236/ijg.201
7.84033.
Sannigrahi S, Zhang Q, Pilla F, Joshi PK, Basu B, Keesstra S, Roy PS, Wang Y, Sutton PC, Chakraborti S et al. 2020. Responses of ecosystem services to natural and anthropogenic forcings: A spatial regression based assessment in the world's largest mangrove ecosystem. Science of the Total Environment. 715: 1-13. doi: https://doi.org/10.1016/j.scitotenv.2020.137004.
Scepan J. 1999. Thematic validation of high-resolution global land-gover data sets. Photogrammetric Engineering and Remote Sensing. 65(9): 1051-1060.
Story M, Congalton RG. 1986. Accuracy assessment: A user's perspective. Remote Sensing Brief. 52(3): 397-399.
Sutarno S, Lymbery A. 2000. Genetic diversity and sequence variations at growth hormone loci among composite and hereford populations of beef cattle. Biodiversitas Journal of Biological Diversity. 1(2): 41-46. doi: https://doi.org/10.13057/biodiv/d010201.
Vo QT, Oppelt N, Leinenkugel P, Kuenzer C. 2013. Remote sensing in mapping mangrove ecosystems - an object-based approach. Remote Sensing. 5(1): 183-201. doi: https://doi.org/10.3390/rs5010183.
Wang D, Wan B, Qiu P, Su Y, Guo Q, Wang R, Sun F, Wu X. 2018. Evaluating the performance of Sentinel-2, Landsat 8 and Pléiades-1 in mapping mangrove extent and species. Remote Sensing. 10(9): 1-27. doi: 1468. https://doi.org/10.3390/rs10091468.
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Authors
Asy’AriR., RahmawatiA. D., Sa’diyyaN., Gustawan A. H., SetiawanY., ZamaniN. P. and PramulyaR. (2022) “Mapping mangrove forest distribution on Banten, Jakarta, and West Java Ecotone Zone from Sentinel-2-derived indices using cloud computing based Random Forest”, Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management). Bogor, ID, 12(1), pp. 97-111. doi: 10.29244/jpsl.12.1.97-111.
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