بهبود تخمين ارتفاع جنگل با روش بهينه‌سازي کوهرنس هوش جمعي ذرات به‌کمک داده‌هاي تداخل‌سنجي پلاريمتريک

سيده سميرا حسيني, حميد عبادي, ياسر مقصودي

چکیده


ازآنجاکه درختان نقش اساسي در تغييرات دي‌اکسيد کربن و شرايط آب‌وهوايي دارند، تخمين زيست‌تودة موجود در درختان بسيار اهميت دارد. روش‌هاي راداري که پارامترهاي ساختاري را در تخمين زيست‌توده در نظر نمی‌گيرند منجر به نتايجي با سطح اشباع پايين می‌شوند.­ ارتفاع از پارامترهاي ساختاري است و يکي از عوامل مهم تأثيرگذار در بهبود تخمين ارتفاع استفاده از کوهرنس بهينه است. در اين مقاله، از داده‌هاي شبيه‌سازي‌شده در باندهاي P و L براي تخمين ارتفاع به روش‌هاي تفاضلي، اندازه همدوسي، ترکيبي و Polarization Coherence Tomography (PCT) استفاده شده است. روش تفاضلي باعث تخمين ارتفاع کمتر از مقدار واقعي به‌اندازة 14 متر در باند P و 11 متر در باند L شده است؛ درحالي‌که روش اندازة همدوسي، به‌نسبت روش تفاضلي، نتايج بهتري به‌دست می‌آورد و اختلاف مقادير میانگین ارتفاعات در اين روش با مقادير واقعي در باند P، 8 متر و در باند L، 2 متر است. روش‌هاي ترکيبي و PCT نتايجی نزديک به هم دارند و اختلاف میانگین مقادير ارتفاعات به‌دست‌آمده با مقدار واقعي کمتر از 2 متر است اما نتايج حاصل از روش PCT به‌دليل استفاده از کوهرنس بهينه، از روش ترکيبي بهتر است. روش بهينه‌سازي کوهرنس به روش PSO که در اين مقاله پيشنهاد شده است نتايجی بهتر از روش‌هاي ديگر حاصل کرده است و اختلاف میانگین مقادير ارتفاعات به‌دست‌آمده با مقدار واقعي به کمتر از 5/0 متر می‌رسد.

واژگان کلیدی


بهينه‌سازي کوهرنس، PSO، تداخل‌سنجي پلاريمتري، PCT، تخمين ارتفاع

تمام متن:

PDF

منابع و مآخذ مقاله


Askne, J.I., Dammert, P.B., Ulander, L.M. & Smith, G., 1997, C-band Repeat-pass Interferometric SAR Observations of the Forest, Geoscience and Remote Sensing, IEEE Transactions on, 35(1), PP. 25-35

Ballester-Berman, J.D., Vicente-Guijalba, F. & Lopez-Sanchez, J.M., 2015, A Simple RVoG Test for PolInSAR Data, Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, 8(3), PP. 1028-1040

Balzter, H., 2001, Forest Mapping and Monitoring with Interferometric Synthetic Aperture Radar (InSAR), Progress in Physical Geography, 25(2), PP. 159-177.

Balzter, H., Rowland, C.S. & Saich, P., 2007, Forest Canopy Height and Carbon Estimation at Monks Wood National Nature Reserve, UK, Using Dual-wavelength SAR Interferometry, Remote Sensing of Environment, 108(3), PP. 224-239.

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.

Clerc, M., 2010, Particle Swarm Optimization, Vol. 93, John Wiley & Sons.

Cloude, S. & Papathanassiou, K., 2003, Three-stage Inversion Process for Polarimetric SAR Interferometry, IEE Proceedings-Radar, Sonar and Navigation, 150(3), PP. 125-134.

Cloude, S.R., 2006, Polarization Coherence Tomography (PCT): A Tutorial Introduction, Radio Science, Vol. 41.

Cloude, S.R. & Papathanassiou, K.P., 1998, Polarimetric SAR Interferometry, Geoscience and Remote Sensing, IEEE Transactions on, 36(5), PP. 1551-1565.

Colin, E., Titin-Schnaider, C. & Tabbara, W., 2006, An Interferometric Coherence Optimization Method in Radar Polarimetry for High-resolution Imagery, Geoscience and Remote Sensing, IEEE Transactions on, 44(1), PP. 167-175.

Dobson, M.C., Ulaby, F.T., LeToan, T., Beaudoin, A., Kasischke, E.S. & Christensen, N., 1992, Dependence of Radar Backscatter on Coniferous Forest Biomass, Geoscience and Remote Sensing, IEEE Transactions on, 30(2), PP. 412-415.

Eberhart, R., & Kennedy, J., 1995, A new optimizer using particle swarm theory, paper presented at the micro machine and human science, AHS, 95, Proceedings of the sixth International symposiamon.

Fomena, R.T. & Cloude, S.R., 2005, On the Role of Coherence Optimization in Polarimetric SAR Interferometry, In Practice, 22(11), P. 9.

Foody, G., 2003, Remote Sensing of Tropical Forest Environments: Towards the Monitoring of Environmental Resources for Sustainable Development, International Journal of Remote Sensing, 24(20), PP. 4035-4046.

Honzák, M., Lucas, R., Do Amaral, I., Curran, P., Foody, G.M. & Amaral, S., 1996, Estimation of the Leaf Area Index and Total Biomass of Tropical Regenerating Forests: Comparison of Methodologies, Amazonian Deforestation and Climate, 1.

Lu, D., 2005, Aboveground Biomass Estimation Using Landsat TM Data in the Brazilian Amazon, International Journal of Remote Sensing, 26(12), PP. 2509-2525.

Mette, T., Kugler, F., Papathanassiou, K. & Hajnsek, I., 2006, Forest and the Random Volume over Ground-nature and Effect of 3 Possible Error Types, EUSAR 2006.

Nelson, B.W., Mesquita, R., Pereira, J.L., De Souza, S.G.A., Batista, G.T. & Couto, L.B., 1999, Allometric Regressions for Improved Estimate of Secondary Forest Biomass in the Central Amazon, Forest Ecology and Management, 117(1), 149-167.

Nelson, R., Krabill, W. & Tonelli, J., 1988, Estimating Forest Biomass and Volume Using Airborne Laser Data, Remote Sensing of Environment, 24(2), PP. 247-267.

Overman, J.P.M., Witte, H.J.L. & Saldarriaga, J.G., 1994, Evaluation of Regression Models for Above-ground Biomass Determination in Amazon Rainforest, Journal of Tropical Ecology, 10(02), PP. 207-218.

Papathanassiou, K.P. & Cloude, S.R., 2001, Single-baseline Polarimetric SAR Interferometry, Geoscience and Remote Sensing, IEEE Transactions on, 39(11), PP. 2352-2363.

Pipia, L., Fabregas, X., Aguasca, A., Lopez-Martinez, C. & Mallorquí, J.J., 2009, Polarimetric Coherence Optimization for Interferometric Differential Applications, Paper presented at the Geoscience and Remote Sensing Symposium, IEEE International, IGARSS 2009.

Qong, M., 2005, Coherence Optimization Using the Polarization State Conformation in PolInSAR, Geoscience and Remote Sensing Letters, IEEE, 2(3), PP. 301-305.

Ranson, K.J. & Sun, G., 1994, Mapping Biomass of a Northern Forest Using Multifrequency SAR Data, Geoscience and Remote Sensing, IEEE Transactions on, 32(2), PP. 388-396.

Sader, S.A., Waide, R.B., Lawrence, W.T. & Joyce, A.T., 1989, Tropical Forest Biomass and Successional Age Class Relationships to a Vegetation Index Derived from Landsat TM Data, Remote Sensing of Environment, 28, PP. 143-198.

Sagues, L., Lopez-Sanchez, J.M., Fortuny, J., Fabregas, X., Broquetas, A. & Sieber, A.J., 2000, Indoor Experiments on Polarimetric SAR Interferometry, Geoscience and Remote Sensing, IEEE Transactions on, 38(2), PP. 671-684.

Santoro, M., Askne, J., Smith, G. & Fransson, J.E., 2002, Stem Volume Retrieval in Boreal Forests from ERS-1/2 Interferometry, Remote Sensing of Environment, 81(1), PP. 19-3.

Santoro, M., Schmullius, C.C., Eriksson, L. & Hese, S., 2003. The SIBERIA and SIBERIA-II Projects: An Overview, Paper presented at the International Symposium on Remote Sensing.

Santos, J.R., Freitas, C.C., Araujo, L.S., Dutra, L.V., Mura, J.C., Gama, F.F., . . . Sant'Anna, S.J., 2003, Airborne P-band SAR Applied to the Aboveground biomass Studies in the Brazilian Tropical Rainforest, Remote Sensing of Environment, 87(4), PP. 482-493.

Schlerf, M., 2006, Determination of Structural and Chemical Forest Attributes Using Hyperspectral Remote Sensing Data—Case Studies in Norway Spruce Forests, Geography/Geosciences. sl: University of Trier.

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, PP. 255-266.

Soja, M.J. & Ulander, L.M., 2014, Polarimetric-interferometric Boreal Forest Scattering Model for BIOMASS End-to-end Simulator, Paper presented at the Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International.

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), PP. 1139-1157.

Tenne, Y. & Goh, C.K., 2010, Computational Intelligence in Expensive Optimization Problems (Vol. 2), Springer Science & Business Media.

Thiel, C., Drezet, P., Weise, C., Quegan, S. & Schmullius, C., 2006, Radar Remote Sensing for the Delineation of Forest Cover Maps and the Detection of Deforestation, Forestry, 79(5), PP. 589-597.

Treuhaft, R.N. & Siqueira, P.R., 2000, Vertical Structure of Vegetated Land Surfaces from Interferometric and Polarimetric Radar, Radio Science, 35(1), PP. 141-177.

Ustin, S., 2004, Manual of Remote Sensing: Remote Sensing for Natural Resource Management and Environmental Monitoring, Wiley Hoboken, NJ, USA.

Xie, Q., Zhu, J., Wang, C. & Fu, H., 2014, Boreal Forest Height Inversion Using E-SAR PolInSAR Data Based Coherence Optimization Methods and Three-stage Algorithm, Paper presented at the Earth Observation and Remote Sensing Applications (EORSA), 2014 3rd International Workshop on.

Yamada, H., Yamaguchi, Y., Rodriguez, E., Kim, Y. & Boerner, W., 2001, Polarimetric SAR Interferometry for Forest Canopy Analysis by Using the Super-resolution Method, Paper presented at the Geoscience and Remote Sensing Symposium, IGARSS'01. IEEE 2001 International.

Zheng, D., Rademacher, J., Chen, J., Crow, T., Bresee, M., Le Moine, J. & Ryu, S.R., 2004, Estimating Aboveground Biomass Using Landsat 7 ETM+ Data across a Managed Landscape in Northern Wisconsin, USA, Remote Sensing of Environment, 93(3), PP. 402-411


ارجاعات

  • در حال حاضر ارجاعی نیست.