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Early Lessons on Combining Lidar and Multi-baseline SAR Measurements for Forest Structure Characterization

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Abstract

The estimation and monitoring of 3D forest structure at large scales strongly rely on the use of remote sensing techniques. Today, two of them are able to provide 3D forest structure estimates: lidar and synthetic aperture radar (SAR) configurations. The differences in wavelength, imaging geometry, and technical implementation make the measurements provided by the two configurations different and, when it comes to the sensitivity to individual 3D forest structure components, complementary. Accordingly, the potential of combining lidar and SAR measurements toward an improved 3D forest structure estimation has been recognised from the very beginning. However, until today there is no established framework for this combination. This paper attempts to review differences, commonalities, and complementarities of lidar and SAR measurements. First, vertical lidar reflectance and SAR reflectivity profiles at different wavelengths are compared in different forest types. Then, current perspectives on their combination for the generation of enhanced structure products are discussed. Two promising frameworks for combining lidar and SAR measurements are reviewed. The first one is a model-based framework where lidar-derived parameters are used to initialize SAR scattering models, and relies on both the validity of the models and on the physical equivalence of the used lidar and SAR parameters. The second one is a structure-based framework based on the ability of lidar and SAR measurements to express physical forest structure by means of appropriate indices. These indices can then be used to establish a link between the two kind of measurements. The review is supported by experimental results achieved using space- and airborne data acquired in recent relevant mission and campaigns.

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Notes

  1. The dependence on the azimuth direction can be readily included. Here it has not been considered for simplicity.

  2. If the dependence of the problem on the azimuth coordinate is taken into account, a similar term would arise also in azimuth. However, the related decorrelation is normally lower than the range one, thus it is considered negligible.

References

  • Aguilera E, Nannini M, Reigber A (2013) Wavelet-based compressed sensing for SAR tomography of forested areas. IEEE Trans Geosci Remote Sens 51(12):5283–5295

    Article  Google Scholar 

  • Armston J, Disney M, Lewis P, Scarth P, Phinn S, Lucas R, Bunting P, Goodwin N (2013) Direct retrieval of canopy gap probability using airborne waveform lidar. Remote Sens Environ 134:24–38

    Article  Google Scholar 

  • Bai Y, Tebaldini S, Minh DHT, Yang W (2018) An empirical study on the impact of changing weather conditions on repeat-pass SAR tomography. J Sel Top Appl Earth Obs Remote Sens 11(10):3505–3511

    Article  Google Scholar 

  • Bamler R, Hartl P (1998) Synthetic aperture radar interferometry. Inverse Probl 14:R1–R54

    Article  Google Scholar 

  • Barbeito I, Montes F, Canellas I (2009) Evaluating the behavior of vertical structure indices in Scots pine forests. Ann For Sci 66(710):1–10

    Google Scholar 

  • Bergen KM, Goetz SJ, Dubayah RO, Henebry GM, Hunsaker CT, Imhoff ML, Nelson RF, Parker GG, Radeloff VC (2009) Remote sensing of vegetation 3-D structure for biodiversity and habitat: review and implications for lidar and radar spaceborne missions. J Geophys Res 114:1–13

    Article  Google Scholar 

  • Blair JB, Hofton MA (1999) Modeling laser altimeter return waveforms over complex vegetation using high-resolution elevation data. Geophys Res Lett 26:2509–2512

    Article  Google Scholar 

  • Blair J, Rabine D, Hofton M (1999) The laser vegetation imaging sensor: a medium altitude, digitization-only, airborne laser altimeter for mapping vegetation and topography. ISPR J Photogramm Remote Sens 54(2/3):115–122

    Article  Google Scholar 

  • Bohn FJ, Huth A (2017) The importance of forest structure to biodiversity-productivity relationships. R Soc Open Sci 4(1):160521

    Article  Google Scholar 

  • Brokaw N, Lent R (1999) Vertical structure. In: Maintaining biodiversity in forest ecosystems. Cambridge University Press, Cambridge, UK

  • Brolly M, Woodhouse IH (2013) Vertical backscatter profile of forests predicted by a macroecological plant model. Int J Remote Sens 34(4):1026–1040

    Article  Google Scholar 

  • Brolly M, Simard M, Tang H, Dubayah R (2016) A lidar-radar framework to assess the impact of vertical forest structure on interferometric coherence. IEEE J Sel Top Appl Earth Obs Remote Sens 9:5830–5841

    Article  Google Scholar 

  • Carabajal CC, Harding DJ (2006) SRTM C-band and ICESat laser altimetry elevation comparisons as a function of tree cover and relief. Photogramm Eng Remote Sens 72(3):287–298

    Article  Google Scholar 

  • Cazcarra Bes V, Tello M, Fischer R, Heym M, Papathanassiou K (2017) Monitoring forest structure dynamics by means of L-band SAR tomography. Remote Sens 9(12):1229

    Article  Google Scholar 

  • Cazcarra Bes V, Pardini M, Tello M, Papathanassiou K (2019) Comparison of tomographic SAR reflectivity reconstruction algorithms for forest applications at L-band. IEEE Trans Geosci Rem Sensing (Accepted for publication)

  • Clark P, Evans C (1954) Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 35(4):445–452

    Article  Google Scholar 

  • Clark ML, Roberts DA, Ewel JJ, Clark DB (2011) Estimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors. Remote Sens Environ 115:2931–2942

    Article  Google Scholar 

  • Couteron P, Pelissier R, Nicolini EA, Paget D (2005) Predicting tropical forest stand structure parameters from Fourier transform of very high-resolution remotely sensed canopy images. J Appl Ecol 42:1121–1128

    Article  Google Scholar 

  • De Grandi EC, Mitchard E, Hoekman D (2016) Wavelet based analysis of TanDEM-X and LiDAR DEMs across a tropical vegetation heterogeneita gradient driven by fire distrurbance in Indonesia. Remote Sens 8:641–667

    Article  Google Scholar 

  • Del Rio M (2016) Characterization of structure, dynamics, and productivity of mixed species stands: review and prespectives. Eur J For Res 135(1):23–49

    Article  Google Scholar 

  • Dubayah RO (2015) The global ecosystem dynamics investigation (GEDI) lidar. In: Proceedings of 1st BIOMASS science workshop

  • Dubayah RO, Drake JB (2000) Lidar remote sensing for forestry. J For 98(6):44–46

    Google Scholar 

  • Dubayah RO, Sheldon SL, Clark DM, Hofton MA, Blair JB, Hurtt GC, Chazdon RL (2010) Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica. J Geophys Res 115:1–17

    Article  Google Scholar 

  • Fatoyinbo TE, Simard M (2013) Height and biomass of mangroves in Africa from ICESat/GLAS and SRTM. Int J Remote Sens 34(2):668–681

    Article  Google Scholar 

  • Fornaro G, Serafino F, Soldovieri F (2003) Three-dimensional focusing with multipass SAR data. IEEE Trans Geosci Remote Sens 41(3):507–517

    Article  Google Scholar 

  • Frey O, Meyer E (2011) Analyzing tomographic SAR data of a forest with respect to frequency, polarization, and focusing techniques. IEEE Trans Geosci Remote Sens 49(10):3648–3659

    Article  Google Scholar 

  • Frolking S, Palace MW, Clark DB, Chambers JQ, Shugart HH, Hurtt GC (2009) 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 114:1–27

    Article  Google Scholar 

  • Garestier F, Dubois-Fernandez P, Champion I (2008) Forest height inversion using high resolution P-band Pol-InSAR data. IEEE Trans Geosci Remote Sens 46(10):3544–3559

    Article  Google Scholar 

  • Gates DM (1991) Water relations of forest trees. IEEE Trans Geosci Remote Sens 29(6):836–842

    Article  Google Scholar 

  • Gini F, Lombardini F, Montanari M (2002) Layover solution in multibaseline SAR interferometry. IEEE Trans Aerosp Electron Syst 38(4):1344–1356

    Article  Google Scholar 

  • Goetz S, Steinberg D, Dubayah RO, Blair B (2007) Laser remote sensing of canopy habitat heterogeneity as a predictor of bird species richness in an eastern temperate forest. Remote Sens Environ 108:254–263

    Article  Google Scholar 

  • Hall FG, Bergen K, Blair JB, Dubayah R, Houghton R, Hurtt G, Kellndorfer J, Lefsky M, Ranson J, Saatchi S, Shugart HH, Wickland D (2011) Characterizing 3D vegetation structure from space: mission requirements. Remote Sens Environ 115:2753–2775

    Article  Google Scholar 

  • Hancock S, Disney M, Muller JP, Lewis P, Foster M (2011) A threshold insensitive method for locating the forest canopy top with waveform lidar. Remote Sens Environ 115:3286–3297

    Article  Google Scholar 

  • Harding DJ, Lefsky MA, Parker GG, Blair JB (2001) Laser altimeter canopy height profiles: methods and validation for closed-canopy, broadleaf forest. Remote Sens Environ 76(3):283–297

    Article  Google Scholar 

  • Kaasalainen S, Holopainen M, Karjalainen M, Vastaranta M, Kankare V, Karila K, Osmanoglu B (2015) Combining lidar and synthetic aperture radar data to estimate forest biomass: status and prospects. Forests 6:252–270

    Article  Google Scholar 

  • Kellndorfer JM, Walker WS, LaPoint E, Kirsch K, Bishop J, Fiske G (2010) 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+, and ancillary data sets. J Geophys Res 115:G00E08

    Article  Google Scholar 

  • Krieger G, Zink M, Bachmann M, Bräutigam B, Schulze D, Martone M, Rizzoli P, Steinbrecher U, Antony JW, De Zan F, Hajnsek Papathanassiou K, Kugler F, Rodriguez Cassola M, Younis M, Baumgartner S, López Dekker P, Prats P, Moreira A (2013) TanDEM-X: a radar interferometer with two formation-flying satellites. Acta Astr 89:83–98

    Article  Google Scholar 

  • Kugler F, Schulze D, Hajnsek I, Pretzsch H, Papathanassiou K (2014) TanDEM-X Pol-InSAR performance for forest height estimation. IEEE Trans Geosci Remote Sens 52(10):6404–6422

    Article  Google Scholar 

  • Lavalle M, Hensley S (2015) Extraction of structural and dynamic properties of forests from polarimetric-interferometric SAR data affected by temporal decorrelation. IEEE Trans Geosci Remote Sens 53(9):4752–4767

    Article  Google Scholar 

  • Le Toan T, Quegan S, Davidson MJW, Baltzer H, Paillou P, Papathanassiou K, Plummer S, Rocca F, Saatchi S, Shugart S, Ulander L (2011) The BIOMASS mission: mapping global forest biomass to better understand the terrestrial carbon cycle. Remote Sens Environ 115:2850–2860

    Article  Google Scholar 

  • Lee SK, Kugler F, Papathanassiou K, Hajnsek I (2013) Quantification of temporal decorrelation effects at L-band for polarimetric SAR interferometry applications. IEEE J Sel Top Appl Earth Obs Remote Sens 6(3):1351–1367

    Article  Google Scholar 

  • Lee SK, Fatoyinbo T, Qi W, Hancock S, Armston J, Dubayah R (2018) GEDI and TanDEM-X fusion for 3D forest structure parameter retrieval. In: Proceedings of international symposium of geoscience and remote sensing (IGARSS)

  • Lefsky MA, Harding DJ, Cohen WB, Parker GG, Shugart HH (1999) Surface lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA. Remote Sens Environ 67(1):83–98

    Article  Google Scholar 

  • Liang J, Buongiorno J, Monserud RA, Kruger EL, Zhou M (2007) Effects of diversity of tree species and size on forest basal area growth, recruitment, and mortality. Forest Ecol Manag 243(1):116–127

    Article  Google Scholar 

  • Lin Y, Sarabandi K (1999) A Monte Carlo coherent scattering model for forest canopies using fractal-generated trees. IEEE Trans Geosci Remote Sens 37(1):440–451

    Article  Google Scholar 

  • Lombardini F, Reigber A (2003) Adaptive spectral estimation for multibaseline SAR tomography with airborne L-band data. In: Proceedings of international symposium of geoscience and remote sensing (IGARSS)

  • Mariotti d’Alessandro M, Tebaldini S, Rocca F (2012) Phenomenology of ground scattering in a tropical forest through polarimetric synthetic aperture radar tomography. IEEE Trans Geosci Remote Sens 51(8):4430–4437

    Article  Google Scholar 

  • McElhinny C, Gibbons P, Brack C, Bauhus J (2005) Forest and woodland stand structural complexity: its definition and measurement. For Ecol Manag 218(1–3):1–24

    Article  Google Scholar 

  • Mitsuhashi R, Murooka J, Sakaizawa D, Imai T, Kimura T, Hayashi M, Mizutani K, Sawada Y, Endo T, Kajiwara K, Honda Y, Asai K (2018). The development of vegetation Lidar mission ‘MOLI’. In: Proceedings of forest SAT

  • Moreira A, Prats-Iraola P, Younis M, Krieger G, Hajnsek I, Papathanassiou KP (2013) A tutorial on synthetic aperture radar. IEEE Geosci Remote Sens Mag 1(1):6–43

    Article  Google Scholar 

  • Moreira A, Krieger G, Hajnsek I, Papathanassiou K, Younis M, Lopez Dekker P, Huber S, Villano M, Pardini M, Eineder M, De Zan F, Parizzi A (2015) Tandem-L: a highly innovative bistatic SAR mission for global observation of dynamic processes on the Earth’s surface. IEEE Geosci Remote Sens Mag 3(2):8–23

    Article  Google Scholar 

  • Neuenschwander A, Magruder L (2018) Mapping global forests using data from NASA’s ICESat-2 Mission. In: Proceedings of Forest SAT

  • Neumann M, Ferro-Famil L, Reigber A (2010) Estimation of forest structure, ground, and canopy layer characteristics from multibaseline polarimetric interferometric SAR data. IEEE Trans Geosci Remote Sens 48(3):1086–1104

    Article  Google Scholar 

  • Neumann M, Saatchi S, Clark DB (2012) Quantifying spatial and temporal dynamics of tropical forest structure using high resolution airborne lidar. In: Proceedings of international symposium of geoscience and remote sensing (IGARSS)

  • Ni-Meister W, Jupp DLB, Dubayah R (2001) Modeling lidar waveforms in heterogeneous and discrete canopies. IEEE Trans Geosci Remote Sens 39(9):1943–1958

    Article  Google Scholar 

  • Oliver MA, Webster R (1990) Kriging: a method of interpolation for geographical information systems. Int J Geogr Inf Syst 4:313–332

    Article  Google Scholar 

  • Papathanassiou K, Cloude S (2001) Single-baseline polarimetric SAR interferometry. IEEE Trans Geosci Remote Sens 39(11):2352–2363

    Article  Google Scholar 

  • Pardini M, Papathanassiou K (2018) TomoSAR vertical profiles at multiple frequencies: an experimental analysis. In: Proceedings of European SAR conference (EUSAR)

  • Pardini M, Qi W, Dubayah R, Papathanassiou KP (2016) Exploiting TanDEM-X Pol-InSAR data for forest structure observation and potential synergies with NASA’s Global Ecosystem Dynamics Investigation Löidar (GEDI) mission. In: Proceedings of European SAR conference (EUSAR)

  • Pardini M, Tello M, Cazcarra-Bes V, Papathanassiou K, Hajnsek I (2018a) L- and P-Band 3-D SAR reflectivity profiles versus lidar waveforms: the AfriSAR case. IEEE J Sel Top Appl Earth Obs Remote Sens 11(10):3386–3401

    Article  Google Scholar 

  • Pardini M, Papathanassiou K, Lombardini F (2018b) Impact of dielectric changes on L-band 3-D SAR reflectivity profiles of forest volumes. IEEE Trans Geosci Remote Sens 56(12):7324–7337

    Article  Google Scholar 

  • Parker GG (1995) Structure and microclimate of forest canopies. In: Lowman M, Nadkarni N (eds) Forest canopies: a review of research on a biological frontier. Academic press, New York, pp 73–106

    Google Scholar 

  • Pastorella F, Paletto A (2013) Stand structure indices as tools to support forest management: an application in Trentino forests (ItalyO). J For Sci 59(4):159–168

    Article  Google Scholar 

  • Persson HJ, Ollson H, Soja MJ, Ulander LMH, Fransson JES (2017) Experiences from large-scale forest mapping of Sweden using TanDEM-X data. Remote Sens 9(12):1253

    Article  Google Scholar 

  • Pommerening A (2002) Approaches to quantifying forest structures. Forestry 75(3):305–324

    Article  Google Scholar 

  • Praks J, Antropov O, Hallikainen M (2012) 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(10):3831–3843

    Article  Google Scholar 

  • Pretzsch H (2009) Forest dynamics, growth and yield. Springer, Berlin

    Book  Google Scholar 

  • Qi W, Dubayah R (2016) Combining Tandem-X InSAR and simulated GEDI lidar observations for forest structure mapping. Remote Sens Environ 187:253–266

    Article  Google Scholar 

  • Qi W, Dubayah R (2017) Forest structure modelling of a coniferous forest using TanDEM-X InSAR and simulated GEDI lidar data. In: Proceedings of IEEE international symposium of geoscience and remote sensing (IGARSS)

  • Reigber A, Moreira A (2000) First demonstration of airborne SAR tomography using multibaseline L-band data. IEEE Trans Geosci Remote Sens 38(5):2142–2152

    Article  Google Scholar 

  • Reineke R (1933) Perfecting a stand-density index for even aged forests. J Agric Res 46(7):627–638

    Google Scholar 

  • Rosen P, Kim Y, Kumar R, Misra T, Bhan R, Raju Sagi T (2017) Global persistent SAR sampling with the NASA-ISRO SAR (NISAR) mission. In: Proceedings of IEEE radar conference

  • Simard M, Denbina M (2018) An assessment of temporal decorrelation compensation methods for forest canopy height estimation using airborne L-band same-day repeat-pass polarimetric SAR interferometry. IEEE J Sel Top Appl Earth Obs Remote Sens 11(1):95–111

    Article  Google Scholar 

  • Snyder M (2010) What is forest stand structure and how is it measured? North Woodl 64:15

    Google Scholar 

  • Spies TA (1998) Forest structure: a key to the ecosystem. Northw Sci 72:34–36

    Google Scholar 

  • Sun G, Ranson KJ, Guo Z, Zhang Z, Montesano P, Kimes D (2011) Forest biomass mapping from lidar and radar synergies. Remote Sens Environ 115:2906–2916

    Article  Google Scholar 

  • Tang H, Dubayah R, Swatantran A, Hofton M, Sheldon S, Clark DB, Blair B (2012) Retrieval of vertical LAI profiles over tropical rain forests using waveform lidar at La Selva, Costa Rica. Remote Sens Environ 124:242–250

    Article  Google Scholar 

  • Tebaldini S, Rocca F (2012) Multibaseline polarimetric SAR tomography of a boreal forest at P- and L-bands. IEEE Trans Geosci Remote Sens 50(1):232–246

    Article  Google Scholar 

  • Tello M, Cazcarra-Bes V, Pardini M, Papathanassiou K (2018) Forest structure characterization from SAR tomography at L-band. IEEE J Sel Top Appl Earth Obs Remote Sens 11(10):3402–3414

    Article  Google Scholar 

  • Thirion L, Colin E, Dahon C (2006) Capabilities of a forest coherent scattering model applied to radiometry, interferometry, and polarimetry at P- and L-Band. IEEE Trans Geosci Remote Sens 44(4):849–862

    Article  Google Scholar 

  • Treuhaft RN, Chapman BD, dos Santos JR, Gonçalves FG, Dutra LV, Graça PMLA, Drake JB (2009) Vegetation profiles in tropical forests from multibaseline interferometric synthetic aperture radar, field, and lidar measurements. J Geophys Res 114:D23110

    Article  Google Scholar 

  • Tsui OW, Coops NC, Wulder MA, Marshall P (2013) Integrating airborne LiDAR and space-borne radar via multivariate kriging to estimate above-ground biomass. Remote Sens Environ 139:340–352

    Article  Google Scholar 

  • Turner W, Spector S, Gardiner N, Fladeland M, Sterling E, Steininger M (2003) Remote sensing for biodiversity science and conservation. Trend Ecol Evol 18:306–314

    Article  Google Scholar 

  • Whitehurst AS, Swatantran A, Blair JB, Hofton MA, Dubayah R (2013) Characterization of canopy layering in forested ecosystems using full waveform lidar. Remote Sens 5(4):2014–2036

    Article  Google Scholar 

  • Yang W, Ni-Meister W, Lee S (2011) Assessment of the impacts of surface topography, off-nadir pointing, and vegetation structure on vegetation structure on vegetation lidar waveforms using an extended geometric optical and radiative transfer model. Remote Sens Environ 115:2810–2822

    Article  Google Scholar 

  • Zebker HA, Villasenor J (1992) Decorrelation in interferometric radar echoes. IEEE Trans Geosci Remote Sens 30(5):950–959

    Article  Google Scholar 

  • Zenner EK, Hibbs D (2000) A new method for modelling the heterogeneity of forest structure. For Ecol Manag 129:75–87

    Article  Google Scholar 

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Acknowledgements

This review originates from the workshop “Space-based Measurement of Forest Properties for Carbon Cycle Research” held at the International Space Science Institute in Bern in November 2017. We thank P. Biber and M. Heym (Technische Universität München, TUM, Munich, Germany), and A. Huth and R. Fischer (Helmholtz Zentrum für Umweltforschung, UFZ, Leipzig, Germany) for inputs and discussions on forest structure and structure metrics. Part of this work was supported by the Helmholtz Alliance Remote Sensing and Earth System Dynamics funded by the Initiative and networking Fund of the Helmholtz Association. Laser Vegetation and Ice Sensor (LVIS) data sets were provided by the team in the Laser Remote Sensing Branch at NASA Goddard Space Flight Center with support from the University of Maryland, College Park. Finally, we thank the two anonymous reviewers that helped to improve the manuscript with their constructive comments.

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Authors and Affiliations

  1. Microwave and Radar Institute, German Aerospace Center, Wessling, 82234, Germany

    Matteo Pardini, Marivi Tello, Victor Cazcarra Bes, Changhyun Choi & Konstantinos P. Papathanassiou

  2. Department of Geographical Sciences, University of Maryland, College Park, MD, 20742, USA

    John Armston, Wenlu Qi, Seung Kuk Lee & Ralph O. Dubayah

  3. Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, 20771, USA

    Seung Kuk Lee & Lola E. Fatoyinbo

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  1. Matteo Pardini

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Appendices

Appendix 1: Derivation of the Relationship Between InSAR Volume Coherences and Vertical Reflectivity Profile

The purpose of this Appendix is to summarize the algebraic steps that lead to the Fourier relationship between the InSAR coherence and the vertical reflectivity profile starting from the volume integral (2).

First of all, it is commonly assumed that the (end-to-end) system point-spread function after focusing is separable in the range and azimuth dimensions, i.e.

$$h\left( {r,x} \right) = h_{r} \left( r \right) \cdot h_{x} \left( x \right)$$
(14)

being\(h_{r} \left( r \right)\) and\(h_{x} \left( x \right)\) the range and azimuth system point-spread functions, usually approximated as ideal rectangular functions with width equal to the respective resolution. The formulation in (14) implies that the viewing geometry is independent of the azimuth coordinate,Footnote1 therefore the integral on\({\text{d}}x\) can be solved, and (1) becomes:

$$y\left( {r^{{\prime }} } \right) = \iint {h_{r} \left( {r^{{\prime }} - r} \right) \cdot \bar{\xi }(r,s) \cdot {\text{e}}^{{ - j\frac{4\pi }{\lambda }R(r,s)}} {\text{d}}r{\text{d}}s},$$
(15)

\(\bar{\xi }\left( {r,s} \right)\) being the integral along azimuth of the complex reflectivity weighted by the azimuth point-spread function. The dependency on the azimuth has been dropped for notational simplicity. From (15), a single SAR image provides a single tomographic projection of the 3D reflectivity filtered by the range response (Bamler and Hartl1998). The interpretation of the convolution integral in (15) is facilitated in the domain of the 2D range-elevation spatial frequencies after a Fourier transform. Indeed, the SAR image spectrum is a 2D slice out of the complete 3D reflectivity spectrum (Bamler and Hartl1998).

In general, an additional 2D spectral slice with minimum overlap increases the amount of information available on the reflectivity spectrum toward its reconstruction. Conventional InSAR configurations realize this by varying the complex exponential in (15), i.e., by collecting one additional SAR image in spatial (incidence angle) diversity. First of all, the two images are co-registered, i.e., both images are resampled onto a common grid so that the co-registered pixels refer to a common position in the 3D space. After co-registration, and in absence of system (including noise) and processing non-idealities, the SLC complex amplitudes for the same pixel become:

$$\begin{aligned} y_{1} \left( {r^{{\prime }} } \right) = \iint {h_{r} \left( {r^{{\prime }} - r} \right) \cdot \bar{\xi }\left( {r,s} \right) \cdot {\text{e}}^{{ - j\frac{4\pi }{\lambda }R_{1} \left( {r,s} \right)}} {\text{d}}r{\text{d}}s}, \hfill \\ y_{2} \left( {r^{{\prime }} } \right) = \iint {h_{r} \left( {r^{{\prime }} - r} \right) \cdot \bar{\xi }\left( {r,s} \right) \cdot {\text{e}}^{{ - j\frac{4\pi }{\lambda }R_{2} \left( {r,s} \right)}} {\text{d}}r{\text{d}}s}, \hfill \\ \end{aligned}$$
(16)

under the additional assumption that\(\bar{\xi }\left( {r,s} \right)\) does not change with the difference of incidence angle induced by the track/orbit displacement and with the (possible) time difference between the two acquisition. The interferogram is defined as

$$\varphi \left( {r^{{\prime }} } \right) = \angle y_{1} \left( {r^{{\prime }} } \right)y_{2}^{*} \left( {r^{{\prime }} } \right),$$
(17)

which for an ideal deterministic point scatterer located at\((r^{{\prime }} ,s^{{\prime }} )\) becomes:

$$\varphi \left( {r^{{\prime }} } \right) = \frac{4\pi }{\lambda }\left[ {R_{2} \left( {r^{{\prime }} ,s^{{\prime }} } \right) - R_{1} \left( {r^{{\prime }} ,s^{{\prime }} } \right)} \right]$$
(18)

Let\(B_{\parallel }\) and\(B_{ \bot }\) be the spatial track/orbit displacements in the direction parallel and orthogonal to the line of sight, respectively. Therefore, one can write:

$$R_{2} \left( {r,s} \right) = \sqrt {\left( {r - B_{\parallel } } \right)^{2} + \left( {s - B_{ \bot } } \right)^{2} } .$$
(19)

Some processing steps and approximations can be applied in order to reach a convenient formulation, which are detailed in Bamler and Hartl (1998) and Fornaro et al (2003). In these operations,\(y_{1} \left( {r^{{\prime }} } \right)\) is taken as a phase reference, and for this reason it is called master;\(y_{2} \left( {r^{{\prime }} } \right)\) is then called slave. The small variation of look angle between the two acquisitions allows to retain the paraxial approximation. By developing (19), a residual\(s^{2}\)-dependent phase contribution is originated by the curvature of the wavefront which can be included in the complex reflectivity, as its (second-order) amplitude distribution is normally of interest. Alternatively, it can simply be neglected if the distance between the sensor and the scatterers is large enough (as it typically is) to make the plane-wave approximation hold. Finally, the phase of the slave image is “flattened” with respect to a reference height by using the interferometric phase (18). In this case, the reference height can be chosen constant for the entire scene, or varying locally according to a reference external DEM. After these manipulations, by further assuming\(B_{\parallel } \ll r\), it results:

$$\begin{aligned} y_{1} \left( {r^{{\prime }} } \right) = \iint {h_{r} \left( {r^{{\prime }} - r} \right) \cdot \xi^{{\prime }} \left( {r,z} \right) {\text{d}}r{\text{d}}z,} \hfill \\ y_{2} \left( {r^{{\prime }} } \right) \cong \iint {h_{r} \left( {r^{{\prime }} - r} \right) \cdot \xi^{{\prime }} \left( {r,z} \right) \cdot e^{{jk_{Z} z}} {\text{d}}r{\text{d}}z.} \hfill \\ \end{aligned}$$
(20)

In (20), a change of variable from elevation to vertical height\(z\) has been performed, with\(s = z /\sin \theta_{0}\).\(\xi^{{\prime }} \left( {r,z} \right)\) is the final complex reflectivity after the change of variables and the inclusion of phase residuals\(\psi\), i.e.,\(\xi^{{\prime }} \left( {r,z} \right) = \bar{\xi }\left( {r,z/\sin \theta_{0} } \right){\text{e}}^{j\psi } /\sin \theta_{0}\), being\(\theta_{0}\) the local incidence angle.\(k_{Z}\) is the so-called vertical wavenumber, defined in (3).

Being composed of distributed scatterers, the scattering from forest scenarios is better described by its statistics, particularly by the complex coherence defined in (4). This results in:

$$\begin{aligned} E\left\{ {y_{1} \left( {r^{{\prime }} } \right)} \right\} = E\left\{ {y_{2} \left( {r^{{\prime }} } \right)} \right\} = 0, \hfill \\ E\left\{ {\left| {y_{1} \left( {r^{{\prime }} } \right)} \right|^{2} } \right\} = E\left\{ {\left| {y_{2} \left( {r^{{\prime }} } \right)} \right|^{2} } \right\} = P_{y} , \hfill \\ E\left\{ {y_{1} \left( {r^{{\prime }} } \right)y_{2}^{*} \left( {r^{{\prime }} } \right)} \right\} = P_{y} \gamma_{1,2} \left( {k_{Z} } \right), \hfill \\ \end{aligned}$$
(21)

and using (20):

$$E\left\{ {y_{1} \left( {r^{{\prime }} } \right)y_{2}^{*} \left( {r^{{\prime }} } \right)} \right\} = \iint {\left| {h_{r} \left( {r^{{\prime }} - r} \right)} \right|^{2} \cdot F_{\xi } \left( {r,z} \right) \cdot {\text{e}}^{{jk_{Z} z}} {\text{d}}r{\text{d}}z}.$$
(22)

\(F_{\xi } \left( {r,z} \right)\) represents the spatial density of backscattered power in the height-range plane. It is interesting to consider the case of surface scattering at a constant height\(z = z_{0}\). Its complex reflectivity can be written as a Dirac-\(\delta\) with power\(P_{S}\). After appropriate algebraic manipulations, it provides the following interferometric coherence:

$$\gamma_{1,2} \left( {k_{Z} } \right) = \frac{{E\left\{ {y_{1} \left( {r^{{\prime }} } \right)y_{2}^{*} \left( {r^{{\prime }} } \right)} \right\}}}{{P_{S} }} = \gamma_{S} \left( {k_{Z} } \right) {\text{e}}^{{jk_{Z} z_{0} }} .$$
(23)

Therefore, the coherence phase is proportional to the surface of the height.\(\gamma_{S} (k_{Z} )\) is a positive (real-valued) geometric decorrelation term that depends on the range point-spread function.Footnote2 However, forest scatterers are distributed within a volume, that can be seen as the coherent superposition of the number of surfaces with different total powers. As a consequence, it simply follows:

$$E\left\{ {y_{1} \left( {r^{{\prime }} } \right)y_{2}^{*} \left( {r^{{\prime }} } \right)} \right\} = \gamma_{S} \left( {k_{Z} } \right)\sum\limits_{n} {P_{S} \left( {z_{n} } \right){\text{e}}^{{jk_{Z} z_{n} }} } .$$
(24)

In the most general case, by letting the number of surfaces tend to infinity, the sum in (24) becomes an integral, and it is readily obtained

$$\gamma_{1,2} \left( {k_{Z} } \right) = \gamma_{S} \left( {k_{Z} } \right) \gamma_{\text{vol}} \left( {k_{Z} } \right),$$
(25)

where:

$$\gamma_{\text{vol}} \left( {k_{Z} } \right): = {\text{e}}^{{jk_{Z} z_{G} }} \frac{{\int_{0}^{{H_{V} }} {F_{vol} \left( z \right) {\text{e}}^{{jk_{Z} z}} {\text{d}}z} }}{{\int_{0}^{{H_{V} }} {F_{\text{vol}} \left( z \right) {\text{d}}z} }},$$
(26)

which provides the Fourier relationship between vertical reflectivity profile and volume coherence anticipated in Sect. 2.1.

Appendix 2: Descriptions of Test Sites and Data Sets

2.1Traunstein Forest (South of Germany)

This temperate forest site is located close to the city of Traunstein in the South of Germany. Most forest stands are structurally complex due to species richness and management practices, with a majority of conifer stands (around 70%). The forest top height can reach 40 m, and mean biomass level is about 200 t/h, significantly higher than other managed forests in the same ecological zone. In this test site, TomoSAR data sets at different frequencies have been collected by means of the German Aerospace Center (DLR) E-SAR and F-SAR airborne platforms since 2008. The data sets used in this paper were acquired at P- and L-band in 2009, and at X-band in 2013 with approximately the same flight tracks and viewing (master) geometry. Due to the 4-year time difference between the P- and X-band data sets, an additional L-band TomoSAR data set of 2013 has been considered. In this way, two direct comparisons can be performed, i.e., P- versus L-band in 2009, and L- versus X-band in 2013. All acquisitions share the same (master) geometry. While the acquisitions at P- and L-band are in repeat-pass mode, the one at X-band is in repeated single-pass mode (with changing\(k_{Z}\) for different passes) to avoid temporal decorrelation (Pardini et al.2018b). The most relevant acquisition parameters are reported in Table 1. On November 18, 2012, discrete return lidar data have been acquired as well. During the total 4-year time, structural changes occurred in some stands due to forest management activities. Additionally, all acquisitions are in leaf-off conditions, but differences of water content and of its distribution in the tree trunk (Gates1991) could have affected the SAR backscattering.

Table 1 Summary of parameters characterizing the airborne SAR acquisitions over the Traunstein and Rabi forests

2.2Rabi and Lopé (Gabon)

The Rabi forest site is located at the southwestern Gamba Complex of Gabon, and consists of a diverse mix of upland and wet-forest with a mean tree height of about 40 m on a fairly flat topography. Within this forest site, a ForestGEO (Smithsonian Institution Forest Global Earth Observatories) plot was established between 2010 and 2012 in which the position and the diameter at breast height (dbh) of more than 175,000 trees belonging to 340 species withdbh ≥ 1 cm were inventoried across 25 ha. P- and L-band TomoSAR acquisitions were carried out with the F-SAR airborne platform during the AfriSAR campaign in 2016 (see Table 1 for the relevant acquisition parameters) (Pardini et al.2018a). During the same campaign, the Rabi site has been covered also by NASA LVIS lidar waveforms. For AfriSAR, LVIS operated at 7315 m altitude, with a wavelength equal to 1064 nm, ~20 m footprint diameter and 10 m footprint spacing along latitude and longitude. Together with the waveforms, ground topography (Digital Terrain Model, DTM) and relative height metrics have been processed.

Table 2 Summary of parameters characterizing the TanDEM-X SAR acquisitions over Lopé and La Selva

The Lopé site lies within the Lopé National Park, and it consists of a mosaic of savannah and (dense) forest, with varying species richness and tree density across stands. The maximum tree height exceeds 50 m in many stands. The forest (above ground) biomass ranges between 10 t/ha in savanna areas up to ~ 400 t/ha in mature forest stands. The terrain is hilly, with many local slopes steeper than 20°. This test site was covered during the AfriSAR campaign by both airborne SAR and LVIS lidar acquisitions similarly to the Rabi site. For the experiments in Sect. 4, a TanDEM-X bistatic single-pass single polarisation (HH) interferometric acquisition has been considered (see Table 2) that was performed a few weeks apart from the LVIS acquisitions.

2.3La Selva (Costa Rica)

La Selva Biological Station (LSBS) forest site is located in northeastern Costa Rica. The site is a protected low-land region of tropical rain forest, covering an area of 1600 ha. The area contains a mixture of old-growth, secondary and selectively logged forests with height up to 60 m. The mean biomass of old-growth forest, which is the major components of total LSBS biomass, is around 169 t/ha (Clark et al.2011). Airborne discrete return lidar data were collected over LSBS in September and October 2009. In 2011, the LSBS was covered by a dual polarisation (HH/VV) TanDEM-X bistatic acquisition on December 5, whose characteristic parameters are reported in Table 2.

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Pardini, M., Armston, J., Qi, W.et al. Early Lessons on Combining Lidar and Multi-baseline SAR Measurements for Forest Structure Characterization.Surv Geophys40, 803–837 (2019). https://doi.org/10.1007/s10712-019-09553-9

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