Forest structure parameter extraction using SPOT-7 satellite data by object- and pixel-based classification methods

  • Naimeh Rahimizadeh
  • Sasan Babaie KafakyEmail author
  • Mahmod Reza Sahebi
  • Asadollah Mataji


Using satellite data to extract forest structure mapping parameters assists forest management. In this research, structural parameters including species, density, canopy, and gaps were extracted from SPOT-7 satellite data over Hyrcanian forests (Iran). A detailed ground inventory was initially conducted, over 12 × 1 ha (100 m × 100 m) plots, in which tree coordinates were plotted, using a differential global positioning system (DGPS), along with data on tree species, diameter-at-breast-height and height, as well as canopy dimensions, and canopy gap shapes, sizes, and positions, for each plot. Then, spectral transformations, vegetation indices, and simple spectral ratios were extracted from SPOT-7 data, and a supervised, pixel-based classification method and a support-vector machine algorithm were used to classify and determine tree species types. In addition, canopy tree borders and gaps were classified, using an object-based method, and tree densities per unit area were determined, using the canopy gravity center. Finally, the original ground data was used to perform an accuracy assessment on the extracted information, with the results showing that forest type could be determined with 95% accuracy and a Kappa coefficient of 0.8. Canopy and gap coverage achieved an overall accuracy of 91% (Kappa coefficient: 0.7), and tree densities per hectare were determined, on average, to be 47 trees fewer than reality. In conclusion, we have shown that forest structural parameters could be extracted, with good accuracy, using a combination of pixel- and object-based methods applied to SPOT-7 imaging.


Fagus orientalis Hyrcanian forest Object-based classification SPOT-7 Support-vector machine algorithm 



The authors would like to appreciate those who assisted in conducting this work. Also thanks to Remote sensing Institute of K.N. Toosi University of Tech. for providing the opportunity to use the remote sensing lab for this research. We thank anonymous reviewers who provided many helpful comments and suggestions for improving this manuscript.


  1. Amini, S., Homayounib, S., Safari, A., & Darvishsefat, A. A. (2018). Object-based classification of hyperspectral data using Random Forest algorithm. Geo-spatial Information Science, 21(2), 127–138. Scholar
  2. Aronoff, S. (2005). Remote sensing for GIS managers. Redlands: ESRI Press xiiii and 487 pp., diagrams, photos, images, appendices, index. ISBN 1-58948-081-3, Scholar
  3. Attarchi, S., & Gloaguen, R. (2014). Classifying complex mountainous forests with L-band SAR and Landsat data integration: A comparison among different machine learning methods in the Hyrcanian Forest. Remote Sensing, 6(5), 3624–3647. Scholar
  4. Awad. M. M. (2018) Forest mapping: a comparison between hyperspectral and multispectral images and technologies. Journal of Forestry Research, 29 (5):1395-1405, Scholar
  5. Bagaram, M., Giuliarelli, D., Chirici, G., Giannetti, F., & Barbati, A. (2018). UAV remote sensing for biodiversity monitoring: Are forest canopy gaps good covariates? Remote Sensing, 10(9), 1397. Scholar
  6. Bayat, M., Thanh Noi, P., Zare, R., & Tien, B. D. (2019). A semi-empirical approach based on genetic programming for the study of biophysical controls on diameter-growth of Fagus orientalis in northern Iran. Remote Sensing, 11, 1680. Scholar
  7. Bettingera, P., Merry, K., Bayat, M., & Tomaštíkc, J. (2019). GNSS use in forestry – a multi-national survey from Iran, Slovakia and southern USA. Computers and Electronics in Agriculture, 158, 369–383. Scholar
  8. Bourque, C. P. A., Bayat, M., & Zhang, C. (2019). An assessment of height–diameter growth variation in an unmanaged Fagus orientalis-dominated forest. European Journal of Forest Research, 1-15, Scholar
  9. Brokaw, N. V. L. (1982). The definition of treefall gap and its effect on measures of forest dynamics. Biotropica, 14. NO, 2, 158–160. Scholar
  10. Camps-Valls, G., & Bruzzone, L. (2009). Methods for remote sensing data analysis. Hoboken: Wiley Scholar
  11. Castillo-Santiago, M. A., Ricker, M., & de Jong, B. H. (2010). Estimation of tropical forest structure from spot-5 satellite images. International Journal of Remote Sensing, 31, 2767–2782. Scholar
  12. Couturier, S., Gastellu-Etchegorry, J., Patiño, P., & Emmanuel, M. (2009). A model-based performance test for forest classifiers on remote-sensing imagery. Forest Ecology and Management, 257, 23–37. Scholar
  13. Dwivedi, R. S., Kandrika, S., & Ramana, K. V. (2004). Comparison of classifiers of remote-sensing data for land-use/land-cover mapping. Current Science, 86(2), 328–335 Scholar
  14. Fadaie, H., Suzuki, R., & Avtar, R. (2012). Estimation tree density as object-based in arid and semi-arid regions using ALOS, Proceedings of the 4th GEOBIA, May 7–9 - Rio de Janeiro - Brazil. p. 668.Google Scholar
  15. Fatehi, P., Damm, A., Schaepman, M. E., & Kneubühler, M. (2015). Estimation of Alpine Forest structural variables from imaging spectrometer data. Remote Sensing, 7(12), 16315–16338. Scholar
  16. Ferreira de Lima, R. A. (2005). Gap size measurement: The proposal of a new field method. Forest Ecology and Management, 214, 413–419. Scholar
  17. Frauman, E., & Wolff, E. (2005). Segmentation of very high spatial resolution satellite images in urban areas for segments-based classification. In In proceedings for 3rd international symposium remote sensing and data fusion over urban areas. Tempe: Arizona. Scholar
  18. Geomatica OrthoEngine Orthorectifying SPOT6 data, Tutorial (2013)
  19. Ghanbari Motlagh, M., Babaie Kafaky, S., Mataji, A., & Akhavan, R. (2018). Estimating and mapping forest biomass using regression models and Spot-6 images (case study: Hyrcanian forests of north of Iran). Environmental Monitoring and Assessment, 190, 352–314. Scholar
  20. Gualtieri, J. A., & Cromp, R. F. (1999). Support vector machines for hyperspectral remote sensing classification. In the 27th AIPR workshop: Advances in computer-assisted recognition. International Society for Optics and Photonics, 3584, 221–232. Scholar
  21. Hay, G. J., & Castilla, G. (2006). Object-based image analysis: Strengths, weaknesses, opportunities and threats (SWOT). Paper presented at the International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences,
  22. Hay, G. J., Castilla, G., Wulde, M. A., & Ruiz, J. R. (2005). An automated object-based approach for the multiscale image segmentation of forest scenes. International Journal of Applied Earth Observation and Geoinformation, 7(4), 339–359. Scholar
  23. Hidayat, S. A., & Wiweka. (2013). Accuracy evaluation orthorectification of SPOT image. International Conference on ICT for Smart Society, 13–14 June, Jakarta, Indonesia,
  24. Huang, C., Davis, L. S., & Townshend, J. R. G. (2002). An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, 23, 725–749. Scholar
  25. Hudak, A. T., Crookston, N. L., Evans, J. S., Falkowski, M. J., Smith, A. M., Gessler, P. E., et al. (2006). Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return LiDAR and multispectral satellite data. Canadian Journal of Remote Sensing, 32, 126–138 Scholar
  26. Immitzer, M., Atzberger, C., & Koukal, T. (2012). Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data. Remote Sensing, 4(9), 2661–2693. Scholar
  27. Izquierdo-Verdiguier, E., Laparra, V., Gómez-Chova, L., & Camps-Valls, G. (2013). Encoding invariances in remote sensing image classification with SVM. Geoscience and Remote Sensing Letters, 10(5. 1 page). Scholar
  28. Jensen, J R. (2000). Remote sensing of the environment (an earth resource perspective). Prentice hall upper saddle river, nj 07458.Google Scholar
  29. Jiang, Y., Carrow, R. N., & Duncan, R. R. (2003). Correlation analysis procedures for canopy spectral reflectance data of seashore paspalum under traffic stress. Journal of American Society, 13, 187–208. Scholar
  30. Kahriman, A., Gunlu, A., & Karahalil, U. (2014). Estimation of crown closure and tree density using Landsat TM satellite images in mixed forest stands. Journal of the Indian Society of Remote Sensing, 42, 559–567. Scholar
  31. Kalbi, S., Fallah, A., & Shataee, S. (2014). Estimation of forest attributes in the Hyrcanian forests, comparison of advanced space-borne thermal emission and reflection radiometer and satellite poure I’observation de la terre high resolution grounding data by multiple linear, and classification and regression tree regression models. Journal of Applied Remote Sensing, 8, 083632–083632. Scholar
  32. Kansanen, K., Vauhkonen, J., Lähivaara, T., Seppänen, A., Maltamo, M., & Mehtätalo, L. (2019). Estimating forest stand density and structure using Bayesian individual tree detection, stochastic geometry, and distribution matching. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 66–78. Scholar
  33. Koch, B., Heyder, U., & Weinacker, H. (2006). Detection of individual tree crowns in airborne LIDAR data. Photogrammetric Engineering and Remote Sensing, 72(4), 357–363. Scholar
  34. Li, M., Zang, S., Zhang, B., Li, S., & Changshan, W. (2014). A review of remote sensing image classification techniques: The role of Spatio-contextual information. European Journal of Remote Sensing, 47, 389–411. Scholar
  35. Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2004). Remote sensing and image interpretation (5th ed.). Hoboken: Wiley.Google Scholar
  36. Lu, D., Mausel, P., Brondízio, E. S., & Moran, E. (2004). Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest Ecology and Management, 198, 149–167. Scholar
  37. Lu, M., Chen, B., Liao, X., Yue, T., Yue, H., Ren, S., et al. (2017). Forest types classification based on multi-source data fusion. Remote Sensing, 9(11), 1153. Scholar
  38. Lu, D., Chen, Q., Wang, G., Liu, L., Li, G., Moran, E. (2014). A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems, International Journal of Digital Earth, 9 (1), pages 63- 105, Scholar
  39. Makela, H., & Pekkarinen, A. (2004). Estimation of forest stand volumes by Landsat TM imagery and stand-level field-inventory data. Forest Ecology and Management, 196, 245–255. Scholar
  40. Malahlelaa, O., Azong Choa, M., & Mutanga, O. (2014). Mapping canopy gaps in an indigenous subtropical coastal forest using high-resolution WorldView-2 data. International Journal of Remote Sensing, 35(17), 6397–6417. Scholar
  41. Marconcini, M., Camps-Valls, G., & Bruzzone, L. (2009). A composite semisupervised SVM for classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters, 6, 234–238. Scholar
  42. Meng, J., Li, S., Wang, W., Liu, Q., Xie, S., & Ma, W. (2016). Estimation of forest structural diversity using the spectral and textural information derived from SPOT-5 satellite images. Remote Sensing, 8(2), 125. Scholar
  43. Moisen, G. G., Freeman, E. A., Blackard, J. A., Frescino, T. S., Zimmermann, N. E., & Edwards Jr., T. C. (2006). Predicting tree species presence and basal area in Utah: A. comparison of stochastic gradient boosting, generalized additive models, and tree based methods. Ecological Modelling, 199, 176–187. Scholar
  44. Molinier, M., Lopez-Sanchez, C. A., Toivanen, T., Korpela, I., Corral-Rivas, J. J., Tergujeff, R., & Häme, T. (2016). Relasphone- Mobile and participative in situ forest biomass measurements supporting satellite image mapping. Remote Sensing, 8(10), 869. Scholar
  45. Noorian, N., Shataee-Jouibary, S., & Mohammadi, J. (2016). Assessment of different remote sensing data for forest structural attributes estimation in the Hyrcanian forests. Forest Systems, 25(3), e074, 11 pages. Scholar
  46. Nyamgeroh, B. B., Groen, T. A., Weir, M. J. C., Dimov, P., & Zlatanov, T. (2018). Detection of forest canopy gaps from very high resolution aerial images. Ecological Indicators, 95, 629–636. Scholar
  47. Pal, M., & Mather, P. M. (2005). Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26, 1007–1011. Scholar
  48. Parma, R. A., & Shataee- Joybari, Sh. (2010). Capability study on mapping the diversity and canopy cover density in Zagros forests using ETM+ images (case study Ghalajeh forests, Kirmanshah province). Iranian Journal of Forest, 2(3)
  49. Petropoulos, G. P., Kontoes, C., & Keramitsoglou, I. (2011). Burnt area delineation from a Uni-temporal perspective based on Landsat TM imagery classification using support vector machines. International Journal of Applied Earth Observation and Geoinformation, 13(1), 70–80. Scholar
  50. Piermattei, L., Karel, W., Wang, D., Wieser, M., Mokroš, M., Surový, P., Koreň, M., Tomaštík, J., Pfeifer, N., & Hollaus, M. (2019). Terrestrial structure from motion photogrammetry for deriving Forest inventory data. Remote Sensing, 11(8), 950. Scholar
  51. Pratihast, A. K., DeVries, B., Avitabile, V., Bruin, S., Kooistra, L., Tekle, M., et al. (2014). Combining satellite data and community-based observations for forest monitoring. Forests, 5, 2464–2489. Scholar
  52. Puletti, N., Perria, R., & Storchi, P. (2014). Unsupervised classification of very high remotely sensed images for grapevine rows detection. European Journal of Remote Sensing, 47, 45–54. Scholar
  53. Rafieyan, O., Darvishsefat, A. A., Babaii, S., & Mataji, A. (2011b). Object-based classification of UltraCamD imagery for identification of tree species in the mixed planted forest. Caspian Journal of Environmental Sciences, 9(1), 67–79 Scholar
  54. Richards, J. A. (1995). Remote sensing digital image analysis: An introduction. Springer.Google Scholar
  55. Salah, M. (2014). Combining pixel-based and object- based support vector machines using Bayesian probability theory. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, volume II-7, ISPRS technical commission VII symposium, 29 September – 2 October, Istanbul, Turkey,
  56. Schneider, T., Elatawneh, A., Rahlf, J., Kindu, M., Rappl, A., Thiele, A., Boldt, M., & Hinz, S. (2013). Parameter determination by RapidEye and TerraSAR-X Data: A Step Toward a Remote Sensing Based Inventory, Monitoring and Fast Reaction System on Forest Enterprise Level. Earth Observation of Global Changes. Scholar
  57. Soenen, S. A., Peddle, D. R., Coburn, C. A., Hall, R. J., & Hall, F. G. (2009). Canopy reflectance model inversion in multiple forward model: Forest structural information retrieval from solution set distributions. Photogramm. Engineering & Remote Sensing, 75, 361–374. Scholar
  58. Sohlbach, M., Weber, M., & Willhauck, G. (2004). eCognition professional: User guide 5. München: Definiens Imaging GmbH.Google Scholar
  59. Tavangar, S., Moradi, H., Massah Bavani, A., & Gholamalifard, M. (2019). A futuristic survey of the effects of LU/LC change on stream flow by CA–Markov model: A case of the Nekarood watershed. Iran. Geocarto International.
  60. Thanh Noi, P., & Kappas, M. (2018). Comparison of random Forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Remote Sensing, 18(1), 18. Scholar
  61. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127–150. Scholar
  62. Vashum, K. T., & Jayakumar, S. (2012). Methods to estimate above-ground biomass and carbon stock in natural forests: A review. Journal of Ecosystem & Ecography, 2, 1–7. Scholar
  63. Wallner, A., Elatawneh, A., Schneider, T., & Knoke, T. (2015). Estimation of forest structural information using RapidEye satellite data. Forestry: An International Journal of Forest Research, 88(1), 96–107. Scholar
  64. Wang, L., Sousa, W. P., & Gong, P. (2004). Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. International Journal of Remote Sensing, 25(24). Scholar
  65. Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., & Wu, X. (2018). Artificial mangrove species mapping using Pléiades-1: An evaluation of pixel-based and object-based classifications with selected machine learning algorithms. Remote Sensing, 10(2), 294. Scholar
  66. Wolter, P. T., & Townsend, P. A. (2011). Multi-sensor data fusion for estimating forest species composition and abundance in northern Minnesota. Remote Sensing of Environment, 115, 671–691. Scholar
  67. Wolter, P. T., Townsend, P. A., & Sturtevant, B. R. (2009). Estimation of forest structural parameters using 5 and 10 m SPOT-5 satellite data. Remote Sensing of Environment, 113, 2019–2036. Scholar
  68. Zhang, L., Shen, H., Gong, W., & Zhang, H. (2012). Adjustable model-based fusion method for multispectral and panchromatic images. IEEE Transactions on system, man, and cybernetics-part B, 42, 1693–1704. Scholar
  69. Zielewska-Büttner, K., Adler, P., Michaela Ehmann, M., & Braunisch, V. (2016). Automated detection of forest gaps in spruce dominated stands using canopy height models derived from stereo aerial imagery. Remote Sensing, 8, 175. Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of environmental and Natural ResourcesScience and Research branch - Islamic Azad UniversityTehranIran
  2. 2.Geodesy & Geomatics engineering facultyK.N.Toosi University of TechnologyTehranIran

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