Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1 Number 2 Number 3
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. 1, pp. 157-168

Investigation of forest growing stock volume estimation possibilities over Russian Primorsky Krai region using Proba-V satellite data

V.O. Zharko 1 , S.A. Bartalev 1 , V.A. Egorov 1 
1 Space Research Institute RAS, Moscow, Russia
Accepted: 23.11.2017
DOI: 10.21046/2070-7401-2018-15-1-157-168
This paper presents a method for forest growing stock volume (GSV) estimation using 100 m Proba-V satellite data, acquired during the period of persistent snow cover. The method is based on the approximation of the relation between GSV and red-band snow-covered surface reflectance. The territory of Russian Primorsky Krai federal subject is selected as a test region. A method for production of Proba-V data-based cloud-free composite images, providing the info on the spatial distribution of snow-covered surface spectral reflectance, is described. An algorithm to prepare a dataset on spatial distribution of various land cover types (using satellite data-based vegetation maps) and terrain characteristics (using digital elevation model data) is presented. This dataset is used to stratify the territory into areas, uniform with respect to the solar illumination conditions, including shadowed northern and illuminated southern slopes, as well as approximately horizontal sites, with subsequent independent processing of satellite data within each stratum. Parameterization of a model, approximating GSV reflectance relation, was performed separately for each forest cover type found on the study area based on a freely available 1 km GSV map under the assumption of inverse GSV-reflectance dependency. Approximation results are used to obtain GSV estimates derived from Proba-V data based snow-covered surface reflectance composite image. Comparison of Proba-V data based GSV estimates with state forest account data at the level of Primorsky Krai forest management units demonstrates determination coefficient R2 = 0.96.
Keywords: remote sensing, forests, growing stock volume, GSV, Proba-V
Full text

References:

  1. Bartalev S. A., Egorov V. A., Ershov D. V., Zharko V. O., Kolbudaev P. A., Loupian E. A., Plotnikov D. E., Razvitie vozmozhnostei regional’nogo sputnikovogo kartografirovaniya rastitel’nogo pokrova na primere Primorskogo kraya (Development of regional satellite vegetation mapping possibilities in the case of Primorie region), 14 konf. “Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa” (14th Conf. “Current Problems in Remote Sensing of the Earth from Space”, Book of Abstracts), Moscow, 2016, p. 326.
  2. Bartalev S. A., Egorov V. A., Ershov D. V., Isaev A. S., Loupian E. A., Plotnikov D. E., Uvarov I. A., Sputnikovoe kartografirovanie rastitel’nogo pokrova Rossii po dannym spektroradiometra MODIS (Mapping of Russia’s Vegetation Cover Using MODIS Satellite Spectroradiometer Data), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2011, Vol. 8, No. 4, pp. 285–302.
  3. Bartalev S. A., Egorov V. A., Zharko V. O., Loupian E. A., Plotnikov D. E., Khvostikov S. A., Shaba­nov N. V. (2016a) Sputnikovoe kartografirovanie rastitel’nogo pokrova Rossii (Land cover mapping over Russia using Earth observation data), Moscow: IKI RAN, 2016, 208 p.
  4. Egorov V. A., Bartalev S. A., Metod radiometricheskoi korrektsii iskazhenii otrazhatel’nykh kharakteristik zemnogo pokrova v dannykh sputnikovykh izmerenii, vyzvannykh vliyaniem rel’efa mestnosti (Radiometric correction for topography-induced distortions in land cover reflectance derived from satellite data), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2016, Vol. 13, No. 5, pp. 192–201.
  5. 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.
  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. Shchepashchenko D. G., Shvidenko A. Z., Perger K., Dresel K., Frits Sh., Lakida P., Mukhortova L. V., Usol’tsev V. A., Bobkova K. S., Osipov A. F., Martynenko O. V., Karminov V. N., Ontikov P. V., Shchepashchenko M. V., Kraksner F., Izuchenie fitomassy lesov: tekushchee sostoyanie i perspektivy (Forest biomass observation: current state and prospective), Sibirskii lesnoi zhurnal, 2017, No. 4, pp. 3–11.
  8. Arino O., Bicheron P., Achard F., Latham J., Witt R., Weber J., GlobCover: the most detailed portrait of Earth, ESA Bulletin — European Space Agency, 2008, No. 136, pp. 24–31.
  9. Bartalev S., Egorov V., Loupian E., Khvostikov S., A new locally-adaptive classification method LAGMA for large-scale land cover mapping using remote-sensing data, Remote Sensing Letters, 2014, Vol. 5, Issue 1, pp. 55–64.
  10. Farr T., Rosen P., Caro E., Crippen R., Duren R., Hensley S., Kobrick M., Paller M., Rodriguez E., Roth L., Seal D., Shaffer S., Shimada J., Umland J., Werner M., Oskin M., Burbank D., Alsdorf D., The Shuttle Radar Topography Mission, Reviews of Geophysics, 2007, Vol. 45, Issue 2.
  11. Hall D., Riggs G., Salomonson V., Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data, Remote Sensing of Environment, 1995, Vol. 54, Issue 2, pp. 127–140.
  12. Rodríguez-Veiga P., Wheeler J., Louis V., Tansey K., Balzter H., Quantifying Forest Biomass Carbon Stocks From Space, Current Forestry Reports, 2017, Vol. 3, Issue 1, pp. 1–18.
  13. Santoro M., Beaudoin A., Beer C., Cartus O., Fransson J., Hall R., Pathe C., Schmullius C., Schepashchenko D., Shvidenko A., Thurner M., Wegmüller U., Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR, Remote Sensing of Environment, 2015, Vol. 168, pp. 316–334.
  14. Santoro M., Beer C., Cartus O., Schmullius C., Shvidenko A., McCallum I., Wegmüller U., Wiesmann A., Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements, Remote Sensing of Environment, 2011, Vol. 115, Issue 2, pp. 490–507.