A review of underlying topography estimation over forest areas by InSAR: Theory, advances, challenges and perspectives

Abstract

The paramount importance and multi-purpose applications of underlying topography over forest areas have gained widespread recognition over recent decades, bringing about a variety of experimental studies on accurate underlying topography mapping. The highly spatial and temporal dynamics of forest scenarios makes traditional measuring techniques difficult to construct the precise underlying topography surface. Microwave remote sensing has been demonstrated as a promising technique to retrieve the underlying topography over large areas within a limited period, including synthetic aperture radar interferometry (InSAR), polarimetric InSAR (PolInSAR) and tomographic SAR (TomoSAR). In this paper, firstly, the main principle of digital elevation model (DEM) generation by InSAR and SAR data acquisition over forest area are introduced. Following that, several methods of underlying topography extraction based on InSAR, PolInSAR, and TomoSAR are introduced and analyzed, as well as their applications and performance are discussed afterwards. Finally, four aspects of challenge are highlighted, including SAR data acquisition, error compensation and correction, scattering model reconstruction and solution strategy of multi-source data, which needs to be further addressed for robust underlying topography estimation.

Keywords

underlying topography / microwave remote sensing / InSAR / PolInSAR / TomoSAR

References

[1] SunQ, ZhangL, DingX-l, HuJ, LiZ-w, ZhuJ-j. Slope deformation prior to Zhouqu, China landslide from Insar time series analysis [J]. Remote Sensing of Environment, 2015, 156: 45-57

[2] BaughC A, BatesP D, SchumannG, TriggM A. SRTM vegetation removal and hydrodynamic modeling accuracy [J]. Water Resources Research, 2013, 49(9): 5276-5289

[3] FuH-qMethod development of InSAR/PolInSAR sub-canopy topography and forest height inversion taking into account trend error correction and observation information enhancement [D], 2018, Changsha, Central South University(in Chinese)

[4] RabusB, EinederM, RothA, BamlerR. The shuttle radar topography mission—A new class of digital elevation models acquired by spaceborne radar [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2003, 57(4): 241-262

[5] RossiC, GonzalezF R, FritzT, YaguemartinezN, EinederM. TanDEM-X calibrated raw DEM generation [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 73: 12-20

[6] TreuhaftR N, MadsenS N, MoghaddamM, VanZ J J. Vegetation characteristics and underlying topography from interferometric radar [J]. Radio Science, 1996, 31(6): 1449-1485

[7] TreuhaftR N, SiqueiraP. Vertical structure of vegetated land surfaces from interferometric and polarimetric radar [J]. Radio Science, 2000, 35(1): 141-178

[8] CloudeS R, PapathanassiouK P. Polarimetric SAR interferometry [J]. IEEE Transactions on Geosciences and Remote Sensing, 1998, 36(5): 1551-1565

[9] O’LoughlinF E, PaivaP C D, DurandM, AlsdorfD E, BatesP D. A multi-sensor approach towards a global vegetation corrected SRTM DEM product [J]. Remote Sensing of Environment, 2015, 182: 49-59

[10] GarestierF, Dubois-FernandezP, ChampionI. Forest height inversion using high-resolution P-band Pol-InSAR data [J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(11): 3544-3559

[11] FuH-q, ZhuJ-j, WangC-c, WangH-q, ZhaoR. Underlying topography estimation over forest areas using high-resolution P-band singlebaseline PolInSAR data [J]. Remote Sensing, 2017, 363(9): 1-17

[12] LiX-w, GuoH-d, WangC-l, LiZ, LiaoJ-j. DEM generation in the densely vegetated area of Hotan, north-west China using SIR-C repeat pass polarimetric SAR interferometry [J]. International Journal of Remote Sensing, 2003, 24(14): 2997-3003

[13] HuaF-f, ZhaoZ, WangM-m, ZhangJ-x, HuangG-m. A global optimal coherence method for multi-baseline InSAR elevation inversion [J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(11): 1263-1270

[14] Lopez-MartinezC, PapathanassiouK P. Cancellation of scattering mechanisms in PolInSAR: Application to underlying topography estimation [J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(2): 953-965

[15] PapathanassiouK P, CloudeS R. Single-baseline polarimetric SAR interferometry [J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11): 2352-2363

[16] CloudeS RPolarisation: Applications in remote sensing [M], 2009, London, UK, Oxford University Press

[17] Ballester-BermanJ, Lopez-SanchezJ. Applying the Freeman-Durden decomposition concept to polarimetric SAR interferometry [J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 1: 466-479 48)

[18] NeumannM, Ferro-FamilL, ReigberA. Estimation of forest structure, ground and canopy layer characteristics from multi-baseline polarimetric interferometric SAR data [J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(3): 1086-1104

[19] ReigberA, MoreiraA. First demonstration of airborne SAR tomography using multibaseline L-band data [J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(5): 2142-2152

[20] BamlerR, HartlP. Synthetic aperture radar interferometry [J]. Inverse Problems, 1998, 14(4): 333-382

[21] GarestierF, Dubois-FernandezP, ChampionI, ToanT L. Pine forest investigation using high resolution P-band Pol-InSAR data [J]. Remote Sensing of Environment, 2011, 115(11): 2897-2905

[22] Dubois-FernandezP, ToanL T, DanielS, OriotH, ChaveJ, BlancL, VillardL, DavidsonW J M, PetitM. The TropiSAR airborne campaign in French Guiana: Objectives, description and observed temporal behavior of the backscatter signal [J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(8): 3228-3241

[23] HajnsekI, KuglerF, LeeS K, PapathanassiouK P. Tropical forest parameter estimation by means of Pol-InSAR: the INDREX-II campaign [J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(2): 481-493

[24] HAJNSEK I, SCHEIBER R, ULANDER L, GUSTAVSSON A, SANDBERG G, TEBALDINI S. BIOSAR 2007: Technical assistance for the development of airborne SAR and geophysical measurements during the BioSAR 2007 experiment [R]. Final Report, ESA Contract No.: 20755/07/NL/CB, 2008.

[25] HAJNSEK I, SCHEIBER R, KELLER M, HORN R, LEE S, ULANDER L. BIOSAR 2008: Technical assistance for the development of airborne SAR and geophysical measurements during the BioSAR 2008 experiment [R]. Final Report, ESTEC Contract 22052/08/NL/CT-002, 2009.

[26] ULANDER L M H, GUSTAVSSON A, FLOOD B, MURDIN D, DUBOIS-FERNANDEZ P, DUPUIS X. BioSAR 2010: Technical assistance for the development of airborne SAR and geophysical measurements during the BioSAR 2010 Experiment [R]. Final Report, ESA contract No. 4000102285/10/NL/JA/ef, 2011.

[27] HAJNSEK I. Technical assistance for the development of airborne SAR and geophysical measurements during the AfriSAR campaign [R]. Final Report, ESA contract No. 4000114293/15/NL/CT, 2017.

[28] ToanT L, QueganS, DavidsonM W J, BalzterH, PaillouP, PapathanassiouK P, PlummerS, RoccaF, SaatchiS, ShugartH, UlanderL. The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle [J]. Remote Sensing of Environment, 2011, 115(11): 2850-2860

[29] QueganS, ToanL T, ChaveJ, DallJ, ExbrayatJ F. The European space agency BIOMASS mission: Measuring forest aboveground biomass from space [J]. Remote Sensing of Environment, 2019, 227: 44-60

[30] ARCIONI M, BENSI P, DAVIDSON M W J, DRINKWATER M, SILVESTRIN P. ESA’s biomass mission candidate system and payload overview [C]// Geoscience and Remote Sensing Symposium (IGARSS), 2012. IEEE International, 2012.

[31] DLR. TanDEM-L: Satellite mission proposal for monitoring dynamic processes on the Earth’s surface [EB/OL]. http://www.dlr.de/hr/en/Portaldata/32/Resources/dokumente/broschueren/Tandem-L_web_Broschuere2014_en.pdf, 2014.

[32] MoreiraA, KriegerG, HajnsekI, PapathanassiouK P, YounisM, Lopez-DekkerP, HuberS, VillanoM, PardiniM. Tandem-L: A highly innovative bistatic SAR mission for global observation of dynamic processes on the Earth’s surface [J]. Geoscience and Remote Sensing Magazine, 2015, 3(2): 8-23

[33] ZanF D, PapathanassiouK P, LeeS K. TanDEM-L forest parameter performance analysis [C]. Proceedings of 4th International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry (Pol-InSAR), 2009, Frascti, Italy, ESA

[34] OleskA, VoormansikK, VainA, NoormaM, PraksJ. Seasonal differences in forest height estimation from interferometric TanDEM-X coherence data [J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2016, 8(12): 5565-5572

[35] ChenH, CloudeS R, GoodenoughD G. Forest canopy height estimation using TanDEM-X coherence data [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(7): 3177-3188

[36] OleskA, PraksJ, AntropovO, ZaliteK, ArumaeT, VoormansikK. Interferometric SAR coherence models for characterization of hemiboreal forests using TanDEM-X data [J]. Remote Sensing, 2016, 8(9): 700-722

[37] ChenH, CloudeS R, GoodenoughD G, HillD A, NesdolyA. Radar forest height estimation in mountainous terrain using TanDEM-X coherence data [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(10): 3443-3452

[38] SchlundM, MagdonP, EatonB, AumannC, ErasmiaS. Canopy height estimation with TanDEM-X in temperate and boreal forests [J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 82: 1-13

[39] QiW-l, DubayahR O. Combining TanDEM-X InSAR and simulated GEDI lidar observations for forest structure mapping [J]. Remote Sensing of Environment, 2016, 187: 253-266

[40] QiW-l, LeeS K, HancockS, LuthckeS, TangH, ArmstonaJ, DubayahR O. Improved forest height estimation by fusion of simulated GEDI Lidar data and TanDEM-X InSAR data [J]. Remote Sensing of Environment, 2019, 221: 621-634

[41] LeiY, SiqueiraP, TorbickN, DuceyM, ChowdhuryD, SalasW. Generation of large-scale moderate-resolution forest height mosaic with spaceborne repeat-pass SAR interferometry and Lidar [J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(2): 770-787

[42] HuangH-b, LiuC-x, WangX-y. Constructing a finer-resolution forest height in China using ICESat/GLAS, Landsat and ALOS PALSAR data and height patterns of natural forests and plantations [J]. Remote Sensing, 2019, 11(15): 1740

[43] SchlundM, BaronD, MagdonP, ErasmiS. Canopy penetration depth estimation with TanDEM-X and its compensation in temperate forests [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 147: 232-241

[44] FlynnT, TabbM, CarandeR. Coherence region shape extraction for vegetation parameter estimation in polarimetric SAR interferometry [C]. Geoscience and Remote Sensing Symposium (IGARSS), 2002, Toronto, Canada, IEEE, 2596-2598

[45] PapathanassiouK P, CloudeS R. The effect of temporal decorrelation on the inversion of forest parameters from PolInSAR data [C]. International Geoscience and Remote Sensing Symposium, 2003, 3: 1429-1431

[46] LavalleM, SimardM, HensleyS. A temporal decorrelation model for polarimetric radar interferometers [J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(7): 2880-2888

[47] LavalleM, HensleyS. Extraction of structural and dynamic properties of forests from polarimetric interferometric SAR data affected by temporal decorrelation [J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(9): 4752-4767

[48] LuH-x, SuoZ-y, GuoR, BaoZ. S-RVoG model for forest parameters inversion over underlying topography [J]. Electronics Letters, 2013, 49(9): 618-620

[49] XieQ-h, ZhuJ-j, WangC-c, FuH-q, Lopez-SanchezJ M, Ballester-BermanJ D. A modified dual-baseline PolInSAR method for forest height estimation [J]. Remote Sensing, 2017, 9: 819

[50] GarestierF, ToanL T. Forest modeling for height inversion using single-baseline InSAR/Pol-InSAR data [J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(3): 1528-1539

[51] FuH-q, WangC-c, ZhuJ-j, XieQ-h, ZhangB. Estimation of pine forest height and underlying DEM using multi-baseline P-band PolInSAR data [J]. Remote Sensing, 2016, 8(10): 820

[52] FuW-x, GuoH-d, SongP-f, TianB-s, LiX-w, SunZ-c. Combination of PolInSAR and LiDAR techniques for forest height estimation [J]. IEEE Geoscience and Remote Sensing Letters, 2017, 1481418-1422

[53] Lopez-SanchezJ M, Vicente-GuijalbaF, ErtenE, Campos-TabernerM, Garcia-HaroF J. Retrieval of vegetation height in rice fields using polarimetric SAR interferometry with Tandem-X data [J]. Remote Sensing of Environment, 2017, 19230-44

[54] Ballester-BermanJ D, Lopez-SanchezJ M. Combination of direct and double-bounce ground responses in the homogeneous oriented volume over ground model [J]. IEEE Geoscience and Remote Sensing Letters, 2010, 8(1): 54-58

[55] CloudeS R, PapathanassiouK P. Three-stage inversion process for polarimetric SAR interferometry [J]. IEEE Proceedings-Radar, Sonar and Navigation, 2003, 150(3): 125-134

[56] IribeK, Lopez-MartinezC, PapathanassiouK P, LeeS K. Estimation of ground topography in forested terrain by means of Pol-InSAR [C]. Geoscience and Remote Sensing Symposium (IGARSS), 2008, Boston, MA, IEEE

[57] LavalleM, KhunK. Three-baseline InSAR estimation of forest height [J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(10): 1737-1741

[58] FuH-q, ZhuJ-j, WangC-c, LiZ-w. Underlying topography extraction over forest areas from multi-baseline PolInSAR data [J]. Journal of Geodesy, 2017, 9: 1-15

[59] ShiLVegetation height and underlying ground altitude estimation based on multi-baseline PolInSAR data [D], 2013, Wuhan, Wuhan University(in Chinese)

[60] MercerB, ZhangQ-p, SchwaebischM, DenbinaM, CloudeS R. Forest height and ground topography at L-band from an experimental single-pass airborne Pol-InSAR system [C]. Proceedings of PolInsar Workshop, 2009, 668: 106-123Frascati, Italy: ESA

[61] LiLForest vertical information extraction based on P-band SAR tomography [D], 2016, Beijing, Chinese Academy of Foresry(in Chinese)

[62] LiW-mForest vertical structure parameters estimation using polarimetric interferometric tomography SAR [D], 2013, Beijing, Chinese Academy of Foresry(in Chinese)

[63] LiX-w, LiangL, GuoH-d, HuangY. Compressive sensing for multibaseline polarimetric SAR tomography of forested areas [J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 54(1): 153-166

[64] TebaldiniSMulti-baseline SAR imaging: models and algorithms [D], 2009, Italy, Milano, Politecnico Di Milano

[65] TebaldiniS. Algebraic synthesis of forest scenarios from multibaseline PolInSAR data [J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(12): 4132-4142

[66] TebaldiniS. Single and multipolarimetric SAR tomography of forested areas: A parametric approach [J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(5): 2375-2387

[67] FreyO, MeierE. Analyzing tomographic SAR data of a forest with respect to frequency, polarization, and focusing technique [J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(10): 3648-3659

[68] PARDINI M, PAPATHANASSIOU K P. Linking SAR tomography and polarization coherence tomography in forest scenarios [C]// Geoscience and Remote Sensing Symposium (IGARSS). IEEE International, 2018.

[69] KuglerF, LeeS K, HajnsekI, PapathanassiouK P. Forest height estimation by means of Pol-InSAR data inversion: The role of the vertical wavenumber [J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(10): 5294-5311

[70] LiaoZ-m, HeB-b, Van DijkA I J M, BaiX-j, QuanX-w. The impacts of spatial baseline on forest canopy height model and digital terrain model retrieval using P-band PolInSAR data [J]. Remote Sensing of Environment, 2018, 210: 403-421

[71] ZhuJ-j, LiZ-w, HuJ. Research progress and methods of InSAR for deformation monitoring [J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1717-1733

[72] FuH-q, ZhuJ-j, WangC-c, WangH-q, ZhaoR. A wavelet decomposition and polynomial fitting-based method for the estimation of time-varying residual motion error in airborne interferometric SAR [J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(1): 49-59

[73] XIE Yan-zhou, FU Hai-qiang, ZHU Jian-jun, WANG Chang-cheng, XIE Qing-hua. A LiDAR-aided multibaseline PolInSAR method for forest height estimation: With emphasis on dual-baseline selection [J]. IEEE Geoscience and Remote Sensing Letters, 2019. DOI: https://doi.org/10.1109/LGRS.2019.2951805.

[74] PourshamsiM, GarciaM, LavalleM, BalzterH. A machine-learning approach to PolInsar and lidar data fusion for improved tropical forest canopy height estimation using NASA AfriSAR campaign data [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(10): 3453-3463

[75] MichaelD, MarcS, BrianH. Forest height estimation using multibaseline PolInSAR and sparse Lidar data fusion [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(10): 3415-3433