Confirmation of post-harvest spectral recovery from Landsat time series using measures of forest cover and height derived from airborne laser scanning data

Remote Sensing of Environment

Volumn 216, Issue , 2018, Pages 262-275

  White, Joanne C ^a,b   Saarinen, Ninni ^b   Kankare, Ville ^b   Wulder, Michael A ^a   Hermosilla, Txomin ^c   Coops, Nicholas C ^c   Pickell, Paul D ^c   Holopainen, Markus ^b   Hyyppä, Juha ^d   Vastaranta, Mikko ^e  

^a PACIFIC FORESTRY CENTRE   (Canada)

^b UNIVERSITY OF HELSINKI   (Finland)

^c UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^d FINNISH GEOSPATIAL RESEARCH INSTITUTE   (Finland)

^e UNIVERSITY OF EASTERN FINLAND   (Finland)

Author keywords

ALS; Boreal; Harvest; Landsat; Lidar; Recovery; Regeneration; Time series

Indexed keywords

ALUMINUM; HARVESTING; LASER APPLICATIONS; OPTICAL RADAR; PIXELS; RECOVERY; REFORESTATION; TIME SERIES;

AIRBORNE LASER SCANNING; BOREAL; FOREST RECOVERY; FOREST STRUCTURE; HARVEST; LANDSAT; LANDSAT TIME SERIES; LASER SCANNING DATA; REGENERATION; TIMES SERIES;

LANDSAT;

ALGORITHM; CLEARCUTTING; FOREST CANOPY; FOREST COVER; FOREST ECOSYSTEM; LASER METHOD; LIDAR; PIXEL; SATELLITE DATA; STAND STRUCTURE; SURFACE REFLECTANCE; TIME SERIES; TIME SERIES ANALYSIS;

FINLAND;

EID: 85049486184     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2018.07.004     Document Type: Article

References (75)

1. Ahmed, O.S., Franklin, S.E., Wulder, M.A., Interpretation of forest disturbance using a time series of Landsat imagery and canopy structure from airborne lidar. Can. J. Remote. Sens. 39:6 (2014), 521–542.
2. Banskota, A., Kayastha, N., Falkowski, M.J., Wulder, M.A., Froese, R.E., White, J.C., Forest monitoring using Landsat time series data: a review. Can. J. Remote. Sens. 40:5 (2014), 362–384.
3. Barrett, F., McRoberts, R.E., Tomppo, E., Cienciala, E., Waser, L.T., A questionnaire-based review of the operational use of remotely sensed data by national forest inventories. Remote Sens. Environ. 174 (2016), 279–289.
4. Bartels, S.F., Chen, H.Y.H., Wulder, M.A., White, J.C., Trends in post-disturbance recovery rates of Canada's forests following wildfire and harvest. For. Ecol. Manag. 361 (2016), 194–207.
5. Baumann, M., Ozdogan, M., Kuemmerle, T., Wendland, K.J., Esipova, E., Radeloff, V.C., Using the Landsat record to detect Forest-cover changes during and after the collapse of the Soviet Union in the temperate zone of European Russia. Remote Sens. Environ. 124 (2012), 174–184.
6. Bolton, D.K., Coops, N.C., Wulder, M.A., Characterizing residual structure and forest recovery following high-severity fire in the western boreal of Canada using Landsat time-series and airborne LiDAR data. Remote Sens. Environ. 163 (2015), 48–60.
7. Bolton, D.K., Coops, N.C., Hermosilla, T., Wulder, M.A., White, J.C., Assessing variability in post-fire forest structure along gradients of productivity in the Canadian boreal using multi-source remote sensing. J. Biogeogr. 44 (2017), 1294–1305.
8. Bolton, D.K., White, J.C., Wulder, M.A., Coops, N.C., Hermosilla, T., Yuan, X., Updating stand-level forest inventories using airborne laser scanning and Landsat time series data. Int. J. Appl. Earth Obs. Geoinf. 66 (2018), 174–183.
9. Chu, T., Guo, X., Remote sensing techniques in monitoring post-fire effects and patterns of forest recovery in boreal forest regions: a review. Remote Sens. 6 (2014), 470–520.
10. Chu, T., Guo, X., Takeda, K., Remote sensing approach to detect post-fire vegetation regrowth in Siberian boreal larch forest. Ecol. Indic. 62 (2016), 32–46.
11. Cohen, W.B., Goward, S.N., Landsat's role in ecological applications of remote sensing. Bioscience 54 (2004), 535–545.
12. 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.
13. Cohen, W.B., Yang, Z., Healey, S.P., Kennedy, R.E., Gorelick, N., A LandTrendr multispectral ensemble for forest disturbance detection. Remote Sens. Environ. 205 (2018), 131–140.
14. Culotta, S., Boncina, A., Carvalho-Ribeiro, S.M., Chauvin, C., Farcy, C., Kurttila, M., Maetzke, F.G., Forest planning across Europe: the spatial scale, tools, and inter-sectoral integration in land-use planning. J. Environ. Plan. Manag. 58:8 (2015), 1384–1411.
15. de Groot, W.J., Cantin, A.S., Flannigan, M.D., Soja, A.J., Gowman, L.M., Newbery, A., A comparison of Canadian and Russian boreal forest fire regimes. For. Ecol. Manag. 294 (2013), 23–34.
16. Escuin, S., Navarro, R., Fernández, P., Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from Landsat TM/ETM images. Int. J. Remote Sens. 29 (2008), 1053–1073.
17. FAO, FRA 2015: terms and definitions. Forest Resource Assessment Working Paper 180, 2012, Food and Agriculture Organization of the United Nations, Rome, Italy Available online http://www.fao.org/docrep/017/ap862e/ap862e00.pdf. (Accessed 5 January 2018)
18. Forest Stewardship Council Finland, FSC standard for Finland. FSC-SECR-0160. Available online https://fi.fsc.org/preview.fsc-standard-for-finland-v1-1-approved-210111.a-84.pdf, 2010. (Accessed 8 May 2018)
19. Frazier, R.J., Coops, N.C., Wulder, M.A., Boreal shield forest disturbance and recovery trends using Landsat time series. Remote Sens. Environ. 170 (2015), 317–327.
20. Frazier, R., Coops, N.C., Wulder, M.A., Hermosilla, T., White, J.C., Analyzing spatial and temporal variability in short-term rates of post-fire vegetation return from Landsat time series. Remote Sens. Environ. 205 (2018), 32–45.
21. Frolking, S., Palace, M.W., Clark, D.B., Chambers, J.Q., Shugart, H.H., Hurtt, G.C., Forest disturbance and recovery: a general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. J. Geophys. Res. 114 (2009), 1–27.
22. Griffiths, P., Kuemmerle, T., Baumann, M., Radeloff, V.C., Abrudan, I.V., Lieskovsky, J., Munteanu, C., Ostapowicz, K., Hostert, P., Forest disturbances, forest recovery, and changes in forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites. Remote Sens. Environ. 151 (2014), 72–88.
23. Goward, S.M., Williams, D.L., Arvidson, T., Rochchio, L.E.P., Irons, J.R., Russel, C.A., Johnston, S.S., Landsat's enduring legacy: pioneering global land observations from space. American Society for Photogrammetry and Remote Sensing, Bethesda, Maryland. 2017 1-57083-101-7, 586.
24. Gustafsson, L., Kouki, J., Sverdrup-Thygeson, A., Tree retention as a conservation measure in clear-cut forests of northern Europe: a review of ecological consequences. Scand. J. For. Res. 25 (2010), 295–308.
25. Healey, S.P., Cohen, W.B., Zhiqiang, Y., Krankina, O.N., Comparison of tasseled cap-based Landsat data structures for use in forest disturbance detection. Remote Sens. Environ. 97 (2005), 301–310.
26. 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. 158 (2015), 220–234.
27. Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sens. Environ. 170 (2015), 121–132.
28. 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. Int. J. Digital Earth 9:11 (2016), 1035–1054.
29. Horler, D.N.H., Ahern, F.J., Forestry information content of Thematic Mapper data. Int. J. Remote Sens., 7(3), 1986 (405–128).
30. Isenburg, M., Efficient LiDAR processing software (version 160910, academic). obtained from http://rapidlasso.com/LAStools, 2017.
31. Kankare, V., Vauhkonen, J., Holopainen, M., Vastaranta, M., Hyyppa, J., Hyyppa, H., Alho, P., Sparse density, leaf-off airborne laser scanning data in aboveground biomass component prediction. Forests 6:6 (2015), 1839–1857.
32. Kennedy, R.E., Cohen, W.B., Schroeder, T.A., Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sens. Environ. 110 (2007), 370–386.
33. 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.
34. 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.
35. Keogh, E., Chu, S., Hart, D., Pazzani, M., An online algorithm for segmenting time series. Proceedings IEEE International Conference on Data Mining, 2001, 2001, 289–296 (ICDM 2001).
36. Key, C.H., Benson, N.C., Landscape assessment (LA). FIREMON: fire effects monitoring and inventory system. Lutes, D.C., Keane, R.E., Carati, J.F., Key, C.H., Benson, N.C., Gangi, L.J., (eds.) General Technical Report RMRS-GTR-164-CD, 2006, USDA Forest Service, Rocky Mountains Research Station, Fort Collins, CO (pp. LA−1-55).
37. Korhonen, L., Pippuri, I., Packalén, P., Heikkinen, V., Maltamo, M., Heikkilä J., Detection of the need for seedling stand tending using high-resolution remote sensing data. Silva Fennica 47:2 (2013), 1–20.
38. Kotivuori, E., Korhonen, L., Packalen, P., Nationwide airborne laser scanning based models for volume, biomass and dominant height in Finland. Silva Fennica 50:4 (2016), 1–28.
39. Kuusinen, N., Stenberg, P., Tomppo, E., Bernier, P., Berninger, F., Variation in understory and canopy reflectance during stand development in Finnish coniferous forests. Can. J. For. Res. 45 (2015), 1077–1085.
40. Li, X., Strahler, A., Geometric-optical modeling of a conifer forest canopy. IEEE Trans. Geosci. Remote Sens. GE-23:5 (1985), 705–721.
41. Madoui, A., Gauthier, S., Leduc, A., Bergeron, Y., Valeria, O., Monitoring forest recovery following wildfire and harvest in boreal forests using satellite imagery. Forests 6 (2015), 4105–4134.
42. Miller, J.R., White, P.H., Chen, J.M., Peddle, D.R., McDermid, G., Fournier, R.A., Shepherd, P., Rubinstein, I., Freemantle, J., Soffer, R., Ledrew, E., Seasonal change in understory reflectance of boreal forests and influence on canopy vegetation indices. J. Geophys. Res. 102:D24 (1997), 475–482.
43. Natural Resources Institute of Finland, The multi-source national forest inventory raster maps of 2013. Available at http://kartta.luke.fi/index-en.html, 2015. (Accessed 1 June 2017)
44. Nilson, T., Peterson, U., Age dependence of forest reflectance: analysis of main driving factors. Remote Sens. Environ. 48 (1994), 319–331.
45. 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.
46. Olsson, H., A method for using Landsat time series for monitoring young plantations in boreal forests. Int. J. Remote Sens. 30:19 (2009), 5117–5131.
47. Ørka, H.O., Gobakken, T., Næsset, E., Predicting attributes of regeneration forests using airborne laser scanning. Can. J. Remote. Sens. 42 (2016), 541–553.
48. Pascual, C., Garcia-Abril, A., Cohen, W.B., Martin-Fernandez, S., Relationship between LiDAR-derived forest canopy height and Landsat images. Int. J. Remote Sens. 31:5 (2010), 1261–1280.
49. PEFC Finland, Criteria for PEFC forest certification. Available online http://pefc.fi/wp-content/uploads/2016/09/PEFC_FI_1002_2014_Criteria_for_Forest_Certification_20141027.pdf, 2014. (Accessed 8 May 2018)
50. Peterson, U., Nilson, T., Successional reflectance trajectories in northern temperate forests. Int. J. Remote Sens. 14:3 (1993), 609–613.
51. Pflugmacher, D., Cohen, W.B., Kennedy, R.E., Using Landsat-derived disturbance history (1972–2010) to predict current forest structure. Remote Sens. Environ. 122 (2012), 146–165.
52. Pflugmacher, D., Cohen, W.B., Kennedy, R.E., Yang, Z., Using Landsat-derived disturbance and recovery history and lidar to map forest biomass dynamics. Remote Sens. Environ. 151 (2014), 124–137.
53. Pickell, P.D., Hermosilla, T., Frazier, R.J., Coops, N.C., Wulder, M.A., Forest recovery trends derived from Landsat time series for North American boreal forests. Int. J. Remote Sens. 37 (2016), 138–149.
54. Potapov, P.V., Turubanova, S.A., Tyukavina, A., Krylov, A.M., McCarty, J.L., Radeloff, V.C., Hansen, M.C., Eastern Europe's forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive. Remote Sens. Environ. 159 (2015), 28–43.
55. Saarinen, N., White, J.C., Wulder, M.A., Kangas, A., Tuominen, S., Kankare, V., Holopainen, M., Hyyppä J., Vastaranta, M., Landsat archive holdings for Finland: Opportunities for forest monitoring. Silva Fennica, 52(3), 2018, 9986, 10.14214/sf.9986.
56. Schmidt, G.L., Jenkerson, C.B., Masek, J., Vermote, E., Gao, F., Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description. U.S. Geol. Surv. Open File Rep., 2013–1057, 2013, 17 Available online http://pubs.usgs.gov/of/2013/1057/.
57. Schroeder, T.A., Cohen, W.B., Yang, Z., Patterns of forest regrowth following clearcutting in western Oregon as determined from Landsat time-series. For. Ecol. Manag. 243 (2007), 259–273.
58. 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. Remote Sens. Environ. 115 (2011), 1421–1433.
59. Senf, C., Pflugmacher, D., Hostert, Pl, Seidl, R., Using Landsat time series for characterizing forest disturbance dynamics in the coupled human and natural systems of Central Europe. ISPRS J. Photogramm. Remote Sens. 130 (2017), 453–463.
60. Song, C., Woodcock, C.E., Monitoring forest succession with multitemporal Landsat images: factors of uncertainty. IEEE Trans. Geosci. Remote Sens. 41:11 (2003), 2557–2567.
61. Song, C., Woodcock, C.E., Li, X., The spectral/temporal manifestation of forest succession in optical imagery: the potential of multitemporal imagery. Remote Sens. Environ. 82 (2002), 285–302.
62. Spake, R., Ezard, T.H.G., Martin, P.A., Newton, A.C., Doncaster, C.P., A meta-analysis of functional group responses to forest recovery outside of the tropics. Conserv. Biol. 29:6 (2015), 1695–1703.
63. Tomppo, E., Satellite image-based National Forest Inventory of Finland. Proceedings of the Symposium on Global and Environmental Monitoring, Techniques and Impacts, Victoria, British Columbia, Canada, 17–21 September 1990 International Archives of Photogrammetry and Remote Sensing, Vol. XXVIII, 1990, 419–424 (part 1–7 (1991)).
64. Tomppo, E., Olsson, H., Ståhl, G., Katila, M., Creation of forest data bases by combining National Forest Inventory Field Plots and Remote Sensing. Remote Sens. Environ. 112 (2008), 1982–1999.
65. Vogeler, J.C., Yang, Z., Cohen, W.B., Mapping post-fire habitat characteristics through the fusion of remote sensing tools. Remote Sens. Environ. 173 (2015), 294–303.
66. White, J.C., Wulder, M.A., The Landsat observation record of Canada: 1972–2012. Can. J. Remote. Sens. 39:6 (2013), 455–467.
67. White, J.C., Wulder, M.A., Hobart, G.W., Luther, J.E., Hermosilla, T., Griffiths, P., Coops, N.C., Hall, R.J., Hostert, P., Dyk, A., Guindon, L., Pixel-based image compositing for large-area dense time series applications and science. Can. J. Remote. Sens. 40:3 (2014), 192–212.
68. White, J.C., Wulder, M.A., Hermosilla, T., Coops, N.C., Hobart, G., A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sens. Environ. 194 (2017), 303–321.
69. Woodcock, C.E., Allen, R.G., Anderson, M., Belward, A., Bindschadler, R., Cohen, W.B., Wynne, R., Free access to Landsat imagery. Science, 320(5879), 2008, 1011.
70. Wulder, M.A., Skakun, R.S., Dymond, C.C., Kurz, W.A., White, J.C., Characterization of the diminishing accuracy in detecting forest insect damage over time. Can. J. Remote. Sens. 31:6 (2005), 421–431.
71. Wulder, M.A., Campbell, C., White, J.C., Flannigan, M., Campbell, I.D., National circumstances in the international circumboreal community. For. Chron. 83:4 (2007), 539–556.
72. 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.
73. Wulder, M.A., White, J.C., Loveland, T.R., Woodcock, C.E., Belward, A.S., Cohen, W.B., Fosnight, E.A., Shaw, J., Masek, J.G., Roy, D.P., The Global Landsat Archive: status, consolidation, and direction. Remote Sens. Environ. 185 (2016), 271–283.
74. Zald, H.S.J., Ohmann, J.L., Roberts, H.M., Gregory, M.J., Henderson, E.B., McGaughey, R.J., Braaten, J., Influence of lidar, Landsat imagery, disturbance history, plot location accuracy, and plot size on accuracy of imputation maps of forest composition and structure. Remote Sens. Environ. 143 (2014), 26–38.
75. Zhu, Z., Woodcock, C.E., Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 118 (2012), 83–94.