Perbandingan Klasifikasi SVM dan Decision Tree untuk Pemetaan Mangrove Berbasis Objek Menggunakan Citra Satelit Sentinel-2B di Gili Sulat, Lombok Timur
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Accepted
25 July 2019
Published
22 October 2019
Abstract
Mangrove is one of the most important objects in wetland ecosystems. Mangrove research has been done, one of them is using remote sensing technology. This study aims to assess accuracy of object based image analysis (OBIA) approach on both Support Vector Machine (SVM) and Decision Tree classification methods to classify mangrove and estimate mangrove area in the field study. We selected Kawasan Konservasi Laut Daerah (KKLD) Gili Sulat as a research site. This research used Sentinel-2B satellite imagery. We took field data using stratified random sampling and the amount of the data we collected were 121 points. The classification analysis result with object based showed that SVM had an overall accuracy of 95 % (kappa = 0.86) and Decision Tree classification had an overall accuracy of 93 % (kappa = 0.82). It is caused SVM can reduce the error of classification than Decision Tree. Estimation result based on assessment showed that mangrove using SVM had 634.62 Ha while using Decision Tree had 590.47 Ha
References
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Blaschke T, Hay GJ, Kelly M, Lang S, Hofmann P, Addink E, Feitosa R, Van Der Meer F, Van Der Werff H, Van Coillie F et al. 2014. Geographic object-based image analysis: a new paradigm in remote sensing and geographic information science. ISPRS Journal Photogrammmetry Remote Sensing. 87 (1): 180-191.
Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS Journal Photogrammetry and Remote Sensing. 65(1): 2-16.
Cao J, Leng W, Liu K, He Z, Zhu Y. 2018. Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens. 10: 89
Chavez PS. 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment. 24 (3): 459–479.
Congalton RG, Green K. 2008. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton (US): CRC Press.
Cui L, Li G, Ren H, He L, Liao H, Ouyang N, Zhang Y. 2014. Assessment of atmospheric correction methods for historical landsat tm images in the coastal zone: a case study in Jiangsu, China. European Journal of Remote Sensing. 47 (1): 701–716.
Drusch M, Del-Bello U, Carlier S, Colin O, Fernandez V, Gascon F, Hoersch B, Isola C, Laberinti P, Martimort P et al. 2012. Sentinel-2: esa’s optical high-resolution mission for gmes operational services. Remote Sensing Environment. 120: 25–36.
Du Y, Zhang Y, Ling F, Wang Q, Li W, Li X. 2016. Water bodies’ mapping from sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the swir band. Remote Sensing. 8: 1-19.
Duro DC, Franklin SE, Dubé MG. 2012. Remote sensing of environment a comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using spot-5 hrg imagery. Remote Sensing of Environment. 118: 259–272.
Friedl MA, Brodleyf CE. 1997. Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment. 61: 499–409.
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.
Gupta B, Rawat A, Jain A, Arora A, Dhami N. 2017. Analysis of various decision tree algorithms for classification in datamining. Int. J. Comput. Appl. 163(8): 15-19.
Idrus AA, Hadiprayitno, Mertha IG, Ihamdi L. 2015. Potensi vegetasi dan arthropoda di kawasan mangrove Gili Sulat Lombok Timur. Biologi Tropis. 15 (2): 183–196.
Jensen JR. 2005. Introductory Digital Image Processing: A Remote Sensing Perspective, 3rd ed. Sydney (AU): Pearson Prentice Hall.
Kamal M, Phinn S, Johansen K. 2015. Object-based approach for multi-scale mangrove composition mapping using multi-resolution image datasets. Remote Sensing. 7: 4753-4783.
Kamal M, Phinn S, Johansen K. 2016. Assessment of multi-resolution image data for mangrove leaf area index mapping. Remote Sensing of Environment. 176: 242–254.
Kamaruddin NA, Fujii S. 2017. Mangrove forest classification using decision tree-learning method. Journal World Applied Science. 35 (9): 1821–1825.
Lantzanakis G, Mitraka Z, Chrysoulakis N. 2016. Comparison of physically & image based atmospheric correction methods for sentinel-2 satellite imagery. Proceeding of SPIE. 9688: 1-6
Larose DT. 2005. Discovering Knowledge in Data: An Introduction to Data Mining. New Jersey (US): John Wiley & Sons.
Laurin GV, Puletti N, Hawthorne W, Liesenberg V, Corona P, Papale D, Chenf Q, Valentini R. 2016. Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral sentinel-2 data. Remote Sensing Environment. 176: 163–176.
Li J, Roy DP. 2017. A global analysis of sentinel-2a, sentinel-2b and landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sensing. 9(9): 1–17.
Liu D, Xia F. 2010. Assessing object-based classification: advantages and limitations. Remote Sensing Letters. 1(4): 187–194.
Madanguit CJG, Oñez JPL, Tan HG, Villanueva MD, Ordaneza JE, Novero AU. 2017. Application of support vector machine (SVM) and quick unbiased efficient statistical tree (QUEST) algorithms on mangrove and agricultural resource mapping using lidar data sets. International Journal of Applied Environmental Sciences. 12(10): 1821–1830.
Mallinis G, Mitsopoulos I, Chrysafi I. 2017. Evaluating and comparing sentinel 2a and landsat-8 operational land imager (OLI) spectral indices for estimating fire severity in a mediterranean pine ecosystem of Greece. GIScience & Remote Sensing. 55 (1): 1-18.
Myint SW, Gober P, Brazel A, Grossman-Clarke S, Weng Q. 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment. 115: 1145–1161.
Neukermans G, Dahdouh-Guebas F, Kairo JG, Koedam N. 2008. Mangrove species and stand mapping in Gazi Bay (Kenya) using quickbird satellite imagery. Spatial Science 53(1): 75–86.
Noi PT, Martin K. 2009. Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using sentinel-2 imagery. Sensors. 18 (18): 1-20.
Nugroho AS, Witarto AB, Handoko D. 2003. Application of support vector machine in bioinformatics. Proceeding Indonesia Scientific Meeting in Central Japan. 1–11.
Putra H, Prasetyo LB, Santoso N. 2016. Monitoring of coastline changes using satellite imagery in Muara Gembong, Bekasi. Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan. 6 (2): 178–186.
Sartika D, Sensuse DI. 2017. Perbandingan algoritma klasifikasi naive bayes, nearest neighbour dan decision tree pada studi kasus pengambilan keputusan pemilihan pola pakaian. Jatisi. 1(2): 151-161.
Son NT, Chen CF, Chang N.Bin, Chen CR, Chang LY. Thanh BX. 2015. Mangrove mapping and change detection in Ca Mau Peninsula, Vietnam, using landsat data and object-based image analysis. IEEE Transactions on Geoscience and Remote Sensing. 8 (2): 503–510.
Stow D, Hamada Y, Coulter LM, Anguelova Z. 2008. Monitoring shrubland habitat changes through object-based change identification with airborne multi-spectral imagery. Remote Sensing of environement. 112: 1051-1061.
Tunggadewi AT, Syaufina L, Puspaningsih N, 2014. Pemanfaatan penginderaan jauh untuk estimasi stok karbon di area reklamasi pt. antam ubpe, Pongkor, Kabupaten Bogor. Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan. 4 (1): 49–59.
Vidhya R, Vijayasekaran D, Farook MA, Jai S, Rohini M, Sinduja A, Vi C, Vi WG. 2014. Improved classification of mangroves health status using hyperspectral remote sensing data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 40: 9–12.
Vo QT, Oppelt N, Leinenkugel P, Kuenzer C. 2013. Remote sensing in mapping mangrove ecosystems — an object based approach. Remote Sensing 5: 183–201.
Wang L, Sousa WP, Gong P, Biging GS. 2004. Comparison of ikonos and quickbird images for mapping mangrove species on the Caribbean Coast of Panama. Remote Sensing Environment. 91: 432–440.
Zhu G, Blumberg DG. 2002. Classification using aster data and svm algorithms: the case study of Beer Sheva, Israel. Remote Sensing of Environment. 80(2): 233–240.
Aziz AA, Phinn S, Dargusch P. 2015. Investigating the decline of ecosystem services in a production mangrove forest using landsat and object-based image analysis. Estuarine, Coastal and Shelf Science. 164: 353-366.
Blaschke T, Hay GJ, Kelly M, Lang S, Hofmann P, Addink E, Feitosa R, Van Der Meer F, Van Der Werff H, Van Coillie F et al. 2014. Geographic object-based image analysis: a new paradigm in remote sensing and geographic information science. ISPRS Journal Photogrammmetry Remote Sensing. 87 (1): 180-191.
Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS Journal Photogrammetry and Remote Sensing. 65(1): 2-16.
Cao J, Leng W, Liu K, He Z, Zhu Y. 2018. Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens. 10: 89
Chavez PS. 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment. 24 (3): 459–479.
Congalton RG, Green K. 2008. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton (US): CRC Press.
Cui L, Li G, Ren H, He L, Liao H, Ouyang N, Zhang Y. 2014. Assessment of atmospheric correction methods for historical landsat tm images in the coastal zone: a case study in Jiangsu, China. European Journal of Remote Sensing. 47 (1): 701–716.
Drusch M, Del-Bello U, Carlier S, Colin O, Fernandez V, Gascon F, Hoersch B, Isola C, Laberinti P, Martimort P et al. 2012. Sentinel-2: esa’s optical high-resolution mission for gmes operational services. Remote Sensing Environment. 120: 25–36.
Du Y, Zhang Y, Ling F, Wang Q, Li W, Li X. 2016. Water bodies’ mapping from sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the swir band. Remote Sensing. 8: 1-19.
Duro DC, Franklin SE, Dubé MG. 2012. Remote sensing of environment a comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using spot-5 hrg imagery. Remote Sensing of Environment. 118: 259–272.
Friedl MA, Brodleyf CE. 1997. Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment. 61: 499–409.
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.
Gupta B, Rawat A, Jain A, Arora A, Dhami N. 2017. Analysis of various decision tree algorithms for classification in datamining. Int. J. Comput. Appl. 163(8): 15-19.
Idrus AA, Hadiprayitno, Mertha IG, Ihamdi L. 2015. Potensi vegetasi dan arthropoda di kawasan mangrove Gili Sulat Lombok Timur. Biologi Tropis. 15 (2): 183–196.
Jensen JR. 2005. Introductory Digital Image Processing: A Remote Sensing Perspective, 3rd ed. Sydney (AU): Pearson Prentice Hall.
Kamal M, Phinn S, Johansen K. 2015. Object-based approach for multi-scale mangrove composition mapping using multi-resolution image datasets. Remote Sensing. 7: 4753-4783.
Kamal M, Phinn S, Johansen K. 2016. Assessment of multi-resolution image data for mangrove leaf area index mapping. Remote Sensing of Environment. 176: 242–254.
Kamaruddin NA, Fujii S. 2017. Mangrove forest classification using decision tree-learning method. Journal World Applied Science. 35 (9): 1821–1825.
Lantzanakis G, Mitraka Z, Chrysoulakis N. 2016. Comparison of physically & image based atmospheric correction methods for sentinel-2 satellite imagery. Proceeding of SPIE. 9688: 1-6
Larose DT. 2005. Discovering Knowledge in Data: An Introduction to Data Mining. New Jersey (US): John Wiley & Sons.
Laurin GV, Puletti N, Hawthorne W, Liesenberg V, Corona P, Papale D, Chenf Q, Valentini R. 2016. Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral sentinel-2 data. Remote Sensing Environment. 176: 163–176.
Li J, Roy DP. 2017. A global analysis of sentinel-2a, sentinel-2b and landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sensing. 9(9): 1–17.
Liu D, Xia F. 2010. Assessing object-based classification: advantages and limitations. Remote Sensing Letters. 1(4): 187–194.
Madanguit CJG, Oñez JPL, Tan HG, Villanueva MD, Ordaneza JE, Novero AU. 2017. Application of support vector machine (SVM) and quick unbiased efficient statistical tree (QUEST) algorithms on mangrove and agricultural resource mapping using lidar data sets. International Journal of Applied Environmental Sciences. 12(10): 1821–1830.
Mallinis G, Mitsopoulos I, Chrysafi I. 2017. Evaluating and comparing sentinel 2a and landsat-8 operational land imager (OLI) spectral indices for estimating fire severity in a mediterranean pine ecosystem of Greece. GIScience & Remote Sensing. 55 (1): 1-18.
Myint SW, Gober P, Brazel A, Grossman-Clarke S, Weng Q. 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment. 115: 1145–1161.
Neukermans G, Dahdouh-Guebas F, Kairo JG, Koedam N. 2008. Mangrove species and stand mapping in Gazi Bay (Kenya) using quickbird satellite imagery. Spatial Science 53(1): 75–86.
Noi PT, Martin K. 2009. Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using sentinel-2 imagery. Sensors. 18 (18): 1-20.
Nugroho AS, Witarto AB, Handoko D. 2003. Application of support vector machine in bioinformatics. Proceeding Indonesia Scientific Meeting in Central Japan. 1–11.
Putra H, Prasetyo LB, Santoso N. 2016. Monitoring of coastline changes using satellite imagery in Muara Gembong, Bekasi. Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan. 6 (2): 178–186.
Sartika D, Sensuse DI. 2017. Perbandingan algoritma klasifikasi naive bayes, nearest neighbour dan decision tree pada studi kasus pengambilan keputusan pemilihan pola pakaian. Jatisi. 1(2): 151-161.
Son NT, Chen CF, Chang N.Bin, Chen CR, Chang LY. Thanh BX. 2015. Mangrove mapping and change detection in Ca Mau Peninsula, Vietnam, using landsat data and object-based image analysis. IEEE Transactions on Geoscience and Remote Sensing. 8 (2): 503–510.
Stow D, Hamada Y, Coulter LM, Anguelova Z. 2008. Monitoring shrubland habitat changes through object-based change identification with airborne multi-spectral imagery. Remote Sensing of environement. 112: 1051-1061.
Tunggadewi AT, Syaufina L, Puspaningsih N, 2014. Pemanfaatan penginderaan jauh untuk estimasi stok karbon di area reklamasi pt. antam ubpe, Pongkor, Kabupaten Bogor. Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan. 4 (1): 49–59.
Vidhya R, Vijayasekaran D, Farook MA, Jai S, Rohini M, Sinduja A, Vi C, Vi WG. 2014. Improved classification of mangroves health status using hyperspectral remote sensing data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 40: 9–12.
Vo QT, Oppelt N, Leinenkugel P, Kuenzer C. 2013. Remote sensing in mapping mangrove ecosystems — an object based approach. Remote Sensing 5: 183–201.
Wang L, Sousa WP, Gong P, Biging GS. 2004. Comparison of ikonos and quickbird images for mapping mangrove species on the Caribbean Coast of Panama. Remote Sensing Environment. 91: 432–440.
Zhu G, Blumberg DG. 2002. Classification using aster data and svm algorithms: the case study of Beer Sheva, Israel. Remote Sensing of Environment. 80(2): 233–240.
Authors
FirmansyahS., GaolJ. L. and SusiloS. B. (2019) “Perbandingan Klasifikasi SVM dan Decision Tree untuk Pemetaan Mangrove Berbasis Objek Menggunakan Citra Satelit Sentinel-2B di Gili Sulat, Lombok Timur”, Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management). Bogor, ID, 9(3), pp. 746-757. doi: 10.29244/jpsl.9.3.746-757.
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