Developing a Random Forest algorithm for MODIS global burned area classification

Remote Sensing

Volumn 9, Issue 11, 2017, Pages

  Ramo, Rubén ^a   Chuvieco, Emilio ^a  

^a UNIVERSITY OF ALCALÁ   (Spain)

Author keywords

Burned area; MODIS; Random Forest

Indexed keywords

DECISION TREES; ERRORS; FIRE HAZARDS; FIRES; FORESTRY; INFRARED DEVICES; PIXELS;

AUXILIARY VARIABLES; BURNED AREAS; MODIS; NEAR-INFRARED BANDS; RANDOM FOREST ALGORITHM; RANDOM FORESTS; REFLECTIVE BANDS; TRAINING DATASET;

RADIOMETERS;

EID: 85034775396     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs9111193     Document Type: Article

References (99)

1. Kloster, S.; Mahowald, N.; Randerson, J.; Lawrence, P. The impacts of climate, land use, and demography on fires during the 21st century simulated by clm-cn. Biogeosciences 2012, 9, 509-525
2. Thonicke, K.; Spessa, A.; Prentice, I.C.; Harrison, S.P.; Dong, L.; Carmona-Moreno, C. The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: Results from a process-based model. Biogeosciences 2010, 7, 1991-2011
3. Van derWerf, G.R.; Randerson, J.T.; Giglio, L.; Collatz, G.; Mu, M.; Kasibhatla, P.S.; Morton, D.C.; DeFries, R.S.; Jin, Y.; van Leeuwen, T.T. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009). Atmos. Chem. Phys. 2010, 10, 11707-11735
4. Schoennagel, T.; Nelson, C.R.; Theobald, D.M.; Carnwath, G.C.; Chapman, T.B. Implementation of national fire plan treatments near the wildland-urban interface in the western united states. Proc. Natl. Acad. Sci. USA 2009, 106, 10706-10711
5. Global Climate Observing System (GCOS). Guideline for the Generation of Datasets and Products Meeting GCOS Requirements; World Meteorological Organization: Geneva, Switzerland, 2010
6. Hollmann, R.; Merchant, C.J.; Saunders, R.W.; Downy, C.; Buchwitz, M.; Cazenave, A.; Chuvieco, E.; Defourny, P.; Leeuw, G.D.; Forsberg, R.; et al. The esa climate change initiative: Satellite data records for essential climate variables. Bull. Am. Meteorol. Soc. 2013, 94, 1541-1552
7. Mouillot, F.; Schultz, M.G.; Yue, C.; Cadule, P.; Tansey, K.; Ciais, P.; Chuvieco, E. Ten years of global burned area products from spaceborne remote sensing-A review: Analysis of user needs and recommendations for future developments. Int. J. Appl. Earth Obs. Geoinf. 2014, 26, 64-79
8. Chuvieco, E.; Yue, C.; Heil, A.; Mouillot, F.; Alonso-Canas, I.; Padilla, M.; Pereira, J.M.; Oom, D.; Tansey, K. A new global burned area product for climate assessment of fire impacts. Glob. Ecol. Biogeogr. 2016, 25, 619-629
9. Riaño, D.; Moreno Ruiz, J.; Isidoro, D.; Ustin, S. Global spatial patterns and temporal trends of burned area between 1981 and 2000 using noaa-nasa pathfinder. Glob. Chang. Biol. 2007, 13, 40-50
10. Tansey, K.; Grégoire, J.M.; Defourny, P.; Leigh, R.; Peckel, J.F.; Bogaert, E.V.; Bartholome, J.E. A new, global, multi-annual (2000-2007) burnt area product at 1 km resolution. Geophys. Res. Lett. 2008, 35, L01401
11. Roy, D.; Jin, Y.; Lewis, P.; Justice, C. Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data. Remote Sens. Environ. 2005, 97, 137-162
12. Bastarrika, A.; Chuvieco, E.; Martín, M.P. Mapping burned areas from landsat tm/etm+ data with a two-phase algorithm: Balancing omission and commission errors. Remote Sens. Environ. 2011, 115, 1003-1012
13. Pu, R.; Gong, P. Determination of burnt scars using logistic regression and neural network techniques from a single post-fire landsat-7 etm+ image. Photogramm. Eng. Remote Sens. 2004, 70, 841-850
14. Koutsias, N.; Karteris, M. Burned area mapping using logistic regression modeling of a single post-fire landsat-5 thematic mapper image. Int. J. Remote Sens. 2000, 21, 673-687
15. Kachmar, M.; Sanchez-Azofeifa, G.A. Detection of post-fire residuals using high-and medium-resolution satellite imagery. For. Chron. 2006, 82, 177-186
16. Mitri, G.H.; Gitas, I.Z. Mapping post-fire forest regeneration and vegetation recovery using a combination of very high spatial resolution and hyperspectral satellite imagery. Int. J. Appl. Earth Obs. Geoinf. 2013, 20, 60-66
17. Van Leeuwen, T.T.; Peters, W.; Krol, M.C.; van derWerf, G.R. Dynamic biomass burning emission factors and their impact on atmospheric co mixing ratios. J. Geophys. Res. Atmos. 2013, 118, 6797-6815
18. Giglio, L.; Randerson, J.T.;Werf, G.R. Analysis of daily, monthly, and annual burned area using the fourth generation global fire emissions database (gfed4). J. Geophys. Res. Biogeosci. 2013, 118, 317-328
19. Giglio, L.; Loboda, T.; Roy, D.P.; Quayle, B.; Justice, C.O. An active-fire based burned area mapping algorithm for the MODIS sensor. Remote Sens. Environ. 2009, 113, 408-420
20. Plummer, S.; Arino, O.; Ranera, F.; Tansey, K.; Chen, J.; Dedieu, G.; Eva, H.; Piccolini, I.; Leigh, R.; Borstlap, G. The globcarbon initiative: Multi-sensor estimation of global biophysical products for global terrestrial carbon studies. In Proceedings of the Envisat & ERS Symposium, Salzburg, Austria, 6-10 September 2004
21. Alonso-Canas, I.; Chuvieco, E. Global burned area mapping from envisat-meris data. Remote Sens. Environ. 2015, 163, 140-152
22. Chang, D.; Song, Y. Comparison of L3JRC and MODIS global burned area products from 2000 to 2007. J. Geophys. Res. 2009, 114
23. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5-32
24. Rodriguez-Galiano, V.F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J.P. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogramm. Remote Sens. 2012, 67, 93-104
25. Belgiu, M.; Draguţ, L. Random forest in remote sensing: A review of applications and future directions. ISPRS J. Photogramm. Remote Sens. 2016, 114, 24-31
26. Gislason, P.O.; Benediktsson, J.A.; Sveinsson, J.R. Random forests for land cover classification. Pattern Recognit. Lett. 2006, 27, 294-300
27. Friedl, M.A.; Sulla-Menashe, D.; Tan, B.; Schneider, A.; Ramankutty, N.; Sibley, A.; Huang, X. MODIS collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ. 2010, 114, 168-182
28. Pelletier, C.; Valero, S.; Inglada, J.; Champion, N.; Dedieu, G. Assessing the robustness of random forests to map land cover with high resolution satellite image time series over large areas. Remote Sens. Environ. 2016, 187, 156-168
29. Immitzer, M.; Atzberger, C.; Koukal, T. Tree species classification with random forest using very high spatial resolution 8-band worldview-2 satellite data. Remote Sens. 2012, 4, 2661-2693
30. Lawrence, R.L.; Wood, S.D.; Sheley, R.L. Mapping invasive plants using hyperspectral imagery and breiman cutler classifications (randomforest). Remote Sens. Environ. 2006, 100, 356-362
31. Ok, A.O.; Akar, O.; Gungor, O. Evaluation of random forest method for agricultural crop classification. Eur. J. Remote Sens. 2012, 45, 421-432
32. Han, H.; Wang, W.-Y.; Mao, B.-H. Borderline-smote: A new over-sampling method in imbalanced data sets learning. In Proceedings of the International Conference on Intelligent Computing, Hefei, China, 23-26 August 2005; Springer: Berlin/Heidelberg, Germany, 2005; pp. 878-887
33. Pommerening, A. Approaches to quantifying forest structures. Forestry 2002, 75, 305-324
34. Mutanga, O.; Adam, E.; Cho, M.A. High density biomass estimation for wetland vegetation using worldview-2 imagery and random forest regression algorithm. Int. J. Appl. Earth Obs. Geoinf. 2012, 18, 399-406
35. Naidoo, L.; Cho, M.; Mathieu, R.; Asner, G. Classification of savanna tree species, in the greater kruger national park region, by integrating hyperspectral and lidar data in a random forest data mining environment. ISPRS J. Photogramm. Remote Sens. 2012, 69, 167-179
36. Oliveira, S.; Oehler, F.; San-Miguel-Ayanz, J.; Camia, A.; Pereira, J.M. Modeling spatial patterns of fire occurrence in mediterranean europe using multiple regression and random forest. For. Ecol. Manag. 2012, 275, 117-129
37. Holden, Z.A.; Morgan, P.; Evans, J.S. A predictive model of burn severity based on 20-year satellite-inferred burn severity data in a large southwestern us wilderness area. For. Ecol. Manag. 2009, 258, 2399-2406
38. Archibald, S.A.; Roy, D.P.; Van Wilgen, B.W.; Scholes, R.J. What limits fire? An examination of drivers of burnt area in southern africa. Glob. Chang. Biol. 2009, 15, 613-630
39. Aldersley, A.; Murray, S.J.; Cornell, S.E. Global and regional analysis of climate and human drivers of wildfire. Sci. Total Environ. 2011, 409, 3472-3481
40. Chuvieco, E.; Riano, D.; Aguado, I.; Cocero, D. Estimation of fuel moisture content from multitemporal analysis of landsat thematic mapper reflectance data: Applications in fire danger assessment. Int. J. Remote Sens. 2002, 23, 2145-2162
41. Trigg, S.; Flasse, S. An evaluation of different bi-spectral spaces for discriminating burned shrub-savannah. Int. J. Remote Sens. 2001, 22, 2641-2647
42. Schaaf, C.B.; Gao, F.; Strahler, A.H.; Lucht, W.; Li, X.; Tsang, T.; Strugnell, N.C.; Zhang, X.; Jin, Y.;Muller, J.-P.; et al. First operational brdf, albedo nadir reflectance products from MODIS. Remote Sens. Environ. 2002, 83, 135-148
43. Liu, Z.; Wimberly, M.C.; Dwomoh, F.K. Vegetation dynamics in the upper guinean forest region of west africa from 2001 to 2015. Remote Sens. 2016, 9, 5
44. Huete, A.R. A soil-adjusted vegetation index (savi). Remote Sens. Environ. 1988, 25, 295-309
45. Huete, A.; Didan, K.; Miura, T.; Rodriguez, E.P.; Gao, X.; Ferreira, L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 2002, 83, 195-213
46. Garcia, M.; Chuvieco, E. Assessment of the potential of sac-c/mmrs imagery for mapping burned areas in spain. Remote Sens. Environ. 2004, 92, 414-423
47. Chuvieco, E.; Martín, M.P.; Palacios, A. Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. Int. J. Remote Sens. 2002, 23, 5103-5110
48. Stroppiana, D.; Bordogna, G.; Carrara, P.; Boschetti, M.; Boschetti, L.; Brivio, P. A method for extracting burned areas from landsat tm/etm+ images by soft aggregation of multiple spectral indices and a region growing algorithm. ISPRS J. Photogramm. Remote Sens. 2012, 69, 88-102
49. Pinty, B.; Verstraete, M.M. Gemi: A non-linear index to monitor global vegetation from satellites. Vegetatio 1992, 101, 15-20
50. Barbosa, P.M.; Grégoire, J.M.; Pereira, J.M.C. An algorithm for extracting burned areas from time series of avhrr gac data applied at a continental scale. Remote Sens. Environ. 1999, 69, 253-263
51. López García, M.J.; Caselles, V. Mapping burns and natural reforestation using thematic mapper data. Geocarto Int. 1991, 1, 31-37
52. Brewer, C.K.;Winne, J.C.; Redmond, R.L.; Opitz, D.W.; Mangrich, M.V. Classifying and mapping wildfire severity: A comparison of methods. Photogramm. Eng. Remote Sens. 2005, 71, 1311-1320
53. Rogan, J.; Franklin, J. Mapping wildfire burn severity in southern california forests and shrublands using enhanced thematic mapper imagery. Geocarto Int. 2001, 16, 89-99
54. Gao, B.C. Ndwi: A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 58, 257-266
55. Chuvieco, E.; Riaño, D.; Danson, F.M.; Martín, M.P. Use of a radiative transfer model to simulate the post-fire spectral response to burn severity. J. Geophys. Res. Biosci. 2006, 111
56. Gitelson, A.A.; Stark, R.; Grits, U.; Rundquist, D.; Kaufman, Y.; Derry, D. Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction. Int. J. Remote Sens. 2002, 23, 2537-2562
57. Gitelson, A.; Kaufmam, J.Y.; Stark, R.; Rundquist, D. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 2002, 80, 76-87
58. Schneider, P.; Roberts, D.; Kyriakidis, P. A vari-based relative greenness from MODIS data for computing the fire potential index. Remote Sens. Environ. 2008, 112, 1151-1167
59. Jin, Y.; Randerson, J.T.; Goetz, S.J.; Beck, P.S.; Loranty, M.M.; Goulden, M.L. The influence of burn severity on postfire vegetation recovery and albedo change during early succession in north american boreal forests. J. Geophys. Res. Biogeosci. 2012, 117, G01036
60. Bastarrika, A.; Alvarado, M.; Artano, K.; Martinez, M.; Mesanza, A.; Torre, L.; Ramo, R.; Chuvieco, E. Bams: A tool for supervised burned area mapping using landsat data. Remote Sens. 2014, 6, 12360-12380
61. Roy, D.P.; Giglio, L.; Kendall, J.D.; Justice, C.O. Multi-temporal active-fire based burn scar detection algorithm. Int. J. Remote Sens. 1999, 20, 1031-1038
62. Fraser, R.H.; Li, Z.; Cihlar, J. Hotspot and ndvi differencing synergy (hands): A new technique for burned area mapping over boreal forest. Remote Sens. Environ. 2000, 74, 362-376
63. Hantson, S.; Arneth, A.; Harrison, S.P.; Kelley, D.I.; Prentice, I.C.; Rabin, S.S.; Archibald, S.; Mouillot, F.; Arnold, S.R.; Artaxo, P. The status and challenge of global fire modelling. Biogeosciences 2016, 13, 3359-3375
64. Hantson, S.; Pueyo, S.; Chuvieco, E. Global fire size distribution is driven by human impact and climate. Glob. Ecol. Biogeogr. 2015, 24, 77-86
65. Danielson, J.J.; Gesch, D.B. Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010); US Geological Survey: Riston, FL, USA, 2011
66. Burrough, P.A.; McDonnell, R.; McDonnell, R.A.; Lloyd, C.D. Principles of Geographical Information Systems; Oxford University Press: Oxford, UK, 2015
67. Defourny, P.; Kirches, G.; Brockmann, C.; Boettcher, M.; Peters, M.; Bontemps, S.; Lamarche, C.; Schlerf, M.; Santoro, M. Land COVER CCI. Product User Guide Version 2.0. 2012. Available online: https://maps.elie. ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (accessed on 7 October 2017)
68. Herold, M.; Woodcock, C.E.; Di Gregorio, A.; Mayaux, P.; Belward, A.S.; Latham, J.; Schmullius, C.C. A joint initiative for harmonization and validation of land cover datasets. IEEE Trans. Geosci. Remote Sens. 2006, 44, 1719-1727
69. Olson, D.M.; Dinerstein, E.; Wikramanayake, E.D.; Burgess, N.D.; Powell, G.V.N.; Underwood, E.C.; D'amico, J.A.; Itoua, I.; Strand, H.E.; Morrison, J.C.; et al. Terrestrial ecoregions of the world: A new map of life on earth. BioScience 2001, 51, 933-938
70. Breiman, L.; Friedman, J.; Stone, C.J.; Olshen, R.A. Classification and Regression Trees; CRC Press: Florida, FL, USA, 1984
71. Bylander, T. Estimating generalization error on two-class datasets using out-of-bag estimates. Mach. Learn. 2002, 48, 287-297
72. Padilla, M.; Stehman, S.V.; Chuvieco, E. Validation of the 2008 MODIS-mcd45 global burned area product using stratified random sampling. Remote Sens. Environ. 2014, 144, 187-196
73. Pereira, J.M.C. A comparative evaluation of noaa/avhrr vegetation indexes for burned surface detection and mapping. IEEE Trans. Geosci. Remote Sens. 1999, 37, 217-226
74. Clark, M.L.; Aide, T.M.; Grau, H.R.; Riner, G. A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the dry chaco ecoregion of south america. Remote Sens. Environ. 2010, 114, 2816-2832
75. Ayala-Izurieta, J.E.; Márquez, C.O.; García, V.J.; Recalde-Moreno, C.G.; Rodríguez-Llerena, M.V.; Damián-Carrión, D.A. Land cover classification in an ecuadorian mountain geosystem using a random forest classifier, spectral vegetation indices, and ancillary geographic data. Geosciences 2017, 7, 34
76. Khoshgoftaar, T.M.; Golawala, M.; Van Hulse, J. An empirical study of learning from imbalanced data using random forest. In Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007), Patras, Greece, 29-31 October 2007; pp. 310-317
77. Breiman, L.; Chen, C.; Liaw, A. Using random forest to learn imbalanced data. J. Mach. Learn. Res. 2004, 1-12
78. Colditz, R.R. An evaluation of different training sample allocation schemes for discrete and continuous land cover classification using decision tree-based algorithms. Remote Sens. 2015, 7, 9655-9681
79. Mellor, A.; Boukir, S.; Haywood, A.; Jones, S. Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin. ISPRS J. Photogramm. Remote Sens. 2015, 105, 155-168
80. Zhou, L.; Wang, H. Loan default prediction on large imbalanced data using random forests. Indones. J. Electr. Eng. 2012, 10, 1519-1525
81. Kuhn, M.; Johnson, K. Applied Predictive Modeling; Springer: Berlin/Heidelberg, Germany, 2013; pp. 74-77
82. Burton, P.J.; Parisien, M.-A.; Hicke, J.A.; Hall, R.J.; Freeburn, J.T. Large fires as agents of ecological diversity in the north american boreal forest. Int. J. Wildland Fire 2009, 17, 754-767
83. Parisien, M.A.; Peters, V.S.; Wang, Y.H.; Little, J.M.; Bosch, E.M.; Stocks, B.J. Spatial patterns of forest fires in canada, 1980-1999. Int. J. Wildland Fire 2006, 15, 361-374
84. Stocks, B.; Mason, J.; Todd, J.; Bosch, E.;Wotton, B.; Amiro, B.; Flannigan, M.; Hirsch, K.; Logan, K.; Martell, D. Large forest fires in canada, 1959-1997. J. Geophys. Res. Atmos. 2002
85. Amiro, B.D.; Todd, J.B.; Wotton, B.M.; Logan, K.A.; Flannigan, M.D.; Stocks, B.J.; Mason, J.A.; Martell, D.L.; Hirsch, K.G. Direct carbon emissions from canadian forest fires, 1959-1999. Can. J. For. Res. 2001, 31, 512-525
86. Padilla, M.; Stehman, S.V.; Hantson, S.; Oliva, P.; Alonso-Canas, I.; Bradley, A.; Tansey, K.; Mota, B.; Pereira, J.M.; Chuvieco, E. Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation. Remote Sens. Environ. 2015, 160, 114-121
87. Padilla, M.; Olofsson, P.; Stehman, S.V.; Tansey, K.; Chuvieco, E. Stratification and sample allocation for reference burned area data. Remote Sens. Environ. 2017
88. Boschetti, L.; Roy, D.P.; Justice, C.O.; Humber, M.L. MODIS-landsat fusion for large area 30m burned area mapping. Remote Sens. Environ. 2015, 161, 27-42
89. Malmström, C.M.; Thompson, M.V.; Juday, G.P.; Los, S.O.; Randerson, J.T.; Field, C.B. Interannual variation in global-scale net primary production: Testing model estimates. Glob. Biogeochem. Cycles 1997, 11, 367-392
90. Chan, J.C.-W.; Paelinckx, D. Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sens. Environ. 2008, 112, 2999-3011
91. Mallinis, G.; Koutsias, N. Comparing ten classification methods for burned area mapping in a mediterranean environment using landsat tm satellite data. Int. J. Remote Sens. 2012, 33, 4408-4433
92. Chawla, N.V. Data mining for imbalanced datasets: An overview. In Data Mining and Knowledge Discovery Handbook; Springer: Berlin/Heidelberg, Germany, 2005; pp. 853-867
93. Liu, H.; Motoda, H. Instance Selection and Construction for Data Mining; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2013; Volume 608
94. Quintano, C.; Fernández-Manso, A.; Roberts, D.A. Multiple endmember spectral mixture analysis (mesma) to map burn severity levels from landsat images in mediterranean countries. Remote Sens. Environ. 2013, 136, 76-88
95. Pereira, J.M.C.; Mota, B.; Privette, J.L.; Caylor, K.K.; Silva, J.M.N.; Sa, A.C.L.; Ni-Meister, W. A simulation analysis of the detectability of understory burns in miombo woodlands. Remote Sens. Environ. 2004, 93, 296-310
96. Genuer, R.; Poggi, J.-M.; Tuleau-Malot, C. Variable selection using random forests. Pattern Recognit. Lett. 2010, 31, 2225-2236
97. Archer, K.J.; Kimes, R.V. Empirical characterization of random forest variable importance measures. Comput. Stat. Data Anal. 2008, 52, 2249-2260
98. Das, S. Filters, wrappers and a boosting-based hybrid for feature selection. In Proceedings of the 18th International Conference on Machine Learning (ICML), San Francisco, CA, USA, 28 June-1 July 2001; Citeseer: State College, PA, USA; pp. 74-81
99. Liu, H.; Motoda, H. Feature Selection for Knowledge Discovery and Data Mining; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2012; Volume 454