Patch-based forest change detection from Landsat time series

Forests

Volumn 8, Issue 5, 2017, Pages

  Joseph Hughes, M ^a   Douglas Kaylor, S ^b   Hayes, Daniel J ^c  

^a OREGON STATE UNIVERSITY   (United States)

^b UNIVERSITY OF TENNESSEE   (United States)

^c UNIVERSITY OF MAINE   (United States)

Author keywords

Change detection; Forest disturbance; Landsat; Remote sensing; Segmentation; Total variation regularization

Indexed keywords

IMAGE DENOISING; IMAGE SEGMENTATION; REFORESTATION; REMOTE SENSING; SATELLITE IMAGERY; TIME SERIES; VEGETATION;

CHANGE DETECTION; DISTURBANCE ESTIMATES; FOREST DISTURBANCES; LANDSAT; SPATIAL AND TEMPORAL SCALE; SPECTRAL VEGETATION INDICES; TOTAL VARIATION REGULARIZATION; VEGETATION REGENERATION;

FORESTRY;

DETECTION METHOD; FOREST COVER; LANDSAT; REMOTE SENSING; SEGMENTATION; TIME SERIES ANALYSIS; VEGETATION STRUCTURE;

FORESTS; REMOTE SENSING; SEGMENTATION;

UNITED STATES;

EID: 85019953593     PISSN: None     EISSN: 19994907     Source Type: Journal    
DOI: 10.3390/f8050166     Document Type: Article

References (56)

1. White, P.S.; Collings, B.S.;Wein, G.R. Natural disturbances and early successional habitats. In Sustaining Young Forest Communities; Greenberg, C.H., Collins, B.S., Thompson, F.R., Eds.; Springer: Dordrecht, The Netherlands, 2011; pp. 27-40.
2. Goward, S.N.; Masek, J.G.; Cohen, W.; Moisen, G.; Collatz, G.J.; Healey, S.; Houghton, R.A.; Huang, C.; Kennedy, R.; Law, B.; et al. Forest disturbance and North American carbon flux. EOS Trans. Am. Geophys. Union 2008, 89, 105-116.
3. Williams, C.A.; Collatz, G.J.; Masek, J.; Huang, C.; Goward, S.N. Impacts of disturbance history on forest carbon stocks and fluxes: Merging satellite disturbance mapping with forest inventory data in a carbon cycle model framework. Remote Sens. Environ. 2014, 151, 57-71.
4. Bleby, T.M.; McElrone, A.J.; Jackson, R.B. Water uptake and hydraulic redistribution across large woody root systems to 20 m depth. Plant Cell Environ. 2010, 33, 2132-2148.
5. Pugh, E.; Gordon, E. A conceptual model of water yield effects from beetle-induced tree death in snow-dominated lodgepole pine forests. Hydrol. Process. 2013, 27, 2048-2060.
6. Aber, J.D.; Ollinger, S.V.; Driscoll, C.T.; Likens, G.E.; Holmes, R.T.; Freuder, R.J.; Goodale, C.L. Inorganic nitrogen losses from a forested ecosystem in response to physical, chemical, biotic, and climatic perturbations. Ecosystems 2002, 5, 648-658.
7. Bernal, S.; Hedin, L.O.; Likens, G.E.; Gerber, S.; Buso, D.C. Complex response of the forest nitrogen cycle to climate change. Proc. Natl. Acad. Sci. USA 2012, 109, 3406-3411.
8. Dale, V.H.; Joyce, L.A.; Mcnulty, S.; Neilson, R.P.; Ayres, M.P.; Flannigan, M.D.; Hanson, P.J.; Irland, L.C.; Lugo, A.E.; Peterson, C.J.; et al. Climate Change and Forest Disturbances. BioScience 2001, 51, 723-734.
9. Weed, A.S.; Ayers, M.P.; Hicke, J.A. Consequences of climate change for biotic disturbances in North American forests. Ecol. Monogr. 2013, 83, 441-470.
10. Pickett, S.T.A.; White, P.S. The Ecology of Natural Disturbance and Patch Dynamics; Academic Press: Orlando, FL, USA, 1985; Volume 1, p. 472.
11. 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. 2015, 355, 109-123.
12. Dale, V.H.; Hughes, M.J.; Hayes, D.J. Climate Change and the Future of Natural Disturbances in the Central Hardwood Region. In Natural Disturbances: Historic Range of Variation and Effects on Upland Hardwood Forest Structure in the Southeastern US; Greenberg, C.H., Collins, B.S., Eds.; Springer International: Cham, Switzerland, 2015; pp. 355-369.
13. Kasischke, E.S.; Verbyla, D.L.; Rupp, T.S.; McGuire, A.D.; Murphy, K.A.; Jandt, R.; Barnes, J.L.; Hoy, E.E.; Duffy, P.A.; Calef, M.; et al. Alaska's changing fire regime-Implications for the vulnerability of its boreal forests. Can. J. For. Res. 2010, 40, 1313-1324.
14. Coomes, D.A.; Holdaway, R.J.; Kobe, R.K.; Lines, E.R.; Allen, R.B. A general integrative framework for modelling woody biomass production and carbon sequestration rates in forests. J. Ecol. 2012, 100, 42-64.
15. Hayes, D.J.; McGuire, A.D.; Kicklighter, D.W.; Gurney, K.R.; Burnside, T.J.; Melillo, J.M. Is the northern high-latitude land-based CO2 sink weakening? Glob. Biogeochem. Cycles 2011, 25, doi:10.1029/2010GB003813.
16. Medvigy, D.; Moorcroft, P.R. Predicting ecosystem dynamics at regional scales: An evaluation of a terrestrial biosphere model for the forests of northeastern North America. Philos. Trans. R. Soc. B Biol. Sci. 2012, 367, 222-235.
17. Smith, M.J.; Vanderwel, M.C.; Lyutsarev, V.; Emmott, S.; Purves, D.W. The climate dependence of the terrestrial carbon cycle including parameter and structural uncertainties. Biogeosci. Discuss. 2012, 9, 13439-13496.
18. Kennedy, R.E.; Andréfouët, S.; Cohen, W.B.; Gómez, C.; Griffiths, P.; Hais, M.; Healey, S.P.; Helmer, E.H.; Hostert, P.; Lyons, M.B.; et al. Bringing an ecological view of change to landsat-based remote sensing. Front. Ecol. Environ. 2014, 12, 339-346.
19. Masek, J.G.; Huang, C.;Wolfe, R.; Cohen,W.; Hall, F.; Kutler, J.; Nelson, P. North American forest disturbance mapped from a decadal Landsat record. Remote Sens. Environ. 2008, 112, 2914-2926.
20. Wilson, E.H.; Sader, S.A. Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sens. Environ. 2002, 80, 385-396.
21. García-Haro, F.J.; Gilabert, M.A.; Meliá, J. Monitoring fire-affected areas using Thematic Mapper data. Int. J. Remote Sens. 2001, 22, 533-549.
22. Lu, D.; Weng, Q. A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 2007, 28, 823-870.
23. Woodcock, C.E.; Allen, R.; Anderson, M.; Belward, A.; Bindschadler, R.; Cohen,W.; Gao, F.; Goward, S.N.; Helder, D.; Helmer, E.; et al. Free access to Landsat imagery. Science 2008, 320, 1011.
24. Vogelmann, J.E.; Tolk, B.; Zhu, Z. Monitoring forest changes in the southwestern United States using multitemporal Landsat data. Remote Sens. Environ. 2009, 113, 1739-1748.
25. Zhao, C.; Hobbs, B.E.; Ord, A. Fundamentals of computational geoscience: Numerical methods and algorithms. Lect. Notes Earth Sci. 2009, 122, 1-257.
26. Brooks, E.B.;Wynne, R.H.; Thomas, V.A.; Blinn, C.E.; Coulston, J.W. On-the-Fly Massively Multitemporal Change Detection Using Statistiacl Quality Control Charts and Landsat Data. IEEE Trans. Geosci. Remote Sens. 2014, 52, 3316-3332.
27. Jamali, S.; Jönsson, P.; Eklundh, L.; Ardö, J.; Seaquist, J. Detecting changes in vegetation trends using time series segmentation. Remote Sens. Environ. 2015, 156, 182-195.
28. Huang, C.; Thomas, N.; Goward, S.N.; Masek, J.G.; Zhu, Z.; Townshend, J.R.G.; Vogelmann, J.E. Automated masking of cloud and cloud shadow for forest change analysis using Landsat images. Int. J. Remote Sens. 2010, 31, 5449-5464.
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. 2010, 114, 2897-2910.
30. Hermosilla, T.; Wulder, M.A.; White, J.C.; Coops, N.C.; Hobart, G.W. An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sens. Environ. 2015, 158, 220-234.
31. Hermosilla, T.; Wulder, M.A.; White, J.C.; Coops, N.C.; Hobart, G.W.; Campbell, L.B. Mass data processing of time series Landsat imagery: Pixels to data products for forest monitoring. Int. J. Digit. Earth 2016, 9, doi:10.1080/17538947.2016.1187673.
32. Zhu, Z.; Woodcock, C.E.; Olofsson, P. Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sens. Environ. 2012, 122, 75-91.
33. Hughes, M.; Hayes, D. Automated detection of cloud and cloud shadow in single-date Landsat imagery using neural networks and spatial post-processing. Remote Sens. 2014, 6, 4907-4926.
34. Zhu, Z.; Woodcock, C.E. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 2012, 118, 83-94.
35. Schwarz, G. Estimating the dimension of a model. Ann. Stat. 1978, 6, 461-464.
36. Cohen, W.B.; Healey, S.P.; Yang, Z.; Stehman, S.V.; Brewer, C.K.; Brooks, E.B.; Gorelick, N.; Huang, C.; Hughes, M.J.; Kennedy, R.E.; et al. How Similar Are Forest Disturbance Maps Derived from Different Landsat Time Series Algorithms? Forests 2017, 8, 98.
37. Oliver, C.D.; Larson, B.C. Forest Stand Dynamics; Updated Edition; John Wiley and Sons: Hoboken, NJ, USA; 1996.
38. Omernik, J. Ecoregions of the conterminous United States. Map (scale 1:7,500,000). Ann. Assoc. Am. Geogr. 1987, 77, 118-125.
39. Chander, G.; Markham, B.L.; Helder, D.L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 2009, 113, 893-903.
40. Chavez, P.S. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens. Environ. 1988, 24, 459-479.
41. Chavez, P.S. Image-based atmospheric corrections-Revisited and improved. Photogramm. Eng. Remote Sens. 1996, 62, 1025-1036.
42. 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. 2010, 114, 1053-1068.
43. Tucker, C.J. Red and photographic infrared linear combinations formonitoring vegetation. Remote Sens. Environ. 1979, 8, 127-150.
44. VanWagtendonk, J.W.; Root, R.R.; Key, C.H. Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity. Remote Sens. Environ. 2004, 92, 397-408.
45. Jin, S.; Sader, S.A. Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sens. Environ. 2005, 94, 364-372.
46. Rudin, L.; Osher, S.; Fatemi, E. Nonlinear total variation based noise removal algorithms. Phys. D Nonlinear Phenom. 1992, 60, 259-268.
47. Goldstein, T.; Osher, S. The split Bregman method for L1-regularized problems. SIAM J. Imaging Sci. 2009, 2, 323-343.
48. Vogel, C.R.; Oman, M.E. Iterative methods for total variation denoising. SIAM J. Sci. Comput. 1996, 17, 227-238.
49. Cohen, W.B.; Yang, Z.; Kennedy, R.E. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync-Tools for calibration and validation. Remote Sens. Environ. 2010, 114, 2911-2924.
50. Cohen, J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 1960, 20, 37-46.
51. Heilman, G.E., Jr.; Strittholt, J.R.; Slosser, N.C.; Dellasala, D.A. Forest fragmentation of the conterminous United States: Assessing forest intactness through road density and spatial characteristics. Bioscience 2002, 52, 411-422.
52. Gómez, C.; White, J.C.; Wulder, M.A.; Alejandro, P. Historical forest biomass dynamics modelled with Landsat spectral trajectories. ISPRS J. Photogramm. Remote Sens. 2014, 93, 14-28.
53. Gamon, J.A.; Field, C.B.; Goulden, M.L.; Griffin, K.L.; Hartley, A.E.; Joel, G.; Penuelas, J.; Valentini, R. Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecol. Appl. 1995, 5, 28-41.
54. Lozano, F.J.; Suárez-Seoane, S.; de Luis, E. Assessment of several spectral indices derived from multi-temporal Landsat data for fire occurrence probability modelling. Remote Sens. Environ. 2007, 107, 533-544.
55. Lozano, F.J.; Surez-Seoane, S.; De Luis-Calabuig, E. Does fire regime affect both temporal patterns and drivers of vegetation recovery in a resilient Mediterranean landscape? A remote sensing approach at two observation levels. Int. J. Wildl. Fire 2012, 21, 666-679.
56. U.S. Geological Survey, Gap Analysis Program. Protected Areas Database of the United States (PAD-US), Version 1.4 Combined Feature Class 2016. Available online: https://gapanalysis.usgs.gov/padus/ (accessed on 10 April 2017).