Land cover and land use mapping of the iSimangaliso Wetland Park, South Africa: Comparison of oblique and orthogonal random forest algorithms

Journal of Applied Remote Sensing

Volumn 10, Issue 1, 2016, Pages

  Bassa, Zaakirah ^a   Bob, Urmilla ^a   Szantoi, Zoltan ^b   Ismail, Riyad ^a  

^a UNIVERSITY OF KWAZULU NATAL   (South Africa)

^b JOINT RESEARCH CENTRE   (Italy)

Author keywords

high resolution imagery; oblique random forest; one against all; one against one; protected areas; random forest

Indexed keywords

CONSERVATION; DECISION TREES; ENVIRONMENTAL PROTECTION; LAND USE;

HIGH RESOLUTION IMAGERY; ONE-AGAINST-ALL; ONE-AGAINST-ONE; PROTECTED AREAS; RANDOM FORESTS;

MAPPING;

EID: 84961844724     PISSN: None     EISSN: 19313195     Source Type: Journal    
DOI: 10.1117/1.JRS.10.015017     Document Type: Article

References (81)

1. IUCN, "What is a protected area?" 2013, https://www.iucn.org/about/work/programmes/gpap-home/pas-gpap/(7 January 2015).
2. H. Nagendra et al., "Remote sensing for conservation monitoring: assessing protected areas, habitat extent, habitat condition, species diversity, and threats," Ecol. Indic. 33, 45-59 (2013).
3. S. J. Leroux et al., "Global protected areas and IUCN designations: do the categories match the conditions," Biol. Conserv. 143(3), 609-616 (2010).
4. G. Sanchez-Azofeifa et al., "Integrity and isolation of Costa Rica's national parks and biological reserves: examining the dynamics of land-cover change," Biol. Conserv. 109(1), 123-135 (2008).
5. L. N. Joopa et al., "On the protection of protected areas," Proc. Natl. Acad. Sci. 105, 6673-6678 (2008).
6. C. Secades et al., Earth Observation for Biodiversity Monitoring: A Review of Current Approaches and Future Opportunities for Tracking Progress Towards the Aichi Biodiversity Targets, Secretariat of the Convention on Biological Diversity, Montreal, Canada, Technical Series No. 72, pp. 183 (2014).
7. S. Woodley et al., "Meeting Aichi Target 11: what does success look like for protected area systems?" Parks 18(1), 23-36 (2012).
8. L. Naughton-Treves, M. Holland, and K. Brandon, "The role of protected areas in conserving biodiversity and sustaining local livelihoods," Ann. Rev. Environ. Res. 30, 219-252 (2005).
9. S. Chape et al., "Measuring the extent and effectiveness of protected areas as an indicator for meeting global biodiversity targets," Philos. Trans. R. Soc. B 360, 443-455 (2005).
10. D. Gross et al., "Monitoring land cover changes in African protected areas in the 21st century," Ecol. Inf. 14, 31-37 (2013).
11. W. D. Newmark, "Isolation of African protected areas," Front. Ecol. Environ. 6(6), 321-328 (2008).
12. N. Clerici et al., "Increased isolation of two biosphere reserves and surrounding protected areas (WAP ecological complex, West Africa)," J. Nat. Conserv. 15, 26-40 (2007).
13. A. J. Hansen and R. DeFries, "Ecological mechanisms linking protected areas to surrounding lands," Ecol. Appl. 17(4), 974-988 (2007).
14. H. M. Kharouba and J. T. Kerr, "Just passing through: global change and the conservation of biodiversity in protected areas," Biol. Conserv. 143, 1094-1101 (2010).
15. C. R. Davis and A. J. Hansen, "Trajectories in land use change around U.S. National Parks and challenges and opportunities for management," Ecol. Appl. 21(8), 3299-3316 (2011).
16. R. DeFries et al., "Increasing isolation of protected areas in tropical forests over the past twenty years," Ecol. Appl. 15(1), 19-26 (2005).
17. N. Fernandez, J. M. Paruelo, and M. Delibes, "Ecosystem functioning of protected and altered Mediterranean environments: a remote sensing classification in Donanã, Spain," Remote Sens. Environ. 114, 211-220 (2010).
18. J. Southworth et al., "Assessing the impact of Celaque National Park on forest fragmentation in western Honduras," Appl. Geogr. 24(4), 303-322 (2004).
19. Y.Wang et al., "Remote sensing of land-cover change and landscape context of the National Parks: a case study of the Northeast Temperate Network," Remote Sens. Environ. 113(7), 1453-1461 (2009).
20. R. E. Kennedy et al., "Remote sensing change detection tools for natural resource managers: understanding concepts and tradeoffs in the design of landscape monitoring projects," Remote Sens. Environ. 113(7), 1382-1396 (2009).
21. P. Mairota et al., "Using landscape structure to develop quantitative baselines for protected area monitoring," Ecol. Indic. 33, 82-95 (2013).
22. C. Giri et al., "Next generation of global land cover characterization, mapping, and monitoring," Int. J. Appl. Earth Obs. Geoinf. 25, 30-37 (2013).
23. H. Nagendra et al., "Monitoring parks through remote sensing: studies in Nepal and Honduras," Environ. Manage. 34(5), 748-760 (2004).
24. J.Wiens et al., "Selecting and conserving lands for biodiversity: the role of remote sensing," Remote Sens. Environ. 113(7), 1370-1381 (2009).
25. M. Sassen et al., "Complex contexts and dynamic drivers: understanding four decades of forest loss and recovery in an East African protected area," Biol. Conserv. 159, 257-268 (2013).
26. P. O. Gislason, J. A. Benediktsson, and J. R. Sveinsson, "Random forests for land cover classification," Pattern Recognit. Lett. 27, 294-300 (2006).
27. T. Kavzoglu and I. Colkesen, "An assessment of the effectiveness of a rotation forest ensemble for land-use and land-cover mapping," Int. J. Remote Sens. 34(12), 4224-4241 (2013).
28. J. C. Chan and D. Paelimckx, "Evaluation of Random forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery," Remote Sens. Environ. 112, 2999-3011 (2008).
29. M. Pal, "Random forest classifier for remote sensing classification," Int. J. Remote Sens. 26(1), 217-222 (2005).
30. V. F. Rodriguez-Galiano et al., "An assessment of the effectiveness of a random forest classifier for land-cover classification," ISPRS J. Photogramm. Remote Sens. 67, 93-104 (2012).
31. B. C. Timm and K. McGarigal, "Fine-scale remotely-sensed cover mapping of coastal dune and coastal ecosystems at Cape Cod National Seashore using random forests," Remote Sens. Environ. 127, 106-117 (2012).
32. M. M. Hayes, S. N. Miller, and M. A. Murphy, "High-resolution landcover classification using random forest," Remote Sens. Lett. 5(2), 112-121 (2014).
33. E. Adam et al., "Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines," Int. J. Remote Sens. 35(10), 3440-3458 (2014).
34. T. Do et al., "Classifying very-high-dimensional data with random forests of oblique decision trees," Adv. Knowl. Discovery Manage. 292, 39-55 (2010).
35. B. H. Menze et al., "On oblique random forests," Mach. Learn. Knowl. Discovery Databases 6912, 453-469 (2011).
36. K. Liu and D. Huang, "Cancer classification using rotation forest," Comput. Biol. Med. 38, 601-610 (2008).
37. L. I Kuncheva and J. J. Rodriguez, "An experimental study on rotation forest ensembles," in Multiple Classifier Systems, M. Haindl, J. Kittler, and F. Roli, Eds., pp. 459-468, Springer, Berlin (2007).
38. J. J. Rodriguez, L. I. Kuncheva, and C. J. Alonso, "Rotation forest: a new classifier ensemble method," IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1619-1630 (2006).
39. C. Zhang, J. Zhang, and G. Wang, "An empirical study of using rotation forest to improve regressors," Appl. Math. Comput. 195, 618-629 (2008).
40. J. Gaudart et al., "Oblique decision trees for spatial patter detection: optimal algorithm and application to malaria risk," BMC Med. Res. Methodol. 5, 22 (2005).
41. UNEP WCMC, "iSimangaliso Wetland Park, KwaZulu-Natal, South Africa," http://www. unep-wcmc.org/medialibrary/2011/06/29/0efed969/iSimangaliso.pdf (2 January 2015).
42. UNESCO, "iSimangaliso Wetland Park," http://whc.unesco.org/en/list/914/(2 January 2015).
43. DigitalGlobeTM, "Worldview-2 specifications," https://www.digitalglobe.com/sites/default/files/DG-WorldView2-DS-PROD.pdf (17 April 2015).
44. L. Breiman, "Random forests," Mach. Learn. 45(1), 5-32 (2001).
45. D. R. Cutler et al., "Random forests for classification in ecology," Ecology 88, 2783-2792 (2007).
46. A. Liaw and M. Wiener, "Classification and regression by random forest," Rnews 2/3, 18-22 (2002).
47. T. Shi and S. Horvath, "Unsupervised learning with random forest predictors," J. Comput. Graph. Stat. 15(1), 118-138 (2006).
48. A. E. Hoerl and R. Kennard, "Ridge regression: biased estimation for non-orthogonal problems," Technometrics 12, 55-67 (1970).
49. K. Chokamni et al., "Comparison of ice-affected streamflow estimates computed using artificial neural networks and multiple regression techniques," J. Hydrol. 349, 383-396 (2008).
50. Y-F. Li, M. Xie, and T-N Goh, "Adaptive ridge regression system for software cost estimating on multi-collinear datasets," J. Syst. Software 83, 2332-2343 (2010).
51. M. Pal and P. M. Mather, "Support vector machines for classification in remote sensing," Int. J. Remote Sens. 26(5), 1007-1011 (2005).
52. V. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York (1995).
53. R. Rifkin and A. Klautau, "In defense of one-vs-all classification," J. Mach. Learn. Res. 5, 101-141 (2004).
54. M. A. Kumar and M. Gopal, "Reduced one-against-all method for multiclass SVM classification," Expert Syst. Appl. 38(11), 14238-14248 (2011).
55. R. G. Congalton, "A review of assessing the accuracy of classifications of remotely sensed data," Remote Sens. Environ. 37, 35-46 (1991).
56. G. M. Foody, "Status of land cover classification accuracy assessment," Remote Sens. Environ. 80, 185-201 (2002).
57. G. M. Foody, "Thematic map comparison: evaluating the statistical significance of differences in classification accuracy," Photogramm. Eng. Remote Sens. 70, 627-633 (2004).
58. J. R. Landis and G. G. Koch, "The measurement of observer agreement for categorical data," Biometrics 33(1), 159-174 (1977).
59. A. Nelson and K. M. Chomitz, "Effectiveness of strict vs. multiple use protected areas in reducing tropical forest fires: a global analysis using matching methods," PLoS ONE 6(8), e22722 (2011).
60. J. Adamczyk and A. Osberger, "Red-edge vegetation indices for detecting and assessing disturbances in Norway spruce dominated mountain forests," Int. J. Appl. Earth Obs. Geoinf. 37, 90-99 (2015).
61. M. A. Cho et al., "Potential utility of the spectral red-edge region of SumbandilaSat imagery for assessing indigenous forest structure and health," Int. J. Appl. Obs. Geoinf. 16, 58-93 (2012).
62. I. Kermaitsoglou et al., "A transferability study of the kernel-based reclassification algorithm for habitat delineation," Int. J. Appl. Earth Obs. Geoinf. 37, 38-47 (2015).
63. P. Mairota et al., "Very high resolution Earth observation features for monitoring plant and animal community structure across multiple spatial scales in protected areas," Int. J. Appl. Earth Obs. Geoinf. 37, 100-105 (2015).
64. H. Nagendra et al., "Satellite Earth observation data to identify anthropogenic pressures in selected protected areas," Int. J. Appl. Earth Obs. Geoinf. 37, 124-132 (2014).
65. G. Fung and O. Mangasarian, "Proximal support vector classifiers," in Proc. KDD-2001: Knowledge Discovery and Data Mining, pp. 77-86 (2001).
66. T. N. Do and F. Poulet, "Classifying one billion data with a new distributed SVM algorithm," in Proc. RIVF-2006: the 4th IEEE Int. Conf. on Computer Science, Research, Innovation and Vision for the Future, pp. 59-66 (2006).
67. M. Kuhnlein et al., "Improving the accuracy of rainfall rates from optical satellite sensors with machine learning-a random forests-based approach applied to MSG SEVIRI," Remote Sens. Env. 141, 129-143 (2014).
68. G. Camps-Valls and L. Bruzzone, "Kernel-based methods for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens. 43(6), 1351-1362 (2005).
69. G. M. Foody and A. Mathur, "A relative evaluation of multiclass image classification by support vector machines," IEEE Trans. Geosci. Remote Sens. 42(6), 1335-1343 (2004).
70. C. Huang, L.S. Davis, and J.R.G. Townshend, "An assessment of support vector machines for land cover classification," Int. J. Remote Sens. 23(4), 725-749 (2002).
71. F. Melgani and L. Bruzzone, "Classification of hyperspectral remote sensing images with support vector machines," IEEE Trans. Geosci. Remote Sens. 42(8), 1778-1790 (2004).
72. A. Driver et al., National Biodiversity Assessment 2011: An Assessment of South Africa's Biodiversity and Ecosystems, Synthesis Report, South African National Biodiversity Institute and Department of Environmental Affairs, Pretoria (2011).
73. O. Adekola and G. Mitchell, "The Niger Delta wetlands: threats to ecosystem services, their importance to dependent communities and possible management measures," Int. J. Biodivers. Sci. Ecosyst. Serv. Manage. 7(1), 50-68 (2011).
74. C. Baker et al., "Mapping wetlands and riparian areas using Landsat ETM+ imagery and decision-tree-based models," Wetlands 26(2), 465-474 (2006).
75. M. McCartney et al., "A study of wetland hydrology and ecosystem service provision: GaMampa wetland, South Africa," Hydrol. Sci. J. 56(8), 1452-1466 (2011).
76. N. Ndlovu et al., "The quantification of human impact on the Dukuduku indigenous forest from 1960 to 2008 using GIS techniques as a basis for sustainable management," in Fifth Natural Forest and Woodland Symp., Department of Agriculture, Forestry and Fisheries, Richards Bay, KwaZulu-Natal, South Africa (2011).
77. E. M. I. Adam, R. Ismail, and O. Mutanga, "A comparison of machine learning classifiers in mapping a South African heterogeneous coastal zone: testing the utility of an object-based classification with WorldView-2 imagery," Proc. SPIE 8538, 85380P (2012).
78. M. Immitzer, C. Atzberger, and T. Koukal, "Tree species classification with random forest using very high spatial resolution 8-band worldview-2 satellite data," Remote Sens. 4(9), 2661-2693 (2012).
79. A.J. Hansen et al., "Delineating the ecosystems containing protected areas for monitoring and managing," BioScience 61(5), 363-373 (2011).
80. R.I. Mcdonald, P. Kareiva, and R.T.T. Forman, "The implications of current and future urbanization for global protected areas and biodiversity conservation," Biol. Conserv. 141(6), 1695-1703 (2008).
81. L.K. Svancara et al., "Assessing the landscape context and conversion risk of protected areas using satellite data products," Remote Sens. Environ. 113(7), 1357-1369 (2009).