Monitoring of forest structure dynamics by means of L-band SAR tomography

Remote Sensing

Volumn 9, Issue 12, 2017, Pages

  Cazcarra Bes, Victor ^a,b   Tello Alonso, Maria ^a   Fischer, Rico ^c   Heym, Michael ^d   Papathanassiou, Konstantinos ^a  

^a GERMAN AEROSPACE CENTER DLR   (Germany)

^b ETH ZURICH   (Switzerland)

^c HELMHOLTZ CENTRE FOR ENVIRONMENTAL RESEARCH UFZ   (Germany)

^d TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

Compressive sensing; Forest dynamics; Forest structure; Horizontal forest structure; L band; Synthetic aperture radar (SAR); Tomography; Vertical forest structure

Indexed keywords

COMPRESSED SENSING; GEOMETRICAL OPTICS; IMAGE RECONSTRUCTION; OPTICAL RADAR; RADAR; REFLECTION; SENSITIVITY ANALYSIS; SYNTHETIC APERTURE RADAR; TOMOGRAPHY;

AIRBORNE LIDAR MEASUREMENTS; COMPRESSIVE SENSING; FOREST DYNAMICS; FOREST STRUCTURE; HIGH-RESOLUTION OPTICAL IMAGES; L-BAND; STRUCTURE INFORMATION; SYNTHETIC APERTURE RADAR TOMOGRAPHIES;

FORESTRY;

EID: 85038213526     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs9121229     Document Type: Article

References (59)

1. Spies, T.A. Forest Structure: A Key to the Ecosystem. Northwest Sci. 1998, 72, 34-36
2. Grace, J. Understanding and managing the global carbon cycle. J. Ecol. 2004, 92, 189-202
3. Gatti, L.V.; Gloor, M.; Miller, J.B.; Doughty, C.E.; Malhi, Y.; Domingues, L.G.; Basso, L.S.; Martinewski, A.; Correia, C.S.C.; Borges, V.F.; et al. Drought sensitivity of Amazonian carbon balance revealed by atmospheric measurements. Nature 2014, 506, 76-80
4. Frolkin, 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. Biogeosci. 2009, 114
5. Beaudoin, A.; Le Toan, T.; Goze, S.; Nezry, E.; Lopes, A.; Mougin, E.; Hsu, C.C.; Han, H.C.; Kong, J.A.; Shin, R.T. Retrieval of forest biomass from SAR data. Int. J. Remote Sens. 1994, 15, 2777-2796
6. Treuhaft, R.N.; Siqueira, P.R. The Vertical Structure of Vegetated Land Surfaces from Interferometric and polarimetric Radar. Radio Sci. 2000, 35, 141-177
7. Garestier, F.; Dubois-Fernandez, P.; Guyon, D.; Le Toan, T. Forest Biophysical Parameter Estimation Using Land P-Band Polarimetric SAR Data. IEEE Trans. Geosci. Remote Sens. 2009, 47, 3379-3388
8. Cloude, S.R.; Papathanassiou, K.P. Polarimetric SAR interferometry. IEEE Trans. Geosci. Remote Sens. 1998, 36, 1551-1565
9. Cloude, S.R.; Papathanassiou, K. Three-stage inversion process for polarimetric SAR interferometry. IEE Proc. Radar Sonar Navig. 2003, 150, 125-134
10. Reigber, A.; Moreira, A. First demonstration of airborne SAR tomography using multibaseline L-band data. IEEE Trans. Geosci. Remote Sens. 2000, 38, 2142-4152
11. Frey, O.; Meier, E. Analyzing Tomographic SAR Data of a Forest with Respect to Frequency, Polarization, and Focusing Technique. IEEE Trans. Geosci. Remote Sens. 2011, 49, 3648-3659
12. Neumann, M.; Ferro-Famil, L.; Reigber, A. Estimation of Forest Structure, Ground, and Canopy Layer Characteristics from Multibaseline Polarimetric Interferometric SAR Data. IEEE Trans. Geosci. Remote Sens. 2009, 48, 1086-1104
13. Toraño, A.; Pardini, M.; Hajnsek, I.; Papathanassiou, K. Forest Above-Ground Biomass Estimation from Vertical Reflectivity Profiles at L-Band. IEEE Trans. Geosci. Remote Sens. Lett. 2015, 12, 2379-2383
14. Tebaldini, S.; Rocca, F. Multibaseline Polarimetric SAR Tomography of a Boreal Forest at P- and L-Bands. IEEE Trans. Geosci. Remote Sens. 2012, 50, 232-246
15. Aguilera, E.; Nannini, M.; Reigber, A. Wavelet-based compressed sensing for SAR tomography of forested areas. IEEE Trans. Geosci. Remote Sens. 2013, 51, 5283-5295
16. Pardini, M.; Papathanassiou, K. Sub-Canopy Topography Estimation: Experiments with Multibaseline SAR Data at L-Band. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, 22-27 July 2012; pp. 4954-4957
17. Dinh, H.T.M.; Tebaldini, S.; Rocca, F.; Koleck, T.; Borderies, P.; Albinet, C.; Villard, L.; Hamadi, A.; Loan, T.L. Ground-Based Array for Tomographic Imaging of the Tropical Forest in P-Band. IEEE Trans. Geosci. Remote Sens. 2013, 51, 4460-4472
18. Ho Tong Minh, D.; Tebaldini, S.; Rocca, F.; Toan, T.L. The Impact of Temporal Decorrelation on BIOMASS Tomography of Tropical Forests. IEEE Trans. Geosci. Remote Sens. Lett. 2015, 12, 1297-1301
19. Albinet, C.; Koleck, T.; Le Toan, T.; Borderies, P.; Villard, L.; Hamadi, A.; Laurin, G.V.; Nicolini, G.; Valentini, R. First Results of AFRISCAT, a Tower-Based Radar Experiment in African Forest. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 26-31 July 2015; pp. 5356-5358
20. Monteith, A.; Soja, M.; Ulander, L.; Eriksson, L. BOREALSCAT: A Tower-Based Tomographic and Polarimetric Radar Experiment in the Boreal Forest at P-, L- and C-Band. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 10-15 July 2016; pp. 7458-7461
21. Lee, S.K.; Kugler, F.; Papathanassiou, K.; Hajnsek, I. Quantification of Temporal Decorrelation Effects at L-Band for Polarimetric SAR Interferometry Applications. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2013, 6, 1351-1367
22. Simard, M.; Hensley, S.; Lavalle, M.; Dubayah, R.; Pinto, N.; Hofton, M. An Empirical Assessment of Temporal Decorrelation Using the Uninhabited Aerial Vehicle Synthetic Aperture Radar over Forested Landscapes. Remote Sens. 2012, 4, 975-986
23. Pardini, M.; Cantini, A.; Kugler, F.; Papathanassiou, K.; Lombardini, F. Monitoring dynamics in time of forest vertical structure with multibaseline PolInSAR data. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, QC, Canada, 13-18 July 2014; pp. 3366-3369
24. Pardini, M.; Cantini, A.; Lombardini, F.; Papathanassiou, K. 3-D Structure of Forests: First Analysis of Tomogram Changes Due toWeather and Seasonal Effects at L-Band. In Proceedings of the 10th European Conference on Synthetic Aperture Radar, Berlin, Germany, 3-5 June 2014
25. Le Toan, T.; Quegan, S.; Davidson, M.W.J.; Balzter, H.; Paillou, P.; Papathanassiou, K.; Plummer, S.; Rocca, F.; Saatchi, S.; Shugart, H.; et al. The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle. Remote Sens. Environ. 2011, 115, 2850-2860
26. Moreira, A.; Krieger, G.; Hajnsek, I.; Papathanassiou, K.; Younis, M.; Lopez-Dekker, P.; Huber, S.; Villano, M.; Pardini, M.; Eineder, M.; et al. A Highly Innovative Bistatic SAR Mission for Global Observation of Dynamic Processes on the Earth's Surface. IEEE Geosci. Remote Sens. Mag. 2015, 3, 8-23
27. Cazcarra-Bes, V.; Tello-Alonso, M.; Papathanassiou, K. 3D Forest Structure Estimation from SAR Tomography by means of a Full Rank Polarimetric Inversion based on Compressive Sensing, POLINSAR. In Proceedings of the 7th International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry, Frascati, Italy, 26-30 January 2015
28. Capon, J. High resolution frequency wavenumber spectrum analysis. Proc. IEEE 1969, 150, 125-134
29. Lombardini, F.; Reigber, A. Adaptive spectral estimation for multibaseline SAR tomography with airborne L-band data. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Toulouse, France, 21-25 July 2003
30. Nannini, M.; Scheiber, R.; Horn, R.; Moreira, A. First 3-D reconstructions of targets hidden beneath foliage by means of polarimetric SAR tomography. IEEE Trans. Geosci. Remote Sens. Lett. 2012, 9, 60-64
31. Budillon, A.; Evangelista, A.; Schirinzi, G. Three-dimensional SAR focusing from multipass signals using compressive sampling. IEEE Trans. Geosci. Remote Sens. 2011, 49, 488-499
32. Zhu, X.; Bamler, R. Tomographic SAR Inversion by L1-norm Regularization-The Compressive Sensing Approach. IEEE Trans. Geosci. Remote Sens. 2010, 48, 3839-3846
33. Aguilera, E.P. SyntheticAperture Radar Tomography-Compressed SensingModels andAlgorithms. Ph.D. Thesis, Technical University of Berlin, Berlin, Germany, 2014
34. Tello-Alonso, M.; Cazcarra-Bes, V.; Pardini, M.; Papathanassiou, K. Assessment of forest structure estimation by means of SAR tomography: Potential and limitations. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 10-15 July 2016; pp. 32-35
35. Snyder, M. What is Forest Stand Structure and How Is It Measured? North. Woodl. 2010, 64, 15
36. Pretzsch, H. Forest Dynamics, Growth and Yield; Springer: Berlin/Heidelberg, Germany, 2008; pp. 1-39
37. Tello-Alonso, M.; Cazcarra-Bes, V.; Pardini, M.; Papathanassiou, K. Structural classification of forests by means of L-band tomographic SAR. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 26-31 July 2015; pp. 5288-5291
38. Bohn, F.J.; Huth, A. The importance of forest structure to biodiversity-Productivity relationships. R. Soc. Open Sci. 2017, 4, 160521
39. Shugart, H.H. A Theory of Forest Dynamics; The Blackburn Press: Caldwell, NJ, USA, 1984; p. 278, ISBN 9781930665750
40. Zenner, E.K.; Hibbs, D.E. A NewMethod forModeling the Heterogeneity of Forest Structure. For. Ecol. Manag. 2000, 129, 75-87
41. Pommerening, A. Approaches to quantifying forest structures. For. Int. J. For. Res. 2002, 75, 305-324
42. Del Rio, M.; Pretzsch, H.; Alberdi, I.; Bielak, K.; Bravo, F.; Brunner, A.; Condés, S.; Ducey, M.J.; Fonseca, T.; von Lüpke, N.; et al. Characterization of the structure, dynamics, and productivity of mixed-species stands: Review and perspectives. Eur. J. For. Res. 2016, 135, 23-49
43. Noss, R.F. Indicators for Monitoring Biodiversity: A Hierarchical Approach. Conserv. Biol. 1990
44. Reineke, L.H. Perfecting a stand-density index for even-aged forest. J. Agric. Res. 1933, 46, 627-638
45. Schmitt, M.; Shahzad, M.; Zhu, X.X. Reconstruction of individual trees from multi-aspect TomoSAR data. Remote Sens. Environ. 2015, 165, 175-185
46. Ponce, O.; Prats-Iraola, P.; Scheiber, R.; Reigber, A.; Moreira, A. First Airborne Demonstration of Holographic SAR Tomography with Fully Polarimetric Multicircular Acquisitions at L-Band. IEEE Trans. Geosci. Remote Sens. 2016, 54, 6170-6196
47. Matthew, B.; Woodhouse, I.H. Vertical backscatter profile of forests predicted by a macroecological plant model. Int. J. Remote Sens. 2013, 34, 1026-1040
48. Palace, M.W.; Sullivan, F.B.; Ducey, M.J.; Treuhaft, R.N.; Herrick, C.; Shimbo, J.Z.; Mota-E-Silva, J. Estimating forest structure in a tropical forest using field measurements, a synthetic model and discrete return lidar data. Remote Sens. Environ. 2015, 161, 1-11
49. Whitehurst, A.S.; Swatantran, A.; Blair, J.B.; Hofton, M.A.; Dubayah, R. Characterization of Canopy Layering in Forested Ecosystems Using Full Waveform Lidar. Remote Sens. 2013, 5, 2014-2036
50. Botkin, D.B. Forest Dynamics: An Ecological Model; Oxford University Press: Oxford, NY, USA, 1993; p. 309, ISBN 0-19-506555-7
51. Bugmann, H. A review of forest gap models. Clim. Chang. 2001, 51, 259-305
52. Fischer, R.; Bohn, F.; Dantas de Paula, M.; Dislich, C.; Groeneveld, J.; Gutiérrez, A.G.; Kazmierczak, M.; Knapp, N.; Lehmann, S.; Paulick, S.; et al. Lessons learned from applying a forest gap model to understand ecosystem and carbon dynamics of complex tropical forests. Ecol. Model. 2016, 326, 124-133
53. Huth, A.; Ditzer, T.; Bossel, H. The Rain Forest Growth Model FORMIX3. Model Description and Analysis of Forest Growth and Logging Scenarios for the Deramakot Forest Reserve (Malaysia); Goltze, E.: Göttingen, Germany, 1998; pp. 1-182, ISBN 9783884523858
54. Fischer, R.; Ensslin, A.; Rutten, G.; Fischer, M.; Schellenberger Costa, D.; Kleyer, M.; Hemp, A.; Paulick, S.; Huth, A. Simulating carbon stocks and fluxes of an African tropical montane forest with an individual-based forest model. PLoS ONE 2015, 10, 1-13
55. Asner, G.P.; Knapp, D.E.; Broadbent, E.N.; Paulo, J.C.; Oliveira, M.K.; Silva, J.N. Selective Logging in the Brazilian Amazon. Science 2005, 310, 480-482
56. Horn, R. The DLR airbone SAR project E-SAR. In Proceedings of the Geoscience and Remote Sensing Symposium-Remote Sensing for a Sustainable Future, Lincoln, NE, USA, 31 May 1996
57. Horn, R.; Nottensteiner, A.; Reigber, A.; Fischer, J.; Scheiber, R. F-SAR-DLR's new multifrequency polarimetric airborne SAR. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, 12-17 July 2009
58. Abdullahi, S.; Kugler, F.; Pretzsch, H. Prediction of stem volume in complex temperate forest stands using TanDEM-X SAR data. Remote Sens. Environ. 2016, 174, 197-211
59. Bayerischen Staatsforsten. Richtlinie Für Die Mittel-Und Langfristige Forstbetriebsplanung in Den Bayerischen Staatsforsten; Bayerischen Staatsforsten: Regensburg, Germany, 2011