Estimation of forest area and its dynamics in Russia based on synthesis of remote sensing products

Contemporary Problems of Ecology

Volumn 8, Issue 7, 2015, Pages 811-817

  Schepaschenko, D G ^a,b   Shvidenko, A Z ^a,c   Lesiv, M Yu ^a,d   Ontikov, P V ^b   Shchepashchenko, M V ^e   Kraxner, F ^a  

^a INTERNATIONAL INSTITUTE FOR APPLIED SYSTEMS ANALYSIS   (Austria)

^b MOSCOW STATE FOREST UNIVERSITY   (Russian Federation)

^c SUKACHEV INSTITUTE OF FOREST   (Russian Federation)

^d LVIV POLYTECHNIC NATIONAL UNIVERSITY   (Ukraine)

^e Fed Autonomous Inst for Additional Prof Educ All Russ Inst of Continuous Education in Forestry   (Russian Federation)

Author keywords

crowdsourcing; forest cover; geographically weighted regression; remote sensing; Russian forest

Indexed keywords

ABANDONED LAND; ARABLE LAND; ESTIMATION METHOD; FOREST COVER; FOREST DYNAMICS; FOREST ECOSYSTEM; METHODOLOGY; REGRESSION ANALYSIS; REMOTE SENSING; SPATIAL RESOLUTION; WILDFIRE;

ASIA; EUROPE; ROSTOV [RUSSIAN FEDERATION]; RUSSIAN FEDERATION;

EID: 84953211559     PISSN: 19954255     EISSN: 19954263     Source Type: Journal    
DOI: 10.1134/S1995425515070136     Document Type: Article

References (25)

1. Aerokosmicheskii monitoring katastroficheskikh pozharov v lesakh Vostochnoi Sibiri (Aerospace Monitoring of Disaster Wildfires in Forests of Eastern Siberia), Sukhinin, A.I., Ed., Krasnoyarsk: Inst. Lesa, Sib. Otd., Ross. Akad. Nauk, 2009.
2. Bartalev, S.A., Development of methods for assessment of status and dynamics of forests based on satellite monitoring, Extended Abstract of Doctoral (Tech.) Dissertation, Moscow: Inst. Kosm. Issled., Ross. Akad. Nauk, 2007.
3. Bartalev, S.A., Belward, A.S., Erchov, D.V., and Isaev, A.S., A new SPOT4-VEGETATION derived land cover map of Northern Eurasia, Int. J. Remote Sens., 2003, vol. 24, no. 9, pp. 1977–1982. doi: 10.1080/0143116031000066297
4. Bartalev, S.A., Stytsenko, F.V., Egorov, V.A., and Lupyan, E.A., Satellite based assessment of post fire mortality of Russian forests, Lesovedenie., 2015, no. 2, pp. 83–94.
5. Brunsdon, C., Fotheringham, S., and Charlton, M., Geographically weighted regression-modeling spatial non-stationarity, J. R. Stat. Soc., D., 1998, vol. 47, no. 3, pp. 431–443. doi: 10.2307/2988625
6. Defourny, P., Vancustem, C., Bicheron, P., Brockmann, C., Nino, F., Schouten, L., and Leroy, M., GLOBCOVER: a 300 m global land cover product for 2005 using ENVISAT MERIS time series, in Proc. VII Mid-Term Symp. the ISPRS Commission “Remote Sensing: from Pixels to Processes,” Enscede, Netherlands, 2006. http://metalib-alibuclacuk/V?func= find-ej-1
7. DiMiceli, C.M., Carroll, M.L., Sohlberg, R.A., Huang, C., Hansen, M.C., and Townshend, J.R.G., Annual Global Automated MODIS Vegetation Continuous Fields (MOD44B) at 250 m Spatial Resolution for Data Years Beginning Day 65, 2000–2010, Collection 5 Percent Tree Cover, Univ. of Maryland., 2011. http://glcfumdedu/data/vcf/
8. Friedl, M.A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., and Huang, X., MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets, Remote Sens. Environ., 2010, vol. 114, no. 1, pp. 168–182. doi: 10.1016/jrse.2009.08.016
9. Fritz, S., McCallum, I., Schill, C., Perger, C., See, L., Schepaschenko, D., van der Velde, M., Kraxner, F., and Obersteiner, M., Geo-Wiki: an online platform for improving global land cover, Environ. Model. Software., 2012, vol. 31, pp. 110–123. doi: 10.1016/jenvsoft. 2011.11.015
10. Jun, C., Ban, Y., and Li, S., China: open access to Earth land-cover map, Nature., 2014, vol. 514, no. 434. doi: 10.1038/514434c
11. Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., and Townshend, J.R.G., High-resolution global maps of 21st-century forest cover change, Science., 2013, vol. 342 (6160), pp. 850–853. doi: 10.1126/science.1244693
12. Krylov, A., McCarty, J.L., Potapov, P., Loboda, T., Tyukavina, A., Turubanova, S., and Hansen, M.C., Remote sensing estimates of stand-replacement fires in Russia, 2002–2011, Environ. Res. Lett., 2014, vol. 9, no. 10, pp. 1–8. doi: 10.1088/1748-9326/9/10/1005007
13. Latham, J., Cumani, R., Rosati, I., and Bloise, M., Global Land Cover SHARE (GLC-SHARE) Database, Beta-Release Version 1.0, 2014, Rome: Food Agric. Org., 2014. http://wwwglcnorg/downs/prj/glcshare/GLC_SHARE_beta_v1.0_2014pdf
14. Laverov, N.P. and Lupyan, E.A., Report presented at the Conference dedicated to the anniversary of Academician R.Z. Sagdeev, Moscow: Akad. Minist. Chrezvychainykh Situats., 2013. http://wwwforestforumru/viewtopicphp?f=9&t=14001
15. Osnovnye pokazateli sel’skogo khozyaistva v Rossii. Byulleten’ (Key Indicators of Agriculture in Russia: Bulletin), Moscow: Rosstat, 2012. http://wwwgksru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/publications/catalog/doc_1140096652250
16. Prognoz ispol’zovaniya i vosproizvodstva lesnykh resursov po ekonomicheskim raionam SSSR do 2010 (Forecast of Use and Reproduction of Forest Resources in Economic Regions of the Soviet Union until 2010), Isaev, A.S., Ed., Moscow: Akad. Nauk SSSR, 1991, vol. 1.
17. Santoro, M., Beaudoin, A., Beer, C., Cartus, O., Fransson, J.E.S., Hall, R.J., and Wegmuller, U., Forest growing stock volume of the northern hemisphere: spatially explicit estimates for 2010 derived from Envisat ASAR data, Remote Sens. Environ., 2015, no. 168, pp. 316–334.
18. See, L., Schepaschenko, D., Lesiv, M., McCallum, I., Fritz, S., Comber, A., Perger, Ch., Schill, Ch., Zhao, Y., Maus, V., Sirajh, M.A., Albrecht, F., Cipriani, A., Vakolyuk, M., Garcia, A., et al., Building a hybrid land cover map with crowdsourcing and geographically weighted regression, ISPRS J. Photogram. Remote Sens., 2015, vol. 103, pp. 48–56. doi: 10.1016/jisprsjprs.2014.06.016
19. Sexton, J.O., Song, X.-P., Feng, M., Noojipady, P., Anand, A., Huang, C., Kim, D.-H., Collins, K.M., Channan, S., DiMiceli, C., and Townshend, J.R., Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error, Int. J. Digital Earth., 2013, vol. 6, no. 5, pp. 427–448. doi: 10.1080/17538947.2013.786146
20. Shimada, M., Isoguchi, O., Motooka, T., Shiraishi, T., Mukaida, A., Okumura, H., Otaki, T., and Itoh, T., Generation of 10 m resolution PALSAR and JERSSAR mosaic and forest/nonforest maps for forest carbon, in Int. IEEE Geoscience and Remote Sensing Symp. (IGARSS)., 2011, pp. 3510–3513. doi: 10.1109/IGARSS.2011.6049978
21. Solontsov, O.N., Targets of the state inventory of forests in Russia, in Mater. II mezhd. konf. “Problemy lesoustroistva i gosudarstvennoi inventarizatsii lesov v Rossii,” Vologda, 8–10 dekabrya 2010 g. (Proc. II Int. Conf. “Problems of Forest Management and State Inventory of Forests in Russia,” Vologda, December 8–10, 2010)., 2010. http://wwwroslesinforg. ru/press/news/35
22. Sukhikh, V.I., Aerokosmicheskie metody v lesnom khozyaistve i landshaftnom stroitel’stve (Aerospace Methods Applied in Forestry and Landscape Construction), Yoshkar-Ola: Mariisk. Gos. Tech. Univ., 2005.
23. Shvidenko, A.Z., Shchepashchenko, D.G., McCallum, I., Lakyda, I.P., Vaganov, E.A., Sukhinin, A.I., and Maksyutov, Sh.Sh., Impact of wildfire in Russia between 1998–2010 on ecosystems and the global carbon budget, Dokl. Earth Sci., 2011, vol. 441, no. 2, pp. 1678–1682.
24. Shvidenko, A.Z. and Schepaschenko, D.G., Climate change and wildfires in Russia, Contemp. Probl. Ecol., 2013, vol. 6, no. 7, pp. 683–692.
25. Vivchar, A.V., Moiseenko, K.B., and Pankratova, N.V., Estimates of carbon monoxide emissions from wildfires in northern Eurasia for air quality assessment and climate modeling, Izv. Atmos. Ocean. Phys., 2010, vol. 46, no. 3, pp. 281–293.