Progresses on SAR Remote Sensing of Tropical Forests: Forest Biomass Retrieval and Analysis of Changing Weather Conditions

doi:10.11947/j.JGGS.2021.0111

Abstract

Abstract:

This paper is intended to report on the progresses made during the Dragon-4 project Three and Four-Dimensional Topographic Measurement and Validation (ID: 32278), sub-project Multi-baseline SAR Processing for 3D/4D Reconstruction (ID: 32278_2). The work here reported focuses on two important aspects of SAR remote sensing of tropical forests, namely the retrieval of forest biomass and the assessment of effects due to changing weather conditions. Recent studies have shown that by using SAR tomography the backscattered power at 30m layer above the ground is linearly correlated to the forest Above Ground Biomass (AGB). However, the two parameters that determine this linear relationship might vary for different tropical forest sites. For purpose of solving this problem, we investigate the possibility of using LiDAR derived AGB to help training the two parameters. Experimental results obtained by processing data from the TropiSAR campaign support the feasibility of the proposed concept. This analysis is complemented by an assessment of the impact of changing weather conditions on tomographic imaging, for which we simulate BIOMASS repeat pass tomography using ground-based TropiSCAT data with a revisit time of 3 days and rainy days included. The resulting backscattered power variation at 30m is within 1.5dB. For this forest site, this error is translated into an AGB error of about 50~80t/hm ², which is 20% or less of forest AGB.

Key words: tropical forest; biomass; SAR tomography; LiDAR; temporal decorrelation

Stefano TEBALDINI,Xinwei YANG,Yu BAI,Mauro Mariotti D’ALESSANDRO,Mingsheng LIAO,Wen YANG. Progresses on SAR Remote Sensing of Tropical Forests: Forest Biomass Retrieval and Analysis of Changing Weather Conditions[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(1): 88-93.

Figures/Tables 7

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

References 17

[1]	SAATCHI S S, HARRIS N L, BROWN S, et al. Benchmark map of forest carbon stocks in tropical regions across three continents[J]. Proceedings of the National Academy of Sciences of the United States of America, 2011,108(24):9899-9904. doi: 10.1073/pnas.1019576108
[2]	PAN Yude, BIRDSEY R A, FANG Jingyun, et al. A large and persistent carbon sink in the world’s forests[J]. Science, 2011,333(6045):988-993. doi: 10.1126/science.1204588
[3]	LETOAN T, QUEGAN S, DAVIDSON M W J, et al. The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle[J]. Remote Sensing of Environment, 2011,115(11):2850-2860. doi: 10.1016/j.rse.2011.03.020
[4]	ESA. Report for mission selection: Biomass[R]. Noordwijk, The Netherlands: ESA, 2012.
[5]	HO TONG MINHD, LE TOAN T, ROCCA F, et al. Relating P-band synthetic aperture radar tomography to tropical forest biomass[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(2):967-979. doi: 10.1109/TGRS.2013.2246170
[6]	HO TONG MINH D, LE TOAN T, ROCCA F, et al. SAR tomography for the retrieval of forest biomass and height: cross-validation at two tropical forest sites in French Guiana[J]. Remote Sensing of Environment, 2016,175:138-147. doi: 10.1016/j.rse.2015.12.037
[7]	DUBOIS-FERNANDEZ P C, LE TOAN T, DANIEL S, et al. 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. doi: 10.1109/TGRS.2011.2180728
[8]	REIGBER A, MOREIRA A. First demonstration of airborne SAR tomography using multibaseline L-band data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000,38(5):2142-2152. doi: 10.1109/36.868873
[9]	GINI F, LOMBARDINI F, MONTANARI M. Layover solution in multibaseline SAR interferometry[J]. IEEE Transactions on Aerospace and Electronic systems, 2002,38(4):1344-1356. doi: 10.1109/TAES.2002.1145755
[10]	FREY O, MAGNARD C, RUEGG M, et al. Focusing of airborne synthetic aperture radar data from highly nonlinear flight tracks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009,47(6):1844-1858. doi: 10.1109/TGRS.2008.2007591
[11]	TEBALDINI S, NAGLER T, ROTT H, et al. Imaging the internal structure of an alpine glacier via l-band airborne SAR tomography[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016,54(12):7197-7209. doi: 10.1109/TGRS.2016.2597361
[12]	TEBALDINI S, GUARNIERI A M. On the role of phase stability in SAR multibaseline applications[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010,48(7):2953-2966. doi: 10.1109/TGRS.2010.2043738
[13]	TEBALDINI S, ROCCA F, D’ALESSANDRO M M, et al. Phase calibration of airborne tomographic SAR data via phase center double localization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016,54(3):1775-1792. doi: 10.1109/TGRS.2015.2488358
[14]	DINH H T M, TEBALDINI S, ROCCA F, et al. Ground-based array for tomographic imaging of the tropical forest in P-band[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013,51(8):4460-4472. doi: 10.1109/TGRS.2013.2246795
[15]	MINH D H T, TEBALDINI S, ROCCA F, et al. Vertical structure of P-band temporal decorrelation at the paracou forest: results from TropiScat[J]. IEEE Geoscience and Remote Sensing Letters, 2014,11(8):1438-1442. doi: 10.1109/LGRS.2013.2295165
[16]	BAI Yu, TEBALDINI S, MINH D H T, et al. An empirical study on the impact of changing weather conditions on repeat-pass SAR tomography[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018,11(10):3505-3511. doi: 10.1109/JSTARS.4609443
[17]	YANG X, D’ALESSANDRO M M, TEBALDINI S, et al. Relating SAR tomography to tropical forest biomass via LiDAR data[C]// Proceedings of IGARSS 2018—2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia:IEEE, 2018: 9034-9037.