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Mapping Forest Height with Multifrequency SAR, InSAR, and Multispectral Datasets

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Technological Innovation for Human-Centric Systems (DoCEIS 2024)

Abstract

Remote Sensing has been widely used for monitoring forests, namely for the retrieval of structural parameters such as the Forest Height (FH). The reason behind the use of remote sensing is the fact that measuring the FH through field campaigns is expensive and non-scalable. The resort to Airborne Laser Scanning campaigns, despite its high accuracy, have the same limitations. Therefore, Synthetic Aperture Radar (SAR) and Multispectral sensors carried by spaceborne platforms are widely used to address this problem. This paper evaluates the effects of combining a dataset that includes multifrequency backscatter (L and C bands) and multispectral variables, with Interferometric SAR (InSAR) variables (Coherence and Phase) for FH mapping resorting to a locally calibrated regression methodology. To make it more suitable for operational scenarios, only free access data is used, and the calibration sets are small. The scope of this study is the Mediterranean forests, and it has achieved a R2/RMSE ranging from 50.33–72.01%/1.55–2.50m in the validation and 56.22–75.48%/0.77–2.34 m for the operational scenarios. The addition of the InSAR variables leads to an improvement of 0.63% in the R2 and 0.02m in the RMSE.

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References

  1. Gomez, C., et al.: Canopy height estimation in mediterranean forests of spain with TanDEM-X data. IEEE J. Selected Topics Appli. Earth Observat. Remote Sensing 14, 2956–2970 (2021). https://doi.org/10.1109/JSTARS.2021.3060691

    Article  Google Scholar 

  2. Parache, H.B., et al.: Estimating forest stand height in Savannakhet, Lao PDR using inSAR and backscatter methods with L-band SAR data. Remote Sens. 13(22), 1–22 (2021). https://doi.org/10.3390/rs13224516

    Article  Google Scholar 

  3. Bruggisser, M., et al.: Potential of sentinel-1 c-band time series to derive structural parameters of temperate deciduous forests. Remote Sens. 13(4), 1–30 (2021). https://doi.org/10.3390/rs13040798

    Article  Google Scholar 

  4. Frey, O., Meier, E.: Analyzing tomographic SAR data of a forest with respect to frequency, polarization, and focusing technique. IEEE Trans. Geosci. Remote Sens. 49(10 PART 1), 3648–3659 (2011). https://doi.org/10.1109/TGRS.2011.2125972

  5. Mao, Y., et al.:  Retrieval of boreal forest heights using an improved random volume over ground (Rvog) model based on repeat-pass spaceborne polarimetric sar interferometry: The case study of saihanba, china. Remote Sens. 13(21) (2021). https://doi.org/10.3390/rs132143068

  6. Chen, W., Zheng, Q., Xiang, H., Chen, X., Sakai, T.:  Forest canopy height estimation using polarimetric interferometric synthetic aperture radar (Polinsar) technology based on full-polarized alos/palsar data. Remote Sens. 13(2), 1–21 (2021). https://doi.org/10.3390/rs13020174

  7. Simard, M., Denbina, M.: 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. Selected Topics in Appli. Earth Observat. Remote Sens. 11(1), 95–111 (2018). https://doi.org/10.1109/JSTARS.2017.2761338

  8. Chen, W., Zheng, Q., Xiang, H., Chen, X., Sakai, T.: Forest canopy height estimation using polarimetric interferometric synthetic aperture radar (Polinsar) technology based on full-polarized alos/palsar data. Remote Sens. 13(2), 1–21 (2021). https://doi.org/10.3390/rs13020174

  9. Bolton, D.K., et al.:  Optimizing Landsat time series length for regional mapping of lidar-derived forest structure. Remote Sens. Environ. 239(2019), 111645 (2020). https://doi.org/10.1016/j.rse.2020.111645

  10. Astola, H., Häme, T., Sirro, L., Molinier, M., Kilpi, J.:  Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region. Remote Sens. Environ. 223(2017), 257–273 v. https://doi.org/10.1016/j.rse.2019.01.019

  11. Radke, D., Radke, D., Radke, J.: Beyond measurement: Extracting vegetation height from high resolution imagery with deep learning. Remote Sens. 12(22), 1–22 (2020). https://doi.org/10.3390/rs12223797

    Article  Google Scholar 

  12. Pereira-Pires, J. E., Mora, A., Aubard, V., Silva, J.M.N.,  Fonseca, J.M.:  Assessment of Sentinel-2 Spectral Features to Estimate Forest Height with the New GEDI Data, pp. 123–131 (2021). https://doi.org/10.1007/978-3-030-78288-7_12

  13. Brede, B., Lau, A., Bartholomeus, H.M., Kooistra, L.: Comparing RIEGL RiCOPTER UAV LiDAR derived canopy height and DBH with terrestrial LiDAR. Sensors (Switzerland) 17(10), 1–16 (2017). https://doi.org/10.3390/s17102371

    Article  Google Scholar 

  14. Fayad, I., et al.: A CNN-based approach for the estimation of canopy heights and wood volume from GEDI waveforms. Remote Sens. Environ. 265, 112652 (2021)

    Google Scholar 

  15. Pourshamsi, M., et al.:  Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning. ISPRS J. Photogrammetry Remote Sens. 172(2020), 79–94 (2021). https://doi.org/10.1016/j.isprsjprs.2020.11.008

  16. Potapov, P., et al.: Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165 (2021)

    Google Scholar 

  17. Fagua, J. C., Jantz, P., Rodriguez-Buritica, S., Duncanson, L.,  Goetz, S.J.:  Integrating LiDAR, multispectral and SAR data to estimate and map canopy height in tropical forests. Remote Sens. 11(22) (2019). https://doi.org/10.3390/rs11222697

  18. Pereira-Pires, J.E., Guida, R., Silva, J.M.N., Mora, A., Fonseca, J.M.: Forest Height Estimation Using Sentinel-1/2 and ALOS-2. In: 2023 8th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), pp. 1–6 (2023). https://doi.org/10.1109/APSAR58496.2023.10388740

  19. Khati, U., Singh, G., Kumar, S.: Potential of space-borne polinsar for forest canopy height estimation over India - a case study using fully polarimetric L-, C-, and X-Band SAR Data. IEEE J. Selected Topics  Appli. Earth Observat. Remote Sens. 11(7), 2406–2416 (2018). https://doi.org/10.1109/JSTARS.2018.2835388

    Article  Google Scholar 

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Acknowledgement

Research Units, Centre of Technology and Systems (CTS) (UIDB/ 00066/2020), Forest Research Centre (UIDB/00239/2020), and the Surrey Space Centre at University of Surrey. João Eduardo Pereira-Pires acknowledges the Fundação para a Ciência e Tecnologia for the Ph.D. Grant 2020.05015.BD.

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Pereira-Pires, J.E., Mora, A., Guida, R., Fonseca, J.M., Silva, J.M.N., Barreira, P. (2024). Mapping Forest Height with Multifrequency SAR, InSAR, and Multispectral Datasets. In: Camarinha-Matos, L.M., Ferrada, F. (eds) Technological Innovation for Human-Centric Systems. DoCEIS 2024. IFIP Advances in Information and Communication Technology, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-031-63851-0_22

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  • DOI: https://doi.org/10.1007/978-3-031-63851-0_22

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