Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 5, pp. 96-109

Mapping of forest site index classes in Primorskiy Krai based on satellite images and terrain characteristics

E.N. Sochilova 1 , N.V. Surkov 1, 2 , D.V. Ershov 1, 3 , V.A. Egorov 3 , S.S. Bartalev 3 , S.A. Bartalev 3 
1 Center for Forest Ecology and Productivity RAS, Moscow, Russia
2 Lomonosov Moscow State University , Moscow, Russia
3 Space Research Institute RAS, Moscow, Russia
Accepted: 23.08.2018
DOI: 10.21046/2070-7401-2018-15-5-96-109
The paper presents the results of the study of the possibility of forest site index mapping using their forest spectral reflectance and parameters of digital elevation model. Primorskiy kray selected as a test region. It is characterised by high diversity of tree species due to local climate and terrain specifics. The sixteen different features were generated. The spectral characteristics of the forests from the red near and middle infrared channels of cloud free winter and summer composite images of the Proba-V instrument were extracted. The parameters of the relief (height, slope and aspect) and relief-derived indices (solar radiation, deep-to-water index, relief curvatures) were used to describe the forest growing conditions. We used also ground data of forest stand parameters for classification training and validation of dominated forest spices and site indices maps. The quality of sampling data was controlled via the relationship between the average age and height of a stand for each specie. We selected 4465 etalons for such tree species as spruce, fir, cedar, larch, birches white and yellow, oak, aspen, birch stone, linden, ash and mountain pine (pinus pumila). Random Forest algorithm used for classification data, which allows to preliminary estimate the training sample and the informative features. We used all sixteen features for recognition of dominated forest species. Total accuracy of dominated species classification was 89.0%. The contribution of each feature in site index classification was determined using the confusion matrix of the recognised classes. As a result, twelve informative features for the site indices classification were selected. The classification accuracy of forest site indices was 84.8%.
The paper presents the results of the study of the possibility of forest sites index mapping using their forest spectral reflectance and parameters of digital elevation model. Primorskiy Krai was selected as a test region. It is characterised by high diversity of tree species due to local climate and terrain specifics. Sixteen different features were generated. The spectral characteristics of the forests from the red near and middle infrared channels of cloud free winter and summer composite images of the Proba-V instrument were extracted. The parameters of the relief (height, slope and aspect) and relief-derived indices (solar radiation, deep-to-water index, relief curvatures) were used to describe the forest growing conditions. We used also ground data of forest stand parameters for classification training and validation of dominated forest spices and site indices maps. The quality of sampling data was controlled via the relationship between the average age and height of a stand for each specie. We selected 4465 etalons for such tree species as spruce, fir, cedar, larch, birches white and yellow, oak, aspen, birch stone, linden, ash and mountain pine (pinus pumila). Random Forest algorithm was used for classification data, which allows to preliminary estimate the training sample and the informative features. We used all sixteen features for recognition of dominated forest species. Total accuracy of dominated species classification was 89.0 %. The contribution of each feature in site index classification was determined using the confusion matrix of the recognised classes. As a result, twelve informative features for the site indices classification were selected. The classification accuracy of forest site indices was 84.8 %. Recognition mistakes occur mostly between adjacent classes. Thus, combination of the spectral characteristics of forests and parameters of digital elevation model can be used with a satisfactory accuracy for classification of forest site indices in mountainous regions. The obtained experience and results is useful for hard-to-reach forest regions of Siberia and the Far East. However, this requires a large sample of ground or forest inventory data to provide a statistical basis for the preparation and training of the classifier.

Keywords: tree species, site index, remote sensing data, Proba-V, digital elevation model, classification, Random Forest
Full text

References:

  1. Atlas lesov Primorskogo kraya (The Atlas of Forests in Primorskiy Kray), Vladivostok: DVO RAN, 2005, 76 p.
  2. Bartalev S. A., Egorov V. A., Zharko V. O., Loupian E. A., Plotnikov D. E., Khvostikov S. A., Shabanov N. V., Sputnikovoe kartografirovanie rastitel’nogo pokrova Rossii (Land cover mapping over Russia using Earth observation data), Moscow, IKI, 2016, 208 p.
  3. Danilova I. V., Ryzhkova V. A., Korets M. A., Algoritm avtomatizirovannogo kartografirovaniya sovremennogo sostoyaniya i dinamiki lesov na osnove GIS (GIS Algorithm for automatic mapping of current conditions and dynamics in forests), Vestnik NGU, Seriya: Informatsionnye tekhnologii, 2010, Vol. 8, Issue 4, P. 15–24.
  4. Zharko V. O., Bartalev S. A., Egorov V. A., Issledovanie vozmozhnostei otsenki zapasov drevesiny v lesakh Primorskogo kraya po dannym sputnikovoi sistemy Proba-V (Investigation of forest growing stock volume estimation possibilities over Russian Primorsky Kray region using Proba-V satellite data), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 1, pp. 157–168.
  5. Lesovedenie: metodicheskie ukazaniya k laboratornym rabotam (Forest Science: instructional guidelines for laboratory work), Rogozin M. V. (ed.), Perm: Permskii gosudarstvennyi natsionalinyi issledovatelskiy universitet, 2012, 36 p.
  6. Loupian E. A., Proshin A. A., Burtsev M. A., Balashov I. V., Bartalev S. A., Efremov V. Yu., Kashnitskii A. V., Mazurov A. A., Matveev A. M., Sudneva O. A., Sychugov I. G., Tolpin V. A., Uvarov I. A., Tsentr kollektivnogo pol’zovaniya sistemami arkhivatsii, obrabotki i analiza sputnikovykh dannykh IKI RAN dlya resheniya zadach izucheniya i monitoringa okruzhayushchei sredy (IKI center for collective use of satellite data archiving, processing and analysis systems aimed at solving the problems of environmental study and monitoring), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, Vol. 12, No. 5, pp. 263–284.
  7. Loupian E. A., Bartalev S. A., Balashov I. V., Bartalev S. S., Burtsev M. A., Egorov V. A., Efremov V. Yu., Zharko V. O., Kashnitskii A. V., Kolbudaev P. A., Kramareva L. S., Mazurov A. A., Oksyukevich A. Yu., Plotnikov D. E., Proshin A. A., Sen’ko K. S., Uvarov I. A., Khvostikov S. A., Khovratovich T. S., Informatsionnaya sistema kompleksnogo distantsionnogo monitoringa lesov “Vega-Primor’e” (Vega-Primorie: complex remote forest monitoring information system), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2016, Vol. 13, No. 5, pp. 11–28.
  8. http://docs.cntd.ru/document/430623554.
  9. Tyurin Yu. N., Makarov A. A., Statisticheskii analiz dannykh na komp’yutere (Statistical analysis of data with the computer), Moscow: INFRA-M, 1998, 528 p.
  10. Chistiakov S. P., Sluchainye lesa: Obzor (Ramdom Forests: An overview), Trudy Karel’skogo nauchnogo tsentra RAN, 2013, No. 1, pp. 117–136.
  11. Brandl S., Falk W., Klemmt H., Stricker G., Bender A., Rotzer T., Pretzsch H., Possibilities and Limitations of Spatially Explicit Site Index Modelling for Spruce Based on National Forest Inventory Data and Digital Maps of Soil and Climate in Bavaria (SE Germany), Forests, 2014, Vol. 5, Issue 11, pp. 2626–2646.
  12. Breiman L., Random forests, Machine Learning, 2001, Vol. 45, No. 1, pp. 5–32.
  13. Burrough P. A., McDonell R. A., Principles of Geographical Information Systems, Oxford University Press, 1998, 332 p.
  14. Crisp D. J., Perry P., Redding N. J., Fast Segmentation of Large Images, Proc. 26th Australasian Computer Science Conference, Adelaide, Australia, 2003, pp. 87–93.
  15. Curt T., Bouchaud M., Agrech G., Predicting site index of Douglas-fir plantations from ecological variables in the Massif central area of France, Forest Ecology and Management, 2001, Vol. 149, pp. 61–74.
  16. Guyon I., Elisseeff A., An Introduction to Variable and Feature Selection, J. Machine Learning Research, 2003, Vol. 3 (1), pp. 1157–1182.
  17. Hamel B. T., Belanger N., Pare D., Productivity of black spruce and Jack pine stands in Quebec as related to climate, site biological features and soil properties, Forest Ecology and Management, 2004, Vol. 191, pp. 239–251.
  18. How to Determine Site Index in Silviculture, Participant’s Workbook, Forest Renewal BC, British Columbia, Ministry of Forests, Forest Practices Branch, 1999, 117 p., URL: https://www.for.gov.bc.ca/hfp/training/00011/acrobat/sicourse.pdf.
  19. Huang S., Ramirez C., Conway S., Kennedy K., Kohler T., Liu J., Mapping site index and volume increment from forest inventory, Landsat, and ecological variables in Tahoe National Forest, California, USA, Canadian J. Forest Research, 2017, Vol. 47, Issue 1, pp. 113 124. DOI: 10.1139/cjfr-2016-0209.
  20. Jarvis A., Reuter H. I., Nelson A., Guevara E., Hole-filled seamless SRTM data V4, International Centre for Tropical Agriculture (CIAT), 2008, URL: http://srtm.csi.cgiar.org.
  21. Korets M. A., Ryzhkova V. A., Danilova I. V., Prokushkin A. S., Vegetation cover mapping based on remote sensing and digital elevation model data, Intern. Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, 2016, Vol. XLI-B8, pp. 699–704.
  22. Liaw A., Wiener M., Classification and Regression by randomForest, R news, 2002, Vol. 2 (3), pp. 18–22.
  23. Monserud R. A., Huang S., Yang Y., Predicting lodgepole pine site index from climatic parameters in Alberta, The Forestry Chronicle, 2006, Vol. 82 (4), pp. 562–571.
  24. Moore I. D., Grayson R. B., Landson A. R., Digital Terrain Modelling: A review of hydrological, geomorphological, and biological applications, Hydrological Processes, 1991, Vol. 5, Issue 1, pp. 3–30.
  25. Murphy P. N. C., Ogilvie J., Arp P. A., Topographic modelling of soil moisture conditions: A comparison and verification of two models, European J. Soil Science, 2009, Vol. 60, Issue 1, pp. 94–109.
  26. Rich P. M., Dubayah R., Hetrick W. A., Saving S. C., Using viewshed models to calculate intercepted solar radiation: applications in ecology, American Society for Photogrammetry and Remote Sensing Technical Papers, 1994, pp. 524–529.
  27. Sharma R. P., Brunner A., Eid T., Oyen B-H., Modelling dominant height growth from national forest inventory individual tree data with short time series and large age errors, Forest Ecology and Management, 2011, Vol. 262 (12), pp. 2162–2175.
  28. Sharma R. P., Brunner A., Eid T., Site index prediction from site and climate variables for Norway spruce and Scots pine in Norway, Scandinavian J. Forest Research, 2012, Vol. 27, pp. 619–636.
  29. Socha J., Pierzchalski M., Bałazy R., Ciesielski M., Modelling top height growth and site index using repeated laser scanning data, Forest Ecology and Management, 2017, Vol. 406, pp. 307–317.
  30. Wang G. G., Klinka K., Use of synoptic variables in predicting white spruce site index, Forest Ecology and Management, 1996, Vol. 80, pp. 95–105.
  31. Waring R. H., Milner K. S., Jolly W. M., Phillips L., McWethy D., Assessment of site index and forest growth capacity across the Pacific and Inland Northwest U. S.A. with a MODIS satellite-derived vegetation index, Forest Ecology and Management, 2006, Vol. 228(1–3), pp. 285–291.
  32. White B., Ogilvie J., Campbell D. M. H., Hiltz D., Gauthier B., Chisholm H. K. H., Wen H. K., Murphy P. N. C., Arp P. A.A., Using the Cartographic Depth-to-Water Index to Locate Small Streams and Associated Wet Areas across Landscapes, Canadian Water Resources J., 2012, Vol. 37, Issue 4, pp. 333–347.
  33. Wolters E., Dierckx W., Iordache M.-D., Swinnen E., PROBA-V Products User Manual v 3.01, 2018, 110 p., URL: http://www.vito-eodata.be/PDF/image/PROBAV-Products_User_Manual.pdf.