• DOI:10.31509/2658-607X-2018-1-1-1-23
  • UDC 528.88

Assessment of biomass of forest species using satellite images of high spatial resolution (on the example of the forest of Khanty-Mansi Autonomous Okrug)

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E.N. Sochilova1, N.V. Surkov1,2, D.V. Ershov1, V.A. Khamedov3

1Center for Forest Ecology and Productivity of the RAS

Profsoyuznaya st. 84/32 bldg. 14, Moscow, 117997, Russia

2Lomonosov Moscow State University, Moscow, 119991, Russia

3Yugra State University, Khanty-Mansiys, 628012, Russia
E-mail: elena@ifi.rssi.ru
Received 30 October 2018

The paper describes assessment of spatial biomass of top wood layer based on combination of high-resolution Landsat-8 satellite images and selected ground forest inventory data measurements. Test area is one of forestry of Khanty-Mansiysk region. Segmentation of satellite images for spectral homogeneous land sites (segments) mapping is applied. Land category, dominated specie, age and wood stock volume for these sites are defined. Ground forest inventory data and segments used for selection of segments for dominated specie classification and validation of obtained map. The first, nine types of land cover are classified, four of them belong to forest cover with dominating of pine, spruce, cider and birch. The reference sample is updated by segments of such non-forest classes as fires, cuts and other non-forested lands, swamps, water internal bodies. Twelve spectral metrics are used for classification: reflectance in blue, green, red and near-infrared bands of Landsat-8. There are following vegetation seasons: and of winter, beginning of spring and middle of summer. The most significant informative metrics are the reflectance in the NIR band of the spring image, also green and red bands of the summer image. Random Forest algorithm is applied for training classification. The total accuracy of land categories and dominated species classification is 86,3%. Cross-validation of the classification based on the control sample was 0.712. In the second stage, we used regression models to relate the reflectance in the red band of the winter image with the taxation characteristics of the wood stock and age of the forest species in the selected reference segments. The level of relationship between the reflectance and wood stock values were equal to 0.80 for pine, 0.56 for dark coniferous species and 0.73 for birch. Between the reflectance and the specie height is following 0.75 for pine, 0.61 for birch and 0.64 for dark coniferous species. A check with control data showed that the error in estimating the wood stock above 250 m3 / ha for birch is 15.4%, for pine – 19.0% and for dark coniferous species – 5.5%. We used regional growth tables and the mean heights reconstructed from the regression equations for calculation mean specie ages. Then the age groups (according regional felling age) for each species are determined and the wood stocks are converted into wood biomass by conversion coefficients. As a result, maps of mean ages, heights, wood stock in m3/ha and biomass in t/ha were created. Based on these maps quarter assessments of the areas and stocks of the main dominated forest species of our test area, including felling age forest stands, were carried out.

Key words: stand biomass, wood stock volume, remote sensing data, Landsat-8, forest classification, Random Forest, forestry


Arroyo L.A, Pascual C., Manzanera J. A., Fire models and methods to map fuel types: The role of remote sensing, Forest Ecology and Management, 2008, No 256, pp. 1239-1252.

Atlas lesov SSSR (The Atlas of Forests in USSR), Moscow: GUGK, 1973, 222 p.

Belova E.I., Ershov D.V., Metod predvaritel’noi obrabotki stsen Landsat-5/7 s izobrazheniem snezhnogo pokrova (The method for processing of Landsat-5/7 scenes with snow cover), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No 4, pp. 9-14.

Breiman L., Random forests, Machine Learning, 2001, Vol. 45, No.1, pp. 5-32.

Chirici G., Barbati A., Corona P., Marchetti M., Travaglini D., Maselli F., Bertini R., Non-parametric and parametric methods using satellite images for estimating growing stock volume in alpine and Mediterranean forest ecosystems, Remote sensing of Environment, 2008, Vol. 112, Issue 5, pp. 2686-2700.

Chistiakov S.P., Sluchainye lesa: Obzor (Ramdom Forests: An overview), Trudy Karel’skogo nauchnogo tsentra RAN, 2013, No 1, pp. 117-136.

Fazakas Z., Nilsson M., Olsson H., Regional forest biomass and wood volume estimation using satellite data and ancillary data, Agricultural and Forest Meteorology, 1999, No. 98 (1), pp. 417-425.

Gharun M., Possell M., Jenkins M.E., Poon L.F., Bell T.L., Adams M.A., Improving forest sampling strategies for assessment of fuel reduction burning, Forest Ecology and Management, 2017, Vol. 392, pp. 78-89.

Guyon I., Elisseeff A., An Introduction to Variable and Feature Selection, Journal of Machine Learning Research, 2003, Vol. 3 (1), pp. 1157-1182. 

Gvozdetskii N.A., Mikhailov N.I., Fizicheskaya geografiya SSSR. Aziatskaya chast’ (Physical geography of USSR. Asian part), Moscow: Mysl’, 1978, 512 p.

Hall R.J., Davidson D.P., Peddle D.R., Ground and remote estimation of leaf area index in Rocky Mountain forest stands, Kananaskis, Alberta, The international journal of remote sensing, 2003, No 29, pp. 411-427.

Hall R.J., Skakun R.S., Arsenault E.J., Case B.S., Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground biomass and stand volume, Forest Ecology and Management, 2006, No 225, pp. 378-390.

Halme M., Tomppo E., Improving the accuracy of multisource forest inventory estimates by reducing plot location error – a multicriteria approach, Remote sensing of Environment, 2001, No 78, pp. 321-327.

Hame T., Salli A., Andersson K., Lohi A., A new methodology for the estimation of biomass of coniferdominated boreal forest using NOAA AVHRR data, International Journal of Remote Sensing, 1997, Vol. 18, No 15, pp. 3211-3243.

Ji L., Wylie B.K., Nossov D.R., Peterson B., Waldrop M.P., McFarland J.W., Rover J., Hollingsworthe T.N., Estimating aboveground biomass in interior Alaska with Landsat data and field measurements, International Journal of Applied Earth Observation and Geoinformation, 2012, Vol. 18, pp. 451-461.

Kozlov D.N., Puzachenko M.Yu., Fedyaeva M.V., Puzachenko Yu.G., Kartografirovanie zapasov drevostoya eli v biogeotsenozakh yuzhnoi taigi (yuzhnaya chast’ Valdaiskoi vozvyshennosti) na osnove distantsionnoi informatsii Landsat-7 i tsifrovoi modeli rel’efa. (Spruce timber stocks mapping using Landsat-7 and DEM data in the South part of Valdai upland) Aerospace methods and gis–technologies in forestry and forest management: Reports of the IV International Conference, Moscow, 17- 19 April 2007, Moscow: Izdatel’stvo Moskovskogo gosudarstvennogo universiteta lesa, 2007, pp. 197-201.

Kuusela K. Poso S., Satellite pictures in the estimation of the growing stock over extensive area, The Photogrammetric Journal of Finland, 1970, Vol. 4, No 1, pp. 3-9.

Markham B.L., Barker J.L., Landsat MSS and TM post-calibration dynamic ranges, exoatmospheric reflectances and at-satellite temperatures, Landsat Technical Notes, Vol.1, No 1, 1986, P. 3–8.

Mura M., Bottalico F., Giannetti F., Bertani R., Giannini R., Mancini M., Orlandini S., Travaglinia D., Chirici G., Exploiting the capabilities of the Sentinel-2 multi spectral instrument for predicting growing stock volume in forest ecosystems, The International Journal of Applied Earth Observation and Geoinformation, 2018, No 66, pp. 126-134.

Nagimov V.Z., Rost i nadzemnaya fitomassa drevostoev sosnyaka lishainikovogo v podzone severnoi taigi Tyumenskoi oblasti. Avtoreferat diss. cand. s.-kh. n. (The growth and phytomass of lichen pine forest in northern taiga forest in Tumen region. Extended abstract of candidate’s agriculture sci. thesis), Ekaterinburg: Ural’skii gosudarstvennyi lesotekhnicheskii universitet, 2011, 23 p.

Onuchin I.E., Lesovodstvenno-taksatsionnaya otsenka kedrovykh nasazhdenii na territorii Khanty-Mansiiskogo avtonomnogo okruga – Yugry. Avtoreferat diss. cand. s.-kh. n. (Forestry examination of Siberian stone pine forests in the Khanty-Mansijsk Autonomous District -YGRA. Extended abstract of candidate’s agriculture sci. thesis), Ekaterinburg: Ural’skii gosudarstvennyi lesotekhnicheskii universitet, 2017, 23 p.

Poso S., Paananen R., Simila M., Forest inventory by compartments using satellite imagery, Silva Fennica, 1987, Vol. 2, No 1, pp. 69-94.

Redding N.J., Crisp D.J., Tang D., Newsam G.N., An efficient algorithm for Mumford-Shah segmentation and its application to SAR imagery, Proc. Conf. «Digital Image Computing: Techniques & Applications» (DICTA-99), Perth, 1999, pp. 35-41.

Sekerin E.M., Puti povysheniya lesoobrazovatel’noi roli sosny sibirskoi v podzone yuzhnoi taigi Urala. Avtoreferat diss. cand. s.-kh. n. (Ways to raise forest-forming role of Siberian stone pine in the South Ural taiga forests. Extended abstract of candidate’s agriculture sci. thesis) Ekaterinburg, Ural’skii gosudarstvennyi lesotekhnicheskii universitet, 2015, 22 p.

Shvidenko A.Z., Shchepashchenko D.G., Nil’sson S., Bului Yu.I., Tablitsy i modeli khoda rosta i produktivnosti nasazhdenii osnovnykh lesoobrazuyushchikh porod severnoi Evrazii (normativno-spravochnye materialy) (Tables and models of growth and productivity of forests of major forest forming species of Northern Eurasia (standard and reference materials), Moscow: Federal’noe agentstvo lesnogo khozyaistva, Mezhdunarodnyi institut prikladnogo sistemnogo analiza, 2008, 886 p.

Sochilova E.N., Ershov D.V., Analiz vozmozhnosti opredeleniya zapasov drevesnykh porod po sputnikovym dannym Landsat ETM (Possibility analysis of stem volume of forests assessment using Landsat ETM data), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No 3, pp. 277-282.

Tokola T., The Influence of field sample data location on growing stock volume estimation in Landsat TM-based forest inventory in Eastern Finland, Remote sensing of environment, 2000, Vol. 74, Issue 3, pp. 422-431.

Tokola T., Heikkilä J., Improving satellite image based forest inventory by using a priori site quality information, Silva Fennica, 1997, No 31 (1), pp. 67-78.

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.

Vinogradov B.V., Aerokosmicheskii monitoring ekosistem (Ecosystem monitoring from air and space), Moscow: Nauka, 1984, 320 p.

URL://m-sosva.ru/? page_id=154


Zamolodchikov D.G., Utkin A.I., Chestnykh O.V., Koeffitsienty konversii zapasov nasazhdenii v fitomassu dlya osnovnykh lesoobrazuyushchikh porod Rossii (Coefficients for conversion of timber stock to phytomass for main tree species in Russia), Lesnaya taksatsiya i lesoustroistvo,2003, Issue 1(32), pp. 119-127.

Zhang J., Huang S., Hogg E.H., Lieffers V. Qin Y., He F., Estimating spatial variation in Alberta forest biomass from a combination of forest inventory and remote sensing data, Biogeosciences, 2014, No 11, pp. 2793-2808.

Zharko V.O., Bartalev S.A., Egorov V.A., Issledovanie vozmozhnostei otsenki zapasov drevesiny v lesakh Primorskogo kraya po dannym sputnikovoi sistemy Proba-V (Possibilities of timber stocks inventory using Proba-V satellite data in Primorsky Krai), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No 1, pp. 157-168.

Zheng S., Cao C., Dang Y., Xiang H., Zhao J., Zhang Y., Wang X., Guo H., Retrieval of forest growing stock volume by two different methods using Landsat TM images, The international journal of remote sensing, 2014, No 35, pp. 29-43.