Estimation of Forest Stand Volume Using ICESat/GLAS Data in Mountainous Forests in the North of Iran

Document Type : علمی - پژوهشی

Authors

1 Faculty of Natural Resources, University of Tehran, Tehran, Iran

2 Researcher, IRSTEA, UMR TETIS, 34093 Montpellier, France

Abstract

Forest volume as an important factor in forest management was aimed to be measured in mountainous forests in the North of Iran using spaceborne LiDar. Two missions of GLAS (L3K and L3I) were preprocessed to remove inappropriate waveforms. Several waveform metrics including waveform extent (Wext), lead edge extent (Hlead), trail edge extent (Htrail) and quartile heights (H25, H50, H75 and H100) were extracted. Principal component analysis (PCA) was also applied to reduce noises from waveform signals and produce new components (PCs). In order to decrease the effect of terrain slope on waveforms, terrain index (TI) describing topographic information was extracted from a digital elevation model (DEM). Forest stand volume was measured on 60 circle plots with diameter of 70 m for developing volume models and their validation. Multiple regression and artificial neural network models were built based on two sets of variables including waveform metrics and PCs. Generally, both multiple regression and neural network methods produced approximately the same result. A neural network combining three first PCs of PCA and Wext estimated forest volume with an RMSE and  of 119.9 m and 0.73, respectively (RMSE%=26.6). Interesting points regards to this model is employing PCs and Wext as input variables which are not affected by terrain slope, achieving slightly better accuracy without adding any ancillary data (DEM), and showing better performance in short sparse stands in comparison with other developed models.

Keywords

References

  1. خرمی، ر.ع.، درویش‌صفت، ع.ا.، طبری کوچکسرایی، م.، شتایی جویباری، ش.، 1393، بررسی قابلیت داده‌های لیدار هوایی در برآورد ارتفاع پایه‌‌های درختی پلت و ممرز، مجلۀ جنگل ایران، سال ششم، شمارۀ 2، صص. 140-127.
  2. درویش‌صفت، ع.‌ا.، پیر باوقار، م.، رجب‌پور رحمتی، م. (مترجمان)، 1390، سنجش از دور برای مدیران GIS، انتشارات دانشگاه تهران، تهران، چاپ اول.
  3. رجب‌پور رحمتی، م.، 1394، برآورد ارتفاع تاج‌پوشش جنگل با استفاده از داده‌های ICESat GLAS (مطالعۀ موردی: جنگل‌های کجور)، رسالۀ دکتری، دانشگاه تهران.
  4. رجب‌پور رحمتی، م.، درویش‌صفت، ع.ا.، بغدادی، ن.، نمیرانیان، م.، صوفی مریو، ح.، 1394، برآورد ارتفاع تاج‌پوشش جنگل در مناطق کوهستانی با استفاده از داده‌های لیدار فضایی ICESat-GLAS (مطالعۀ موردی: حوزۀ 46 جنگل‌های شمال ایران)، فصلنامۀ علمی- پژوهشی تحقیقات جنگل و صنوبر ایران، سال بیست‌وسوم، شمارۀ 1، صص. 103-90.
  5. سازمان جنگل‌ها، مراتع و آبخیزداری کشور، 1381، جدول حجم گونه‌های جنگلی شمال کشور به‌جز راش، دفتر فنی جنگلداری.
  6. سازمان جنگل‌ها، مراتع و آبخیزداری کشور، 1364، جدول حجم گونۀ راش برای جنگل‌های مازندران، دفتر فنی جنگلداری.
  7. نمیرانیان، م.، 1385، اندازه‌گیری درخت و زیست‌سنجی جنگل، انتشارات دانشگاه تهران، تهران.
  8. Ahmadi, K., Alavi, S.J., Kouchaksaraei, M.T. & Aertsen, W., 2013, Non-Linear Height-Diameter Models for Oriental Beech (Fagus Orientalis Lipsky) in the Hyrcanian Forests, Iran, Biotechnology, Agronomy, Society and Environment, 17(3): 431-440.
  9. Andersen, H.E., Reutebuch, S.E. & McGaughey, R.J., 2006, A Rigorous Assessment of Tree Height Measurements Obtained Using Airborne Lidar and Conventional Field Methods, Canadian Journal of Remote Sensing, 32(5): 355-366. www.fs.fed.us/pnw/pubs/journals/pnw_2006_andersen001.pdf.
  10. Attarchi, S. & Gloaguen, R., 2014, Improving the Estimation of Above Ground Biomass Using Dual Polarimetric Palsar and ETM+ Data in the Hyrcanian Mountain Forest (Iran), Remote Sensing, 6: 3693-3715. http://dx.doi.org/10.3390/rs6053693.
  11. Baghdadi, N., Maire, G., Fayad, I., Bailly, J.S., Nouvellon, Y., Lemos & C., Hakamada, R., 2014, Testing Different Methods of Forest Height and Aboveground Biomass Estimations from ICESat/GLAS Data in Eucalyptus Plantations in Brazil, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1): 290-299. http://dx.doi.org/10.1109/JSTARS.2013.2261978.
  12. 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: 224-239. http://dx.doi.org/10.1016/j.rse.2006.11.014.
  13. Burnham, K.P. & Anderson, D.R., 2002, Information and Likelihood Theory: A Basis for Model Selection and Inference, in Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd ed. New York: Springer-Verlag Press, 49-97.
  14. Cannon, A.J., 2012, Package ‘Monmlp’: Monotone Multi-Layer Perceptron Neural Network, Comprehensive R Archive Network (CRAN), 1-14. https://cran.r-project.org/web/packages/monmlp/monmlp.pdf.
  15. Chen, Q., 2010, Retrieving Vegetation Height of Forests and Woodlands over Mountainous Areas in the Pacific Coast Region Using Satellite Laser Altimetry, Remote Sensing of Environment, 14: 1610-1627. http://www2.hawaii.edu/~qichen/cv/Chen%202010%20RSE.pdf.
  16. Chen, G. & Hay, G.J., 2011, A Support Vector Regression Approach to Estimate Forest Biophysical Parameters at the Object Level Using Airborne Lidar Transects and QuickBird Data, Photogrammetric Engineering & Remote Sensing, 77(7): 733-741. https://clas-pages.uncc.edu/gang-chen/wp-content/uploads/sites/184/2012/12/Chen_2011_PERS_77.pdf.
  17. Duncanson, L.I., Niemann, K.O. & Wulder, M.A., 2010, Estimating Forest Canopy Height and Terrain Relief from GLAS Waveform Metrics, Remote Sensing of Environment, 114: 138-154. http://geog.uvic.ca/olaf/ON_personal/PUblications_files/Duncanson_et_al_2010.pdf.
  18. Fang, Z. & Bailey, R.L., 1998, Height-Diameter Models for Tropical Forests on Hainan Island in Southern China, Forest Ecology and Management, 110: 315-327. http://dx.doi.org/10.1016/S0378-1127(98)00297-7.
  19. Fayad, I., Baghdadi, N., Bailly, J-S., Barbier, N., Gond, V., El Hajj, M., Fabre, F. & Bourgine, B., 2014, Canopy Height Estimation in French Guiana with LiDAR ICESat/GLAS Data Using Principal Component Analysis and Random Forest Regressions, Remote Sensing, 6: 11883-11914. http://dx.doi.org/10.3390/rs61211883.
  20. Fu, A., Guoqing, S. & Zhifeng, G., 2009, Estimating Forest Biomass with GLAS Samples and MODIS Imagery in Northeastern China, Proc. SPIE, 7498: 1-8.
  21. Gobakken, T., Bollandsås, O.M. & Næsset, E., 2015, Comparing Biophysical Forest Characteristics Estimated from Photogrammetric Matching of Aerial Images and Airborne Laser Scanning Data, Scandinavian Journal of Forest Research, 30(1): 73-86.
  22. Healey, S.P., Patterson, P.L., Saatchi, S., Lefsky, M.A., Lister, A.J. & Freeman, E.A., 2012, A Sample Design for Globally Consistent Biomass Estimation Using Lidar Data from the Geoscience Laser Altimeter System (GLAS). Carbon Balance and Management, 7:10. http://www.cbmjournal.com/content/7/1/10.
  23. Huang, S., Titus, S.J. & Wiens, D.P., 1992, Comparison of Nonlinear Height-Diameter Functions for Major Alberta Tree Species, Canadian Journal of Forest Research, 22: 1297-1304.
  24. Husson, F., Josse, J., Le, S. & Mazet, J., 2015, Package ‘FactoMineR’: Multivariate Exploratory Data Analysis and Data Mining, Comprehensive R Archive Network (CRAN), 1-95.
  25. Kaastra, I. & Boyd, M., 1996, Designing a Neural Network for Forecasting Financial and Economic Time Series, Neurocomputing, 10: 215-236. http://www.eecs.harvard.edu/~parkes/cs286r/spring08/cours.pdf.
  26. katz, J.O., 1992, Developing Neural Network Forecasters for Trading, Technical analysis for stocks and commodities, 10(4): 160-168.
  27. Lefsky, M.A., Keller, M., Pang, Y., de Camargo, P.B. & Hunter, M.O., 2007, Revised Method for Forest Canopy Height Estimation from Geoscience Laser Altimeter System Waveform, Journal of Applied Remote Sensing, 1: 1-18. http://www.fs.fed.us/global/iitf/pubs/ja_iitf_2007_lefsky001.pdf.
  28. Lefsky, M.A., Harding, D.J., Keller, M., Cohen, W.B., Carabajal, C.C., Espirito-Santo, F.D., Hunter, M.O., de Oliveira, R. & de Camargo, P.B., 2005, Estimates of Forest Canopy Height and Aboveground Biomass Using lCESat, Geophysical Research Letters, 32(22), 1-4. http://www.fs.fed.us/global/iitf/pubs/ja_iitf_2005_Lefsky001.pdf.
  29. Miller, D.R., Quineb, Ch.P. & Hadley, W., 2000, An Investigation of the Potential of Digital Photogrammetry to Provide Measurements of Forest Characteristics and Abiotic Damage, Forest Ecology and Management, 135: 279-288. http://dx.doi.org/10.1016/S0378-1127(00)00286-3.
  30. Nelson, R., 2010, Model Effects on GLAS-Based Regional Estimates of Forest, Biomass and Carbon, International Journal of Remote Sensing, 31(5): 1359-1372. http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20110015334.pdf.
  31. Nelson, R., Ranson, K.J., Sun, G., Kimes, D.S., Kharuk, V. & Montesano, P., 2009, Estimating Siberian Timber Volume Using MODIS and ICESat/GLAS, Remote Sensing of Environment, 113: 691-701. http://dx.doi.org/10.1016/j.rse.2008.11.010.
  32. NSIDC, 2012, GLAS altimetry product usage guidance, Available at:
  33. http://nsidc.org/data/docs/daac/glas_altimetry/pdf/NSIDC_AltUserGuide_Rel33.pdf.
  34. Rajabpour rahmati, M., Baghdadi, N., Darvishsefat, A.A., Namiranian, M., Fayad, I. Bailly, J.S. & Gond, V., 2016, Capability of GLAS/ICESat Data to Estimate Forest Canopy Height and Volume in Mountainous Forests of Iran, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(11): 5246 - 5261. http://dx.doi.org/10.1109/JSTARS.2015.2478478.
  35. Rao, R.B., Fung, G. & Rosales, R., 2008, On the Dangers of Cross-Validation, an Experimental Evaluation, In: Proceedings of the SIAM International Conference on Data Mining, SDM, Atlanta, Georgia, USA, 588-596.
  36. Rosette, J.A.B., North, P.R.J. & Suarez, J.C., 2008a, Vegetation Height Estimates for a Mixed Temperate Forest Using Satellite Laser Altimetry, International Journal of Remote Sensing, 29(5): 1475-1493. DOI: 10.1080/01431160701736380.
  37. Rosette, J.A.B., North, P.R.J. & Suarez, J.C., 2008b, Satellite Lidar Estimation of Stemwood Volume; A Method Using Waveform Decomposition, The photogrammetric Journal of Finland, 21(1): 76-85. http://foto.hut.fi/seura/julkaisut/pjf/pjf_e/2008/Rosette_et_al_2008_PJF.pdf.
  38. Silva Scaranello, M.A., Alves, L.F., Vieira, S.A., Camargo, P.B., Joly, C.A. & Martinelli, L.A., 2012, Height-Diameter Relationships of Tropical Atlantic Moist Forest Trees in Southeastern Brazil, Scientia Agricola, 69(1): 26-37.
  39. Sun, G., Ranson, K.J., Kimes, D.S., Blair, J.B. & Kovacs, K., 2008, Forest Vertical Structure from GLAS: An Evaluation Using LVIS and SRTM Data, Remote Sensing of Environment, 112: 107-117. http://dx.doi.org/10.1016/j.rse.2006.09.036.
  40. Zhifeng, G., Hong, CH. & Guoqing, S., 2010, Estimating Forest Aboveground Biomass Using HJ-1 Satellite CCD and ICESat GLAS Waveform Data, Science China Earth Sciences, 53(1): 16-25.
Iranian Journal of Remote Sensing & GIS
Volume 7, Issue 4 - Serial Number 4
November 2015
Pages 85-98

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