Mapping forest change using stacked generalization: An ensemble approach

Remote Sensing of Environment

Volumn 204, Issue , 2018, Pages 717-728

  Healey, Sean P ^a   Cohen, Warren B ^b   Yang, Zhiqiang ^c   Kenneth Brewer, C ^d   Brooks, Evan B ^e   Gorelick, Noel ^f   Hernandez, Alexander J ^g   Huang, Chengquan ^h   Joseph Hughes, M ^c   Kennedy, Robert E ^c   Loveland, Thomas R ⁱ   Moisen, Gretchen G ^a   Schroeder, Todd A ^j   Stehman, Stephen V ^k   Vogelmann, James E ⁱ   Woodcock, Curtis E ^l   Yang, Limin ^n,o   Zhu, Zhe ^m  

^a ROCKY MOUNTAIN RESEARCH STATION   (United States)

^b US Forest Service Pacific Northwest Research Station   (United States)

^c OREGON STATE UNIVERSITY   (United States)

^d US Forest Service Washington Office   (United States)

^e VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

^f GOOGLE INC   (United States)

^g UTAH STATE UNIVERSITY   (United States)

^h UNIVERSITY OF MARYLAND   (United States)

ⁱ USGS Earth Resources Observations and Science Center   (United States)

^j US Forest Service Research and Development   (United States)

^k STATE UNIVERSITY OF NEW YORK   (United States)

^l BOSTON UNIVERSITY   (United States)

^m TEXAS TECH UNIVERSITY   (United States)

ⁿ SGT INC   (United States)

^o US Geological Survey Earth Resources Observation and Science Center   (United States)

more..

Author keywords

Change detection; Ensemble; Landsat; Multiple classifier systems

Indexed keywords

CHANGE DETECTION; DECISION TREES; FORESTRY; PARAMETER ESTIMATION; PHOTOMAPPING; REMOTE SENSING; SIGNAL DETECTION; VEGETATION MAPPING;

CHANGE DETECTION; ENSEMBLE; ENSEMBLE APPROACHES; ERROR RATE; FOREST CHANGE; LANDSAT; MULTIPLE CLASSIFIER SYSTEMS; PERFORMANCE; STACKED GENERALIZATION; STACKINGS;

LANDSAT;

ACCESSIBILITY; ACCURACY ASSESSMENT; DETECTION METHOD; ENSEMBLE FORECASTING; ENVIRONMENTAL FACTOR; LANDSAT; MAP GENERALIZATION; PARAMETERIZATION; PERFORMANCE ASSESSMENT; REMOTE SENSING; SATELLITE DATA; SATELLITE IMAGERY; TIME SERIES ANALYSIS; VEGETATION COVER; VEGETATION MAPPING;

EID: 85028374060     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2017.09.029     Document Type: Article

References (71)

1. Ahmed, O.S., Wulder, M.A., White, J.C., Hermosilla, T., Coops, N.C., Franklin, S.E., Classification of annual non-stand replacing boreal forest change in Canada using Landsat time series: a case study in northern Ontario. Remote Sens. Lett. 8 (2017), 29–37.
2. Antropov, O., Rauste, Y., Väänänen, A., Mutanen, T., Häme, T., Mapping forest disturbance using long time series of Sentinel-1 data: case studies over boreal and tropical forests. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016, 3906–3909.
3. Breiman, L., Bagging predictors. Mach. Learn. 24 (1996), 123–140.
4. Breiman, L., Stacked regressions. Mach. Learn. 24 (1996), 49–64.
5. Breiman, L., Random forests. Mach. Learn. 45 (2001), 5–32.
6. Brooks, E.B., Thomas, V.A., Wynne, R.H., Coulston, J.W., Fitting the multitemporal curve: A Fourier series approach to the missing data problem in remote sensing analysis. IEEE Trans. Geosci. Remote Sens. 50:9 (2012), 3340–3353.
7. Brooks, E.B., Wynne, R.H., Thomas, V.A., Blinn, C.E., Coulston, J.W., On-the-fly massively multitemporal change detection using statistical quality control charts and Landsat data. IEEE Trans. Geosci. Remote Sens. 52 (2014), 3316–3332.
8. Chambers, J.Q., Fisher, J.I., Zeng, H., Chapman, E.L., Baker, D.B., Hurtt, G.C., Hurricane Katrina's Carbon Footprint on U.S. Gulf Coast Forests. Science, 318, 2007, 1107.
9. Cohen, W.B., Goward, S.N., Landsat's role in ecological applications of remote sensing. Bioscience 54 (2004), 535–545.
10. Cohen, W.B., Yang, Z., Kennedy, R., Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync—tools for calibration and validation. Remote Sens. Environ. 114 (2010), 2911–2924.
11. Cohen, W.B., Yang, Z., Stehman, S.V., Schroeder, T.A., Bell, D.M., Masek, J.G., Huang, C., Meigs, G.W., Forest disturbance across the conterminous United States from 1985–2012: the emerging dominance of forest decline. For. Ecol. Manag. 360 (2016), 242–252.
12. Cohen, W., Healey, S., Yang, Z., Stehman, S., Brewer, C., Brooks, E., Gorelick, N., Huang, C., Hughes, M., Kennedy, R., Loveland, T., Moisen, G., Schroeder, T., Vogelmann, J., Woodcock, C., Yang, L., Zhu, Z., How similar are forest disturbance maps derived from different Landsat time series algorithms?. Forests, 8, 2017, 98.
13. Cutler, D.R., Edwards, T.C., Beard, K.H., Cutler, A., Hess, K.T., Gibson, J., Lawler, J.J., Random forests for classification in ecology. Ecology 88 (2007), 2783–2792.
14. DeVries, B., Verbesselt, J., Kooistra, L., Herold, M., Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series. Remote Sens. Environ. 161 (2015), 107–121.
15. Engler, R., Waser, L.T., Zimmermann, N.E., Schaub, M., Berdos, S., Ginzler, C., Psomas, A., Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution. For. Ecol. Manag. 310 (2013), 64–73.
16. Finco, M.V., Quayle, B., Zhang, Y., Lecker, J., Megown, K.A., Brewer, C.K., Monitoring Trends in Burn Severity (MTBS): monitoring wildfire activity for the past quarter century using Landsat data. Morin, R.S., Liknes, G.C., (eds.) Forest Inventory and Analysis (FIA) Science Symposium, 2012, USDA Forest Service, Northern Research Station, Baltimore, MD, 222–228.
17. Foody, G.M., Boyd, D.S., Sanchez-Hernandez, C., Mapping a specific class with an ensemble of classifiers. Int. J. Remote Sens. 28 (2007), 1733–1746.
18. Freeman, E.A., Moisen, G.G., A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecol. Model. 217 (2008), 48–58.
19. González-Olabarria, J.-R., Pukkala, T., Integrating fire risk considerations in landscape-level forest planning. For. Ecol. Manag. 261 (2011), 278–287.
20. Grinand, C., Rakotomalala, F., Gond, V., Vaudry, R., Bernoux, M., Vieilledent, G., Estimating deforestation in tropical humid and dry forests in Madagascar from 2000 to 2010 using multi-date Landsat satellite images and the random forests classifier. Remote Sens. Environ. 139 (2013), 68–80.
21. Healey, S.P., Yang, Z.Q., Cohen, W.B., Pierce, D.J., Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Landsat data. Remote Sens. Environ. 101 (2006), 115–126.
22. Healey, S.P., Cohen, W.B., Spies, T.A., Moeur, M., Pflugmacher, D., Whitley, M.G., Lefsky, M., The relative impact of harvest and fire upon Landscape-level dynamics of older forests: lessons from the Northwest Forest Plan. Ecosystems 11 (2008), 1106–1119.
23. Healey, S.P., Urbanski, S.P., Patterson, P.L., Garrard, C., A framework for simulating map error in ecosystem models. Remote Sens. Environ. 150 (2014), 207–217.
24. Huang, C., Goward, S.N., Masek, J.G., Thomas, N., Zhu, Z., Vogelmann, J.E., An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sens. Environ. 114 (2010), 183–198.
25. Hughes, M., Kaylor, S., Hayes, D., Patch-based forest change detection from Landsat time series. Forests, 8, 2017, 166.
26. Jin, S., Yang, L., Danielson, P., Homer, C., Fry, J., Xian, G., A comprehensive change detection method for updating the National Land Cover Database to circa 2011. Remote Sens. Environ. 132 (2013), 159–175.
27. Kandel, K., Huettmann, F., Suwal, M.K., Ram Regmi, G., Nijman, V., Nekaris, K.A.I., Lama, S.T., Thapa, A., Sharma, H.P., Subedi, T.R., Rapid multi-nation distribution assessment of a charismatic conservation species using open access ensemble model GIS predictions: red panda (Ailurus fulgens) in the Hindu-Kush Himalaya region. Biol. Conserv. 181 (2015), 150–161.
28. Kavzoglu, T., Sahin, E.K., Colkesen, I., Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11 (2014), 425–439.
29. Kennedy, R.E., Yang, Z., Cohen, W.B., Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—temporal segmentation algorithms. Remote Sens. Environ. 114 (2010), 2897–2910.
30. Kennedy, R.E., Yang, Z., Cohen, W.B., Pfaff, E., Braaten, J., Nelson, P., Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan. Remote Sens. Environ. 122 (2012), 117–133.
31. Kennedy, R.E., Yang, Z., Braaten, J., Copass, C., Antonova, N., Jordan, C., Nelson, P., Attribution of disturbance change agent from Landsat time-series in support of habitat monitoring in the Puget Sound region, USA. Remote Sens. Environ. 166 (2015), 271–285.
32. Kurz, W.A., Dymond, C.C., White, T.M., Stinson, G., Shaw, C.H., Rampley, G.J., Smyth, C., Simpson, B.N., Neilson, E.T., Trofymow, J.A., Metsaranta, J., Apps, M.J., CBM-CFS3: a model of carbon-dynamics in forestry and land-use change implementing IPCC standards. Ecol. Model. 220 (2009), 480–504.
33. Li, W., Ciais, P., MacBean, N., Peng, S., Defourny, P., Bontemps, S., Major forest changes and land cover transitions based on plant functional types derived from the ESA CCI Land Cover product. Int. J. Appl. Earth Obs. Geoinf. 47 (2016), 30–39.
34. Li, Z.-W., Xin, X.-P., Tang, H., Yang, F., Chen, B.-R., Zhang, B.-H., Estimating grassland LAI using the Random Forests approach and Landsat imagery in the meadow steppe of Hulunber, China. J. Integr. Agric. 16 (2017), 286–297.
35. Liaw, A., Wiener, M., Classification and regression by randomForest. R News 2 (2002), 18–22.
36. Loveland, T.R., Dwyer, J.L., Landsat: building a strong future. Remote Sens. Environ. 122 (2012), 22–29.
37. Masek, J.G., Hayes, D.J., Hughes, M.J., Healey, S.P., Turner, D.P., The role of remote sensing in process-scaling studies of managed forest ecosystems. For. Ecol. Manag. 355 (2015), 109–123.
38. Masek, J.G., Vermote, E.F., Saleous, N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., Lim, T.K., A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geosci. Remote Sens. Lett. 3 (2006), 68–72.
39. McRoberts, R.E., Næsset, E., Gobakken, T., Estimation for inaccessible and non-sampled forest areas using model-based inference and remotely sensed auxiliary information. Remote Sens. Environ. 154 (2014), 226–233.
40. Meigs, G.W., Kennedy, R.E., Gray, A.N., Gregory, M.J., Spatiotemporal dynamics of recent mountain pine beetle and western spruce budworm outbreaks across the Pacific Northwest Region, USA. For. Ecol. Manag. 339 (2015), 71–86.
41. Meyer, M.C., Semi-parametric additive constrained regression. J. Nonparametric Stat., 25, 2013, 715.
42. Moisen, G.G., Meyer, M.C., Schroeder, T.A., Liao, X., Schleeweis, K.G., Freeman, E.A., Toney, J.C., Shape selection in Landsat time series: a tool for monitoring forest dynamics. Glob. Chang. Biol. 22 (2016), 3518–3528.
43. Olofsson, P., Foody, G.M., Herold, M., Stehman, S.V., Woodcock, C.E., Wulder, M.A., Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 148 (2014), 42–57.
44. Oza, N.C., Tumer, K., Classifier ensembles: select real-world applications. Information Fusion 9 (2008), 4–20.
45. Powell, S.L., Cohen, W.B., Healey, S.P., Kennedy, R.E., Moisen, G.G., Pierce, K.B., Ohmann, J.L., Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: a comparison of empirical modeling approaches. Remote Sens. Environ. 114 (2010), 1053–1068.
46. R Core Team, R: A language and environment for statistical computing. 2015, R Foundation for Statistical Computing, Vienna, Austria http://www.R-project.org/.
47. Prasad, A., Iverson, L., Liaw, A., Newer classification and regression tree techniques: bagging and Random Forests for ecological prediction. Ecosystems 9 (2006), 181–199.
48. Reid, S., Grudic, G., Regularized linear models in stacked generalization. Benediktsson, J., Kittler, J., Roli, F., (eds.) Multiple Classifier Systems, 2009, Springer, Berlin Heidelberg, 112–121.
49. Ruefenacht, B., Finco, M.V., Nelson, M.D., Czaplewski, R., Helmer, E.H., Blackard, J.A., Holden, G.R., Lister, A.J., Salajanu, D., Weyermann, D., Winterberger, K., Conterminous U.S. and Alaska forest type mapping using forest inventory and analysis data. Photogramm. Eng. Remote. Sens. 74 (2008), 1379–1388.
50. Sáez, J.A., Galar, M., Luengo, J., Herrera, F., Tackling the problem of classification with noisy data using Multiple Classifier Systems: analysis of the performance and robustness. Inf. Sci. 247 (2013), 1–20.
51. Schroeder, T.A., Wulder, M.A., Healey, S.P., Moisen, G.G., Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data. Remote Sens. Environ. 115 (2011), 1421–1433.
52. Schroeder, T.A., Schleeweis, K.G., Moisen, G.G., Toney, C., Cohen, W.B., Freeman, E.A., Yang, Z., Huang, C., Testing a Landsat-based approach for mapping disturbance causality in U.S. forests. Remote Sens. Environ. 195 (2017), 230–243.
53. Schwind, B., Brewer, K., Quayle, B., Eidenshink, J.C., Establishing a nationwide baseline of historical burn-severity data to support monitoring of trends in wildfire effects and national fire policies. Pye, J.M., Rauscher, H., Sands, Y., Lee, D.C., Beatty, J.S., (eds.) Advances in Threat Assessment and Their Application to Forest and Rangeland Management, 2010, USDA Forest Service, Pacific Northwest Research Station, Portland, OR, 381–396.
54. Seidl, R., Schelhaas, M.-J., Rammer, W., Verkerk, P.J., Increasing forest disturbances in Europe and their impact on carbon storage. Nat. Clim. Chang. 4 (2014), 806–810.
55. Seilheimer, T.S., Zimmerman, P.L., Stueve, K.M., Perry, C.H., Landscape-scale modeling of water quality in Lake Superior and Lake Michigan watersheds: how useful are forest-based indicators?. J. Great Lakes Res. 39 (2013), 211–223.
56. Smith, W.B., Miles, P.D., Perry, C.H., Pugh, S.A., Forest Resources of the United States, 2007. 2009, F.S. U.S. Department of Agriculture, Washington, DC, 336.
57. Spies, T.A., Miller, J.D., Buchanan, J.B., Lehmkuhl, J.F., Franklin, J.F., Healey, S.P., Hessburg, P.F., Safford, H.D., Cohen, W.B., Kennedy, R.S.H., Knapp, E.E., Agee, J.K., Moeur, M., Underestimating risks to the northern spotted owl in fire-prone forests: response to Hanson et al. Conserv. Biol. 24 (2010), 330–333.
58. Stahl, G., Saarela, S., Schnell, S., Holm, S., Breidenbach, J., Healey, S., Patterson, P., Magnussen, S., Naesset, E., McRoberts, R., Gregoire, T., Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation. For. Ecosystems, 3, 2016, 5.
59. Stehman, S.V., Model-assisted estimation as a unifying framework for estimating the area of land cover and land-cover change from remote sensing. Remote Sens. Environ. 113 (2009), 2455–2462.
60. Thomas, N.E., Huang, C., Goward, S.N., Powell, S., Rishmawi, K., Schleeweis, K., Hinds, A., Validation of North American Forest Disturbance dynamics derived from Landsat time series stacks. Remote Sens. Environ. 115 (2011), 19–32.
61. Turner, B.L., Lambin, E.F., Reenberg, A., The emergence of land change science for global environmental change and sustainability. Proc. Natl. Acad. Sci. 104 (2007), 20666–20671.
62. Verhegghen, A., Eva, H., Ceccherini, G., Achard, F., Gond, V., Gourlet-Fleury, S., Cerutti, P., The potential of sentinel satellites for burnt area mapping and monitoring in the Congo Basin forests. Remote Sens., 8, 2016, 986.
63. Vogelmann, J.E., Xian, G., Homer, C., Tolk, B., Monitoring gradual ecosystem change using Landsat time series analyses: case studies in selected forest and rangeland ecosystems. Remote Sens. Environ. 122 (2012), 92–105.
64. Wolpert, D.H., Stacked generalization. Neural Netw. 5 (1992), 241–259.
65. Wolpert, D.H., Macready, W.G., No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1 (1997), 67–82.
66. Woźniak, M., Graña, M., Corchado, E., A survey of multiple classifier systems as hybrid systems. Information 16 (2014), 3–17.
67. Wulder, M.A., Masek, J.G., Cohen, W.B., Loveland, T.R., Woodcock, C.E., Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sens. Environ. 122 (2012), 2–10.
68. Xing, W., Chen, X., Stein, J., Marcinkowski, M., Temporal predication of dropouts in MOOCs: reaching the low hanging fruit through stacking generalization. Comput. Hum. Behav. 58 (2016), 119–129.
69. Zhu, Z., Woodcock, C.E., Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 118 (2012), 83–94.
70. Zhu, Z., Woodcock, C.E., Continuous change detection and classification of land cover using all available Landsat data. Remote Sens. Environ. 144 (2014), 152–171.
71. Zhu, Z., Woodcock, C.E., Holden, C., Yang, Z., Generating synthetic Landsat images based on all available Landsat data: predicting Landsat surface reflectance at any given time. Remote Sens. Environ. 162 (2015), 67–83.