Combining Tandem-X InSAR and simulated GEDI lidar observations for forest structure mapping

Remote Sensing of Environment

Volumn 187, Issue , 2016, Pages 253-266

  Qi, Wenlu ^a   Dubayah, Ralph O ^a  

^a UNIVERSITY OF MARYLAND   (United States)

Author keywords

Forest height; GEDI; InSAR; Lidar; LVIS; RVoG; TanDEM X

Indexed keywords

IMAGE RESOLUTION; NASA; OPTICAL RADAR; SPACE STATIONS; SYNTHETIC APERTURE RADAR; VEGETATION;

FOREST HEIGHT; GEDI; INSAR; LVIS; RVOG; TANDEM-X;

FORESTRY;

DATA SET; DIGITAL TERRAIN MODEL; ELEVATION; ERROR ANALYSIS; LIDAR; SATELLITE DATA; SPATIAL RESOLUTION; SYNTHETIC APERTURE RADAR; TANDEM-X; TOPOGRAPHY; TRANSECT; WAVEFORM ANALYSIS;

EID: 84992052854     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2016.10.018     Document Type: Article

References (56)

1. Abdullahi, S., Kugler, F., Pretzsch, H., Prediction of stem volume in complex temperate forest stands using TanDEM-X SAR data. Remote Sens. Environ. 174 (2016), 197–211.
2. Agrawal, A., Nepstad, D., Chhatre, A., Reducing emissions from deforestation and forest degradation. Annu. Rev. Environ. Resour. 36 (2011), 373–396.
3. Anderson, J., Martin, M.E., Smith, M.L., Dubayah, R.O., Hofton, M.A., Hyde, P., Peterson, B.E., Blair, J.B., Knox, R.G., The use of waveform lidar to measure northern temperate mixed conifer and deciduous forest structure in New Hampshire. Remote Sens. Environ. 105 (2006), 248–261.
4. Askne, J.I.H., Fransson, J.E.S., Santoro, M., Soja, M.J., Ulander, L.M.H., Model-based biomass estimation of a hemi-boreal forest from multitemporal TanDEM-X acquisitions. Remote Sens. 5 (2013), 5574–5597.
5. Blair, J.B., Rabine, D.L., Hofton, M.A., The laser vegetation imaging sensor: a medium-altitude, digitisation-only, airborne laser altimeter for mapping vegetation and topography. ISPRS J. Photogramm. Remote Sens. 54 (1999), 115–122.
6. Campbell, J., Hubbard Brook Experimental Forest (USDA Forest Service): Daily Mean Temperature Data, 1955-present. 2014, Hubbard Brook Data Archive, Durham, NH.
7. CEOS, CEOS Strategy for Carbon Observations from Space. 2014 (The Committee on Earth Observation Satellites (CEOS) Response to the Group on Earth Observations (GEO) Carbon Strategy).
8. Clark, D.B., Kellner, J.R., Tropical forest biomass estimation and the fallacy of misplaced concreteness. J. Veg. Sci. 23 (2012), 1191–1196.
9. Cloude, S.R., Papathanassiou, K.P., Polarimetric SAR interferometry. IEEE Trans. Geosci. Remote Sens. 36 (1998), 1551–1565.
10. Cloude, S.R., Papathanassiou, K.P., Three-stage inversion process for polarimetric SAR interferometry. IEE Proc. Radar Sonar Navig. 150 (2003), 125–134.
11. Cloude, S.R., Chen, H., Goodenough, D.G., Forest height estimation and validation using Tandem-X polinsar. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), 2013, 1889–1892.
12. Dolan, K.A., Masek, J.G., Huang, C.Q., Sun, G.Q., Regional forest growth rates measured by combining ICESat GLAS and Landsat data. J. Geophys. Res. Biogeosci., 114, 2009, 7.
13. Dolan, K.A., Hurtt, G.C., Chambers, J.Q., Dubayah, R.O., Frolking, S., Masek, J.G., Using ICESat's Geoscience Laser Altimeter System (GLAS) to assess large-scale forest disturbance caused by hurricane Katrina. Remote Sens. Environ. 115 (2011), 86–96.
14. Drake, J.B., Knox, R.G., Dubayah, R.O., Clark, D.B., Condit, R., Blair, J.B., Hofton, M., Above-ground biomass estimation in closed canopy neotropical forests using lidar remote sensing: factors affecting the generality of relationships. Glob. Ecol. Biogeogr. 12 (2003), 147–159.
15. Dubayah, R.O., Sheldon, S.L., Clark, D.B., Hofton, M.A., Blair, J.B., Hurtt, G.C., Chazdon, R.L., Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica. J. Geophys. Res. Biogeosci., 115, 2010, 17.
16. Dubayah, R.O., Goetz, S.J., Blair, J.B., Fatoyinbo, T.E., Hansen, M., Healey, S.P., Hofton, M.A., Hurtt, G.C., Kellner, J., Luthcke, S.B., Swatantran, A., The Global Ecosystem Dynamics Investigation. In, AGU Fall Meeting Abstracts, 2014, USA, San Francisco.
17. Duncanson, L.I., O., D.R., J., E.B., Assessing the general patterns of forest structure: quantifying tree and forest allometric scaling relationships in the United States. Glob. Ecol. Biogeogr. 24 (2015), 1465–1475.
18. Duncanson, L.I., Dubayah, R.O., Cook, B.D., Rosette, J., Parker, G., The importance of spatial detail: assessing the utility of individual crown information and scaling approaches for lidar-based biomass density estimation. Remote Sens. Environ. 168 (2015), 102–112.
19. Englhart, S., Keuck, V., Siegert, F., Aboveground biomass retrieval in tropical forests — the potential of combined X- and L-band SAR data use. Remote Sens. Environ. 115 (2011), 1260–1271.
20. Fatoyinbo, T.E., Simard, M., Height and biomass of mangroves in Africa from ICESat/GLAS and SRTM. Int. J. Remote Sens. 34 (2013), 668–681.
21. Garestier, F., Dubois-Fernandez, P.C., Papathanassiou, K.P., Pine forest height inversion using single-pass X-band PolInSAR data. IEEE Trans. Geosci. Remote Sens. 46 (2008), 59–68.
22. Goetz, S.J., Dubayah, R.O., Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change. Carbon Manag. 2 (2011), 231–244.
23. Hajnsek, I., Kugler, F., Lee, S.K., Papathanassiou, K.P., Tropical-forest-parameter estimation by means of Pol-InSAR: the INDREX-II campaign. IEEE Trans. Geosci. Remote Sens. 47 (2009), 481–493.
24. Hall, F.G., Bergen, K., Blair, J.B., Dubayah, R.O., Houghton, R., Hurtt, G., Kellndorfer, J., Lefsky, M., Ranson, J., Saatchi, S., Shugart, H.H., Wickland, D., Characterizing 3D vegetation structure from space: mission requirements. Remote Sens. Environ. 115 (2011), 2753–2775.
25. Huang, W.L., Sun, G.Q., Dubayah, R.O., Cook, B., Montesano, P., Ni, W.J., Zhang, Z.Y., Mapping biomass change after forest disturbance: applying LiDAR footprint-derived models at key map scales. Remote Sens. Environ. 134 (2013), 319–332.
26. Hurtt, G.C., Fisk, J., Thomas, R.Q., Dubayah, R., Moorcroft, P.R., Shugart, H.H., Linking models and data on vegetation structure. J. Geophys. Res. Biogeosci., 115, 2010, 11.
27. Keller, M., Palace, M., Hurtt, G., Biomass estimation in the Tapajos National Forest, Brazil — examination of sampling and allometric uncertainties. For. Ecol. Manag. 154 (2001), 371–382.
28. Kellndorfer, J.M., Walker, W.S., Pierce, L., Dobson, C., Fites, J.A., Hunsaker, C., Vona, J., Clutter, M., Vegetation height estimation from shuttle radar topography mission and national elevation datasets. Remote Sens. Environ. 93 (2004), 339–358.
29. Kellndorfer, J.M., Walker, W.S., LaPoint, E., Kirsch, K., Bishop, J., Fiske, G., Statistical fusion of lidar, InSAR, and optical remote sensing data for forest stand height characterization: a regional-scale method based on LVIS, SRTM, Landsat ETM plus, and ancillary data sets. J. Geophys. Res. Biogeosci., 115, 2010, 10.
30. Kellndorfer, J.M., Walker, W.S., LaPoint, E., Bishop, J., Cormier, T., Fiske, G., NACP aboveground biomass and carbon baseline data (NBCD 2000). U.S.A. Data Set, 2012 (In: Oak Ridge, Tennessee, U.S.A.: ORNL DAAC).
31. Kenyi, L.W., Dubayah, R.O., Hofton, M., Schardt, M., Comparative analysis of SRTM-NED vegetation canopy height to LIDAR-derived vegetation canopy metrics. Int. J. Remote Sens. 30 (2009), 2797–2811.
32. Kugler, F., Schulze, D., Hajnsek, I., Pretzsch, H., Papathanassiou, K.P., TanDEM-X Pol-InSAR performance for forest height estimation. IEEE Trans. Geosci. Remote Sens. 52 (2014), 6404–6422.
33. Kugler, F., Lee, S.K., Hajnsek, I., Papathanassiou, K.P., Forest height estimation by means of Pol-InSAR data inversion: the role of the vertical wavenumber. IEEE Trans. Geosci. Remote Sens. 53 (2015), 5294–5311.
34. Lee, S.K., Fatoyinbo, T.E., TanDEM-X Pol-InSAR inversion for mangrove canopy height estimation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8 (2015), 3608–3618.
35. Lee, S.K., Kugler, F., Papathanassiou, K.P., Hajnsek, I., Quantification of temporal decorrelation effects at L-band for polarimetric SAR interferometry applications. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6 (2013), 1351–1367.
36. Lu, D., Chen, Q., Wang, G., Liu, L., Li, G., Moran, E., A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. Int. J. Digit. Earth, 2014, 1–43.
37. Mitchard, E.T.A., Saatchi, S.S., White, L.J.T., Abernethy, K.A., Jeffery, K.J., Lewis, S.L., Collins, M., Lefsky, M.A., Leal, M.E., Woodhouse, I.H., Meir, P., Mapping tropical forest biomass with radar and spaceborne LiDAR in Lope National Park, Gabon: overcoming problems of high biomass and persistent cloud. Biogeosciences 9 (2012), 179–191.
38. Montesano, P.M., Cook, B.D., Sun, G., Simard, M., Nelson, R.F., Ranson, K.J., Zhang, Z., Luthcke, S., Achieving accuracy requirements for forest biomass mapping: a spaceborne data fusion method for estimating forest biomass and LiDAR sampling error. Remote Sens. Environ. 130 (2013), 153–170.
39. Moreira, A., Prats-Iraola, P., Younis, M., Krieger, G., Hajnsek, I., Papathanassiou, K.P., A tutorial on synthetic aperture radar. IEEE Geosci. Remote Sens. Mag. 1 (2013), 6–43.
40. NOAA, Daily Record of Climatological Observations. 2011–2012, National Oceanic and Atmospheric Administration.
41. NOAA, United States Interagency Elevation Inventory (USIEI). 2015, NOAA's Ocean Service, Office for Coastal Management (OCM), Charleston, SC.
42. Olesk, A., Voormansik, K., Vain, A., Noorma, M., Praks, J., Seasonal differences in forest height estimation from interferometric TanDEM-X coherence data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8 (2015), 5565–5572.
43. Oliver, M.A., Webster, R., Kriging: a method of interpolation for geographical information systems. Int. J. Geogr. Inf. Syst. 4 (1990), 313–332.
44. Papathanassiou, K.P., Cloude, S.R., Single-baseline polarimetric SAR interferometry. IEEE Trans. Geosci. Remote Sens. 39 (2001), 2352–2363.
45. Praks, J., Kugler, F., Papathanassiou, K.P., Hajnsek, I., Hallikainen, M., Height estimation of boreal forest: interferometric model-based inversion at L- and X-band versus HUTSCAT profiling scatterometer. IEEE Geosci. Remote Sens. Lett. 4 (2007), 466–470.
46. Praks, J., Antropov, O., Hallikainen, M.T., LIDAR-aided SAR interferometry studies in boreal forest: scattering phase center and extinction coefficient at X- and L-band. IEEE Trans. Geosci. Remote Sens. 50 (2012), 3831–3843.
47. Saatchi, S.S., Harris, N.L., Brown, S., Lefsky, M., Mitchard, E.T.A., Salas, W., Zutta, B.R., Buermann, W., Lewis, S.L., Hagen, S., Petrova, S., White, L., Silman, M., Morel, A., Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl. Acad. Sci. U.S.A. 108 (2011), 9899–9904.
48. Seymour, M.S., Cumming, I.G., Maximum likelihood estimation for SAR interferometry. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS) 4 (1994), 2272–2275.
49. Siccama, T.G., Fahey, T.J., Johnson, C.E., Sherry, T.W., Denny, E.G., Girdler, E.B., Likens, G.E., Schwarz, P.A., Population and biomass dynamics of trees in a northern hardwood forest at Hubbard brook. Can. J. For. Res. Rev. Can. Rech. For. 37 (2007), 737–749.
50. Soja, M.J., Ulander, L.M.H., Digital canopy model estimation from TanDEM-X interferometry using high-resolution lidar DEM. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), 2013, 165–168.
51. Solberg, S., Astrup, R., Breidenbach, J., Nilsen, B., Weydahl, D., Monitoring spruce volume and biomass with InSAR data from TanDEM-X. Remote Sens. Environ. 139 (2013), 60–67.
52. Stysley, P.R., Coyle, D.B., Kay, R.B., Frederickson, R., Poulios, D., Cory, K., Clarke, G., Long term performance of the High Output Maximum Efficiency Resonator (HOMER) laser for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar. Opt. Laser Technol. 68 (2015), 67–72.
53. Sun, G.Q., Ranson, K.J., Guo, Z., Zhang, Z., Montesano, P., Kimes, D., Forest biomass mapping from lidar and radar synergies. Remote Sens. Environ. 115 (2011), 2906–2916.
54. Torano Caicoya, A., Kugler, F., Hajnsek, I., Papathanassiou, K., Large scale biomass classification in Boreal forests with TanDEM-X data. IEEE Trans. Geosci. Remote Sens. 54 (2016), 5935–5951.
55. Tsui, O.W., Coops, N.C., Wulder, M.A., Marshall, P.L., Integrating airborne LiDAR and space-borne radar via multivariate kriging to estimate above-ground biomass. Remote Sens. Environ. 139 (2013), 340–352.
56. Zhao, F., Guo, Q.H., Kelly, M., Allometric equation choice impacts lidar-based forest biomass estimates: a case study from the Sierra National Forest, CA. Agric. For. Meteorol. 165 (2012), 64–72.