Optimized rule-based logistic model tree algorithm for mapping mangrove species using ALOS PALSAR imagery and GIS in the tropical region

Environmental Earth Sciences

Volumn 77, Issue 5, 2018, Pages

  Pham, Tien Dat ^a,b   Bui, Dieu Tien ^c   Yoshino, Kunihiko ^d   Le, Nga Nhu ^e  

^a UNIVERSITY OF TSUKUBA   (Japan)

^b VIETNAM NATIONAL UNIVERSITY OF AGRICULTURE   (Viet Nam)

^c UNIVERSITY OF SOUTH EASTERN NORWAY   (Norway)

^d UNIVERSITY OF TOKYO   (Japan)

^e Inst of Mechanics   (Viet Nam)

Author keywords

ALOS PALSAR; Decision tree GIS; Hai Phong City; Logistic model tree; Mangrove species

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION TREES; LEARNING SYSTEMS; MAPPING; POPULATION DISTRIBUTION; SPATIAL DISTRIBUTION; TROPICAL ENGINEERING; TROPICS;

ALOS PALSAR; HAI PHONG CITY; LOGISTIC MODELS; MACHINE LEARNING TECHNIQUES; MANGROVE SPECIES; OVERALL ACCURACIES; RULE-BASED DECISION TREES; STATE-OF-THE-ART MACHINE LEARNING METHODS;

TREES (MATHEMATICS);

ALGORITHM; ALOS; DETECTION METHOD; GIS; MACHINE LEARNING; MANGROVE; PALSAR; SATELLITE IMAGERY; SPATIAL DISTRIBUTION; TROPICAL REGION; URBAN AREA;

HAI PHONG; VIET NAM;

EID: 85042436390     PISSN: 18666280     EISSN: 18666299     Source Type: Journal    
DOI: 10.1007/s12665-018-7373-y     Document Type: Article

References (84)

1. Ahmed N, Glaser M (2016) Coastal aquaculture, mangrove deforestation and blue carbon emissions: is REDD+ a solution? Mar Policy 66:58–66. https://doi.org/10.1016/j.marpol.2016.01.011
2. Alongi DM (2002) Present state and future of the world’s mangrove forests. Environ Conserv 29:331–349. https://doi.org/10.1017/S0376892902000231
3. Arnesen AS, Silva TSF, Hess LL, Novo EMLM, Rudorff CM, Chapman BD, McDonald KC (2013) Monitoring flood extent in the lower Amazon River floodplain using ALOS/PALSAR ScanSAR images. Remote Sens Environ 130:51–61. https://doi.org/10.1016/j.rse.2012.10.035
4. Attarchi S, Gloaguen R (2014) Classifying complex mountainous forests with L-band SAR and Landsat data integration: a comparison among different machine learning methods in the Hyrcanian Forest. Remote Sens 6:3624
5. Barbier EB, Cox M (2004) An economic analysis of shrimp farm expansion and mangrove conversion in Thailand. Land Econ 80:391–407
6. Binh TNKD, Vromant N, Hung NT, Hens L, Boon EK (2005) Land cover changes between 1968 and 2003 in Cai Nuoc, Ca Mau Peninsula, Vietnam. Environ Dev Sustain 7:519–536
7. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees, 1st edn. Chapman and Hall/CRC. ISBN: 978-0412048418
8. Chapi K, Singh VP, Shirzadi A, Shahabi H, Bui DT, Pham BT, Khosravi K (2017) A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environ Model Softw 95:229–245. https://doi.org/10.1016/j.envsoft.2017.06.012
9. Colkesen I, Kavzoglu T (2017) The use of logistic model tree (LMT) for pixel- and object-based classifications using high-resolution WorldView-2 imagery. Geocarto Int 32:71–86. https://doi.org/10.1080/10106049.2015.1128486
10. Conchedda G, Durieux L, Mayaux P (2008) An object-based method for mapping and change analysis in mangrove ecosystems. ISPRS J Photogramm Remote Sens 63:578–589. https://doi.org/10.1016/j.isprsjprs.2008.04.002
11. Congalton RG, Green K (1999) Assessing the accuracy of remotely sensed data—principles and practices. Lewis Publishers, New York
12. Daniel MA (2008) Mangrove forests: resilience, protection from tsunamis, and responses to global climate change. Estuar Coast Shelf Sci 76:1–13
13. Darmawan S, Takeuchi W, Vetrita Y, Wikantika K, Sari DK (2015) Impact of topography and tidal height on ALOS PALSAR polarimetric measurements to estimate aboveground biomass of mangrove forest in Indonesia. J Sens 2015:13. https://doi.org/10.1155/2015/641798
14. Dat PT, Yoshino K (2013) Comparing mangrove forest management in Hai Phong City, Vietnam towards sustainable aquaculture. Proc Environ Sci 17:109–118. https://doi.org/10.1016/j.proenv.2013.02.018
15. de Leeuw MR, de Carvalho LMT (2009) Performance evaluation of several adaptive speckle filters for SAR imaging. Anais XIV Simpósio Brasileiro de Sensoriamento Remoto 7299–7305
16. de Souza Rocha, Pereira F, Kampel M, Cunha-Lignon M (2012) Mapping of mangrove forests on the southern coast of São Paulo, Brazil, using synthetic aperture radar data from ALOS/PALSAR. Remote Sens Lett 3:567–576. https://doi.org/10.1080/01431161.2011.641511
17. Doetsch P, Buck C, Golik P, Hoppe N, Kramp M, Laudenberg J, Oberdörfer C, Steingrube P, Forster J, Mauser A (2009) Logistic model trees with AUC split criterion for the KDD Cup 2009 small challenge. KDD Cup, pp 77–88
18. Donato DC, Kauffman JB, Murdiyarso D, Kurnianto S, Stidham M, Kanninen M (2011) Mangroves among the most carbon-rich forests in the tropics. Nat Geosci 4:293–297
19. Foody GM (2004) Thematic map comparison: evaluating the statistical significance of differences in classification accuracy. Photogramm Eng Remote Sens 70:627–633
20. Friedl MA, Brodley CE (1997) Decision tree classification of land cover from remotely sensed data. Remote Sens Environ 61:399–409. https://doi.org/10.1016/S0034-4257(97)00049-7
21. Friedl MA, Brodley CE, Strahler AH (1999) Maximizing land cover classification accuracies produced by decision trees at continental to global scales. IEEE Trans Geosci Remote Sens 37:969–977. https://doi.org/10.1109/36.752215
22. Frohn RC, Arellano-Neri O (2005) Improving artificial neural networks using texture analysis and decision trees for the classification of land cover. GISci Remote Sens 42:44–65. https://doi.org/10.2747/1548-1603.42.1.44
23. Giri C, Muhlhausen J (2008) Mangrove forest distributions and dynamics in Madagascar (1975–2005). Sensors 8:2104–2117
24. Hansen M, DeFries R, Townshend JR, Sohlberg R (2000) Global land cover classification at 1 km spatial resolution using a classification tree approach. Int J Remote Sens 21:1331–1364
25. Heumann BW (2011) An object-based classification of mangroves using a hybrid decision tree—support vector machine approach. Remote Sens 3:2440–2460
26. Hong PN (1991) Ecology of mangrove vegetation in Vietnam. Hanoi Pedagogic University
27. Hong PN, San HT (1993) Mangroves of Vietnam. IUCN, Bangkok, Thailand
28. Hue L (2008) Economic reforms and mangrove forests in central Vietnam. Soc Nat Resour 21:106–119. https://doi.org/10.1080/08941920701617775
29. JAXA (2014) ALOS-2/PALSAR-2 Level 1.1/1.5/2.1/3.1 CEOS SAR Product Japan Aerospace Exploration Agency
30. Jensen JR (1996) Introductory digital image processing: a remote sensing perspective. Prentice-Hall, New York
31. Kauffman JB, Heider C, Norfolk J, Payton F (2013) Carbon stocks of intact mangroves and carbon emissions arising from their conversion in the Dominican Republic. Ecol Appl 24:518–527. https://doi.org/10.1890/13-0640.1
32. Landwehr N, Hall M, Frank E (2005) Logistic model trees. Mach Learn 59:161–205. https://doi.org/10.1007/s10994-005-0466-3
33. Li X, Gar-On Yeh A (2004) Data mining of cellular automata’s transition rules. Int J Geogr Inf Sci 18:723–744. https://doi.org/10.1080/13658810410001705325
34. Li M, Im J, Beier C (2013) Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest. GISci Remote Sens 50:361–384. https://doi.org/10.1080/15481603.2013.819161
35. Lim T-S, Loh W-Y, Shih Y-S (2000) A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Mach Learn 40:203–228. https://doi.org/10.1023/a:1007608224229
36. Liu K, Li X, Shi X, Wang S (2008) Monitoring mangrove forest changes using remote sensing and GIS data with decision-tree learning. Wetlands 28:336–346. https://doi.org/10.1672/06-91.1
37. Long JB, Giri C (2011) Mapping the Philippines’ mangrove forests using Landsat imagery. Sensors 11:2972–2981
38. Lu L, Di L, Ye Y (2014) A decision-tree classifier for extracting transparent plastic-mulched landcover from Landsat-5 TM images. IEEE J Sel Top Appl Earth Observ Remote Sens 7:4548–4558. https://doi.org/10.1109/JSTARS.2014.2327226
39. Lucas RM, Mitchell AL, Rosenqvist A, Proisy C, Melius A, Ticehurst C (2007) The potential of L-band SAR for quantifying mangrove characteristics and change: case studies from the tropics. Aquat Conserv Mar Freshw Ecosyst 17:245–264. https://doi.org/10.1002/aqc.833
40. Mazda Y, Magi M, Kogo M, Hong PN (1997) Mangroves as a coastal protection from waves in the Tong King delta, Vietnam. Mangroves Salt Marshes 1:127–135. https://doi.org/10.1023/a:1009928003700
41. Mazda Y, Magi M, Nanao H, Kogo M, Miyagi T, Kanazawa N, Kobashi D (2002) Coastal erosion due to long-term human impact on mangrove forests. Wetl Ecol Manag 10:1–9. https://doi.org/10.1023/a:1014343017416
42. Mishra P, Singh D, Yamaguchi Y (2011) Land cover classification of PALSAR images by knowledge based decision tree classifier and supervised classifiers based on SAR observables. Progr Electromagn Res B 30:47–70
43. Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens 66:247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001
44. Myles AJ, Feudale RN, Liu Y, Woody NA, Brown SD (2004) An introduction to decision tree modeling. J Chemom 18:275–285. https://doi.org/10.1002/cem.873
45. Neumann J, Emanuel K, Ravela S, Ludwig L, Verly C (2015) Risks of coastal storm surge and the effect of sea level rise in the Red River delta, Vietnam. Sustainability 7:6553
46. Nguyen-Thanh S, Chi-Farn C, Ni-Bin C, Cheng-Ru C, Ly-Yu C, Bui-Xuan T (2015) Mangrove mapping and change detection in Ca Mau peninsula, Vietnam, using Landsat data and object-based image analysis. IEEE J Sel Top Appl Earth Observ Remote Sens 8:503–510. https://doi.org/10.1109/JSTARS.2014.2360691
47. Otukei JR, Blaschke T (2010) Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. Int J Appl Earth Observ Geoinform 12(Suppl 1):S27–S31. https://doi.org/10.1016/j.jag.2009.11.002
48. Pal M, Mather PM (2003) An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens Environ 86:554–565. https://doi.org/10.1016/S0034-4257(03)00132-9
49. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830
50. Pendleton L, Donato DC, Murray BC, Crooks S, Jenkins WA, Sifleet S, Craft C, Fourqurean JW, Kauffman JB, Marbà N, Megonigal P, Pidgeon E, Herr D, Gordon D, Baldera A (2012) Estimating global “blue carbon” emissions from conversion and degradation of vegetated coastal ecosystems. PLoS ONE 7:e43542. https://doi.org/10.1371/journal.pone.0043542
51. Pham LTH, Brabyn L (2017) Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms. ISPRS J Photogramm Remote Sens 128:86–97. https://doi.org/10.1016/j.isprsjprs.2017.03.013
52. Pham TD, Yoshino K (2015) Mangrove mapping and change detection using multi-temporal landsat imagery in Hai Phong city, Vietnam. In: International symposium on cartography in internet and ubiquitous environments 2015, The University of Tokyo, Japan
53. Pham TD, Yoshino K (2016) Impacts of mangrove management systems on mangrove changes in the Northern Coast of Vietnam. Tropics 24:141–151. https://doi.org/10.3759/tropics.24.141
54. Pham TD, Yoshino K (2017) Aboveground biomass estimation of mangrove species using ALOS-2 PALSAR imagery in Hai Phong City, Vietnam. APPRES 11:026010. https://doi.org/10.1117/1.JRS.11.026010
55. Pham BT, Tien Bui D, Prakash I, Nguyen LH, Dholakia MB (2017a) A comparative study of sequential minimal optimization-based support vector machines, vote feature intervals, and logistic regression in landslide susceptibility assessment using GIS. Environ Earth Sci 76:371. https://doi.org/10.1007/s12665-017-6689-3
56. Pham TD, Yoshino K, Bui DT (2017b) Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks. GISci Remote Sens 54:329–353. https://doi.org/10.1080/15481603.2016.1269869
57. Pham TD, Yoshino K, Kaida N (2018) Monitoring mangrove forest changes in cat ba biosphere reserve using ALOS PALSAR imagery and a GIS-based support vector machine algorithm. In: Tien Bui D, Ngoc Do A, Bui H-B, Hoang N-D (eds) Advances and applications in geospatial technology and earth resources: proceedings of the international conference on geo-spatial technologies and earth resources 2017. Springer, Cham, pp 103–118
58. Phan NH, Vu DT (2007) The role of mangroves in mitigating natural disasters. Annual report of FY 2006. The Core University Program between Japan Society for the Promotion of Science (JSPS) and Vietnamese Academy of Science and Technology (VAST) Osaka University, Japan, pp 147–155. http://hdl.handle.net/11094/13068. Accessed date 4 Sept 2017
59. Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365. https://doi.org/10.1016/j.cageo.2012.08.023
60. Pradhan B, Tehrany MS, Jebur MN (2016) A new semiautomated detection mapping of flood extent from TerraSAR-X satellite image using rule-based classification and Taguchi optimization techniques. IEEE Trans Geosci Remote Sens 54:4331–4342. https://doi.org/10.1109/TGRS.2016.2539957
61. Pradhan B, Sameen MI, Kalantar B (2017) Optimized rule-based flood mapping technique using multitemporal RADARSAT-2 images in the tropical region. IEEE J Sel Top Appl Earth Observ Remote Sens. https://doi.org/10.1109/jstars.2017.2676343
62. Qiu F, Berglund J, Jensen JR, Thakkar P, Ren D (2004) Speckle noise reduction in SAR imagery using a local adaptive median filter. GISci Remote Sens 41:244–266. https://doi.org/10.2747/1548-1603.41.3.244
63. Quinlan JR (1993) C4.5: Programming for machine learning. Morgan Kauffmann, Los Altos, p 38
64. Sameen MI, Pradhan B, Shafri HZM, Mezaal MR, Hb Hamid (2017) Integration of ant colony optimization and object-based analysis for LiDAR data classification. IEEE J Sel Top Appl Earth Observ Remote Sens 10:2055–2066. https://doi.org/10.1109/JSTARS.2017.2650956
65. Seto KC, Fragkias M (2007) Mangrove conversion and aquaculture development in Vietnam: a remote sensing-based approach for evaluating the Ramsar Convention on Wetlands. Glob Environ Change 17:486–500
66. Sharma R, Ghosh A, Joshi P (2013) Decision tree approach for classification of remotely sensed satellite data using open source support. J Earth Syst Sci 122:1237
67. Shimada M, Isoguchi O, Tadono T, Isono K (2009) PALSAR radiometric and geometric calibration. IEEE Trans Geosci Remote Sens 47:3915–3932
68. Shiraishi T, Motohka T, Thapa RB, Watanabe M, Shimada M (2014) Comparative assessment of supervised classifiers for land use—land cover classification in a tropical region using time-series PALSAR mosaic data. IEEE J Sel Top Appl Earth Observ Remote Sens 7:1186–1199. https://doi.org/10.1109/JSTARS.2014.2313572
69. Stehman SV (1997) Selecting and interpreting measures of thematic classification accuracy. Remote Sens Environ 62:77–89. https://doi.org/10.1016/S0034-4257(97)00083-7
70. Tachikawa T, Kaku M, Iwasaki A, Gesch DB, Oimoen MJ, Zhang Z, Danielson JJ, Krieger T, Curtis B, Haase J (2011) ASTER global digital elevation model version 2-summary of validation results. NASA
71. Thomas N, Lucas R, Bunting P, Hardy A, Rosenqvist A, Simard M (2017) Distribution and drivers of global mangrove forest change, 1996–2010. PLoS ONE 12:e0179302
72. Tien Bui D, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13:361–378. https://doi.org/10.1007/s10346-015-0557-6
73. Tien Bui D, Tuan TA, Hoang N-D, Thanh NQ, Nguyen DB, Van Liem N, Pradhan B (2017) Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides 14:447–458. https://doi.org/10.1007/s10346-016-0711-9
74. Tien Dat P, Kunihiko Y (2016) Characterization of mangrove species using ALOS-2 PALSAR in Hai Phong city, Vietnam. IOP Conf Ser Earth Environ Sci 37:012036
75. Tien Dat P, Yoshino K (2012) Mangrove analysis using ALOS imagery in Hai Phong City, Vietnam. https://doi.org/10.1117/12.977261
76. Tuan LX, Munekage Y, Dao QTQ, Tho NH, Dao PTA (2003) Environmental management in mangrove areas. Environ Inf Arch 1:38–52
77. Valiela I, Bowen JL, York JK (2001) Mangrove forests: one of the world’s threatened major tropical environments at least 35% of the area of mangrove forests has been lost in the past two decades, losses that exceed those for tropical rain forests and coral reefs, two other well-known threatened environments. Bioscience 51:807–815. https://doi.org/10.1641/0006-3568(2001)051[0807:MFOOTW]2.0.CO;2
78. Vieira MA, Formaggio AR, Rennó CD, Atzberger C, Aguiar DA, Mello MP (2012) Object based image analysis and data mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas. Remote Sens Environ 123:553–562. https://doi.org/10.1016/j.rse.2012.04.011
79. Wang L, Silván-Cárdenas JL, Sousa WP (2008) Neural network classification of mangrove species from multi-seasonal Ikonos imagery. Photogramm Eng Remote Sens 74:921–927. https://doi.org/10.14358/PERS.74.7.921
80. Yang C-C, Prasher SO, Enright P, Madramootoo C, Burgess M, Goel PK, Callum I (2003) Application of decision tree technology for image classification using remote sensing data. Agric Syst 76:1101–1117. https://doi.org/10.1016/S0308-521X(02)00051-3
81. Zhang X, Treitz PM, Chen D, Quan C, Shi L, Li X (2017) Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure. Int J Appl Earth Obs Geoinf 62:201–214. https://doi.org/10.1016/j.jag.2017.06.010
82. Zhao Y, Zhang Y (2008) Comparison of decision tree methods for finding active objects. Adv Space Res 41:1955–1959. https://doi.org/10.1016/j.asr.2007.07.020
83. Zhong H, Li Y, Jiao L (2011) SAR image despeckling using bayesian nonlocal means filter with sigma preselection. IEEE Geosci Remote Sens Lett 8:809–813. https://doi.org/10.1109/LGRS.2011.2112331
84. Zhu HM, Zhong WQ, Jiao LC (2013) Combination of target detection and block-matching 3D filter for despeckling SAR images. Electron Lett 49:495–497. https://doi.org/10.1049/el.2012.3160