عنوان مقاله [English]
Estimation of forest biomass has received much attention in recent decades including assessing the capability of different sensor data (e.g., optical, radar, and LiDAR)and the development of advanced techniques such as synthetic aperture radar (SAR),polarimetry and polarimetric SAR interferometry for forest biomass estimation. Accurate estimation of forest biomass is of vital importance to model global carbon cycle. Deforestation and forest degradation will result in the loss of forest biomass and consequently increases the greenhouse gases. Radar systems including SAR have a great potential to quantify biomass and structural diversity because of its penetration capability. These systemsare also independent of weather and external illumination condition and can be designed for different frequencies and resolutions.Moreover, SAR systems operating at lower frequencies such as L- and P-band have shown relatively good sensitivity to forest biomass. Regression analysis is among thecommon methods for evaluation forest biomass which have been investigated for many years on different areas. This analysis is based on the correlation between backscattering coefficient values and the forest biomass. However, previous studies demonstratedthat such approaches are very simple and they do not consider structural effects of different species. One of the restrictions and limitations of these methods is the low saturation level. The level of saturation is lower in higher frequencies and vice versa. Considering the structural parameters, researchers have tried to use the interferometry techniques.Forest canopy height is one of the important parameters that can be used to estimate Above Ground Biomass (AGB) using allometric equations.
Recentforest height retrieval methods rely on model based interferometric SAR analysis. The random volume over ground (RVOG) model is one of the most common algorithms. This method considers two layers, one for the ground under the vegetation and one for the volumetric canopy. This model has been investigated in different forest environments (e.g. tropical, temperate and boreal forests). Estimation of forest biomass based on forest height using allometric equations can overcome radar signal saturation to some extent.Improvement of Forest height estimation can play an important role to retrieve accurate forest biomass estimation. In this paper, a new method using scattering matrix optimization is introduced to extract forest height by changing polarization bases. Scattering matrices for slave and master images have been extracted by changing polarization bases. Then polarimetric interferometry coherences have been calculated and forest height was estimated by various forest height methods including DEM Difference, coherence amplitude inversion, RVOG Phase, Combined and RVOG.
P-band full Polarimetric synthetic aperture radar (SAR) images acquired by SETHI sensor over Remningstorp (a boreal forest in south of Sweden) were investigated for forest biomass estimation.Mean of Lidar height values which fall in each shapefile was used to check corresponding results with the heights of retrieval methods.
The results of tree height retrieval methods without changing polarization bases between PolInSAR tree height and LIDAR height show that three methods including coherence amplitude inversion, RVOG Phase and RVOG have low R2 value. DEM Difference and combined methods yielded better results in comparison with the other three aforementioned methods; however the results are not satisfactory.DEM Difference method underestimated the tree height compared to that of LIDAR. This is perhaps due to the fact that volume phase center does not lie at the top of the tree.Temporal decorrelation decreases volume correlation, consequently small values in the SINC function lead to generate large values in results; therefore RMSE of coherence amplitude method is relatively high.New master and slave scattering matrices in arbitrary polarization basis were extracted by alteringandin transformation matrix.Results show that RVOG phase has the best result with R2=0.76 and RMSE=3.76. Following this method, DEM difference method shows R2=-0.69.It is likely that methods which include phase information by changing geometricalparameters, in transformation matrix (e.g. RVOG phase and DEM difference) significantly increase the tree height accuracy.sOn the other hand, methods that only apply magnitude of coherence such as coherence amplitude method do not show notable improvementfor retrieving tree height.
Robustness of forest height estimation using Scattering Matrix Optimization by changing Polarization Bases was studied in this paper.PolInSAR data was acquired by SETHI on Remningstorp, a boreal forest in south of Sweden. Results indicated that forest height retrieval methods which included phase parameter shows remarkable improvement by changing the geometrical parameters for height estimation.Therefore RVOG phase method with R2=0.76, RMSE=3.76m and DEM Difference method with R2=-0.69 gave the best results, whereas coherence amplitude method which only included magnitude of coherence with R2=0.17 showed the lowest correlation.
1-Avitabile, V., Herold, M., Henry, M., & Schmullius, C. (2011). Mapping biomass with remote sensing: a comparison of methods for the case study of Uganda. Carbon Balance and Management, 6.(1), 1.
2- Ballester-Berman, J. D., Vicente-Guijalba, F., & Lopez-Sanchez, J. M. (2015). A simple RVoG test for PolInSAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(3), 1028-1040.
3- Basuki, T. M., Skidmore, A. K., Hussin, Y. A., & Van Duren, I. (2013). Estimating tropical forest biomass more accurately by integrating ALOS PALSAR and Landsat-7 ETM+ data. International Journal of Remote Sensing, 34 (13), 4871-4888.
4- Brown, S. (2002). Measuring carbon in forests: current status and future challenges. Environmental pollution, 116(3), 363-372.
5-Chehade, B. E. H., Ferro-Famil, L., Minh, D. H. T., Le Toan, T., & Tebaldini, S. (2015). Tropical Forest Biomass Retrieval using P-Band PolTomSAR Data. Paper presented at the POLinSAR 2015 Workshop.
6- Cloude, S., & Papathanassiou, K. (2003). Three-stage inversion process for polarimetric SAR interferometry. IEE Proceedings-Radar, Sonar and Navigation, 150(3), 125-134.
7- Cloude, S. R. (2006). Polarization coherence tomography. Radio Science, 41(4).
8-Cloude, S. R., & Papathanassiou, K. P. (1998). Polarimetric SAR interferometry. IEEE transactions on Geoscience and Remote Sensing, 36(5), 1551-1565.
9- De Grandi, G., Lee, J.-S., Schuler, D., & Nezry, E. (2003). Texture and speckle statistics in polarimetric SAR synthesized images. Geoscience and Remote Sensing, IEEE Transactions on, 41(9), 2070-2088.
10- Florian, K., Kostas, P., Irena, H., & Dirk, H. (2006). Forest height estimation in tropical rain forest using Pol-InSAR techniques. Paper presented at the 2006 IEEE International Symposium on Geoscience andRemote Sensing.
11- Hajnsek, I., Kugler, F., Lee, S.-K., & Papathanassiou, K. P. (2009). Tropical-forest-parameter estimation by means of Pol-InSAR: The INDREX-II campaign. IEEE transactions on Geoscience and Remote Sensing, 47 (2), 481-493.
12- Hyyppä, J., Hyyppä, H., Inkinen, M., Engdahl, M., Linko, S., & Zhu, Y.-H. (2000). Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128(1), 109-120.
13- Kajisa, T., Murakami, T., Mizoue, N., Top, N., & Yoshida, S. (2009). Object-based forest biomass estimation using Landsat ETM+ in Kampong Thom Province, Cambodia. Journal of Forest Research, 14(4), 203-211.
14- Ketterings, Q. M., Coe, R., van Noordwijk, M., & Palm, C. A . (2001) Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests. Forest Ecology and management, 146(1), 199-209.
15- Le Toan, T., Quegan, S., Davidson, M., Balzter, H., Paillou, P., Papathanassiou, K., . . . Shugart, H. (2011). The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle. Remote sensing of environment, 115(11), 2850-2860.
16-Lee, J.-S., & Pottier, E. (2009). Polarimetric radar imaging: from basics to applications: CRC press.
17- Lee, S.-K., Kugler, F., Papathanassiou, K. P., & Hajnsek, I. (2008). Quantifying temporal decorrelation over boreal forest at L-and P-band. Paper presented at the Synthetic Aperture Radar (EUSAR 7) 2008,th European Conference on.
18- Lu, D. (2006). The potential and challenge of remote sensing based biomass estimation. International Journal of Remote Sensing, 27(7), 1297-1328.
19- Lu, D., Chen, Q., Wang, G., Moran, E., Batistella, M., Zhang, M., . . . Saah, D. (2012). Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates. International Journal of Forestry Research, , 2012.
20- Mahgoun, H., & Ouarzeddine, M. (2016). Volume Height Estimation based on Fusion of Discrete Fourier Transform (DFT) and Least Square (LS) in a Tomographic SAR Application. Journal of the Indian Society of Remote Sensing, 1-12.
21- Mette, T., Kugler, F., Papathanassiou, K., & Hajnsek, I. (2006). Forest and the random volume over ground-nature and effect of 3 possible error types. Paper presented at the European Conference on Synthetic Aperture Radar (EUSAR).
22- Minh, D. H. T., Le Toan, T., Rocca, F., Tebaldini, S., Villard, L., Réjou-Méchain, M., . . . Scipal, K. (2016). SAR tomography for the retrieval of forest biomass andheight: Cross-validation at two tropical forest sites in French Guiana. Remote sensing of environment, 175, 138-147.
23- Mutanga, O., Adam, E., & Cho, M. A. (2012). High density biomass estimation for wetland vegetation using WorldView-2 imagery and randomforest regression algorithm. International Journal of Applied Earth Observation and Geoinformation, 18, 399-406.
24- Praks, J., Kugler, F., Papathanassiou, K. P., Hajnsek, I., & Hallikainen, M. (2007). Height estimation of boreal forest: Interferometric model-based inversion at L-and X-band versus HUTSCAT profiling scatterometer. IEEE Geoscience and remote sensing letters, 4(3), 466-470.
25- Roy, P., & Ravan, S. A. (1996). Biomass estimation using satellite remote sensing data—an investigation on possible approaches for natural forest. Journal of Biosciences. (4) 21, 535-561.
26- Schlund, M., von Poncet, F., Kuntz, S., Schmullius, C., & Hoekman, D. H. (2015). TanDEM-X data for aboveground biomass retrieval in a tropical peat swamp forest. Remote sensing of environment, 158, 255-266.
27- Soja, M. J., & Ulander, L. M. (2014). Polarimetric-interferometric boreal forest scattering model for BIOMASS end-to-end simulator. Paper presented at the 2014 IEEE Geoscience and Remote Sensing Symposium.
28- Steininger, M. (2000). Satellite estimation of tropical secondary forest above-ground biomass: data from Brazil and Bolivia. International Journal of Remote Sensing, 21(6-7), 1139-1157.